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Currently, the use of industrial seawater reverse osmosis desalination (ISROD) plants has increased in popularity in light of the growing global demand for freshwater. In ISROD plants, any fault in the components of their control systems can lead to a plant malfunction, and this condition can originate safety risks, energy waste, as well as affect...
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... RO system is the most complex and the one that has the greatest importance in the production of quality fresh water [5]. Figure 4 shows a diagram of this industrial seawater RO desalination plant. The pretreatment subsystem is made up of the following items: feed pumps, flocculation/sedimentation to eliminate suspended material, dissolved air flotation (DAF) to eliminate potential algal biomass or potential hydrocarbons, granular media filtration (GMF), low-pressure ultrafiltration (UF) or microfiltration (MF) to eliminate suspended particulate matter, and additives tanks. ...
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... He compared the merits and drawbacks of the four algorithms in finding the MSOs, analyzed the computational complexity of each algorithm, and used the dual-capacity water tank system; thus, he simulated and verified the performance of each algorithm's performance and complexity [50]. Pere-Zuniga converted the fault diagnosis problem into an MSO problem based on the linear programming algorithm of binary integers that solves the MSO set and analyzes the fault detection and isolation [51]. ...
Structural analysis, a model-based fault diagnosis approach, has been extensively highlighted since it does not depend on an exact analytical system model. The structural analysis approach focuses on obtaining the required information for fault detection and fault-tolerant control from the structural model of the control system. Diagnosability is a design attribute that indicates the fault diagnosis capabilities. Diagnosability design not only guarantees that the structure of the control system gives adequate fault information but also that the control system achieves the fault diagnosis with the least cost and the most excellent efficiency. In this paper, in combination with the research progress of structural analysis, the study first designs the structural diagnosability model of the control system based on structural analysis; secondly, the essential techniques of diagnosability evaluation and diagnosability design based on structural analysis are analyzed and summarized, and the diagnosability index system based on structural analysis is designed based on the structural detectability and structural isolation; this lays the foundation for the development of the diagnosability; lastly, the study summarizes the problems of diagnosability research based on structural analysis and the future development direction.
... The results indicated that their approach could effectively detect and locate faults while remaining robust for residue analysis and insensitive to false alarms. Pérez-Zuñiga et al. (2020) demonstrated a fault detection and isolation system for an industrial seawater reverse osmosis desalination plant located in Lima, Peru. The system applies a structural analysis approach to generate diagnostic tests and binary integer linear programming to select fault-driven minimal structurally overdetermined sets, which ensures isolation of all faults, including sensor and actuator faults. ...
... If this occurs, it can lead to not only safety risks and energy waste but also serious damage to the quality of the produced water. To address this issue, the authors of [172] considered the pre-installed sensors and developed a system for fault detection and isolation (FDI) by considering a structural analysis approach for industrial seawater reverse osmosis desalination (ISROD). The mentioned plant, located in Peru, is equipped with a range of sensors and actuators, including temperature, pH, pressure, flow, conductivity, and ultrasonic level sensors, additive dosing pumps, and high-pressure feed pumps. ...
Desalination is the process of making the saltwater of the Earth's oceans drinkable and could be a solution to droughts, especially in warmer and drier regions. However, the concerns of high cost and ecosystem degradation have always restricted the growth of the desalination industry and necessitated further studies and corrective actions. As desalination is becoming essential for our planet, the development of sustainable and energy-efficient technologies to produce clean water is a key research topic. Considering the worth of seawater desalination to fulfill the needs of the Earth habitants, this article explores how this process can be made less problematic to use as a real solution, not just a temporary opportunity. Desalination methods will play a key role, so all the major options and their environmental footprints are discussed, considering the energy requirements in particular of each process. A brief but up-to-date summary of the current status and future trends of desalination technologies is provided. Available equipment and procedures that are being developed to make desalination a sustainable process are explored. Finally, based on a detailed review, we highlight the future trends and issues to make informed decisions pertaining to the future research and development projects of this sector.
... where H c (s) is the transfer function of the controller, e(s) is the error value, and (s) is the resultant output. k p , k I and k D are the gains for proportional, integral, and derivative terms for the FOPID controller [22]. The expression λ is the fractional element of integral parts and μ is the fractional element of derivative parts as clearly shown in Fig. 4. ...
The main aim of this work is to design a suitable Fractional Order Proportionl Integral Derivative (FOPID) controller with Chaotic Whale Optimization Algorithm (CWOA) for a RO desalination system. Continuous research on Reverse Osmosis (RO) desalination plants is a promising technique for satisfaction with sustainable and efficient RO plants. This work implements CWOA based FOPID for the simulation of reverse osmosis (RO) desalination process for both servo and regulatory problems. Mathematical modeling is a vital constituent of designing advanced and developed engineering processes, which helps to gain a deep study of processes to predict the performance, more efficiently. Numerous approaches have been employed for mathematical models based on mass and heat transfer and concentration of permeable flow rate. Incorporation of FOPID controllers is broadly used to improve the dynamic response of the system, at the same time, to reduce undershoot or overshoot, steady state error and hence improve the response. The performances of the FOPID controller with optimization is compared in terms of measures such as Integral Time Absolute Error (ITAE) and Integral Square Error (ISE). Simulation results with FOPID on desalination process achieved rise time of 0.0311 s, settling time of 0.0489 s and 0.7358% overshoot, better than the existing techniques available in the literatures.
... The major contribution of this research is the extension of the structural analysis approach [17,23,37] to propose a distributed FDI approach for the OPS, considering an optimization process in the selection of tests and the inclusion of neighbors subsystems at different levels to achieve the diagnostic goals. In order to guarantee maximum diagnosability and least exchange of sensor information between subsystems, local diagnosers should be efficiently built for each subsystem, in this sense, two algorithms for the design of local diagnosers are proposed, first only with local information and if necessary with progressive incorporation of information from neighboring subsystems to achieve detection and isolation of a set of faults of interest in OPS. ...
Fault detection and isolation (FDI) in oil pipeline systems (OPS) is a very critical issue because faults in these systems such as leaks or equipment malfunctions may cause significant safety accidents and economic losses. These are the challenging factors, along with the environmental regulations for developing efficient FDI approaches for OPS. This paper proposes a model-based distributed FDI approach, which uses a structural model of the system in conjunction with algorithms to generate diagnostic tests that may be implemented in local diagnosers along the OPS. The proposed approach allows detection and isolation of faults in pipeline sections (pipeline segments), pump stations, as well as process control equipment. In this way, simulation of the obtained diagnostic tests in a benchmark application shows that all faults of interest (pipeline segment faults and sensor faults) are detected and isolated.
... Nevertheless, the existing literature on the subject of reverse osmosis process monitoring is limited. Recently, a new approach was proposed for fault detection based on a structural analysis approach for an industrial seawater reverse osmosis plant [16]. Another approach proposed hybrid minimal structurally overdetermined sets for hybrid systems formulated as a binary integer linear programming optimization problem [17]. ...
The water purification process is becoming increasingly important to ensure the continuity and quality of subsequent production processes, and it is particularly relevant in pharmaceutical contexts. However, in this context, the difficulties arising during the monitoring process are manifold. On the one hand, the monitoring process reveals various discontinuities due to different characteristics of the input water. On the other hand, the monitoring process is discontinuous and random itself, thus not guaranteeing continuity of the parameters and hindering a straightforward analysis. Consequently, further research on water purification processes is paramount to identify the most suitable techniques able to guarantee good performance. Against this background, this paper proposes an application of kernel principal component analysis for fault detection in a process with the above-mentioned characteristics. Based on the temporal variability of the process, the paper suggests the use of past and future matrices as input for fault detection as an alternative to the original dataset. In this manner, the temporal correlation between process parameters and machine health is accounted for. The proposed approach confirms the possibility of obtaining very good monitoring results in the analyzed context.
... Furthermore, it could also be used to perform fault prediction for improving the maintenance plan and to avoid undesired malfunctions. In the fault detection literature, a great number of methods have been dedicated to modelbased techniques [1,2] with great success in many cases, although a known drawback of such methods is their limitation to processes with relatively simple dynamics and sensiblity to model approximations. Examples of model-based methods are: the structural approach, which searches for irreducible relations that attains sensibility to the selected faults [3,4], and eigenstructure-based fault diagnosis, which deals with a minimization problem for the sensibility of perturbations against faults [5,6]. ...
Implementation of model-based fault diagnosis systems can be a difficult task due to the complex dynamics of most systems, an appealing alternative to avoiding modeling is to use machine learning-based techniques for which the implementation is more affordable nowadays. However, the latter approach often requires extensive data processing. In this paper, a hybrid approach using recent developments in neural ordinary differential equations is proposed. This approach enables us to combine a natural deep learning technique with an estimated model of the system, making the training simpler and more efficient. For evaluation of this methodology, a nonlinear benchmark
system is used by simulation of faults in actuators, sensors, and process. Simulation results show that the proposed methodology requires less processing for the training in comparison with conventional machine learning approaches since the data-set is directly taken from the measurements and inputs. Furthermore, since the model used in the essay is only a structural approximation of the plant; no advanced modeling is required. This approach can also alleviate some pitfalls of training data-series,
such as complicated data augmentation methodologies and the necessity for big amounts of data.
... This Special Issue also encompasses eleven research articles covering the optimal sizing of hybrid solar photovoltaic, fuel cells, hydrogen storage, and reverse osmosis seawater desalination system [4]; design of unconfined dense plunging jets used for brine disposal from desalination plants [5]; mode-based analysis and optimal operation of a multi-stage flash desalination system [6]; simulation analysis and optimization of a coupled reverse osmosis and membrane capacitive deionization plant for seawater desalination [7]; optimal operating parameters of a capacitive deionization desalination system via radial movement optimization [8]; design and thermodynamic analysis of a scraped surface crystallizer plant for freeze desalination [9]; design of a fault detection and isolation control system for industrial seawater reverse osmosis desalination plants based on structural analysis [10]; simulation and economic feasibility evaluation of an ocean thermal energy conversion system for electricity and freshwater production [11]; design and performance investigation of a closed-cycle humidification-dehumidification desalination system [12]; two-dimensional modelling approach and performance assessment of ion-exchange membrane in membrane capacitive deionization [13]; and, advanced exergy, exergoeconomic, and exergoenvironmental analyses of a combined cycle power plant integrated with multi-effect distillation and reverse osmosis desalination units [14]. ...
Water scarcity due to the ever-increasing worldwide demand and climate change is one of the greatest hurdles of our time [...]
... { y 1 = h 1 , y 2 = h 2 , measurement sensor of h 1 measurement sensor of h 2 (19) It is assumed that these two sensors are biased in their measurements, that is to say we consider two failures 1 and 2 affecting respectively the two DIAGNOSTYKA, Vol. 21, No. 4 (2020) AChbi MS, Mhamdi L, Kechida S, Dhouibi H.: Methodology to knowledge discovery for fault diagnosis of … 120 sensors of ℎ 1 and ℎ 2 and because of this, the measurement equations become: ...
The work carried out in this article concerns on the implementation off a diagnostic procedure for hybrid dynamic systems (HDS) whose objective is to guarantee the proper functioning of industrial installations. In this context, the main contributions of this work are summarized into three parts: The first part is oriented to the modeling approach dedicated to HDS. The aim is to find an adequate model combining both aspects (continuous and discrete dynamics). The use of Neuro-fuzzy networks makes it possible to build a model of the system and to follow all the modes without it being necessary to identify or discern them. The second part concerns the synthesis of a fault diagnostic technique based on a fuzzy inference system. A Neuro-Fuzzy network based is used for residual generation, while for the residual evaluation, a fuzzy reasoning model is used which can mainly introduce heuristic information into the analysis scheme and takes the appropriate decision regarding the actual behaviour of the process. The proposed approach is successfully applied to monitoring faults of a non-linear three-tank system and the results confirm the effectiveness of this approach.
We all agree that freshwater is essential to our lives (drinking, domestic activities), our
socio-economic activities (industry, agriculture and energy production). However, under the
impact of climate change on our conventional freshwater resources, which depend on rainfalls
and rivers runoff, associated with demographic growth, we are witnessing a water scarcity in
most world regions. Therefore, to minimize the gap between demand and supply, we began to
use other alternatives; one of the attractive options that we got is seawater desalination, as an
unconventional water resource, to narrow this gap. Our production of desalinated water is
around 95 million m3/day. So far, that seems positive, but we need to take into consideration
the environmental impact of these desalination facilities on marine environment. One of the environmental challenges associated with desalination technologies is the production of a hyper-salty charged water as a byproduct, which also called briefly “the brine. The latest studies estimate that today, brine production is around 142 million m3/day. In other words, we produce brine more than we produce desalinated water. What we need is just to adapt standard EIA procedures for evaluating and minimizing the negative effects of desalination projects; also, we have to improve the brine management, to limit the negative environmental impacts of seawater desalination. We should also think of other alternatives (wastewater recycling, water efficiency …).
