Combining CSP and MPC for the operational control of water networks

Sun, Congcong; Puig, Vicenç; Cembraño, Gabriela

doi:10.1016/j.engappai.2015.12.006

Cited by 20 publications

(4 citation statements)

References 39 publications

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“…Pressure-based networks lead to optimization problems with nonconvex constraints. These can be approximated by solving a constraints satisfaction problem (CSP) as discussed in [60]. The problem can be then transformed into a convex SSMPC problem which can be solved by RapidNet.…”

Section: Discussionmentioning

confidence: 99%

“…The cost for the operation of the water network is quantified in terms of three individual costs which have been proposed in the literature [5,60,16,61]: the economic cost which is related to the treatment cost and electricity required for pumping, the smooth operating cost which penalizes the abrupt operation of pumps and valves and the safety storage cost which penalizes the use of water from the reserves (i.e., allowing the level in the tanks to drop below the safety level).…”

Section: Control Objectivesmentioning

confidence: 99%

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Uncertainty-aware demand management of water distribution networks in deregulated energy markets

Sopasakis¹,

Sampathirao²,

Bemporad³

et al. 2018

Environmental Modelling & Software

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We present an open-source solution for the operational control of drinking water distribution networks which accounts for the inherent uncertainty in water demand and electricity prices in the day-ahead market of a volatile deregulated economy. As increasingly more energy markets adopt this trading scheme, the operation of drinking water networks requires uncertainty-aware control approaches that mitigate the effect of volatility and result in an economic and safe operation of the network that meets the consumers' need for uninterrupted water supply. We propose the use of scenario-based stochastic model predictive control: an advanced control methodology which comes at a considerable computation cost which is overcome by harnessing the parallelization capabilities of graphics processing units (GPUs) and using a massively parallelizable algorithm based on the accelerated proximal gradient method.

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Section: Discussionmentioning

confidence: 99%

Section: Control Objectivesmentioning

confidence: 99%

Uncertainty-aware demand management of water distribution networks in deregulated energy markets

Sopasakis¹,

Sampathirao²,

Bemporad³

et al. 2018

Environmental Modelling & Software

View full text Add to dashboard Cite

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“…Some researchers have thus opted for linear approaches [22,23] in an attempt to simplify the problem; however, the solution's reliability and validity then become questionable. Metaheuristic algorithms such as ant colony optimisation (ACO), particle swarm optimization (PSO), and genetic algorithm (GA) were applied as a solution to non-linear problems by some researchers [24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approach to Identify Important Parameters Influencing Pumping Load Shift in a Complex Dewatering System of a Deep-Level Mine

Olifant,

Hancock,

du Plessis

et al. 2024

Mining

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This study investigated the application of machine learning to optimise the pumping load shift of a complex dewatering system in a deep-level mine, aiming to reduce energy costs associated with the dewatering process, which consumes an average of 14% of the mine’s electricity. Traditional practices, reliant on human control and simulations, often lead to inconsistent savings and occasional losses. The study employed multivariate linear regression (MLR) and extreme gradient boosting (XGBoost) on a mine dewatering system, to identify important parameters influencing the pumping load shift performance. Critical parameters significantly impacting the energy consumption of the dewatering system were identified by the best-performing model, XGBoost. Implementing a pumping schedule based on XGBoost insights resulted in consistent load shifting and enhanced energy cost savings. These findings highlight the potential of machine learning in comprehending and optimising complex systems in deep-level mines, with the case study approach proving effective in quantifying and validating real-world impacts. This approach could offer substantial energy savings through data-driven decision-making.

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“…Contribución al modelado e implementación de un control avanzado para un proceso de cloración de una Estación de Tratamiento de Agua Potable agua de salida de la ETAP a las cuatro horas y la estimación en base a las ecuaciones (38) y (39) para el conjunto de valores de muestras recogidas cada día durante dos años a la misma hora. La Figura 4-2 muestra cuatro pantallas de la interfaz de la aplicación que se ha diseñado e implementado en el marco de la presente tesis y que tiene por objetivo estimar la demanda de cloro en línea a la salida de la ETAP a partir de la parametrización de la dosificación en el depósito 1…”

Section: Validación Del Modelo De Reacción Del Clorounclassified

Contribución al modelado e implementación de un control avanzado para un proceso de cloración de una estación de tratamiento de agua potable

Gámiz¹

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The effective disinfection in chlorination tanks of drinking water treatment plants depends on many factors that must be taken into account when designing a suitable control system. The chemical characteristics of the water to be treated, the possible flow disturbances or the contact time of the chlorine with the outgoing water must be taken into account in order to establish a control strategy that contemplates adaptation to the different possible scenarios. An excessive dosage of chlorine, or on the other hand a deficit in the dose, may lead to a legal failure to comply with the existing regulations that establish obligatory limit values. Likewise, an overdose generates an unnecessary expenditure of chlorine and, as a collateral effect, problems due to an increase in the maximum values allowed for by-products such as trihalomethanes. The contribution of the present work regarding chlorination in drinking water treatment tanks is divided into two areas: simulation and control. The studies and field work have been carried out on one of the largest drinking water treatment plants in southern Europe. The design and implementation of a simulator has made it possible to reproduce the behaviour of the system to test the proposed controller without the need to interfere in the production process, thus avoiding possible effects on the supply. The control implemented at the Plant is based on a control advance that compensates the disturbance of ammonium and other compounds and a feedback with planned gains depending on the flow rate and the temperature and origin of the water to be treated. The two control blocks (feedforward and feedback) are supervised by a diffuse system that, depending on the characteristics of the water to be treated, combines a control feedforward with a gains planning on a PI control.The simulation and experimental results obtained have been validated with real plant data from the Supervision System database. The control presented in this work has been integrated into the current Plant Control System. The results obtained, apart from being satisfactory, have allowed the Plant Control Room operator to move from the role of Process Control to that of Supervision of the Chlorination Process. La desinfección efectiva en depósitos de cloración de Estaciones de Tratamiento de Agua Potable, depende de numerosos factores que hay que tener en cuenta a la hora de diseñar un sistema de control adecuado. Las características químicas del agua a tratar, las posibles perturbaciones de caudal o el tiempo de contacto del cloro con el agua de salida se deberán tener en cuenta para establecer una estrategia de control que contemple la adaptación a los diferentes posibles escenarios. Una dosificación excesiva de cloro o por el contrario un déficit en la dosis, puede incurrir en un incumplimiento legal de la normativa existente que fija unos valores límites de obligado cumplimiento. Así mismo, una sobredosificación genera un gasto innecesario de cloro y de forma colateral problemas por un incremento en los valores máximos permitidos de subproductos como los trihalometanos. La contribución del presente trabajo respecto a la cloración en depósitos de tratamiento de agua potable se divide en dos ámbitos: la simulación y el control. Los estudios y trabajos en campo se han realizado sobre una Estación de Tratamiento de Agua Portable de las más grandes del sur de Europa. El diseño y la implementación de un simulador ha permitido reproducir el comportamiento del sistema para testear el controlador propuesto sin necesidad de interferir en el proceso productivo, evitando así posibles afectaciones en el suministro. El control que se ha implementado en la Planta está basado en un avance del control que compensa la perturbación del amonio y otros compuestos y una realimentación con ganancias planificadas en función del caudal , la temperatura y el origen del agua a tratar. Los dos bloques de control (avance y realimentación) son supervisados por un sistema difuso que, en función de las características del agua a tratar, combina un avance del control (feedforward) junto a una planificación de ganancias sobre un control PI. Los resultados de simulación y experimentales obtenidos han sido validados con datos reales de Planta a partir de la base de datos del Sistema de Supervisión. El control presentado en este trabajo ha sido integrado en el actual Sistema de Control de la Planta. Los resultados obtenidos además de ser satisfactorios han permitido al técnico de la Sala de Control de Planta pasar del rol de Control del Proceso, al de Supervisión del Proceso de Cloración.

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Combining CSP and MPC for the operational control of water networks

Cited by 20 publications

References 39 publications

Uncertainty-aware demand management of water distribution networks in deregulated energy markets

Uncertainty-aware demand management of water distribution networks in deregulated energy markets

Machine Learning Approach to Identify Important Parameters Influencing Pumping Load Shift in a Complex Dewatering System of a Deep-Level Mine

Contribución al modelado e implementación de un control avanzado para un proceso de cloración de una estación de tratamiento de agua potable

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