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Enabling wastewater treatment process automation: Leveraging innovations in real-time sensing, data analysis, and online controls
Abstract
The primary mandate of wastewater treatment facilities is the limitation of pollutant discharges, however both continued tightening of permit limits and unique challenges associated with improving sustainability (i.e., resource recovery) demand innovation. Enabling increasingly sophisticated treatment processes in a cost-effective and energy-efficient way requires expanding capabilities for rapid, accurate real-time quantification of a broadened range of wastewater constituents as well as envisioning novel feedback control strategies based on these signals. This manuscript quantitatively compares results of early adoption of instrumentation and process upgrades at operating wastewater treatment facilities and proof-of-concept research results, with a focus on leveraging real-time sensing of wastewater chemistry for process monitoring and control. Up to 10% improvement in nutrient removal and energy savings are already being achieved, yet shortfalls in hardware readiness, lack of field-relevant context of research results, and a widening gap between the training of environmental engineers and the skillsets required to develop and maintain sensor-driven solutions present challenges. A forward-looking roadmap highlights opportunities for accelerating innovation, including (1) ensuring research results are published in units and context that allow operators to make an informed cost–benefit analysis with explicit comparison to existing operational baselines, (2) promoting integrated design of hardware and software to generate novel approaches for improved sensing of target analytes, (3) strengthening partnerships nationally, including for data sharing, field testing of new hardware, and expanding educational curricula, and (4) building forums for sharing of expertise and data among plant operators to enable smaller facilities to more cost-effectively collect information required to design and evaluate upgrades.
... Traditional sensor technologies used in the water systems of WEIHN have primarily focused on water-based applications, geared towards enhancing wastewater treatment efficiency and producing clean drinking water. 79,80 However, for the next-generation WEIHN, the application of sensors must extend beyond the water dimension and incorporate the perspectives of infrastructure and humans to meet the divergent demands of centralized and decentralized systems ( Figure 2c). ...
... 83,171 Real-time monitoring and control can augment the capabilities of failure detection and diagnosis and enable swift response and reorganization of individual components without disrupting the overall system. 80 Furthermore, the incorporation of emerging technologies such as artificial intelligence (AI) and MLA can foster the development of predictive maintenance strategies. 172 These strategies will enhance the overall infrastructure performance and mitigate the risk of cascading failures, thereby fostering more robust and efficient decentralized WEIHNs. ...
The escalating challenge of water scarcity, intensified by water-energy interdependency, demands an urgent shift towards sustainable solutions. As this concern heightens, the focus on Water-Energy-Infrastructure-Human Nexus (WEIHN) becomes pivotal to...
... For this reason, WRRFs are increasingly applying real-time sensing technologies in their operational facilities to improve process characterization and achieve energy efficiency gains. 9 While the benefits of carbon monitoring are clear, existing technologies such as online sensors have faced difficulties due to high upfront and maintenance costs, limited sensitivity, and short lifetime. In contrast, bioelectrochemical sensors (BESs), which utilize electrogenic biofilms to convert organic matter to electrical current, are gaining increasing attention due to their sensitivity, fast response time, and low maintenance requirements. ...
... In contrast, bioelectrochemical sensors (BESs), which utilize electrogenic biofilms to convert organic matter to electrical current, are gaining increasing attention due to their sensitivity, fast response time, and low maintenance requirements. 9 BESs can provide real-time amperometric sensory data of soluble carbon concentrations and metabolic activity, 10,11 and act as an early warning system for toxic shocks or overloading events. [12][13][14] BES sensing of soluble carbonaceous compounds has especially been used for volatile fatty acids (VFA) monitoring in anaerobic digestion (AD) units, as the failure of an AD process is often marked by an accumulation of VFA due to a disrupted symbiosis of fermentation reactions in the system. ...
Water resource recovery facility (WRRF) operations personnel increasingly rely on sensor networks for automated control. Bio-electrochemical sensors (BESs), which leverage electrogenic biofilms to generate an amperometric signal of carbon metabolism, are being developed to monitor changes in wastewater composition and detect toxic shock events. This study presents for the first time, a long-term evaluation of a BES installed in the primary effluent channel of a WRRF for 247 days to quantify its sensitivity to organic load variations and assess the impact of abiotic factors on the BES response signal and biofilm composition, using advanced data analysis and microbial techniques. While the BES signal showed a strong correlation to volatile fatty acid (VFA) concentration, other environmental factors impacted the signal significantly. Principal component analysis identified pH, VFA concentration, and temperature to be the main contributors to the total variance of the signal in the entire dataset. 16S rRNA amplicon sequencing of samples obtained from the anodic biofilm of the biosensor determined Trichococcus and Lactococcus to be the dominant genera in the biofilm. While the precision of in situ carbon monitoring was impacted by environmental conditions, singular spectrum analysis of the biosensor signal identified four underlying cycles in the primary effluent (287, 92, 44, and 7 days). These results aligned closely with cycles identified in dissolved oxygen readings in an aeration basin downstream of the BES.
... For NH 4 -N removal, aerobic and anoxic biological nutrient removal (BNR) strategies have commonly been employed in centralized treatment plants. In these large-scale settings, BNR works very well because parameters such as dissolved oxygen (DO), pH, and oxidation/reduction potential (ORP) can be precisely monitored and controlled by operators, and where the costs of operation are generally non-prohibiting (Moore, 2009;Zhang et al., 2020). Newer methods, such as partial nitritation/anammox, have also been implemented at full-scale treatment plants to treat high strength nitrogen wastewater. ...
... However, performance is highly influenced by the varying influent solids concentration and maintaining precise DO levels in the system is critical (Lackner et al., 2014;Vlaeminck et al., 2009). For small-scale NSSS, aerobic and anoxic BNR processes have limited application due to their high energy demand for operation, the requirement of high precision control, the availability of reliable small-scale sensors for system monitoring, and the intermittent nature of NSSS (WSDH, 2005;Zhang et al., 2020). Furthermore, these BNR processes typically do not recover nitrogen for downstream reuse. ...
Natural zeolite clinoptilolite was used as the primary ammonium removal method from the permeate of an anaerobic membrane bioreactor (AnMBR) treating high-strength blackwater generated from a community toilet facility. This zeolite-based nutrient capture system (NCS) was a sub-component of a non-sewered sanitation system (NSSS) called the NEWgenerator and was field tested for 1.5 years at an informal settlement in South Africa. The NCS was operated for three consecutive loading cycles, each lasting 291, 110, and 52 days, respectively. Both blackwater (from toilets) and blackwater with yellow water (from toilets and urinals) were treated during the field trial. Over the three cycles, the NCS was able to remove 80 ± 28%, 64 ± 23%, and 94 ± 11%, respectively, of the influent ammonium. The addition of yellow water caused the rapid exhaustion of zeolite and the observed decrease of ammonium removal during Cycle 2. After Cycles 1 and 2, onsite regeneration was performed to recover the sorption capacity of the spent zeolite. The regenerant was comprised of NaCl under alkaline conditions and was operated as a recycle-batch to reduce the generation of regenerant waste. Modifications to the second regeneration process, including an increase in regenerant contact time from 15 to 30 hours, improved the zeolite regeneration efficiency from 76 ± 0.7% to 96 ± 1.0%. The mass of recoverable ammonium in the regenerant was 2.63 kg NH4-N and 3.15 kg NH4-N after Regeneration 1 and 2, respectively. However, the mass of ammonium in the regenerant accounted for 52.8% and 54.4% of the estimated NH4-N originally sorbed onto the zeolite beds after Cycles 1 and 2, respectively. The use of zeolite clinoptilolite is a feasible method for ammonium removal by NSSS that observe variable nitrogen loading rates, but further research is still needed to recover the nitrogen from the regenerant waste.
... An online monitoring approach would be preferred instead. Advances in instrumentation over the past two decades have enabled online monitoring of flows and physicochemical characteristics, pushing forward the digitalization of the water sector (Duffy & Regan 2017;Zhang et al. 2020). Online monitoring has the advantage of capturing wastewater composition dynamics at reduced costs compared to grab sampling for long-term studies. ...
This study aims to investigate the differences in intra-urban catchments with different characteristics through real-time wastewater monitoring. Monitoring stations were installed in three neighbourhoods of Barcelona to measure flow, total chemical oxygen demand (COD), pH, conductivity, temperature, and bisulfide (HS-) for 1 year. Typical wastewater profiles were obtained for weekdays, weekends, and holidays in the summer and winter seasons. The results reveal differences in waking up times and evening routines, commuting behaviour during weekends and holidays, and water consumption. The pollutant profiles contribute to a better understanding of pollution generation in households and catchment activities. Flows and COD correlate well at all stations, but there are differences in conductivity and HS- at the station level. The article concludes by discussing the operational experience of the monitoring stations.
... A wide range of physical and chemical parameters relevant to operation of WWTPs can be measured continuously or semi-continuously with commonly used sensors and analysers (Ingildsen & Olsson 2016). Development of novel instruments is still taking place, for instance, for measurement of organic carbon, metals, and emerging contaminants in wastewaters, for more affordable and improved nutrient sensors (Zhang et al., 2020), and for enabling continuous monitoring of some parameters relevant to optimization of anaerobic digestion processes (e.g., measuring individual volatile fatty acid species) (Jimenez et al., 2015). Figure 1 shows a scheme of the water line and the sludge line including the typical sensors that could usually be deployed within a standard wastewater facility. ...
To protect human health and natural ecosystems, wastewater treatment plants (WWTPs) have been traditionally designed to remove pollutants from wastewater. With remarkable success WWTPs have adapted to increasingly stringent discharge limits over the years. Nowadays, municipal wastewater treatment facilities are facing a double transition. On the one hand, the transition towards sustainability and the circular water economy, in which resource recovery from wastewater (water recovery, energy recovery and nutrient recovery) plays a fundamental role for its effective implementation. Note that the incorporation of any resource recovery process in a WWTP will immediately turn it into a water resource recovery facility (WRRF). On the other hand, the digital transition, which aims at making the operation of these facilities smart and that undoubtedly could have a synergistic effect together with the paradigm shift towards the effective implementation of circular water economy. To make our current facilities smart, there is a growing interest in finding the way to convert the collected process data into intelligent actions for improving their operation. This is not an easy task for many reasons: - the harsh environment in which the instrumentation has to work (corrosive, sludgy, biofilm formation with biological activity…), - almost complete absence of metadata that would make it easy the interpretation of the process data that it is being collected and that would enable its future use, - the almost complete absence of automated data quality assurance, required to avoid “garbage in – garbage out”- the ever-increasing number of process sensors available (data overload), that must be properly processed and made easily available for further use to make them useful- large amounts of data are collected and stored in databases but not wisely used, thus, resulting in data graveyards, - the excessive cost of nutrient and organic matter sensors/analysers which moreover are labour maintenance intensive, fact that restrict their availability to the range of large facilities, thus, they are not usually available for small size facilities (which are the vast majority). - the intelligent sensors and data-driven models must be maintainable by the plant workers (not by Data scientists), - the lack of process expertise in the development of the artificial intelligent tools, - plant operators are often accustomed to their operational routines and, therefore, cultural change is needed in the organization for successful digital transition and adopting new intelligent tools. The progress in computing capabilities together with the large amount of collected process data in WWTPs have created the perfect storm for the machine learning boom we are observing, but all the aforementioned issues can make the incredible digital transition opportunity that exists today completely lost. In an attempt to avoid this disaster, this paper tries to shed light on the path towards increasing the value of the large amount of data that nowadays are being collected in WWTPs and WWRFs. Thus, digital transition could be safely embraced and the enormous potential of data analytics fully exploited, enabling it to play an essential role in the future automation and operation of our municipal facilities.
... In addition, it is very difficult to clean the chemicals, oil and petroleum wastes in the wastes by themselves [3]. Such wastes need to be subjected to a special cleaning process [4]. Waste water treatment plants are established in order to clean the waste water generated after use in places where there is collective life and production [5]. ...
After using clean water, giving it back to nature causes environmental pollution. Treatment processes are used to clean the used wastewater. The water passed through various processes is cleaned and given to the environment. Some organizations are in charge of providing and cleaning water in collective settlements. However, the inspection is carried out by a ministry from a single point. The sensor data of the wastewater treatment plants from a single point are continuously monitored and a sample request can be made from the treated water at any time. In this study, an automation has been developed by the Ministry of Environment, Urbanization and Climate Change of the Republic of Turkey to automatically monitor the wastewater treatment plant. This automation was operated by applying it to the treatment plant of Bursa Hasanağa Organized Industrial Zone.
... Most studies use 60-80% data for training and the remaining data for testing. Some researchers have made an attempt to design various sensors to enable rapid and accurate real-time monitoring and WWT process automation by real-time sensing, data analysis and online controls [57][58][59][60]. For the monitoring of influent water, some water quality parameters, such as BOD, COD, pH, DO, flow rate, temperature and initial pollutant concentration, are easily obtained and used for the inputs of AI models, while for the monitoring of effluent water, some water quality parameters, such as effluent BOD, COD, pH, DO and pollutant concentration, are usually used to evaluate the effect of WWT or the performance of wastewater treatment plants (WWTPs). ...
In recent years, artificial intelligence (AI), as a rapidly developing and powerful tool to solve practical problems, has attracted much attention and has been widely used in various areas. Owing to their strong learning and accurate prediction abilities, all sorts of AI models have also been applied in wastewater treatment (WWT) to optimize the process, predict the efficiency and evaluate the performance, so as to explore more cost-effective solutions to WWT. In this review, we summarize and analyze various AI models and their applications in WWT. Specifically, we briefly introduce the commonly used AI models and their purposes, advantages and disadvantages, and comprehensively review the inputs, outputs, objectives and major findings of particular AI applications in water quality monitoring, laboratory-scale research and process design. Although AI models have gained great success in WWT-related fields, there are some challenges and limitations that hinder the widespread applications of AI models in real WWT, such as low interpretability, poor model reproducibility and big data demand, as well as a lack of physical significance, mechanism explanation, academic transparency and fair comparison. To overcome these hurdles and successfully apply AI models in WWT, we make recommendations and discuss the future directions of AI applications.
... Despite the plethora of literature on the applications of ML techniques to address individual problems within the WWTFs, a paucity of information remains regarding how those applications can be organised. In the wastewater community, gaps exist between the education of practitioners and the understanding necessary to implement advanced ML applications within wastewater treatment [42]. Thus, the objective of this review is to facilitate the application of machine learning techniques for WWTFs through a presentation of a logical organisational framework for ML applications in WWTFs. ...
... In general, knowing the pollutant load of influent waters in sanitation systems in real time enables the implementation and development of different management strategies that can help protect the environment and help the water bodies to receive lower pollution loads, in both dry weather as well as during rainfall events (Gruber et al., 2006;Meyer et al., 2019). Decisions such as when to start or end a discharge during a rainfall event, as well as the oxygen requirements and internal recirculation in the biological processes of a WWTP can be predicted and managed once the pollution load information is known in sufficient time (Hochedlinger et al., 2006b;Zhang et al., 2020). Moreover, real-time monitoring of the quality of circulating water throughout industrial areas can help to control and enforce compliance with the maximum pollutant loads allowed (Thomas and Thomas, 2022). ...
Interest is growing in simple, fast and inexpensive systems to analyze urban wastewater quality in real time. In this research project, a methodology is presented for the characterization of COD, BOD5, TSS, TN, and TP of wastewater samples, without the need to alter the samples or use chemical reagents, from a few wavelengths, belonging to the different color groups that compose the visible spectrum in isolation: (380-700 nm): violet (380-427 nm), blue (427-476 nm), cyan (476-497 nm), green (497-570 nm), yellow (570-581 nm), orange (581-618 nm), and red (618-700 nm). In this study, about 650 raw and treated urban wastewater samples from over 43 WWTPs and a total of 36 estimation models based on genetic algorithms have been calculated. Seven models were calculated for each pollutant parameter; one model for each color group of the visible spectrum, except for TN, which includes an additional model combining the wavelengths of the violet and red region of the spectrum. All the calculated models showed high accuracy, with an R2 between 80 and 85 % for COD, BOD5 and TSS, and 66-74 % for TN and TP. The tests carried out have shown the accuracy of the models of the different color groups to be very close to each other. However, it is noted that the models making use of the wavelengths between 497 and 570 nm (green) were the ones that showed the best performance in all the parameters under study. This research work lays the foundations for the development of cheaper, faster, and simpler wastewater monitoring and characterization equipment.
... Image preprocessing and feature extraction are the critical steps in image recognition technology. Image preprocessing determines the quality of the subsequent image processing stages, and the image recognition result depends mainly on the image feature extraction process [4]. However, all feature extraction algorithms have advantages in computational speed and accuracy. ...
This paper presents an in-depth study of a statistical system using image feature extraction technology for cultural heritage tourism information. Based on the specificity of the tourism industry, it combines the technical architecture, framework of image feature extraction, GIS basic design, database design, and other related theories; realizes visual mapping through relevant tourism statistics in GIS; and uses the spatial analysis capability of ArcGIS software to complete the statistical scheme of cultural heritage tourism information data in the context of Internet big data. In this paper, the image preprocessing process is accelerated by the parallel optimization of the filtering operation and down-sampling operation in the relevant image preprocessing algorithm. The preprocessed images can effectively improve the recognition effect of the image feature extraction and classification stage. Based on cultural heritage tourism information mining results, the design and development of the tourism information statistics platform are carried out. The platform is built and developed using GIS secondary development technology and HTML + CSS front-end development technology API, Echarts, and other image feature components. The platform enables the massive and disorganized tourism text information to be displayed in a way that is easier to understand in both maps and text. This paper’s research on critical technologies provides technical support for image feature extraction of tourism statistics, which has specific practical value. Implementing the cultural heritage tourism platform offers a visual operating environment for tourism, enabling cultural heritage tourism businesses to tap into user preferences so that users can effectively access the travel options that interest them, which is conducive to promoting the development of travel recommendation platforms.
... While the feasibility of pollutant detection through spectroscopic methods has been already established, most if not all studies have stagnated at the proof-of-concept stage due to limitations of the methodology, reduced testing equipment availability or lack of procedural optimization (Chen et al., 2020;Zhang et al., 2020). Spectroscopy diagnostic methods have matured to a good enough level where taking the next step towards commercial applications must be considered. ...
PFAS are a very diverse group of anthropogenic chemicals used in various consumer and industrial products. The properties that characterize are their low degradability as well as their resistance to water, oil and heat. This results in their high persistence in the environment and bioaccumulation in different organisms, causing many adverse effects on the environment as well as in human health. Some of their effects remain unknown to this day. As there are thousands of registered PFAS, it is difficult to apply traditional technologies for an efficient removal and detection for all. This has made it difficult for wastewater treatment plants to remove or degrade PFAS before discharging the effluents into the environment. Also, monitoring these contaminants depends mostly on chromatography-based methods, which require expensive equipment and consumables, making it difficult to detect PFAS in the environment. The detection of PFAS in the environment, and the development of technologies to be implemented in tertiary treatment of wastewater treatment plants are topics of high concern. This study focuses on analyzing and discussing the mechanisms of occurrence, migration, transformation, and fate of PFAS in the environment, as well the main adverse effects in the environment and human health. The following work reviews the recent advances in the development of PFAS detection technologies (biosensors, electrochemical sensors, microfluidic devices), and removal/degradation methods (electrochemical degradation, enzymatic transformation, advanced oxidation, photocatalytic degradation). Understanding the risks to public health and identifying the routes of production, transportation, exposure to PFAS is extremely important to implement regulations for the detection and removal of PFAS in wastewater and the environment.
... However, those algorithms require large volumes of representative training data relied upon capable sensors that are severely under developed. 12 A critical step is developing reliable sensors and data processing algorithms to present realtime LTCM sensor data for variable inputs and complex dynamics. ...
... There are also other algorithms for both regression and classification, which include support vector machine, decision tree, random forest and new hybrid models [22,23]. Aside from the above algorithms, artificial neural network (ANN), especially feedforward back-propagation neural network (FFNN in short), is the most widely used method in the prediction of effluent water quality due to its fast operation speed and good fitting effect for non-linear problems [18,19,[24][25][26][27][28][29]. These ANN models have also been applied for predicting the effluent quality of processes including a membrane bioreactor [30], a sequential batch reactor [31], an anoxic/oxic system [32][33][34] and aerobic granular sludge reactors [35]. ...
To provide real-time prediction of wastewater treatment plant (WWTP) effluent water quality, a machine learning (ML) model was developed by combining an improved feedforward neural network (IFFNN) with an optimization algorithm. Data used as input variables of the IFFNN included hourly influent water quality parameters, influent flow rate and WWTP process monitoring and operational parameters. Additionally, input variables included historical effluent water quality parameters for future prediction. The model was demonstrated in a WWTP in Jiangsu Province, China, where prediction of effluent chemical oxygen demand (COD) and total nitrogen (TN) with large variations were tested. Relative to the traditional feedforward neural network (FFNN) model without considering historical effluent water quality parameter input, the IFFNN enhanced prediction performance by 52.3% (COD) and 72.6% (TN) based on the mean absolute percentage errors of test datasets, after its model structure was optimized with a genetic algorithm (GA). The problem of over-fitting could also be overcome through the use of the IFFNN, with the determination of coefficient increased from 0.20 to 0.76 for test datasets of effluent COD. The GA-IFFNN model, which was efficient in capturing complex non-linear relationships and extrapolation, could be a useful tool for real-time direction of regulatory changes in WWTP operations.
... With increased focus on the United Nations Sustainable Development Goals (SDGs) and increasing energy prices, there has been an increased interest in optimizing the performance of Wastewater Treatment Plants (WWTP) and Wastewater Recovery Facilities, prospectively referred to as WWTPs in this article. Optimizing the operation of WWTPs is the topic of several studies [1] where some of the more complex control systems include the energy usage and economics [2,3]. Several companies offer software for real time control of WWTP; however, few focus on the data quality involved [4]. ...
Sensor drift in Wastewater Treatment Plants (WWTPs) reduces the efficiency of the plants and needs to be handled. Several studies have investigated anomaly detection and fault detection in WWTPs. However, these solutions often remain as academic projects. In this study, the gap between academia and practice is investigated by applying suggested algorithms on real WWTP data. The results show that it is difficult to detect drift in the data to a sufficient level due to missing and imprecise logs, ad hoc changes in control settings, low data quality and the equality in the patterns of some fault types and optimal operation. The challenges related to data quality raise the question of whether the data-driven approach for drift detection is the best solution, as this requires a high-quality data set. Several recommendations are suggested for utilities that wish to bridge the gap between academia and practice regarding drift detection. These include storing data and select data parameters at resolutions which positively contribute to this purpose. Furthermore, the data should be accompanied by sufficient logging of factors affecting the patterns of the data, such as changes in control settings.
... Compared with preventive testing, condition monitoring systems use more sensitive sensors to collect insulation deterioration information in power equipment operation and rely on computer networks and rich software support to process and identify the information [12]. The online condition monitoring system uses some online preventive test programs to reflect the operating status of power equipment more accurately, to achieve a comprehensive diagnosis of the operating status of power equipment, and to promote the transition from periodic maintenance to condition maintenance of power equipment [13]. ...
In this paper, we use wireless sensing technology to conduct an in-depth study and analysis of data-driven power energy equipment in the context of carbon neutrality. For the high-order uncertainty of renewable energy power generation and the nonlinearity of the tidal equation, a set of orthogonal bases under arbitrary probability space can be constructed by itself using the high-order information of renewable energy power generation statistics, and then, polynomials fit the state variables such as voltage in the tidal equation using the orthogonal bases and calculate the fitting parameters using the stochastic Gallatin integration method. Based on the analysis of the online monitoring project of the main power equipment in the substation, a substation power equipment condition monitoring system is designed to realize the real-time monitoring of the temperature status of power equipment, substation smoke and temperature, and humidity environment, and the feasibility and advanced of the system are verified by elaborating the analysis. Using wireless transmission to send real-time temperature information to the monitoring background, the infrared thermal imaging online monitoring system is designed around the front-end data acquisition system, transmission network, background data processing, and display module. Saving-investment equilibrium means that total investment equals total savings; government budget balance can be achieved through government savings or deficits. When both supply chain approaches enter into the same environmental competition, the added value that consumers value the product because of the carbon-neutral approach becomes smaller in the coefficient constraint of green effort investment cost due to the competition between the two supply chains, which in turn increases the green investment cost of the supply chain with the green effort carbon-neutral approach, indicating that in the case of competition, consumers, when faced with two products produced by two supply chains, are interested in the product with the carbon-neutral approach has less value-added, and some of the demand is shifted to the product not produced with the carbon-neutral approach.
The combination of biological processes with electrical monitoring and control mechanisms has resulted in bioelectronics, a technology that holds significant potential for improving waste treatment systems. In this study, the authors investigate the use of bioelectronics in waste treatment systems and their potential to contribute to environmental sustainability and resource recovery using TAM theory. Bioelectronics, which combines biological processes with electrical monitoring and control systems, has emerged as a viable method for waste treatment optimization. Waste treatment facilities can use bioelectronic sensors, actuators, and monitoring devices to optimize different process parameters, improve treatment efficiency, and reduce their environmental impact. Bioelectronics improves waste treatment operations by merging modern electronic monitoring and control systems with biological treatment mechanisms.
This review emphasizes the significance of formulating control strategies for biological and advanced oxidation process (AOP)-based wastewater treatment systems. The aim is to guarantee that the effluent quality continuously aligns with environmental regulations while operating costs are minimized. It highlights the significance of understanding the dynamic behaviour of the process in developing effective control schemes. The most common process control strategies in wastewater treatment plants (WWTPs) are explained and listed. It is emphasized that the proper control scheme should be selected based on the process dynamic behaviour and control goal. This study further discusses the challenges associated with the control of wastewater treatment processes, including inadequacies in developed models, the limitations of most control strategies to the simulation stage, the imperative requirement for real-time data, and the financial and technical intricacies associated with implementing advanced controller hardware. It is discussed that the necessity of the availability of real-time data to achieve reliable control can be achieved by implementing proper, accurate hardware sensors in suitable locations of the process or by developing and implementing soft sensors. This study recommends further investigation on available actuators and the criteria for choosing the most appropriate one to achieve robust and reliable control in WWTPs, especially for biological and AOP-based treatment approaches.
Integrating the Internet of Things (IoT) and Artificial Intelligence (AI) in smart water management revolutionizes sustainable water resource utilization. This comprehensive review explores these technologies' benefits, challenges, regulatory implications, and future trends. Smart water management enhances operational efficiency, predictive maintenance, and resource conservation while addressing data security and infrastructure investment challenges. Regulatory frameworks play a pivotal role in shaping the responsible deployment of AI and IoT, ensuring data privacy and ethical use. Future trends include advanced sensors, decentralized systems, quantum computing, and blockchain for enhanced water data security. The alignment with Sustainable Development Goals (SDGs) underscores the transformative potential of smart water management in achieving universal access to clean water, climate resilience, and inclusive, sustainable development. As we embrace these technologies, collaboration, public awareness, and ethical considerations will guide the evolution of intelligent and equitable water management systems.
Water reuse is rapidly becoming an integral feature of resilient water systems, where municipal wastewater undergoes advanced treatment, typically involving a sequence of ultrafiltration (UF), reverse osmosis (RO), and an advanced oxidation process (AOP). When RO is used, a concentrated waste stream is produced that is elevated in not only total dissolved solids but also metals, nutrients, and micropollutants that have passed through conventional wastewater treatment. Management of this RO concentrate—dubbed municipal wastewater reuse concentrate (MWRC)—will be critical to address, especially as water reuse practices become more widespread. Building on existing brine management practices, this review explores MWRC management options by identifying infrastructural needs and opportunities for multi-beneficial disposal. To safeguard environmental systems from the potential hazards of MWRC, disposal, monitoring, and regulatory techniques are discussed to promote the safety and affordability of implementing MWRC management. Furthermore, opportunities for resource recovery and valorization are differentiated, while economic techniques to revamp cost-benefit analysis for MWRC management are examined. The goal of this critical review is to create a common foundation for researchers, practitioners, and regulators by providing an interdisciplinary set of tools and frameworks to address the impending challenges and emerging opportunities of MWRC management.
Purpose: This study investigates how the absorptive management control system affects in the development of radical innovation. The purpose of this paper is to analyze the influence of using different types of management control (enabling levers and constraining levers) on radical innovation, which is mediated by knowledge sharing and moderated by technological turbulence. Methods: This study uses a methodology partial least squares structural equation modeling and qualitative comparative analysis have been applied to a sample of 253 Indonesian startups. Results and Conclusion: Enabling levers and constraining levers have an influence in generating the necessary knowledge sharing for a company to transfer information within, which affects the production of radical innovation, meaning innovation that is in line with market development and demand. Technological turbulence, as a moderator, has a relationship between knowledge sharing and radical innovation. Implication of research: The results can be useful for managers of startups in the traction stage, as the research highlights different management controls and possible combinations that can be used to drive radical innovation. Additionally, the research highlights the role of knowledge sharing in promoting radical innovation, especially in the context of technological turbulence.
Developing advanced onsite wastewater treatment systems (OWTS) requires accurate and consistent water quality monitoring to evaluate treatment efficiency and ensure regulatory compliance. However, off-line parameters such as chemical oxygen demand (COD), total suspended solids (TSS), and Escherichia coli (E. coli) require sample collection and time-consuming laboratory analyses that do not provide real-time information of system performance or component failure. While real-time COD analyzers have emerged in recent years, they are not economically viable for onsite systems due to cost and chemical consumables. This study aimed to design and implement a real-time remote monitoring system for OWTS by developing several multi-input and single-output soft sensors. The soft sensor integrates data that can be obtained from well-established in-line sensors to accurately predict key water quality parameters, including COD, TSS, and E. coli concentrations. The temporal and spatial water quality data of an existing field-tested OWTS operated for almost two years (n = 56 data points) were used to evaluate the prediction performance of four machine learning algorithms. These algorithms, namely, partial least square regression (PLS), support vector regression (SVR), cubist regression (CUB), and quantile regression neural network (QRNN), were chosen as candidate algorithms for their prior application and effectiveness in wastewater treatment predictions. Water quality parameters that can be measured in-line, including turbidity, color, pH, NH4⁺, NO3–, and electrical conductivity, were selected as model inputs for predicting COD, TSS, and E. coli. The results revealed that the trained SVR model provided a statistically significant prediction for COD with a mean absolute percentage error (MAPE) of 14.5% and R² of 0.96. The CUB model provided the optimal predictive performance for TSS, with a MAPE of 24.8% and R² of 0.99. None of the models were able to achieve optimal prediction results for E. coli; however, the CUB model performed the best with a MAPE of 71.4% and R² of 0.22. Given the large fluctuation in the concentrations of COD, TSS, and E. coli within the OWTS wastewater dataset, the proposed soft sensor models adequately predicted COD and TSS, while E. coli prediction was comparatively less accurate and requires further improvement. These results indicate that although water quality datasets for the OWTS are relatively small, machine learning-based soft sensors can provide useful predictive estimates of off-line parameters and provide real-time monitoring capabilities that can be used to make adjustments to OWTS operations.
Widespread implementation of on-site water reuse systems is hindered by the limited ability to ensure the level of treatment and protection of human health during operation. In this study, we tested the ability of five commercially available online sensors (free chlorine (FC), oxidation-reduction potential (ORP), pH, turbidity, UV absorbance at 254 nm) to predict the microbial water quality (removal of bacteria, removal of viruses, regrowth of bacteria) in membrane bioreactors followed by chlorination using logistic regression-based and mechanism-based models. We found that FC and ORP alone could predict the microbial water quality well, with ORP-based models generally performing better. We further observed that prediction accuracy did not increase when data from multiple sensors were integrated. We propose a methodology to link these online measurements to risk-based water quality targets, providing operation setpoints protective of human health for specific combinations of wastewaters and reuse applications. For instance, we recommend a minimum ORP of 705 mV to ensure a virus log-removal of 5, and an ORP of 765 mV for a log-removal of 6. These setpoints were selected to ensure that the percentage of events where the water is predicted to meet the quality target when it does not remains below 5%. Such a systematic approach to set sensor targets could be used in the development of water reuse guidelines and regulations that aim to cover a range of reuse applications with differential risks to human health.
Long-term continuous monitoring (LTCM) of water quality can provide high-fidelity datasets essential for executing swift control and enhancing system efficiency. One roadblock for LTCM using solid-state ion-selective electrode (S-ISE) sensors is biofouling on the sensor surface, which perturbs analyte mass transfer and deteriorates the sensor reading accuracy. This study advanced the anti-biofouling property of S-ISE sensors through precisely coating a self-assembled channel-type zwitterionic copolymer poly(trifluoroethyl methacrylate-random-sulfobetaine methacrylate) (PTFEMA-r-SBMA) on the sensor surface using electrospray. The PTFEMA-r-SBMA membrane exhibits exceptional permeability and selectivity to primary ions in water solutions. NH4+ S-ISE sensors with this anti-fouling zwitterionic layer were examined in real wastewater for 55 days consecutively, exhibiting sensitivity close to the theoretical value (59.18 mV/dec) and long-term stability (error <4 mg/L). Furthermore, a denoising data processing algorithm (DDPA) was developed to further improve the sensor accuracy, reducing the S-ISE sensor error to only 1.2 mg/L after 50 days of real wastewater analysis. Based on the dynamic energy cost function and carbon footprint models, LTCM is expected to save 44.9% NH4+ discharge, 12.8% energy consumption, and 26.7% greenhouse emission under normal operational conditions. This study unveils an innovative LTCM methodology by integrating advanced materials (anti-fouling layer coating) with sensor data processing (DDPA).
Energy demand of wastewater treatment plants (WWTPs) is counted as an important energy consumer especially in developed and developing countries. Environmental concerns and pressure on freshwater resources have caused to implement energy-intensive wastewater treatment processes and thus highly energy demanding WWTPs. Although wastewater itself holds a substantial amount of chemical and thermal energy, conventional management practices are not capable of recovery this energy content. Since there has been a paradigm shift in wastewater treatment, wastewater is considered as an important energy source. Several new applications have been developed to recover energy content of wastewater as much as possible. The recent developments in materials and equipment also promise a significant opportunity to improve energy balance of WWTPs. Adopting new measures and enhancing energy recovery show that WWTPs do not have to be energy consumers no longer, instead they can be energy-neutral and even energy-positive systems.
Per- and polyfluoroalkyl substances (PFAS) make up a large group of persistent anthropogenic chemicals which are difficult to degrade and/or destroy. PFAS are an emerging class of contaminants, but little is known about the long-term health effects related to exposure. In addition, technologies to identify levels of contamination in the environment and to remediate contaminated sites are currently inadequate. In this opinion-type discussion paper, a team of researchers from the University of Connecticut and the University at Albany discuss the scientific challenges in their specific but intertwined PFAS research areas, including rapid and low-cost detection, energy-saving remediation, the role of T helper cells in immunotoxicity, and the biochemical and molecular effects of PFAS among community residents with measurable PFAS concentrations. Potential research directions that may be employed to address those challenges and improve the understanding of sensing, remediation, exposure to, and health effects of PFAS are then presented. We hope our account of emerging problems related to PFAS contamination will encourage a broad range of scientific experts to bring these research initiatives addressing PFAS into play on a national scale.
Leveraging the potential flexibility of large electrical loads has become an attractive option for maintaining grid reliability, especially in electric grids with high penetrations of variable renewable energy. The water sector is a particularly attractive option for demand‐side flexibility due to its vast water storage infrastructure, large interruptible pumping loads, and energy generation opportunities. Shifting the timing of water supply and wastewater utility operations can reduce peak load and temporally align energy‐consuming activities with periods of cheap electricity prices and/or high renewable energy generation. This paper presents a general overview of demand‐side management strategies in water and wastewater systems, focusing primarily on demand‐response measures. We find that while there is consensus in the literature about the potential for water systems to provide flexible demand‐side management services, there is a need for developing comprehensive water‐energy models to examine the value of load management as a source of revenue for water utilities and as a source of flexibility for the electric grid. More experimental studies and simulation efforts are also needed to address the technical complexities and water quality concerns associated with interrupting water and wastewater utility operations.
This article is categorized under:
• Engineering Water > Sustainable Engineering of Water
• Engineering Water > Planning Water
Abstract
Flexible electric loads can serve as valuable resources to respond to electric grid needs, and especially to balance variable renewables. We review the literature to highlight the potential and limitations of demand response (DR) opportunities in the water and wastewater sectors.
Tissue engineering endeavors to regenerate tissues and organs through appropriate cellular and molecular interactions at biological interfaces. To this aim, bio-mimicking scaffolds have been designed and practiced to regenerate and repair dysfunctional tissues by modifying cellular activity. Cellular activity and intracellular signaling are performances given to a tissue as a result of the function of elaborated electrically conductive materials. In some cases, conductive materials have exhibited antibacterial properties; moreover, such materials can be utilized for on-demand drug release. Various types of materials ranging from polymers to ceramics and metals have been utilized as parts of conductive tissue engineering scaffolds, having conductivity assortments from a range of semi-conductive to conductive. The cellular and molecular activity can also be affected by the microstructure; therefore, the fabrication methods should be evaluated along with an appropriate selection of conductive materials. This review aims to address the research progress toward the use of electrically conductive materials for the modulation of cellular response at the material-tissue interface for tissue engineering applications.
Wastewater treatment systems are nowadays evolving into systems where energy and resources are recovered from wastewater. This work presents the long term operation of a demo-scale pilot plant (7.8 m3) with a novel configuration named as mainstream SCEPPHAR (ShortCut Enhanced Phosphorus and polyhydroxyalkanoate (PHA) Recovery) and based on two sequencing batch reactors (R1-HET and R2-AUT). This is the first report of an implementation at demo scale and under relevant operational conditions of the simultaneous integration of shortcut nitrification, P recovery and production of sludge with a higher PHA content than conventional activated sludge. An operating period under full nitrification mode achieved successful removal efficiencies for total N, P and CODT (86 ± 12%, 93 ± 9% and 79 ± 6%). In the following period, nitrite shortcut (with undetectable activity of nitrite oxidising bacteria) was achieved by implementing automatic control of the aerobic phase length in R2-AUT using ammonium measurement and operating at a lower sludge retention time. Similar N, P and CODT removal efficiencies to the full nitrification period were obtained. P-recovery from the anaerobic supernatant of R1-HET was achieved in a separate precipitator by increasing pH and dosing MgCl2, recovering an average value of 45% of the P in the influent as struvite precipitate, with a peak up to 63%. These values are much higher than the typical values of sidestream P-recovery (12%). Regarding PHA, a percentage in the biomass in the range 6.9-9.2% (gPHA·g-1TSS) was obtained.
Since anaerobic wastewater treatment is a nonlinear and complex biochemical process, reasonable monitoring and control are needed to keep it operating stably and efficiently. In this paper, a least-square support-vector machine (LS-SVM) was employed to construct models for the prediction of effluent chemical oxygen demand (COD) in an anaerobic wastewater treatment system. The result revealed that the performance of the steady-state model based on LS-SVM for predicting effluent COD was acceptable, with the maximum relative error (RE) of 11.45%, the mean average percentage error (MAPE) of 0.79% and the root mean square error (RMSE) of 3.08 when training, and the performance fell slightly when testing. Even though, the correlation coefficient value (R) between the predicted value and the actual value of 0.9752 could be achieved, which means this model can predict the variation of effluent COD in general. The dynamic-state models under three kinds of shock loads, which were concentration, hydraulic, and bicarbonate buffer absent, showed good forecasting performance, the correlation coefficient values (R) all excelled 0.99. Among these three shocks, the dynamic LS-SVM model under bicarbonate buffer absent shock achieved the optimal performance and followed by the dynamic-state model under hydraulic shock. This paper provides a meaningful reference to improve the monitoring level of the anaerobic wastewater treatment process.
This work aims to obtain full-scale N2O emission characteristics translatable into viable N2O control strategies and conduct full-scale testing of the proposed N2O control concepts. Data of a long-term monitoring campaign was firstly used to quantify full-scale N2O emission and probe into the seasonal pattern. Then trends between N2O production/emission and process variables/conditions during typical operating cycles were revealed to explore the dynamic N2O emission behavior. A multivariate statistical analysis was performed to find the dependency of N2O emission on relevant process variables. The results show for the first time that relatively low/high N2O emission took place in seasons with a decreasing/increasing trend of water temperature, respectively. Aerobic phase contributed to N2O production/emission probably mainly through the hydroxylamine pathway. Comparatively, heterotrophic bacteria had a dual role in the anoxic phase and could be responsible for both net N2O production and consumption. Incomplete denitrification might contribute mainly to the N2O production/emission in the anoxic phase and the accumulation of N2O to be significantly emitted in the following cycle due to the competition between different denitrification steps for electron donors. Therefore, properly extending the length of anoxic phase could serve as a potential control means to regulate N2O accumulation in the anoxic phase. The full-scale testing not only verified the efficacy of reduced dissolved oxygen set-point in reducing N2O emission by 60%, but also confirmed the proposed concepts of control over the aerobic and anoxic phases collectively.
Gaining rapid knowledge of dissolved organic matter (DOM) proves to be decisive for wastewater treatment plant operators in efforts to achieve good treatment efficiency in light of current legislation. DOM can be monitored by application of fluorescence spectroscopy both online and in real time in order to derive an assessment of DOM oxidation potential. This work presents an eco-friendly alternative method for measuring the soluble chemical oxygen demand (COD) in raw sewage by means of three-dimensional fluorescence spectroscopy. A peak-picking approach has been developed based on a previous parallel factor analysis (PARAFAC) model dedicated to Paris raw sewage. Fluorescence spectroscopy parameters were used to obtain a good prediction model of soluble COD (r2 = 0.799; p < 0.0001; n = 80) for raw sewage. The approach employed in this study serves as a guideline for purposes of implementing online wastewater monitoring and conducting environmentally friendly soluble COD measurements in the laboratory.
The present study was carried out to evaluate the performance of 16.1 MLD sewage treatment plant (STP) located at Brari Nambal (J&K), India. The STP is based on sequential batch reactor (SBR) technology. Wastewater (influent and effluent) samples were analyzed for 14 different physicochemical parameters. Significant variation (P < 0.05) was recorded within and among the wastewaters in pH (F11,1 = 7.49, 26), electrical conductivity (F11,1 = 12.13, 49.94), calcium (F11,1 = 8.58, 91.66), magnesium (F11,1 = 4.68, 132.37), chloride (F11,1 = 10.18, 74.85), sodium (F11,1 = 11.31, 192.64), potassium (F11,1 = 5.98, 52.22) and chemical oxygen demand (F11,1 = 4.16, 267.65), whereas among the wastewaters in total suspended solids (F1 = 165.21), total dissolved solids (F1 = 150.40), biological oxygen demand (F1 = 307.89), ortho-phosphate (F1 = 624.54), total phosphorous (F1 = 336.85) and nitrate nitrogen (F1 = 68.10). Significant negative correlation exists between TSS and EC (r = − 0.796; P < 0.01) and Cl and Ca (r = − 0.646; P < 0.05), whereas significant positive correlation between BOD5 and Ca (r = 0.579; P < 0.05), COD and TSS (r = 0.728; P < 0.01) and ortho-phosphate and pH (r = 0.791; P < 0.01). Maximum decrease was recorded in TP (68.37%) followed by NO3-N (64.88%), COD (63.79%), BOD (59.38%), OP (55.94%), TDS (44.82%) and least in TSS (38%) among parameters which are of prime concern. Six principal components (PCs) have been identified by factor analysis which explained 90.30% of total variance, representing alkaline factor, salts/ions factor, household/water usage factor, dissolved salts factor, soaps/detergents factor and catchment factor. Thus, least reduction in concentration of ortho-phosphate, TDS and TSS is concern when the effluent is disposed off in a water body which is already under the stress of nutrient enrichment/pollution.
Constructed wetlands receiving treated wastewater (CWtw) are placed between wastewater treatment plants and receiving water bodies, under the perception that they increase water quality. A better understanding of the CWtw functioning is required to evaluate their real performance. To achieve this, in situ continuous monitoring of nitrate and ammonium concentrations with ion-selective electrodes (ISEs) can provide valuable information. However, this measurement needs precautions to be taken to produce good data quality, especially in areas with high effluent quality requirements. In order to study the functioning of a CWtw instrumented with six ISE probes, we have developed an appropriate methodology for probe management and data processing. It is based on an evaluation of performance in the laboratory and an adapted field protocol for calibration, data treatment and validation. The result is an operating protocol concerning an acceptable cleaning frequency of 2 weeks, a complementary calibration using CWtw water, a drift evaluation and the determination of limits of quantification (1 mgN/L for ammonium and 0.5 mgN/L for nitrate). An example of a 9-month validated dataset confirms that it is fundamental to include the technical limitations of the measuring equipment and set appropriate maintenance and calibration methodologies in order to ensure an accurate interpretation of data.
Hexavalent chromium (Cr(VI)) is a well-known toxic heavy metal in industrial wastewater, but in situ and real time monitoring cannot be achieved by current methods used during industrial wastewater treatment processes. In this study, a Sediment Microbial Fuel Cell (SMFC) was used as a biosensor for in situ real-time monitoring of Cr(VI), which was the organic substrate is oxidized in the anode and Cr(VI) is reduced at the cathode simultaneously. The pH 6.4 and temperature 25 °C were optimal conditions for the operation. Under the optimal conditions, linearity (R² = 0.9935) of the generated voltage was observed in the Cr(VI) concentration range from 0.2 to 0.7 mg/L. The system showed high specificity for Cr(VI), as other co-existing ions such as Cu2+, Zn2+, and Pb2+did not interfere with Cr(VI) detection. In addition, when the sediment MFC-based biosensor was applied for measuring Cr(VI) in actual wastewater samples, a low deviation (<8%) was obtained, which indicated its potential as a reliable biosensor device. MiSeq sequencing results showed that electrochemically active bacteria (GeobacterandPseudomonas) were enriched at least two-fold on the biofilm of the anode in the biosensor as compared to the SMFC without Cr(VI). Cyclic voltammetry curves indicated that a pair of oxidation/reduction peaks appeared at -111 mV and 581 mV, respectively. These results demonstrated that the proposed sediment microbial fuel cell-based biosensor can be applied as an early warning device for real time in situ detection of Cr(VI) in industrial wastewaters.
The removal of phosphorus (P) from domestic wastewater is primarily to reduce the potential for eutrophication in receiving waters, and is mandated and common in many countries. However, most P-removal technologies have been developed for use at larger wastewater treatment plants that have economies-of-scale, rigorous monitoring, and in-house operating expertise. Smaller treatment plants often do not have these luxuries, which is problematic because there is concern that P releases from small treatment systems may have greater environmental impact than previously believed. Here P-removal technologies are reviewed with the goal of determining which treatment options are amenable to small-scale applications. Significant progress has been made in developing some technologies for small-scale application, namely sorptive media. However, as this review shows, there is a shortage of treatment technologies for P-removal at smaller scales, particularly sustainable and reliable options that demand minimal operating and maintenance expertise or are suited to northern latitudes. In view of emerging regulatory pressure, investment should be made in developing new or adapting existing P-removal technologies, specifically for implementation at small-scale treatment works.
In this study, three-layered feedforward-backpropagation artificial neural network (BPANN) model was developed and employed to evaluate COD removal in upflow anaerobic sludge blanket (UASB) reactor treating industrial starch processing wastewater. At the end of UASB operation, microbial community characterization revealed satisfactory composition of microbes whereas morphology depicted rod-shaped archaea. pH, COD, NH4+, VFA, OLR and biogas yield were selected by principal component analysis and used as input variables. Whilst tangent sigmoid function (tansig) and linear function (purelin) were assigned as activation functions at the hidden-layer and output-layer, respectively, optimum BPANN architecture was achieved with Levenberg-Marquardt algorithm (trainlm) after eleven training algorithms had been tested. Based on performance indicators such as mean squared errors, fractional variance, index of agreement and coefficient of determination (R2), the BPANN model demonstrated significant performance with R2 reaching 87%. The study revealed that, control and optimization of an anaerobic digestion process with BPANN model was feasible.
A novel floc sensor prototype was tested in a Norwegian municipal wastewater treatment plant. The resulting images of flocs, captured using a specially designed software, were analysed by texture image analysis technique—grey level co-occurrence matrix (GLCM). The results of image analysis were merged with the coagulation process measurement data—inlet and outlet wastewater parameters. The data based only on GLCM textural features resulted in 96.6% explained total variance by two principal components and distinguished two classes in the data—low and high outlet turbidity values. The predicted by partial least squares regression (PLSR) coagulant dosages precisely followed the reference dosages, explained Y total variance by 3 factors equals 91.8% for calibration and 77.9% for validation. Results of the studies indicate that the GLCM method and sensor prototype can be used for an improvement of coagulant dosage control. Tested sensor prototype gives a solid basis for development of the low-cost floc sensor.
The online monitoring of dissolved organic matter (DOM) in raw sewage water is expected to better control wastewater treatment processes. Fluorescence spectroscopy offers one possibility for both the online and real-time monitoring of DOM, especially as regards the DOM biodegradability assessment. In this study, three-dimensional fluorescence spectroscopy combined with a parallel factor analysis (PARAFAC) has been investigated as a predictive tool of the soluble biological oxygen demand in 5 days (BOD5) for raw sewage water. Six PARAFAC components were highlighted in 69 raw sewage water samples: C2, C5, and C6 related to humic-like compounds, along with C1, C3, and C4 related to protein-like compounds. Since the PARAFAC methodology is not available for online monitoring, a peak-picking approach based on maximum excitation-emission (Ex-Em) localization of the PARAFAC components identified in this study has been used. A good predictive model of soluble BOD5 using fluorescence spectroscopy parameters was obtained (r² = 0.846, adjusted r² = 0.839, p < 0.0001). This model is quite straightforward, easy to automate, and applicable to the operational field of wastewater treatment for online monitoring purposes.
Emerging contaminants, such as antibiotics, pharmaceuticals, personal care products, hormones, and artificial sweeteners, are recognized as new classes of water contaminants due to their proven or potential adverse effects on aquatic ecosystems and human health. This review provides comprehensive data on the occurrence of 60 emerging contaminants (ECs) in influent, treated effluent, sludge, and biosolids in wastewater treatment plants (WWTPs). In particular, data on the occurrence of ECs in the influents and effluents of WWTPs are systematically summarized and categorized according to geographical regions (Asia, Europe, and North
America). The occurrence patterns of ECs in raw influent and treated effluents of WWTPs between geographical regions were compared and evaluated. Concentrations of most ECs in raw influent in Asian region tend to be higher than those in European and North American countries. Many antibiotics were detected in the influents and effluents of WWTPs at concentrations close to or exceeding the predicted no-effect concentrations (PNECs) for resistance selection. The efficacy of EC removal by sorption and biodegradation during wastewater treatment processes are discussed in light of kinetics and parameters, such as sorption coefficients (Kd) and biodegradation constants (kbiol), and physicochemical properties (i.e. log Kow and pKa). Commonly used sampling and monitoring strategies are critically reviewed. Analytical research needs are identified, and novel investigative approaches for future monitoring studies are proposed.
Biosensors are the devices that capture the biological signal and convert it into a detectable electrical signal. It involves the combination of biological entities like DNA, RNA, and proteins/enzymes to the electrochemical transducers in order to detect and observe certain biological analytes like antibody-antigen interaction. Several types of biosensors have been known that have been successfully employed in the fields of environment, biomedical and food industries to detect and remove certain contaminants, weather non-living or living entities. Amperometric, Optical, Surface Plasmon Resonance, enzymatic, DNA, Phage, and bacterial sensors are the common sensors being employed today. These biosensors can be used for the detection of the broad spectrum of biological analytes and have shown greater responses and success in medical laboratories, food bioanalysis, microbial detection etc. Detection of the lower or higher limits of glucose in body, microbial invasion in body and food, heavy metals detection in soil, water and air-borne microbes, pesticides in water and soil and various harmful chemicals produced by body, can be easily and timely monitored with high precision using the different types of biosensors with few modifications.
Urban wastewater sector is being pushed to optimize processes in order to reduce energy consumption without compromising its quality standards. Energy costs can represent a significant share of the global operational costs (between 50% and 60%) in an intensive energy consumer. Pumping is the largest consumer of electrical energy in a wastewater treatment plant. Thus, the optimal control of pump units can help the utilities to decrease operational costs. This work describes an innovative predictive control policy for wastewater variable-frequency pumps that minimize electrical energy consumption, considering uncertainty forecasts for wastewater intake rate and information collected by sensors accessible through the Supervisory Control and Data Acquisition system. The proposed control method combines statistical learning (regression and predictive models) and deep reinforcement learning (Proximal Policy Optimization). The following main original contributions are produced: (i) model-free and data-driven predictive control; (ii) control philosophy focused on operating the tank with a variable wastewater set-point level; (iii) use of supervised learning to generate synthetic data for pre-training the reinforcement learning policy, without the need to physically interact with the system. The results for a real case-study during 90 days show a 16.7% decrease in electrical energy consumption while still achieving a 97% reduction in the number of alarms (tank level above 7.2 m) when compared with the current operating scenario (operating with a fixed set-point level). The numerical analysis showed that the proposed data-driven method is able to explore the trade-off between number of alarms and consumption minimization, offering different options to decision-makers.
The IWA Task Group on Good Modelling Practice (GMP) has analysed current practice and experience on the use of activated sludge models. The group developed a framework to make modelling more straightforward and that helps structuring the interaction between modellers and the project stakeholders. The GMP protocol is a synthesis of existing procedures, and contains five major steps: (i) Project definition, (ii) Data collection and reconciliation, (iii) Plant model set-up, (iv) Calibration and validation, and (v) Simulation and result interpretation.
Real-time detection of phosphate ions is critical for monitoring the eutrophication status of surface water. We report an ultrasensitive phosphate sensor based on a reduced graphene oxide field-effect transistor (rGO-FET). In this work, rGO nanosheets were employed as sensing channels and an aluminum oxide (Al2O3) film was applied as a passivation layer to separate the sensing channels from the sample solution. The critical thickness of passivation layer – defining a continuous barrier film on the FET sensor was identified, which fully covers the sensing platform and also enables sensitive responses. The detection results showed that ferritin probes have good affinity to phosphate ions in water. It was also found that providing both a high ferritin concentration and an acidic environment improves the immobilized probe density, which increases the analyte adsorption sites and improves the sensing performance. The sensing platform was used to detect phosphate ions in industrial discharge with a lower detection limit of 10 µg/L (105 nM). Ultimately, this inexpensive and micro-sized sensor can provide new opportunities for real-time monitoring of phosphate ions in agriculture and wastewater discharge.
The communities of Bacteria, Archaea and Fungi in a full-scale moving bed biofilm reactor in Rukatunturi (Ruka, Finland, in the Polar Arctic Circle) were analyzed in order to observe their ecological role in the operation of this system at low temperature. The bioreactor achieved efficient removal performances organic matter (> 96%) and for ammonium (> 95%), however a modest removal for total nitrogen (> 63-75%). Bacteria dominant phylo-types changed in relation to the different functions: Trichococcus and Polaromonas for denitrification, Trichococcus and Simplicispira for organic matter removal, and Devosia and Thermomonas for nitrification. Bacteria predicted metagenome related to nitrification confirmed ammonium oxidation in nitrification chamber, and reduction of nitrate and nitrite in the denitrification and organic matter removal chambers. Dynamics of Fungi, dominated by Trichosporonaceae and Ascomycota clones, were linked to aeration conditions but not to substrate concentrations. Archaea communities, dominated by Methanobrevibacter genus and Thermoplasmatales-related clones, were not affected by aeration or substrate concentrations. The results obtained offer a valuable insight into the ecological role of microbial communities in full-scale moving bed biofilm reactor operating under extremely low temperatures.
In water resource recovery facilities, sidestream biological nitrogen removal via anaerobic ammonium oxidation (anammox) is more energy and cost efficient than conventional nitrification-denitrification. However, under mainstream conditions, nitrite oxidizing bacteria (NOB) out-select anammox bacteria for nitrite produced by ammonium oxidizing bacteria (AOB). Therefore, nitrite production is the bottleneck in mainstream anammox nitrogen removal. Nitrate-dependent denitrifying anaerobic methane oxidizing archaea (n-damo) oxidize methane and reduce nitrate to nitrite. The nitrite supply challenge in mainstream anammox implementation could be solved with a microbial community of AOB, NOB, n-damo, and anammox with methane from anaerobic sludge digestion or a mainstream anaerobic membrane bioreactor (AnMBR). The cost and environmental impact of traditional nitrification/dentrification relative to AOB/anammox and AOB/anammox/n-damo systems, with and without an AnMBR were compared with a stoichiometric model. AnMBR implementation reduced costs and emission rates at moderate to high nutrient loading by lowering aeration and sludge handling demands while increasing methane available for cogeneration. AnMBR/AOB/anammox systems reduced cost and GHG emission by up to $0.303/d/m3 and 1.72 kg equiv. CO2/d/m3, respectively, while AnMBR/AOB/anammox/n-damo systems saw a similar reduction of at least $0.300/d/m3 and 1.65 kg equiv. CO2/d/m3 in addition to alleviating the necessity to stop nitrification at nitrate, allowing easier aeration control.
Undergraduate civil engineering curricula must satisfy a variety of constraints, notably any institutional requirements and, to be accredited by ABET, the minimum requirements defined in the ABET General Criterion 3: Student Outcomes, General Criterion 5: Curriculum, and the Program Criteria for Civil and Similarly Named Engineering Programs (also referred to as the civil engineering program criteria, or CEPC). The American Society of Civil Engineers (ASCE) serves as the lead society responsible for content of the CEPC. ASCE also maintains and publishes the Civil Engineering Body of Knowledge (CEBOK), with the third edition (CEBOK3) being published in the spring of 2019. ASCE developed and proposed changes to the CEPC following completion of both prior editions of the CEBOK. ASCE has also established a timeline to consider possible changes to the CEPC based on future editions of the CEBOK, including the CEBOK3. Regardless if or when ASCE actually proposes any changes to the CEPC or whether any changes in the CEPC even occur based on the CEBOK3, many civil engineering programs may consider modifying their curriculum based on the CEBOK3. Therefore, an important step in evaluating the CEBOK3 in terms of undergraduate education, whether considering accreditation or curricular design, is to complete a gap analysis between current ABET engineering accreditation criteria and that portion of the CEBOK3 that is proposed to be attained through undergraduate education. Therefore, the purpose of this paper is to present a comprehensive assessment of the commonalities and differences between the curricular and student outcomes defined by ABET engineering accreditation criteria and those defined in the CEBOK3. Additionally, the paper will include an assessment of the significance and potential impact of the identified differences on civil engineering curricula.
Several potential problems associated with emissions such as odour nuisance may occur in sewer systems and wastewater treatment plants (WWTP). Improved understanding of prediction of emitted gases (e.g. hydrogen sulphide, H2S) is of great importance for better evaluation of such problems in WWTPs. The present paper provides a survey of the feasibility of utilising soft computing models in predicting emission factors (gaseous H2S) of based on five input parameters including the total dissolved sulphides, biochemical oxygen demand (BOD5), temperature, flow rate and pH. Multivariate nonlinear autoregressive exogenous (NARX) neural networks were developed and applied to predict weekly H2S in four WWTPS. Due to the nonlinearity nature of the H2S emission, the principle component analysis, and the average mutual information (AMI) method was employed to determine the effective independent parameters and the optimal time lags in state space for the H2S data. In addition to the NARX models, three standard regressive models (multiple linear regression model, stepwise regression and two-variate logarithmic regression model) were also applied to the same dataset for evaluating the capability and reliability of the developed NARX models. Based on several statistical measures (RMSE, MAPE, PCC, NSE & GRI), it was found that the NARX models were superior to the other applied models in predicting emitted H2S. Although the temperature and wastewater flow rate were found to be the most effective parameters for modelling H2S, but the results indeed showed that taking into account of all wastewater parameters would lead to more accurate H2S prediction.
Process monitoring is crucial for maintaining favorable operating conditions and has received considerable attention in previous decades. Currently, a plant-wide process generally consists of multiple operational units and a large number of measured variables. The correlation among the variables and units is complex and results in the imperative but challenging monitoring of such plant-wide processes. With the rapid advancement of industrial sensing techniques, process data with meaningful process information are collected. Data-driven multivariate statistical plant-wide process monitoring (DMSPPM) has become popular. The key idea of DMSPPM is first decomposing a plant-wide process into multiple subprocesses and then establishing data-driven model for monitoring the process, in which process variable decomposition is important for guaranteeing the monitoring performance. In the current review, we first introduce basics of multivariate statistical process monitoring and highlight the necessity of designing a distributed monitoring scheme. Then state-of-the-art DMSPPM methods are revisited. Finally, opportunities of and challenges to the DMSPPM methods are discussed.
The assessment of Chromium ion in the field of leather tanning industry; either in leathers or waste forms, using fluorescent sensor method, has not been thoroughly explored. Herein, a simple and sensitive fluorescent sensor based on x mol Eu³⁺:BaZrO3 Nano-phosphor (EBZO) (x = 0.00150, 0.0150, 0.0300, 0.0500 mol) has been prepared and characterized for Cr³⁺ ion detection in tanning leathers or waste. Eu³⁺:BaZrO3 Nano-sensor has strong and pure red emission with long lifetime upon UVC excitation; either in solid state or aqueous solutions. The sensing results reveal that EBZO Nano-sensor is quite selective and sensitive towards chromium ion over other metal ions. The assessment of chromium ion has been done using Stern-Volmer quenching method on the red fluorescence emitted from the sensor upon the addition of chromium in aqueous solution. Calibration plot has been achieved over the concentration 1.0–10 × 10⁻⁹ molL⁻¹ with a correlation coefficient of 0.978 and a detection limit of 3.8 × 10⁻⁹ mol L⁻¹. Optical fluorescence and lifetime measurements confirm that EBZO sensor is statically quenched via columbic interaction mechanism with chromium ions. Optimized EBZO Nano-sensor is successfully applied for Chromium detection in real tanning of leather or other waste samples with high recovery values.
Wastewater treatment plants are the main release sources of pharmaceutical compounds present in surface waters. Even at low concentrations, many of these substances have long-term adverse effects on the environment. For an efficient control of pharmaceutical removal, a real-time recognition is a prerequisite. Currently, quantification of such compounds is done in special equipped laboratories and is rather time-consuming and expensive. Here, we introduce a novel biosensor for the detection of the pharmaceutical compound diclofenac, which can be produced with low costs, is easy in handling and can be applied directly on-site. Recognition of diclofenac is based on genetically engineered yeast cells which produce green fluorescent protein in a diclofenac concentration-dependent manner. Centerpiece of the sensor is a foil-based microfluidic flow cell, which allows supply with nutrient solution and analyte while preventing loss of reporter cells. Readout of data is accomplished by a newly developed spectrometric detection unit. With this device, we are able to determine diclofenac concentrations in a range from 10 to 50 μM.
This study investigated membrane capacitive deionization (MCDI) at a pilot-scale using ion-selective polymer-coated carbon electrodes for wastewater reuse. Several issues have been addressed to verify the suitability of MCDI for wastewater reclamation: electrosorption performance, removal efficiency and selectivity of ions present in wastewater, optimization of operating conditions, and performance degradation in long-term caused by the accumulation of organic contaminants. The coated electrodes had better adsorption capacities and charge efficiencies than the conventional MCDI system, which was attributed to their low electrical resistance induced by the thin coated layer. The pilot-scale MCDI test cell involved 50 pairs of anion- and cation-selective electrodes and achieved good removal efficiency of ions from the wastewater effluent, particularly for problematic charged impurities, such as nitrate (NO 3⁻ ) (up to 91.08% of NO 3⁻ was removed). Increasing the flow rate and reducing the applied potential were shown to be efficient for achieving better water quality by enhancing the NO 3⁻ selectivity. Last, the 15 d operation showed good reproducibility in electrosorption and regeneration for the coated electrodes, despite the fact that high concentrations of organics were contained in the wastewater feed solution (12.4 mg/L of dissolved organic carbon).
Current wastewater treatment plants (WWTPs)paradigm is moving towards the so-called water resource recovery facilities in which sewage is considered a source of valuable resources. In particular, urban WWTPs are crucial systems to enhance phosphorus (P)recycling. This paper evaluates the implementation of a P-recovery system in Calahorra WWTP combining the operation of a new sludge line configuration coupled to a struvite crystallisation reactor at demonstration-scale. This new configuration consisted in the elutriation in the gravity thickener of the mixed sludge contained in the mixing chamber in order to reduce the phosphate load to the anaerobic digestion. The results indicated that the P available in the primary sludge overflow was nearly five times more than the obtained for the conventional configuration (1.88 vs. 0.39 gP/kg sludge treated), and the uncontrolled P precipitation inside the anaerobic digester was reduced by 43%. Regarding the total P entering the WWTP, 19% of the total P could be recovered with the new configuration proposed in comparison with 9% in the previous conventional configuration. The average recovery efficiency in the crystallisation plant was 86.9 ± 0.4%, yielding a struvite recovery of 8.0 ± 0.6 kg/d (0.67 ± 0.04 kg/m ³ fed to the crystalliser). The potential struvite production with the new configuration would be around 41 kg/d (15 t/y)crystallising the thickener supernatant which could be increased up to around 103 kg/d (38 t/y)treating all the P-enriched streams (thickener supernatant and centrate streams). The paper demonstrates that WWTPs can contribute to reduce P scarcity, resulting in environmental and economic benefits.
Recent advancements in data-driven process control and performance analysis could provide the wastewater treatment industry with an opportunity to reduce costs and improve operations. However, big data in wastewater treatment plants (WWTP) is widely underutilized, due in part to a workforce that lacks background knowledge of data science required to fully analyze the unique characteristics of WWTP. Wastewater treatment processes exhibit nonlinear, nonstationary, autocorrelated, and co-correlated behavior that (i) is very difficult to model using first principals and (ii) must be considered when implementing data-driven methods. This review provides an overview of data-driven methods of achieving fault detection, variable prediction, and advanced control of WWTP. We present how big data has been used in the context of WWTP, and much of the discussion can also be applied to water treatment. Due to the assumptions inherent in different data-driven modeling approaches (e.g., control charts, statistical process control, model predictive control, neural networks, transfer functions, fuzzy logic), not all methods are appropriate for every goal or every dataset. Practical guidance is given for matching a desired goal with a particular methodology along with considerations regarding the assumed data structure. References for further reading are provided, and an overall analysis framework is presented.
This paper presents a thorough review of control technologies that have been applied to wastewater treatment processes in the environmental engineering regime in the past four decades. It aims to provide a comprehensive technological review for both water engineering professionals and control specialists, giving rise to a suite of up-to-date pathways to impact this field in light of the classified technology hubs. The assessment was conducted with respect to linear control, linearizing control, nonlinear control, and artificial intelligence-based control. The application domain of each technology hub was summarized into a set of comparative tables for a holistic assessment. Challenges and perspectives were offered to these field engineers to help orient their future endeavor.
Traditional wastewater treatment plants (WWTPs) are increasingly regarded as water resource recovery facilities (WRRFs), reflecting the value of water, nutrients, energy and other resources, besides ensuring the required effluent quality. Resource recovery techniques involve biochemical, physical and physico-chemical processes, and even previously unexploited biological conversions. Biopolymers and bioplastics production also reveal the remarkable potential present in our microbial cultures. Models have demonstrated their usefulness to optimize WWTP operation to achieve better effluent quality at lower costs; they also constitute a useful tool to support the transition of WWTPs into WRRFs that maximize the valorization of products recovered from the wastewater. In this paper, it is discussed to which extent the new techniques and unit processes applied for resource recovery could be modelled with conventional activated sludge models (ASMs) and which additional modelling challenges are faced while providing recommendations of potential approaches to address current modelling research gaps.
Ammonia-based aeration control (ABAC) is a cascade control concept for controlling total ammonia nitrogen (NHx-N) in the activated sludge process. Its main goals are to tailor the aeration intensity to the NHx-N loading and to maintain consistent nitrification, to meet effluent limits but minimize energy consumption. One limitation to ABAC is that the solids retention time (SRT) control strategy used at a water resource recovery facility (WRRF) may not be consistent with the goals of ABAC. ABAC-SRT control is a strategy for aligning the goals of ammonia-based aeration control and SRT control. A supervisory controller is used to ensure that the SRT is always optimal for ABAC. The methodology has the potential to reduce aeration energy consumption by over 30% as compared to traditional dissolved oxygen (DO) control. Practical implementation aspects are highlighted for implementation at full scale, such as proper selection of the set point for the supervisory controller, proper calculation of the rate of change in sludge inventory, using a mixed liquor suspended solids (MLSS) controller, and tuning of the controllers. In conclusion, ABAC-SRT is a promising approach for coordinated control of SRT, total ammonia nitrogen, and dissolved oxygen in the activated sludge process that balances both treatment performance and energy savings.
Real-time sensing of minor, or difficult to measure, components in complex systems is a challenge faced across disciplines. For environmental applications, this often entails measurement of target chemicals—that may be harmful and/or of interest at very low concentrations—in a complex medium (tens to thousands of constituent components). Sensor arrays and machine learning (ML) approaches have demonstrated some success but remain limited by the application context and high labor costs of chemical assays for creating large training datasets. This article explores a lower overhead approach, employing data fusion techniques to extract information from a relatively small training dataset. Creation of statistically relevant synthetic training samples is utilized to reduce dependence on costly analysis of samples from the target system. Samples were characterized using eight sensor modalities to create a training set for several ML algorithms, namely artificial neural networks (ANN), support vector regression (SVR), and random forests (RF), to measure NH
$_4^+$
concentrations (
${\leq }$
50 µM) online in a game-changing wastewater treatment process. Hyperparameters for each method were tuned using a particle swarm optimization approach, and both the accuracy and consistency of results were evaluated. ANN achieved the lowest mean absolute error (MAE) of ∼6 µM, but all methods had a minimum MAE within 20% of this value. When evaluated on computational demand, SVR outperformed other approaches. ANN and RF showed wide variation in resulting MAE for a given parameterization, demonstrating strong dependence on initialization and training process. Overall RF provided the best balance of accuracy and consistency, and therefore, in applications where data are expected to be updated frequently or computational resources are not infinite, RF may provide the best tradeoff in speed, accuracy, and consistency.
This study compares the biosensing performance of a microbial fuel cell (MFC) and a microbial electrolysis cell (MEC). Initial tests provided a qualitative comparison of MFC and MEC currents after the anode compartment liquid (anolyte) was spiked with acetate, or sulphates of NH4+, Na+, Mg2+, Fe2+, or a fertilizer solution. Current measurements showed that the MFC sensor had a faster response time, higher sensitivity, and faster recovery time after the spike. Following the spike tests, the MFC and MEC were operated in a continuous flow mode at several influent concentrations of acetate, and sulphates of NH4+, Na+, and Fe2+. The continuous flow tests confirmed the better performance of the MFC sensor, which was selected for further experiments. Two MFC sensors were used for real-time (on-line) COD measurements of brewery wastewater. Regression analysis showed a strong correlation between the MFC power output and COD concentrations in the anode compartment with a coefficient of determination (R2) of 0.97. Overall, results of this study suggest that an MFC-based sensor can be successfully used as a simple and cost-efficient real-time monitoring tool.
The potential of multisensor array in continuous on-line monitoring of processed water quality at aeration plant is explored. The responses of 23 potentiometric sensors were continuously registered every seven seconds through the 26 days of experiment in a special container having connection with outlet water line of the aeration plant. Using chemometric tools it is shown that potentiometric “electronic tongue” is capable of evaluation of two important parameters of water quality: ammonium nitrogen and nitrate nitrogen. Unlike traditional sampling-based analysis the results of multisensor are available immediately in a real-time mode. Moreover, the achieved precision is sufficient to monitor possible alarm events. The employment of topological data analysis allowed for exploration of the very large dataset (295,828 measurements) accumulated through the long period of continuous measurements with sensors and for judgment on stability of water quality.
Soft-sensor is the most common strategy to predict hard-to-measure variables in the wastewater treatment processes. However, existence of a large number of hard-to-measure variables always renders a generic single-output soft-sensor inadequate. This study developed multi-output soft-sensors using Multivariate Linear Regression model (MLR), Multivariate Relevant Vector Machine (MRVM) and Multivariate Gaussian Processes Regression (MGPR) models aiming to predict multiple hard-to-measure variables simultaneously and to capture the joint distribution of the response variables. This, in turn, ensures that the proposed soft-sensors are not just able to obtain prediction values, but also to indicate the credibility of information for hard-to-measure quantities. To further compromise the computational overhead of multi-output soft-sensors, improved Variable Importance in Projection (VIP) and Least Absolute Shrinkage and Selection Operator (Lasso) are proposed to reduce the dimensions of data, thereby alleviating the complexity of predicted models. The proposed methodologies were firstly demonstrated by applying the design algorithm to a wastewater plant (WWTP) simulated with the well-established model, BSM1, then extended to a full-scale WWTP with data collecting from the field. Results showed that the proposed strategy significantly improved the prediction performance.
Excess boron in drinking and irrigation water is a serious environmental and health problem because it can be toxic to many crops and lead to various diseases in humans and animals upon long-term consumption. In this work, the removal of boron from aqueous solutions was achieved by electrocoagulation using aluminium as the anode and cathode. The operating parameters influencing the efficiency of boron removal, namely, initial pH (pH 0), current density, and treatment time, were investigated. An optimum removal efficiency of 70% was achieved at a current density of 18.75 mA/cm 2 and pH 0 = 4 within 90 min of treatment time. An artificial neural network (ANN) was utilised for modelling the experimental data. The model with a topology of 3-10-1 (cor-responding to input, hidden, and output neurons, respectively) provided satisfactory results in the identification of the optimal conditions. The sum of squared error and correlation coefficient (R 2) were 0.616 and 0.973, respectively, confirming the good fit of the ANN model.
The quantification of pollutants, as pharmaceuticals, in wastewater is an issue of special concern. Usually, typical methods to quantify these products are time and reagent consuming. This paper describes the development and validation of a Fourier transform near-infrared (FT-NIR) spectroscopy methodology for the quantification of pharmaceuticals in wastewaters. For this purpose, 276 samples obtained from an activated sludge wastewater treatment process were analysed in the range of 200 cm⁻¹ to 14,000 cm⁻¹, and further treated by chemometric techniques to develop and validate the quantification models. The obtained results were found adequate for the prediction of ibuprofen, sulfamethoxazole, 17β-estradiol and carbamazepine with coefficients of determination (R²) around 0.95 and residual prediction deviation (RPD) values above four, for the overall (training and validation) data points. These results are very promising and confirm that this technology can be seen as an alternative for the quantification of pharmaceuticals in wastewater.
Ion selective electrodes for diclofenac monitoring in both pharmaceutical wastewater and dosage form were described; that are considered environmental friendly analytical method. The sensors development depended on comparative performance evaluation of membranes that were based on functionalized magnetite nanoparticles with the classical sensors; this approach provided that nanoparticles in the inner solution of sensor membrane were highly dispersed and coated with ionophore to enhance a complete ion-pairing interaction between the ionophore and the analyte. The optimum membrane was that containing β‑cyclodextrin coupled with magnetite ferric oxide as inner filling solution, dibutylphthalate as plasticizer and crystal violet as ion exchanger in poly (vinylchloride) matrix. This sensor (CV-Fe-β-CD) exhibited high sensitivity, Nernstian slope of the calibration curve, as well as fast, stable response and good selectivity. The sensor exhibits a Nernstian slope of −58.7 ± 1 mV/decade over the concentration range 1.0 × 10⁻⁷ to 1.0 × 10⁻² M of Diclofenac with a minimal limit of detection of 1.1 × 10⁻⁷ M. The electrode showed a good potentiometric selectivity for diclofenac with respect to a number of interfering ions and organic species. The membrane sensor was successfully applied for the determination of diclofenac in wastewater samples and dosage form without sample pretreatment steps prior to its analysis.
Aerobic granulation is a complex process that, while proven to be more effective than conventional treatment methods, has been a challenge to control and maintain stable operation. This work presents a static data-driven model to predict the key performance indicators of the aerobic granulation process. The first sub-model receives influent characteristics and granular sludge properties. These predicted parameters then become the input for the second sub-model, predicting the effluent characteristics. The model was developed with a dataset of 2600 observations and evaluated with an unseen dataset of 286 observations. The prediction R2 and RMSE were >99% and <5% respectively for all predicted parameters. The results of this paper show the effectiveness of data-driven models for simulating the complex aerobic granulation process, providing a great tool to help in predicting the behaviour, and anticipating failures in aerobic granular reactors.
Water pollution and habitat degradation are the cause of increasing water scarcity and decline in aquatic biodiversity. While the freshwater availability has been declining through past decades, water demand has continued to increase particularly in areas with arid and semi-arid climate. Monitoring of pollutants in wastewater effluents are critical to identifying water pollution area for treatment. Conventional detection methods are not effective in tracing multiple harmful components in wastewater due to their variability along different times and sources. Currently, the development of biosensing instruments attracted significant attention because of their high sensitivity, selectivity, reliability, simplicity, low-cost and real-time response. This paper provides a general overview on reported biosensors, which have been applied for the recognition of important organic chemicals, heavy metals, and microorganisms in dark waters. The significance and successes of nanotechnology in the field of biomolecular detection are also reviewed. The commercially available biosensors and their main challenges in wastewater monitoring are finally discussed.
Aerobic granulation is a recent technology with high level of complexity and sensitivity to environmental and operational conditions. Artificial neural networks (ANN), computational tools capable of describing complex nonlinear systems, are the best fit to simulate aerobic granular bioreactors. In this study, two feedforward backpropagation ANN models were developed to predict chemical oxygen demand (COD) (Model I) and total nitrogen (TN) removal efficiencies (Model II) of aerobic granulation technology under steady-state condition. Fundamentals of ANN models and the steps to create them were briefly reviewed. The models were respectively fed with 205 and 136 data points collected from laboratory-, pilot-, and full-scale studies on aerobic granulation technology reported in the literature. Initially, 60, 20, and 20%, and 80, 10, and 10% of the points in the corresponding datasets were randomly chosen and used for training, testing, and validation of Model I, and Model II, respectively. Overall coefficient of determination (R²) value and mean squared error (MSE) of the two models were initially 0.49 and 15.5, and 0.37 and 408, respectively. To improve the model performance, two data division methods were used. While one method is generic and potentially applicable to other fields, the other can only be applied to modeling the performance of aerobic granular reactors. R² value and MSE were improved to 0.90 and 2.54, and 0.81 and 121.56, respectively, after applying the new data division methods. The results demonstrated that ANN-based models were capable simulation approach to predict a complicated process like aerobic granulation.
Water pollution occurs mainly due to inorganic and organic pollutants, such as nutrients, heavy metals and persistent organic pollutants. For the modeling and optimization of pollutants removal, artificial intelligence (AI) has been used as a major tool in the experimental design that can generate the optimal operational variables, since AI has recently gained a tremendous advance. The present review describes the fundamentals, advantages and limitations of AI tools. Artificial neural networks (ANNs) are the AI tools frequently adopted to predict the pollutants removal processes because of their capabilities of self-learning and self-adapting, while genetic algorithm (GA) and particle swarm optimization (PSO) are also useful AI methodologies in efficient search for the global optima. This article summarizes the modeling and optimization of pollutants removal processes in water treatment by using multilayer perception, fuzzy neural, radial basis function and self-organizing map networks. Furthermore, the results conclude that the hybrid models of ANNs with GA and PSO can be successfully applied in water treatment with satisfactory accuracies. Finally, the limitations of current AI tools and their new developments are also highlighted for prospective applications in the environmental protection.
Because of existing uncertainties and non-linearity characteristics of wastewater plants, highly affected by the state of the bacteria involved in each stage, their real-time control must follow an adaptive control rule. This paper presents the control strategy and fuzzy logic rules for a two stage anaerobic wastewater treatment plant in the food sector. PH, temperature and NaOH are controlled in the acidogenic reactor, and a second stage for ph and temperature control takes place in the methanogenic. The solution was tested ina simulated environment and was implemented on a pilot plant on a S7-300 PLC using simple IF.. THEN rules. We connected the plant to a remote Decision Support System to perform better state analysis, process optimization and abnormal situation management.
The aim of this paper is to describe the state-of-the art computer-based techniques for data analysis to improve operation of wastewater treatment plants. A comprehensive review of peer-reviewed papers shows that European researchers have led academic computer-based method development during the last two decades. The most cited techniques are artificial neural networks, principal component analysis, fuzzy logic, clustering, independent component analysis and partial least squares regression. Even though there has been progress on techniques related to the development of environmental decision support systems, knowledge discovery and management, the research sector is still far from delivering systems that smoothly integrate several types of knowledge and different methods of reasoning. Several limitations that currently prevent the application of computer-based techniques in practice are highlighted.
Soft sensors are inferential estimators, drawing conclusions from process observations when hardware sensors are unavailable or unsuitable; they have an important auxiliary role in sensor validation when performance declines through senescence or fault accumulation.
The non-linear behaviour exhibited by many industrial processes can be usefully modelled with the techniques of computational intelligence: neural networks; fuzzy systems and nonlinear partial least squares.
Soft Sensors for Monitoring and Control of Industrial Processes underlines the real usefulness of each approach and the sensitivity of the individual steps in soft-sensor design to the choice of one or the other. Design paths are suggested and readers shown how to evaluate the effects of their choices. All the case studies reported, resulting from collaborations between the authors and a number of industrial partners, raised challenging soft-sensor-design problems. The applications of soft sensors presented in this volume are designed to cope with the whole range from measuring system backup and what-if analysis through real-time prediction for plant control to sensor diagnosis and validation. Some of the soft sensors developed here are implemented on-line at industrial plants.
Features:
• soft-sensor design;
• advice on data selection and choice of model structure;
• model validation;
• strategies for the improvement of soft-sensor performance;
• uses of soft sensors in fault detection and sensor validation;
• soft sensors in use in industrial applications such as a debutanizer column and a sulfur recovery unit.
This monograph guides interested readers – researchers, graduate students and industrial process technologists – through the design of their own soft sensors. It is self-contained with full references and appraisal of existing literature and data sets for some of the case studies can be downloaded from springer.com.
Advances in Industrial Control aims to report and encourage the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.
