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A Smart Sustainable Decision Support System for Water Management oF Power Plants in Water Stress Regions
... They showed that XGBoost outperformed other methods considering the employed statistical performance criteria. Furthermore, the removal of biological oxygen demand and chemical oxygen demand by various levels of geranular activated carbon-and genetic algorithm-based simulation have been conducted by Sasan et al. (2023); a smart Decision Support System (DSS) was developed by Mahdi et al. (2023) for water monitoring and management based on AI and integration of the PESTEL matrix and Multi Criteria Decision Making (MCDM) methods. ...
... As a result, the study provided a way forward for convenient water quality prediction for drinking water treatment plants, not merely under the regional context but in terms of advancing machine learning algorithms elsewhere. These inferences have yielded novel approaches to the ongoing trend where researchers have rigorously employed machine learning techniques in water resources management [for example, Elbeltagi et al. 2022Elbeltagi et al. , 2023Sasan et al. 2023;Mahdi et al. 2023, Gad et al. 2023. While the study like El Bilali and Taleb (2020) is one of the recently published work employing machine learning models for predicting water quality variables, thereby indicating their significance on better prediction accuracy, as compared to conventional approaches. ...
This study aims to evaluate the performance of four ensemble machine learning methods, i.e., Random Committee, Discretization Regression, Reduced Error Pruning Tree, and Additive Regression, to estimate water quality parameters of Biochemical Oxygen Demand BOD and Dissolved Oxygen DO. Data from Anbar City on the Euphrates River in western Iraq was employed for the model's training and validation. The best subset regression analysis and correlation analysis were used to determine the best input combinations and to ascertain variable correlation, respectively. Besides, sensitivity analysis was employed to determine the standardized coefficient for BOD and DO predictions, hence knowing the significance of the relevant physical and chemical parameters. Results revealed that temperature , turbidity, electrical conductivity, Ca ++ , and chemical oxygen demand were identified as the best input combinations for BOD prediction. In contrast, the variable combination of temperature, turbidity, chemical oxygen demand, SO4 −1 , and total suspended solids was identified as the best input combination for DO prediction. It was also demonstrated that the random committee model was superior for predictions of BOD and DO, followed by the discretization regression model. For predicting BOD (DO), the correlation coefficient and root mean square error were 0.8176 (0.7833) and 0.3291 (0.3544), respectively, during the testing stage. The present investigation provided approaches for addressing difficulties in irrigation water quality prediction through artificial intelligence techniques and thence serve as a tool to overcome the obstacles towards better water management.
... The study conducted by Nakhaei et al. [47] aimed to develop a smart DSS for the monitoring, prediction, and control of water consumption in power plants (PPs) utilizing ML and multi-criteria decision making (MCDM) methods. The primary objectives were to enhance efficiency and resource management in PP operations. ...
Statement of Problem: Environmental engineering confronts complex challenges characterized by significant uncertainties. Traditional modeling methods often fail to effectively address these uncertainties. As a promising direction, this study explores fuzzy machine learning (ML) as an underutilized alternative. Research Question: Although the potential of fuzzy logic is widely acknowledged, can its capabilities truly enhance environmental engineering applications? Purpose: This research aims to deepen the understanding of the role and significance of fuzzy logic in managing uncertainty within environmental engineering applications. The objective is to contribute to both theoretical insights and practical implementations in this domain. Method: This research performs a systematic review carried out in alignment with PRISMA guidelines, encompassing 27 earlier studies that compare fuzzy ML with other methods across a variety of applications within the field of environmental engineering. Results: The findings demonstrate how fuzzy-based models consistently outperform traditional methods in scenarios marked by uncertainty. The originality of this research lies in its systematic comparison and the identification of fuzzy logic’s transparent, interpretable nature as particularly suited for environmental engineering challenges. This approach provides a new perspective on integrating fuzzy logic into environmental engineering, emphasizing its capability to offer more adaptable and resilient solutions. Conclusions: The analysis reveals that fuzzy-based models significantly excel in managing uncertainty compared to other methods. However, the study advocates for a case-by-case evaluation rather than a blanket replacement of traditional methods with fuzzy models. This approach encourages optimal selection based on specific project needs. Practical Implications: Our findings offer actionable insights for researchers and engineers, highlighting the transparent and interpretable nature of fuzzy models, along with their superior ability to handle uncertainties. Such attributes position fuzzy logic as a promising alternative in environmental engineering applications. Moreover, policymakers can leverage the reliability of fuzzy logic in developing ML-aided sustainable policies, thereby enhancing decision-making processes in environmental management.
Keywords: environmental engineering; machine learning; fuzzy systems
... • Nakhaei et al. (2023), the research builds an advanced decision aid system for overseeing, forecasting, and managing divisions using AI. • Khan et al. (2023), this study emphasizes the validation characteristic of IoT gadgets from literature investigations and assesses the crucial characteristic using COPRAS method to aid the institution in enhancing the safety of IoT gadgets. ...
This study proposes a fuzzy multi-criteria model to assess the risk of unemployment among professionals in the communication sector in Turkey, prompted by the rapid development and evolution of artificial intelligence (AI) technologies. The method integrates Fuzzy The Decision Making Trial and Evaluation Laboratory (F-DEMATEL) and Fuzzy Logarithm Methodology of Additive Weights (F-LMAW) procedures. Data were collected from 20 experts representing professions such as public relations, advertising, journalism, and design through a 12-question survey. In the analysis, the F-DEMATEL procedure was initially employed to determine attitudes towards AI technologies, followed by the application of the F-LMAW procedure to assess the magnitude of AI's impact on occupational groups. Findings reveal a nuanced stance: while professionals acknowledge the necessity of AI for their work, they are unwilling to accept unemployment due to more advanced AI. This newly identified structure, termed Post-Luddism, highlights concerns over technological unemployment, particularly pronounced in professions like journalism where job prospects are limited and creative thinking is paramount. In other communication fields, the intensive use of technology mitigates fears of AI harm. However, even in journalism, there exists a propensity to perceive AI as detrimental. These insights shed light on communication professionals' apprehensions and attitudes towards AI's effects. Policymakers and stakeholders can leverage this understanding to formulate strategic measures, considering the divergent perspectives among professional groups regarding AI, towards mitigating potential unemployment risks and fostering AI-adaptive
Numerous water supply utilities around the world face challenges in successfully distributing water in distribution networks due to increased urbanization, population growth, and climate change. The age of the water supply facilities is a particular issue, which is aggravated by their inadequate maintenance and operation. Due of this, many water utilities have recently adopted integrated and intelligent water supply solutions that leverage information technology, artificial intelligence, big data, and IOT (Internet of Things) to handle water supply system issues. In this study, a smart dashboard of water distribution network operation was developed to improve the effectiveness of Tehran, Iran's water delivery system. In order to properly manage water resources, the article proposes adopting knowledge management systems in Tehran's municipal water distribution and transmission networks.
By locating useful characteristics and determining the perfect circumstances to meet ideal water quality criteria, this study seeks to improve the operation of a water treatment facility. The research comprises gathering data from personnel and exposure to system events, as well as from explicit and tacit knowledge sources. The problem at hand is a multi-objective, multi-criteria problem with many variables in spatial and temporal dimensions, requiring the use of powerful tools for analysis. All engineering problems have an objective function consisting of smaller sub-functions, typically in the form of cost or error minimization. To solve such problems, optimization methods based on natural patterns have been introduced, including genetic algorithms, evolutionary algorithms, and particle mass optimization. By optimizing the operation process of the water treatment plant, the quality of the water provided can be improved to meet standards set by organizations such as Iran 1053, WHO, and EPA. The study's findings could be used to implement changes to the plant's management and operation processes to achieve more ideal water quality conditions. Ultimately, the optimization of water treatment plant processes could have significant positive impacts on public health and well-being, as well as the environment.
Filtration is a crucial step in the water treatment process, typically preceding disinfection. Filters trap microorganisms and suspended solids, reducing their amount in the environment. The latest technology in filtration is ceramic filters, and in this study, the performance of silicon carbide ceramic filters (SIC) is evaluated. These filters were installed at three different locations within a water treatment plant (entrance storage, raw water, and backwash water), and changes in physical and chemical water parameters were measured. Results indicate high efficiency in turbidity removal, effectively clarifying volatile suspended solids (VSS) and fixed suspended solids (FSS). The turbidity removal efficiency was 99% for entrance storage and 65% for raw water. The SWOT (Strengths, Weaknesses, Opportunities, and Threats) matrix was used to analyse the results of the SIC and highlight its strengths, weaknesses, opportunities, and threats.
In this paper, the quadrotor stabilization under time and state constraints is studied. The objective is to design a nonlinear controller under time and state constraint for quadrotor. The nonlinear quadrotor model is built by the Euler-Lagrange approach while ignoring the Coriolis terms, hub moment and force. Based on quadrotor’s dynamic model, a nonlinear feedback controller is designed for the quadrotor stabilization under time and state constraints. This feedback is an implicit PID controller where the feedback gains are obtained from LMIs (Linear matrix inequalities). LMI system characterizing the system stability and convergence properties is built based on convex embedding approach and implicit Lyapunov function method. To demonstrate the application prospects of implicit PID controller, robustness analysis is provided to show the property of implicit PID controller under external disturbance. The key novelty of this paper is that the implicit PID controller is proven feasible for applying to the quadrotor under time and state constraints, which is also the main outcome.
The entry conditions of severe accident management guidelines (SAMGs) in pressurized water reactors (PWRs) rely on the indication of core exit temperature (CET). Yet, the setpoints for the CET may be different from plant to plant. Most Westinghouse PWR designs adopt the setpoint of CET at 650℃ as the entry condition of the SAMGs, since this setpoint is an effective indicator of core damage in a wide spectrum of accident sequences. Motivated by the interest in the verification & validation of SAMS after the Fukushima accidents, the present study is conducted numerically to verify the effectiveness of the CET setpoint for the transition from emergency operation procedures (EOPs) to SAMGs in a Swedish nuclear power plant. For this purpose, six representative severe accident sequences covering the main contributors to the core damage frequency (CDF) are analyzed using the MELCOR code. Moreover, different CET readings and alternative entry conditions are also investigated. The simulation results show that the average CET = 650 °C is the effective setpoint as the entry condition of SAMGs, i.e., given this setpoint the transition from EOPs to SAMGs will take place slightly before the occurrence of core degradation, which secures the intended mitigation of SAMGs while keeping EOPs active as long as possible. On the other hand, it is too conservative if the maximum CET = 650 °C is used as the setpoint of entry condition of SAMGs, i.e., it will result in an excessive realization of SAMGs over EOPs. The coolant temperature in the primary circuits, the water level in the RPV and the hydrogen concentration in the containment can also be applied as reference indications of core damage states in the accident management.
In this investigation, metals of environmental concern, Cd, Cr, Co, Pb and Cu, are identified in the feed coal of thermal power plant from eastern India. The concentrations (0.56–10.3 µg/g) of these metals are enriched (one- to twofold) in fly and bottom ash during combustion. Additionally, a noticeable presence of Cd, Cr, Co, Pb, and Cu was also observed on the surface (0.26–5.6 µg/l) and groundwater (0.12–2.65 µg/l) of the neighbouring power plant, which focuses on the contribution of combustion ashes in the water contamination of these elements. The analytical result shows that rainwater has a greater influence on the rapid transportation of these elements into the surface water than groundwater. The mobility of such metals of environmental concern from the combustion ashes into the surface and groundwater may pose a risk to the ecosystem. A regular monitoring of chemical quality of coal as well as expert dumping custom, chemical treatment before disposal and recycling of combustion residues are the essential steps for clean energy generation and safe environment.
Recent years have seen rapid development in industrialization and urbanization with huge growth in the population throughout the world. In this regard, an efficient and robust framework for the concept of a green city and sustainable development goals to manage municipal plastic wastes is still needed. This study models a bio-recovery of municipal different plastic wastes management based on a new integrated Multi-Criterion Decision-Making (MCDM) approach through a case study in Mashahd, Iran. The proposed integrated MCDM framework includes the Shannon Entropy (SE), Ordered Weighted Aggregation (OWA), Analytic Hierarchy Process (AHP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and, ELimination Et Choice Translating REality (ELECTRE) systems in an intelligent way. Through decision-making computations, all criteria are approved after extraction from the literature review by experts with more than 60% agreement percentage. Different scenarios of economic, energy, and environmental crises are created. One finding of this paper is to create a new entrance in economic competition with plastic biodegradation to present a novel, environmental-friendly product with high-quality and low-cost advantages. Another finding determines that with an application of plastic wastes bio-recovery, citizens' satisfaction from urban management system will be increased from 49% to 64%. Whereas, based on the outcomes of this investigation, the rate of municipal waste industries development, smart city goals’ meeting, and rate of hazardous material emission from municipal solid wastes are increased to 58%, 25%, and 70%, respectively. The declared numerical outcomes illustrate the effectiveness of plastic waste bio-recovery on the smart city approach.
Mexico, as many countries, relies on its aquifers to provide at least 60% of all irrigation water to produce crops every year. Often, the water withdrawal goes beyond what the aquifer can be replenished by the little rainfall. Mexico is a country that has experienced a successful process of regional development based on the adoption of intensive agricultural systems. However, this development has occurred in an unplanned way and displays shortcomings in terms of sustainability, particularly in the management of water resources. This study analysed the case of Costa de Hermosillo, which is one of the Mexican regions in which this model of intensive agriculture has been developed and where there is a high level of overexploitation of its groundwater resources. Based on the application of a qualitative methodology involving different stakeholders (farmers, policymakers, and researchers), the main barriers and facilitators for achieving sustainability in water resources management have been identified. A series of consensus-based measures were contemplated, which may lead to the adoption of sustainable practices in water management. Useful lessons can be drawn from this analysis and be applied to other agricultural areas where ground and surface water resources are overexploited, alternative water sources are overlooked, and where stakeholders have conflicting interests in water management.
Light Steel Frame (LSF) system is mainly used for construction of short and intermediate-height buildings in developed countries whereas considerable heed is not given to it in developing countries. Unfamiliarity to LSF risks is one of the main reasons for this averseness so risk management can remedy this challenge and develop its application. Hence, this paper investigates the risk management of LSF considering design, construction and operation phase. Three steps entailing risk identification, assessment and responding using fuzzy Failure Mode and Effect Analysis (FMEA) technique are suggested for risk management implementation and validation of responses, a novel index with respect to weighted combination of project quality, time and cost is calculated. The methodology is demonstrated on a pilot study in a developing country. By using interview, 29 significant risks are extracted in design, construction and operation and then evaluated by proposed fuzzy method. Results showed that the share of the risks in these steps are 21%, 31% and 48% respectively. The results revealed that the risks in the construction and operation phases are higher than those in the design phase. The results also show that involving safety as a project object in the risk management process could eventuate acceptable results.
The determination of the water chemistry for cooling systems of nuclear fusion plants is under debate. It should be tailored for different types of fusion reactors: either experimental, e.g., ITER, JT-60SA, and DTT, or aimed at power generation, e.g., DEMO, given the different operation requirements. This paper presents the dual approach involving experiments and computer simulations chosen for the definition of DEMO water chemistry. Experimental work was performed to assess the corrosion susceptibility of reduced activation ferritic martensitic EUROFER 97 and AISI 316L in different water chemistry regimes. At the same time, the low corrosivity requirement brings an additional safety aspect for the radiation protection since some neutron-activated corrosion products (ACPs) create a gamma radiation when deposited outside the plasma chamber in components accessible to operators and these must be minimized. To evaluate the ACP inventory for DEMO, assessments were carried out using a reference computer code. Preliminary experimental activities to define the water chemistry of DTT under construction at ENEA were also conducted. The comparison of code results with experiments is two-fold important: for the validation of the computer code models and to determine data that are necessary to perform calculations.
Over the last decade, there has been a constant development in control techniques for DC-DC power converters which can be classified as linear and nonlinear. Researchers focus on obtaining maximum efficiency using linear control techniques to avoid complexity although nonlinear control techniques may achieve full dynamic capabilities of the converter. This paper has a similar purpose in which a novel hybrid metaheuristic optimization algorithm (AEONM) is proposed to design an optimal PID controller for DC-DC buck converter’s output voltage regulation. The AEONM employs artificial ecosystem-based optimization (AEO) algorithm with Nelder-Mead (NM) simplex method to ensure optimal PID controller parameters are efficiently tuned to control output voltage of the buck converter. Initial evaluations are performed on benchmark functions. Then, the performance of AEONM-based PID is validated through comparative results of statistical boxplot, non-parametric test, transient response, frequency response, time-domain integral-error-performance indices, disturbance rejection and robustness using AEO, particle swarm optimization and differential evolution. A comparative performance analysis of transient and frequency responses is also performed against simulated annealing, whale optimization and genetic algorithms for further performance assessment. The comparisons have shown the proposed hybrid AEONM algorithm to be superior in terms of enhancing the buck converter’s transient and frequency responses.
Fossil fuel power plants can cause numerous environmental issues, owing to exhaust emissions and substantial water consumption. In a thermal power plant, heat and water recovery from flue gas can reduce CO2 emissions and water demand. High-humidity flue gas averts the diffusion of pollutants, enhances the secondary transformation of air pollutants, and leads to smog weather; hence, water recovery from flue gas can also help to lessen the incidence of white plumes and smog near and around the power plant. In this study, a lab-scale system for heat and water recovery from flue gas was tested. The flue gas was initially cooled by an organic Rankine cycle (ORC) system to produce power. This gas was further cooled by an aftercooler, using the same working fluid to condense the water and condensable particulate matter in the flue gas. The ORC system can produce approximately 220 W of additional power from flue gas at 140 °C, with a thermal efficiency of 10%. By cooling the flue gas below 30–40 °C, the aftercooler can recover 60% of the water in it.
ir pollution is one of the most important problems of urban life. Since a large proportion of airborne pollutants originate from industry, it is important to address emission removal systems. One of the growing industries is the production of aluminum, which requires attention and planning since emits dangerous pollutants such as particulate matter, SO2, NOx, dioxins, furans, mercury chloride, and fluorine compounds. The present study investigates the production life cycle of this metal and analyzes the production of gaseous pollutants and particles in different production units. Large amount of pollution is produced in the alumina production and the aluminum electrolysis units, which in the best case, for the production of one ton of final aluminum, Emit 1.07, 4.73, and 1.32 kg of particulate pollutants, sulfur dioxide, and nitrogen dioxide respectively. In the next step, in the search of the optimal system for controlling particulate pollutants, SO2, NOx caused by aluminum production, by reviewing the research background and related articles and books, ranked these systems using ELECTRE, TOPSIS and SAW methods. Sedimentation chamber, internal separators, cyclones, fabric filters, electrostatic precipitators, and wet collectors in particle removal and condensation, absorption, adsorption, incineration, and wet washing in SO2 and NOx removal were reviewed and compared. The results show the superiority of cyclones in particle removal, wet washing system for removing SO2, and adsorption for removing NOX.
With the growth of industrialization and urbanization in megacities, some emerging disasters occur such as air pollution mortality, increasing cancer risks, decreasing life expectancy, descending prosperity, and Human Development Indexes (HDI). In addition, with the raising population of cities, the quantity and quality of air pollutions are increased based on vehicle application rate, industrial activities, agricultural efforts and etc. In this research, with the application of Dose-Response Functions in air pollution, some parameters such as chronic disease-based mortality, life expectancy reduction based on chronic and acute effects, and ozone gas health risks are computed in a case study of Mashhad city, Iran. The outcomes have illustrated the life expectancy is reduced in a case study around 8.22 and 8.51 years for men and women, respectively. Plus, the results of statistical health scrutinizing have demonstrated that the mortality of chronic effects based on air pollution emissions is calculated around 20 percentages in the case study. Likewise, with the application of two different methods in Multi-Criteria Decision Making (MCDM) containing Analytic Hierarchy Process (AHP) and ELimination Et Choice Translating Reality (ELECTRE) the responsibility of each pollution is determined. As per the mentioned computations, Particle Matter 2.5 (PM 2.5) has the most role in increasing the health risk of air pollution in Mashhad City, Iran.
The sugarcane industry is technologically pioneering in the area of food production. On the other side, this industry produces a huge amount of by-products. Proper handling of these by-products has remained a challenge. An efficient multi-echelon Sugarcane Supply Chain Network (SSCN) is designed and proposed in this paper to handle the by-products produced by the sugarcane industry that can be utilized further with little modification. It helps to reduce the overall working cost of the network. Usually, the supply chain problems are complex in nature, and complexity further increases with increasing problem instances. Metaheuristics techniques are, in general, applied to handle such NP-hard problems. This work proposes three hybrid metaheuristics algorithms, namely H-GASA, a hybrid of Genetic Algorithm with Simulated Annealing, H-KASA, a hybrid of Keshtel Algorithm with Simulated Annealing, and H-RDASA, a hybrid of Red Deer Algorithm with Simulated Annealing to handle the complexity of the problem. The algorithms’ performance is probed using the Taguchi experiments, and the best combinations of parameters are identified. This hybrid algorithms’ efficacy is compared with their basic version of the algorithms, i.e. GA, KA, RDA, and SA using different criteria. A set of test problems is generated to ensure the capability of the presented model. The obtained results suggest that H-KASA significantly outperforms in small-sized problems, while the H-RDASA significantly outperforms in medium- and large-sized problem instances. In addition, the sensitivity analysis confirms that by adopting this proposed multi-echelon SSCN, decision-makers can achieve a significant cost reduction of 8.3% in terms of the total cost.
Considering the importance of water in local communities and the development of Concentrated Solar Thermal Power (CSP) projects in arid regions, water footprint analysis and finding water-saving solutions towards optimized use of raw water resources in CSP plants are essential. Within this study, we present an integrated simulation tool for a comprehensive simulation of CSP plants including the water and wastewater streams, treatment units, and the cost of treatment. The models are compared to a set of measured water consumption data from a commercial CSP plant. Furthermore, strategies to reduce raw water consumption are identified and then investigated through the detailed simulation of alternative water management plans. The results show that with optimized solutions, raw water consumption can be reduced by 14% while the cost of electricity remains the same or is even slightly reduced.
Recent developments in food industries have attracted both academic and industrial practitioners. Shrimp as a well-known, rich, and sought-after seafood, is generally obtained from either marine environments or aquaculture. Central prominence of Shrimp Supply Chain (SSC) is brought about by numerous factors such as high demand, market price, and diverse fisheries or aquaculture locations. In this respect, this paper considers SSC as a set of distribution centers, wholesalers, shrimp processing factories, markets, shrimp waste powder factory, and shrimp waste powder market. Subsequently, a mathematical model is proposed for the SSC, whose aim is to minimize the total cost through the supply chain. The SSC model is NP- hard and is not able to solve large-size problems. Therefore, three well-known metaheuristics accompanied by two hybrid ones are exerted. Moreover, a real-world application with 15 test problems are established to validate the model. Finally, the results confirm that the SSC model and the solution methods are effective and useful to achieve cost savings.
This paper considers the issue of rapid automated decision making in changing factory environments, situations including human-robot collaboration, mass customisation and the need to rapidly adapt activities to new conditions. The approach taken is to adapt the Monte Carlo Tree Search (MCTS) algorithm to provide online choices for the possible actions of machines and workers, interleaving them dynamically in response to the changing conditions of the production process. This paper describes how the MCTS algorithm has been adapted for use in production environments and then the proposed method is illustrated by two examples of the system in use, one simulated and one in a physical test cell.
The article addresses the problems of local power plants operating on fossil fuels such as gas and coal. As a solution to the problem, it is proposed to consider dual-fuel combined cycle power plants with a parallel operating scheme. This type of combined cycle technology is good variant for integration into coal predominating districts. Therefore, the efficiency of dual-fuel combined cycle power plants operating at subcritical ( p 0 = 13 MPa, t 0 = 540 °C) and supercritical ( p 0 = 24 MPa, t 0 = 545 °C) initial steam parameters are discussed in the article. The thermal model and a calculation methodology for the installation under consideration are proposed. The results of the installation’s technical and economic indicators analysis are presented. Additionally article sets out the assessment of capital costs, Net Present Value and the possibility of using the technology under study within the framework of the considering region.
This paper describes an efficient and low cost material for treatment of Cu²⁺, Pb²⁺ and malachite green from aquatic environments. Aerated Autoclave Concrete (AAC) was used as a low cost bed and modified by chitosan to synthesize of AAC-CH composite. Then, it was analyzed by the Energy Dispersive X-ray spectroscopy (EDX), X-Ray Fluorescence (XRF), Field Emission Scanning Electron Microscopy (FE-SEM) and Fourier Transform Infrared spectrophotometry (FT-IR) analysis. The key factors influencing on the removal percentage among each run including, sample's pH, amounts of adsorbent and contact time were optimized by the Central Composite Design in Response Surface Methodology (CCD-RSM). Based on the obtained results, the optimum conditions for the removal of both Cu²⁺ and Pb²⁺ are pH 5.8, amounts of adsorbent: 14 mg and 58 min contact time. Also, for MG, its maximum removal percentage obtained at pH 9.7, 13 mg adsorbent and 55 min removal time. In addition, the variations of adsorptive behaviors were scrutinized by Adaptive Network-based Fuzzy Inference System (ANFIS) based on Sugeno model with focusing on removal percentage predictions. Also, to evaluate the adsorption mechanism, Dubinin–Radushkevich (D-R), Langmuir, Temkin, Freundlich (two parameter equations) and Sips, Khan and Toth isotherms (Three parameter equations) were appraised and the outcomes demonstrate that the adsorptive reaction of Pb²⁺, Cu²⁺ and MG governed by the Freundlich isotherm with the maximum adsorption capacities of 78.12, 56.82 and 833.33 mg g⁻¹, correspondingly. Likewise, outputs of the D-R and Temkin isotherms show that the physiosorption process is the main interaction of analytes with AAC-CH composite. The consequences of the kinetic modeling illustrated that the adsorptive reaction of Cu²⁺, Pb²⁺ and MG followed by the pseudo second order adsorption kinetic. The geometry kinetic computing depicted that the adsorption/desorption rates don't have any interfering during reaction process. The advantages such as high efficiency, low cost, reusability and green aspect, make AAC-CH composite as a high performance adsorbent for decontamination of Pb²⁺, Cu²⁺ and MG from water resources.
A smart city (SC) is a sustainable and efficient urban center that provides a high quality of life to its inhabitants through optimal management of its resources that nowadays have been wider and wider. In modern societies, municipal solid waste management (MSWM) is an important part of SCs, the main problem of MSWM is the cost that it generates and must be reduced. To solve this situation in this paper are considered two sub-models. The first sub-model uses vehicle routing problem (VRP) for routing fleet among generation waste to separation facilities. The second sub-model is designed to allocate resources from separation facilities to set of recovery plants or landfill centers. From the best of our knowledge, most of the past studies related to this topic have focused only on deterministic implementations. Also, recent studies usually focus on uncertain parameters in the area of waste generation. In addition, a few related studies have developed the uncertain parameter which has focused on facilitating separation. This study considers the uncertain parameters in the output rate of separation facilities as well as the importance of value recovery from each bin; the aim is to enhance the efficiency of operations. The purpose of this study is to minimize the total transportation cost and to maximize recycled revenue. Chance-constrained programming has been used to deal with stochastic optimization model. Four metaheuristic algorithms are employed to identify the best solution. Besides, the performance of the proposed algorithms is evaluated. Finally, sensitivity analyses along with number of scenarios have developed to measure the tightness of the proposed problem. The results of the study illustrate the optimized number of vehicles that can help the managers and decision-makers in various tightness conditions.
Improvements in irrigated areas' classification accuracy are critical to enhance agricultural water management and inform policy and decision-making on irrigation expansion and land use planning. This is particularly relevant in water-scarce regions where there are plans to increase the land under irrigation to enhance food security, yet the actual spatial extent of current irrigation areas is unknown. This study applied a non-parametric machine learning algorithm, the random forest, to process and classify irrigated areas using images acquired by the Landsat and Sentinel satellites, for Mpumalanga Province in Africa. The classification process was automated on a big-data management platform, the Google Earth Engine (GEE), and the R-programming was used for post-processing. The normalised difference vegetation index (NDVI) was subsequently used to distinguish between irrigated and rainfed areas during 2018/19 and 2019/20 winter growing seasons. High NDVI values on cultivated land during the dry season are an indication of irrigation. The classification of cultivated areas was for 2020, but 2019 irrigated areas were also classified to assess the impact of the Covid-19 pandemic on agriculture. The comparison in irrigated areas between 2019 and 2020 facilitated an assessment of changes in irrigated areas in smallholder farming areas. The approach enhanced the classification accuracy of irrigated areas using ground-based training samples and very high-resolution images (VHRI) and fusion with existing datasets and the use of expert and local knowledge of the study area. The overall classification accuracy was 88%.
Groundwater resources play a key role in supplying urban water demands in numerous societies. In many parts of the world, wells provide a reliable and sufficient source of water for domestic, irrigation, and industrial purposes. In recent decades, artificial intelligence (AI) and machine learning (ML) methods have attracted a considerable attention to develop Smart Control Systems for water management facilities. In this study, an attempt has been made to create a smart framework to monitor, control, and manage groundwater wells and pumps using a combination of ML algorithms and statistical analysis. In this research, 8 different learning methods and regressions namely support vector regression (SVR), extreme learning machine (ELM), classification and regression tree (CART), random forest (RF), artificial neural networks (ANNs), generalized regression neural network (GRNN), linear regression (LR), and K-nearest neighbors (KNN) regression algorithms have been applied to create a forecast model to predict water flow rate in Mashhad City wells. Moreover, several descriptive statistical metrics including mean squared error (MSE), root mean square error (RMSE), mean absolute error (MAE), and cross predicted accuracy (CPA) are calculated for these models to evaluate their performance. According to the results of this investigation, CART, RF, and LR algorithms have indicated the highest levels of precision with the lowest error values while SVM and MLP are the worst algorithms. In addition, sensitivity analysis has demonstrated that the LR and RF algorithms have produced the most accurate models for deep and shallow wells respectively. Finally, a Petri net model has been presented to illustrate the conceptual model of the smart framework and alarm management system.
Water resource management may restrict economic development, and a preference for economic development may weaken the effect of water resource management. The Water Eco-Civilization City (WEC) is a significant pilot policy in China, and is designed to achieve both effective water resource management and economic development. This study assessed possible win-win outcomes of this policy by applying a water eco-efficiency (WEE) perspective. First, a slacks-based measure (SBM) was used to calculate WEE. Then, a difference-in-differences (DID) strategy and mediating effect model were applied to examine whether and how the WEC policies improved WEE and achieved desired policy outcomes in cities. The panel dataset used for the study covered 275 prefecture-level cities from 2008 to 2017. The major findings were as follows: (1) The WEC policies achieved win-win outcomes in pilot cities, meaning it achieved both positive water resource management and economic outcomes; specifically, it contributed 48.63% of the total increase in WEE. (2) Three effective WEC policies realized win-win outcomes: upgrading the industrial structure; scaling up drainage infrastructure; and encouraging centralized wastewater treatment. Upgrading the industrial structure was the most effective policy. (3) WEC policy outcomes were heterogeneous under different city conditions. In cities with more abundant water resources, scaling up drainage infrastructures and encouraging centralized wastewater treatment were more effective. In cities with a larger industrial scale, the WEC policies could not achieve win-win outcomes, because it was not possible to upgrade the industrial structure or encourage centralized wastewater treatment. In cities with more intense wastewater regulations, upgrading the industrial structure and encouraging centralized wastewater treatment were more effective. These findings exemplify the policies that achieve win-win outcomes, and highlight the fact that governments should consider water resource abundance, industrial scale, and the current intensity of wastewater regulations before designing new policies.
Current national and international regulations, along with growing environmental concerns, have deeply influenced the design of supply chain networks. These decisions stem from the fact that decision-makers try to design the supply chain network to align with their economic and environmental objectives. In this paper, a new closed-loop supply chain network with sales agency and customers is formulated. The proposed model has four echelons in the forward direction and five echelons in the backwards direction. The model not only considers several constraints from previous studies, but also addresses new constraints in order to better explore real-life problems that employ different transportation modes and that rely on sale agency centers. The objective function is to maximize the total profit. In addition, this study firstly considers distinct cluster of customers based on the product life cycle. These customers are utilized in different levels of the proposed network in order to purchase the final products, returned products, and recycled products. The structure of the model is based on linear mixed-integer programming, and the proposed model has been investigated through a case study regarding the manufacturing industry. To verify the model efficiency, a set of metaheuristics and hybrid algorithm are applied in various test problems along with a data from a real-world case study in a building construction industry. The findings of the proposed network illustrated that using the attributes of sale agency centers and clusters of customers both increase the problem total revenue and the number of the collected returned products.
The contradiction of water resource shortage is increasingly prominent in the process of electric power production. In order to fully understand the implementation of water-saving strategy in a power plant, this paper takes a coal-fired power plant as an example to analyze and evaluate the water-saving effect of “zero discharge” of waste water in the whole plant through the water balance test. The results show that the coal-fired power plant not only protects the water environment, but also saves about 38 % of the cost of production water by implementing special water modification such as “zero discharge” of waste water in the whole plant.
This paper presents reliability, availability, and maintainability (RAM) analysis framework for evaluating the performance of a circulation system of water (WCS) used in a coal-fired power plant (CFPP). The performance of WCS is evaluated using a reliability block diagram (RBD), fault tree analysis (FTA), and Markov birth–death probabilistic approach. In this work, the system under study consists of five subsystems connected in series and parallel configuration namely condensate extraction pump (CEP), low-pressure feed water heater (LPH), deaerator (DR), boiler feed pump (BFP), high-pressure feed water heater (HPH). The reliability block diagram (RBD) and fault tree approach (FTA) have been employed for the performance evaluation of WCS. The Markov probabilistic approach based simulation model is developed. The transition diagram of the proposed model represented several states with full working capacity, reduced capacity, and failed state. The ranking of critical equipment is decided on the basis of criticality level of equipment. The study results revealed that the boiler feed pump affects the system availability at most, while the failure of deaerator affects it least. The availability of the system is optimized using the particle swarm optimization method. The optimized availability parameter (TBF, TTR) based modified maintenance strategy is recommended to enhance the availability of the plant system. The optimized failure rate and repair rate parameters of the subsystem are used to suggest a suitable maintenance strategy for the water circulation system of the thermal power plant. The proposed RAM framework helps the decision-makers to plan the maintenance activity as per the criticality level of subsystems and allocate the resources accordingly.
Controlling air pollution in megacities is crucial from both a health and environmental perspective. Air pollution is a pervasive problem in these densely populated cities and must be effectively managed to promote sustainability. This study presents a framework for the intelligent management of air pollution control systems in developing nations. The framework was developed through the use of an expert agreement model involving five managers from Tehran. The results indicate that the Decision Support System (DSS) is equipped with calibrated sensors for monitoring air quality. Additionally, the concentration of air pollutants can be determined through the application of machine learning algorithms and the analysis of historical data. Rapid response methods are then applied to mitigate the acute and chronic effects of air pollution. The DSS also incorporates citizens' feedback to evaluate the effectiveness of air pollution control measures. Implementing this model in developing nations' smart cities can help achieve several of the United Nations' Sustainable Development Goals (SDGs), such as good health and well-being, sustainable cities and communities, climate action, and life on land.
Urbanization is one of the major transformations, along with industrialization and economic development, profoundly impacting human society and its living environment. Among the environmental elements, air quality is one of the most intuitively perceived. Most low- and mid-income countries currently suffer from air pollution, especially in urban areas. This review therefore examined the history of air pollution under different stages of urbanization in typical high-, mid-, and low-income countries and summarized the general understanding of the relationship between urbanization and air pollution to suggest the future development of the trade-off between urbanization and air pollution. Based on the review, we highlighted that the development of precautionary measures against environmental pollution during the process of urbanization. In particular, adequate attention and efficient local practices to control air pollution before the onset of heavy pollution, and local practices for multi-stakeholder satisfaction would allow sustainable urbanization with favourable air quality. We hope this review will provide powerful examples of countries that have considered and acted to balance urbanization and air quality in recent centuries, and thus serve as a reference for countries to design more appropriate actions to fulfil the 11th Sustainable Development Goal of the United Nations (UNSDG).
PID controller is used in applications like motor speed control, liquid level control, temperature control, etc. It works well in both closed-loop and open-loop system configurations. To set gain parameters in the PID controller, the main issue is with the computation of gain parameters in offline mode. So, the controller may not be adjusted itself when deployed online. The disturbance that occurs in a system is spontaneous, thereby the PID gain parameters should be adjusted in tune with the disturbance online. So, the online mode of controller tuning has become popular. Artificial intelligence (AI)-based logical techniques like fuzzy logic, neural networks, and genetic algorithm-based methods have gained prominence in the process of tuning the gain parameters of a PID controller. There is a provision to train the controller in presence of disturbance and hence a better disturbance rejection can be obtained. In the present work, the fuzzy logic is used for tuning the gain parameters of the PID controller adaptively to control a DC shunt motor. From the time domain characteristics, it can be concluded that the controller tuned using fuzzy logic which performs better when compared to a controller tuned using conventional tuning methods.
A digital PID (Proportional-Integral-Derivative) controller to maintain a constant hot water temperature in the tank was proposed. The thermometer's indications are subject to random errors. The water temperature is determined by solving the inverse problem for a thermometer in the form of a solid cylinder inside which the temperature is measured. The water temperature is determined using a first or second-order thermometer model. The use of differential quotients to calculate the first or second derivative after time causes the controller to be unstable. To increase the stability of the PID system operation, digital filters were used to eliminate the influence of random disturbances of the temperature indicated by the thermometer. This ensures stable, much faster, and more accurate operation of the controller. The operation of the PID controller with the proposed technique for determining the medium temperature was illustrated by using the example of an electric storage heater in which the water temperature should be equal to the pre-set temperature. The novelty of the presented PID control system is the determination of the fluid temperature based on the solution of the inverse problem of heat conduction formulated for a thermometer. As a result, the speed and accuracy of the PID controller are significantly improved.
Nowadays, energy crisis is considered an essential active issue for future urbanization in megacities. While the rate of population growth increases, the volume of municipal solid waste production increases significantly. This highlights the need of Sustainable Development Goals (SDGs) for both developed and developing countries. This paper constructs a novel smart framework for supplying biogas energy. Our study is applicable for fields of waste management and energy supply in green buildings. The proposed framework integrates the Response Surface Methodology (RSM), Artificial Intelligence (AI), and Petri net modeling. In this regard, the AI techniques including the Random Tree (RT), Random Forest (RF), Artificial Neural Network (ANN) and, Adaptive-Network-based Fuzzy Inference System (ANFIS) are employed. In addition, for creating the optimum condition, a dynamic control system using the Petri Net modeling is applied. Among all machine learning methods, ANFIS with 0.99 correlation coefficient had the best accuracy for Accumulated Biogas Production (ABP) based on effective factors. Finally, the main findings of this paper are to introduce a novel framework for addressing different scientific issues such as supplying the clean energy in green buildings, the development of a smart and sustainable biogas production control system, integration of solid waste management with the SDGs in green buildings.
Water and energy are elementary requirements for well-being and prosperity of humans. They are mutually dependent on each other. Energy production involves a huge quantity of water and in turn, water structures need a huge amount of energy. This dependency is termed as ‘water-energy nexus'. As a result of population and economic growth, and higher living standard, future demand for both water and energy is anticipated to upsurge, particularly in the emerging economies like India. The majority of energy-linked water usage is for power generation, which is led by water-concentrated electricity generation from coal, natural gas, and nuclear materials. Majority of the water utilised at thermal power plants is for cooling purpose in addition to ash handling, boiler feed water, flue gas desulphurisation and for other applications in coal-fired power plants. Governing authorities shall impose limits on freshwater usage by power plants by framing regulatory norms. The present stress on freshwater supplies and increasing demand is compelling the power generating units to search for alternative sources or supplementary sources of water. Utilising water from such sources will help conserve fresh water for other uses, such as drinking and agriculture. The current paper discusses water resource management for coal-based thermal power plants. If further evaluates various alternatives or measures for water conservation in thermal power plants.
The Water-Energy Nexus is gaining considerable interest with growing environmental concerns, especially in developing countries such as Pakistan that are now under a state of water stress. Thermal power plants consume large amounts of freshwater, thereby making their water usage optimization immensely important. For potential repowering
featuring reduced cooling water consumption, a clear understanding of the influence of key condenser parameters on the power plant is required. This study investigates a degraded combined cycle gas turbine power plant for reduced cooling water usage from a repowering perspective. The degraded power plant under consideration draws 13% more
freshwater for recirculation-based cooling from a canal linked with the River Indus in Pakistan. The power plant has been modeled using 120 hours of real-time probabilistic data at baseload by generating three study cases stemming from 24-hour parametric variations. The commercial tool Cycle-Tempo has been deployed for modeling and simulating the power plant performance with the incorporation of physical uncertainties and errors originating from the measurement sensors. The model is validated and compared with the 23 years old design case data to highlight the degradations via
an exergy analysis. A tri-parametric optimization is later performed to reduce the cooling water consumption through specialized performance maps that relate exergy, low-pressure section steam turbine performance, and condenser performance to highlight potential repowering opportunities while retaining the baseload performance. The optimized cases result in approximately 9.5-11% lesser water consumption in terms of mass flow rate while retaining the baseload performance and key heat recovery steam generator operating points.
The global epidemic caused by novel coronavirus continues to be a crisis in the world and a matter of concern. The way the epidemic has wreaked havoc on the international level has become difficult for the healthcare systems to supply adequately personal protection equipment for medical personnel all over the globe. In this paper, considering the COVID-19 outbreak, a multi-objective, multi-product, and multi-period model for the personal protection equipment demands satisfaction aiming to optimize total cost and shortage, simultaneously, is developed. The model is embedded with instances and validated by both modern and classic multi-objective metaheuristic algorithms. Moreover, the Taguchi method is exploited to set the metaheuristic into their best performances by finding their parameters’ optimum level. Furthermore, fifteen test examples are designed to prove the established PPE supply chain model and tuned algorithms’ applicability. Among the test examples, one is related to a real case study in Iran. Finally, metaheuristics are evaluated by a series of related metrics through different statistical analyses. It can be concluded from the obtained results that solution methods are practical and valuable to achieve the efficient shortage level and cost.
This paper is aimed to design a PID controller for reference signal tracking. Design problem is formulated to control the angular position of a single-link manipulator at any particular desired set value. Implementation of the overall feedback control system and the proposed controller has been carried out in Python language. Additive uncertainty is considered with the transfer function model. Simulation results illustrate the usefulness of the proposed controller for angular velocity tracking in presence of uncertainty. Average mean square error and settling time have been calculated. Overall stability of the feedback system has been tested by obtaining the eigenvalues of the closed-loop system.
This study presents a comprehensive framework for evaluating renewable and non-renewable power plants' performance using the Life Cycle Analysis (LCA) and emergy analysis. The emergy analysis is used to consider the free ecosystem services in the sustainability of the systems as a supplement to the LCA. The results indicate that the wind and photovoltaic power plants have the best performance in terms of the LCA analysis, while the wind and combined cycle power plants have the highest emergy sustainability index. The best scenario is chosen under a two-objective optimization problem, including the single score and emergy sustainability as objective functions. Here, the wind power plant is the most interesting option while the combined cycle power plant with CO2 capture is the least interesting alternative. In this study, a novel framework is developed to assess the Cost of Avoided Carbon emissions (CACe) using the integrated LCA-emergy analysis for the combined cycle power plant in which its value is evaluated to be about $151.65/ton of CO2. Eventually, an uncertainty analysis is performed on the solar transformities using Monte Carlo simulation. The framework presented in this paper provides insights to increase power plants' sustainability for managers and authorities.
Energy supply of megacities is considered as an active research topic in the new aspects of urban management, especially in developing countries like Iran. With an introduction to the sustainable development goals, the smart city concept presents a novel idea for providing energy in a city with the use of Artificial Intelligence (AI), renewable energy, such as Photovoltaic (PV) technologies, and Transformational Participation (TP) based on motivational programs for citizens. This study aims to evaluate the electrical energy consumption in Mashhad, Iran, based on machine learning tools and present the dynamic strategies for promoting citizens’ willingness for renewable energy generation based on the experts’ knowledge. The main novelty of this research is simultaneous application of Artificial Neural Network (ANN) and statistical analysis for creating a Decision Support System (DSS). Then, the solar energy potential is appraised by the PV system simulation tool during one year in our case study in Mashhad, Iran. Furthermore, a Classical Delphi (CD) method is applied for motivational strategies and further TP implementation. In particular, the motivational strategies are suggested by 45 experts and then are prioritized in sequential expert meetings. The outcomes of this research indicate that the ANN model can successfully forecast the electrical energy consumption in summer and winter periods with a 99% accuracy. Then, based on the solar energy computations in the PV system, the peak of electrical energy consumption can be controlled in the hottest and coldest months. Last but not least, the superposition of experts’ and citizens’ opinions reveal A4 (sharing benefits of optimized costs with the citizens by solar energy generation), B2 (reducing the electrical energy cost for solar energy generation, especially in peak times) and C1 (creating the energy coin in the city with credits instead of spending money in urban activities fits to solar energy generation) as the main motivational strategies for solar energy generation in short, middle and long-term planning horizons.
Recently, supply chain network design has been a demanding question and attracted great interest in a wide range of fields, including the medical industry. The heart of the entire discipline of the medical industry is the concept of blood management which nowadays brought striking attention among managers and decision-makers. Thus, a bi-objective and sustainable blood supply chain network is introduced in this study by considering both social and environmental factors of blood decomposition. In addition, some aspects of uncertainty are firstly considered to the problem in terms of both the amount of gathered blood from blood transfusion centers and the decomposition ratio in the blood decomposition center. Considering bi-objective programming in the developed network, the problem simultaneously considers optimizing the CO2 emission, balancing the flow of all utilized vehicles, penalty coefficient of non-visited centers, total costs of using all vehicles, and more importantly the social and environmental factors of blood decomposition. Therefore, the total cost is minimized by the first objective function along with the environmental factor of greenhouse gas emission while maximizing the social factor of blood decomposition is done by the second objective function which gathers and produces the maximum amount of blood sub-products for patients using stochastic programming. Several sensitivity analyses are conducted for a better implication of the problem in various conditions. The findings of the study suggest that expecting more social factors convey more costs in most cases. Therefore, blood supply chain managers must adjust their aims and scope to achieve the best-desired results for society.
Zero liquid discharge (ZLD) of wet flue gas desulphurization (WFGD) wastewater in coal-fired power plants (CFPPs) would contribute to sustainable development of fossil electricity generation. In the present work, improved flue gas-driven forced-circulation multi-effect distillation and crystallization (FCMEDC) systems were proposed for achieving low-cost ZLD of WFGD wastewater. Different types of integration scenarios were investigated, including FCMEDC systems driven by waste heat recovered from boiler and economizer exhaust gases, and incorporated with low-pressure economizer and flue gas bypass high-pressure economizer systems. Thermodynamic and techno-economic analysis models were developed for comprehensive evaluation of the technical and economic feasibility of the integrated ZLD schemes. The levelized cost of wastewater (LCOW) and operating expenditure (OPEX) were found to be significantly reduced by 41.5% and 53.6%, respectively, when plant auxiliary steam was substituted by boiler exhaust gas as the heat source in a 600 MW unit. A parametric study was further conducted to investigate the impact of wastewater salinity and flow ratio on the process design. The flue gas-driven FCMEDC system demonstrated favorable adaptability to variations in wastewater quality. Moreover, sensitivity analysis suggested that the LCOWs of the proposed ZLD systems were explicitly subject to the expected lifetime, brine disposal options, and brine pre-concentration. The methodology and results could provide insights into the process development of WFGD wastewater ZLD systems based on the FCMEDC technology.
Renewable energy resources are considered as alternative sources of energy for future generations. Wells turbines can be used to convert the reciprocating energy of waves in oceans and seas into rotational energy. Low aerodynamic efficiency is the main drawback of Wells turbines. The present study showed that the addition of the guide vanes, endplate, or end ring to the base blade significantly enhanced the performance of Wells turbines. Also, we used ANFIS-GA-CFD for optimization of the several design variables (the blade tip thickness, the blade form, and the input-output angels of guide vanes) simultaneously. Therefore, artificial neural fuzzy networks were used to forecast the three benchmark parameters (pressure drop coefficient, torque coefficient, and efficiency) and a genetic algorithm was utilized to approximate the optimized design variables when there are maximum torque, maximum efficiency, and minimum pressure drop. The optimal blade increased the upper limit of efficiency up to 18%, torque coefficient up to 8%, and reduced the pressure drop coefficient up to almost 3.45% compared to the best previous models. Moreover, flow separation was delayed.
This study aims to reveal the status quo and future trend of thermoelectric water use and water stress in India. We compiled a bottom-up geo-database for all thermal power plants in India and identified the type of cooling technology used. We then estimated thermoelectric water withdrawal and water consumption in India from 2009 to 2018 and projected future trends in thermoelectric water use up to 2027 using the integrated power planning and dispatch model, SWITCH-India. Results show that thermoelectric power generation in India is not a major source of water stress in most basins until 2027. Freshwater withdrawal varied from 14 to 16 billion m3 during the study period, while freshwater consumption increased with growing thermal power generation. The catchment in the middle of the Ganga River basin has the largest freshwater withdrawal and consumption. The volume of water withdrawal accounts for less than 1% of blue water availability in most catchments. It is also likely that a larger proportion of power generation and water withdrawal will occur in catchments that are under lower water stress in the future. Policy interventions should target stressed catchment areas and improve the resilience of thermal power plants to outages due to water stress.
In this research, graphene oxide-cyanuric acid (GO-CA) nanocomposite as an efficient and novel adsorbent was used for solid phase extraction of Pb2+ followed by electrothermal atomic absorption spectrometry (ETAAS). The synthesised adsorbent was characterised by the Fourier transform-infrared spectrophotometry (FT-IR), field emission scanning electron microscopy (FE-SEM) and Energy dispersive X-Ray spectroscopy (EDX). In order to optimise the parameters including pH of sample solution, amounts of adsorbent, extraction and desorption times; the response surface methodology based on central composite design (RSM-CCD) was used.
Under the optimum conditions, the calibration curve was linear in the range of 0.15-3.0 µg L-1 Pb2+. Also, the limit of detection (LOD) and the relative standard deviation were 0.021µg L-1 (n=7) and 3.1 % (seven replicate analysis of 1 µg L-1 Pb2+), respectively. To evaluate the adsorption mechanism of Pb2+ onto the GO-CA nanocomposite, two parameter (Langmuir and Freundlich) and three parameter isotherms were evaluated and based on the obtained results, the adsorption of Pb2+ onto the GO-CA nanocomposite governed by the Freundlich isotherm with a the maximum adsorption capacities of 333.0 mg g-1. According to the kinetic models including the Pseudo First Order (PFO), Pseudo Second Order (PSO), Intra-particle diffusion, Elovich and Boyd models; adsorption of Pb2+ followed by the second-order kinetic model and film diffusion is the rate controlling step. Moreover, based on the geometric computations, the adsorption and desorption processes don’t have any interfering together during the contact time.
In the following, Artificial Neural Network (ANN) and Random Forest algorithm (RFA) are employed for prediction of adsorption performance based on effective parameters such as pH, amounts of adsorbent, extraction and desorption times. The outcomes of soft computing (ANN and RFA) illustrated acceptable accuracy (R2>0.92) for estimation and prediction of extraction recovery of Pb2+
Affordable and environmentally sustainable sanitation practices are required to improve public health and contribute to resource security. Human urine recovery can optimize resource demand and minimize waste in the sanitation sector while providing agricultural producers access to recycled nutrients. Nexus thinking was applied in resource management related to human urine to assess the energy demand using a life cycle approach and economic performance using a cost-benefit approach. Business as usual practices regarding water supply and wastewater management in flushing toilets and the use of synthetic fertilizers in agriculture were compared to a proposed optimization with reduced water flushing in a toilet or urinal, and the proposed urine logistics with a waterless urinal and the use of urine fertilizer. The energy demand was 92-192 kWh in business as usual, 94-117 kWh in proposed optimization and 38 kWh in proposed urine logistics, resulting in a potential saving of 25-154 kWh per person a year in the proposed scenarios. The financial cost was 67-108 USD in business as usual, 9-57 USD in proposed optimization and 32 USD in proposed urine logistics, resulting in a potential benefit of 30-96 USD per person a year. Sensitivity analysis of key parameters was considered in the proposed urine logistics to support decision-making. Urine recovery as presented in this study contributes to multi-sectoral integrated management of resources mainly through natural resource savings, pollution prevention and fertilizer provision optimizing the water-energy-nutrient nexus.
The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is a classical multi-attribute decision-making method, which is widely used in various fields for decision-making or evaluation. The entropy method (EM) is frequently used in determining attribute weights for TOPSIS, and the weight determined by the EM is always called entropy weight (EW). In this paper, based on a large number of data and theoretical analysis, the effects of the EW on TOPSIS are analyzed. It is found that the EW can enhance the function of the attribute with the highest diversity of attribute data (DAD) as well as weaken the function of the attributes with a low DAD in decision-making or evaluation. Sometimes the EW even causes the decision-making or evaluation result to be seriously affected by the attribute with the highest DAD (called primacy attribute, abbreviated as PA). Since the EW can enhance the function of the PA in decision-making or evaluation, it is conducive to increase the dipartite degree of the relative closeness (RC), but reduces the comprehensiveness of the RC, and may even lead to unreasonable decision-making or evaluation result. In order to adjust the effects of the EW on TOPSIS, the entropy-based TOPSIS with adjustable weight coefficient is proposed in this paper. Some discussions on the application of the proposed method are also given.