Multi-criteria analysis applied to multi-objective optimal pump scheduling in water systems

This work presents a multi-criteria-based approach to automatically select specific non-dominated solutions from a Pareto front previously obtained using multi-objective optimization to find optimal solutions for pump control in a water supply system. Optimal operation of pumps in these utilities is paramount to enable water companies to achieve energy efficiency in their systems. The Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS) is used to rank the Pareto solutions found by the Non-Dominated Sorting Genetic Algorithm (NSGA-II) employed to solve the multi-objective problem. Various scenarios are evaluated under leakage uncertainty conditions, resulting in fuzzy solutions for the Pareto front. This paper shows the suitability of the approach for quasi real-world problems. In our case-study, the obtained solutions for scenarios including leakage represent the best trade-off among the optimal solutions, under some considered criteria, namely, operational cost, operational lack of service, pressure uniformity and network resilience. Potential future developments could include the use of clustering alternatives to evaluate the goodness of each solution under the considered evaluation criteria

Optimización de la gestión de redes de riego a presión a diferentes escalas mediante Inteligencia Artificial

Factors such as climate change, world population growth or the competition for the water resources make freshwater availability become an increasingly large and complex global challenge. Under this scenario of reduced water availability, increasing droughts frequency and uncertainties associated with a changing climate, the irrigated agriculture sector, particularly in the Mediterranean region, will need to be even more efficient in the use of the water resources. In Spain, many irrigation districts have been modernized in recent years, replacing the obsolete open channels by pressurized water distribution networks towards improvements in water use efficiency. Thanks to this, water use has reduced but the energy demand and the water costs have dramatically increased. Thus, strategies to reduce simultaneously water and energy uses in irrigation districts are required. This thesis consists of nine chapters, which include several models to optimize the management of the irrigation districts and increase the efficiency of water and energy use.Factores tales como el cambio climático, el crecimiento de la población mundial o la competencia por los recursos hídricos hacen que la disponibilidad de agua se esté convirtiendo en un desafío global cada vez más grande y complejo. En este escenario de reducción de la disponibilidad de agua, aumento de la frecuencia de las sequías y de las incertidumbres asociadas a un cambio climático, el sector de la agricultura de regadío, en particular en la región mediterránea, tendrá que ser aún más eficiente en el uso de los recursos hídricos. En España, muchas comunidades de regantes se han modernizado en los últimos años, sustituyendo los obsoletos canales abiertos por redes de distribución de agua a presión con el objetivo de mejorar la eficiencia en el uso del agua. Gracias a esto, el uso del agua se ha reducido, pero la demanda de energía y los costos del agua se han incrementado drásticamente. Por lo tanto, se requieren estrategias para reducir simultáneamente el uso de agua y energía en las comunidades de regantes. Esta tesis consta de nueve capítulos que incluyen varios modelos para optimizar la gestión de las comunidades de regantes y aumentar la eficiencia en el uso del agua y la energía

The integration of pumped hydro storage systems into PV microgrids in rural areas

Photovoltaic (PV) systems are popular in rural areas because they provide low cost and clean electricity for homes and irrigation systems. The primary challenge of PV systems is their intermittent nature. The typical solution is storing energy in batteries; however, they are expensive and possess a short lifespan. This research proposes a new type of pumped hydro storage (PHS) which can be implemented as an alternative to batteries. The components of the system are modelled to consider losses of the system accurately. The mathematic model developed in this project assists the management system to make more efficient decisions. The proposed storage is integrated into a farmhouse that has a PV pumping system where economic aspects of implementing the proposed storage is investigated. The integration of the proposed PHS into a microgrid needs a management system to make this system efficient and 3 cost-effective. This research proposes a multi-stage management system to schedule and control the microgrid components for optimal integration of the PHS. The designed management system is able to manage the pump, turbine, and irrigation time on real-time taking into account both present and future conditions of the microgrid. This study investigates the technical aspects of the proposed system. The PHS and the management system are tested experimentally in a setup installed at smart energy laboratory at Edith Cowan university. Data used in this project are real data collected in the laboratory in order to have a realistic analysis. Economic analysis is done in different sizes with different conditions. Results indicate that the proposed system has a short payback period and a large lifetime benefit, featuring as a cost-effective and sustainable energy storage system for use in rural areas. Video abstract: https://youtu.be/VuyEvHRY7W

Optimisation of a water company’s waste pumping asset base with a focus on energy reduction

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonWater companies use a significant quantity of electricity for the operation of their clean and wastewater assets. Rising energy prices have led to higher energy bills within the water companies, which has increased operating costs. Thus, improvements in demand side energy management are needed to increase efficiency and reduce costs, which forms the premise for this research project. Thames Water Utilities Ltd has identified that improvements in demand side energy management is required and is currently researching various methods to reduce energy consumption. One initiative included the upgrade of a variety of site telemetry assets. By deploying these new telemetry assets, Thames Water Utilities Ltd are more able to liberate the asset data and as such, be able to make informed decisions on how better to control and optimise the target sites, which is where this research project has seen further opportunities. This enhanced telemetry and SCADA infrastructure will enable successful research to further develop an intelligent integrated system that tackles pump scheduling and process control with the emphasis on energy management. The use of modern techniques, such as artificial intelligence, to optimise the network operation is gradually gaining traction. The balance between implementing new technology (with the benefits it may bring) and reluctance to change from the incumbent operating model will always provide challenges in the technology adoption agenda. The main work of this research project included the physical surveying of a wastewater hydraulic catchment, inclusive of all wet well dimensions, lidar overlays, and pump electrical power characteristics. These survey results where then able to be programmed by the research into the company’s' hydraulic model to enable a higher degree of accuracy in the modelling, as well as enabling electrical power as a measurable output. From here, the model was then able to be optimised, focussing on electrical energy as an output variable for reduction. The research concluded that electrical energy consumption over time can be reduced using the aforementioned strategies and as such recommends further work to move from the model environment to physical architecture. It does so with the key message that risk tolerances on water levels must be pre-agreed with hydraulic specialists prior to deployment

Multi-criteria analysis applied to multi-objective optimal pump scheduling in water systems

[EN] This work presents a multi-criteria-based approach to automatically select specific non-dominated solutions from a Pareto front previously obtained using multi-objective optimization to find optimal solutions for pump control in a water supply system. Optimal operation of pumps in these utilities is paramount to enable water companies to achieve energy efficiency in their systems. The Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS) is used to rank the Pareto solutions found by the non-dominated sorting genetic algorithm (NSGA-II) employed to solve the multi-objective problem. Various scenarios are evaluated under leakage uncertainty conditions, resulting in fuzzy solutions for the Pareto front. This paper shows the suitability of the approach for quasi real-world problems. In our case-study, the obtained solutions for scenarios including leakage represent the best trade-off among the optimal solutions, under some considered criteria, namely, operational cost, operational lack of service, pressure uniformity and network resilience. Potential future developments could include the use of clustering alternatives to evaluate the goodness of each solution under the considered evaluation criteria.Carpitella, S.; Brentan, BM.; Montalvo Arango, I.; Izquierdo Sebastián, J.; Certa, A. (2019). Multi-criteria analysis applied to multi-objective optimal pump scheduling in water systems. Water Science & Technology: Water Supply. 19(8):2338-2346. https://doi.org/10.2166/ws.2019.115S23382346198Ancău, M., & Caizar, C. (2010). The computation of Pareto-optimal set in multicriterial optimization of rapid prototyping processes. Computers & Industrial Engineering, 58(4), 696-708. doi:10.1016/j.cie.2010.01.015Aşchilean, I., Badea, G., Giurca, I., Naghiu, G. S., & Iloaie, F. G. (2017). Choosing the Optimal Technology to Rehabilitate the Pipes in Water Distribution Systems Using the AHP Method. Energy Procedia, 112, 19-26. doi:10.1016/j.egypro.2017.03.1109Brentan, B., Meirelles, G., Luvizotto, E., & Izquierdo, J. (2018). Joint Operation of Pressure-Reducing Valves and Pumps for Improving the Efficiency of Water Distribution Systems. Journal of Water Resources Planning and Management, 144(9), 04018055. doi:10.1061/(asce)wr.1943-5452.0000974Certa, A., Enea, M., Galante, G. M., & La Fata, C. M. (2017). ELECTRE TRI-based approach to the failure modes classification on the basis of risk parameters: An alternative to the risk priority number. Computers & Industrial Engineering, 108, 100-110. doi:10.1016/j.cie.2017.04.018Chen, C.-T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems, 114(1), 1-9. doi:10.1016/s0165-0114(97)00377-1Cruz-Reyes, L., Fernandez, E., Sanchez, P., Coello Coello, C. A., & Gomez, C. (2017). Incorporation of implicit decision-maker preferences in multi-objective evolutionary optimization using a multi-criteria classification method. Applied Soft Computing, 50, 48-57. doi:10.1016/j.asoc.2016.10.037Farmani, R., Ingeduld, P., Savic, D., Walters, G., Svitak, Z., & Berka, J. (2007). Real-time modelling of a major water supply system. Proceedings of the Institution of Civil Engineers - Water Management, 160(2), 103-108. doi:10.1680/wama.2007.160.2.103Hadas, Y., & Nahum, O. E. (2016). Urban bus network of priority lanes: A combined multi-objective, multi-criteria and group decision-making approach. Transport Policy, 52, 186-196. doi:10.1016/j.tranpol.2016.08.006Hamdan, S., & Cheaitou, A. (2017). Supplier selection and order allocation with green criteria: An MCDM and multi-objective optimization approach. Computers & Operations Research, 81, 282-304. doi:10.1016/j.cor.2016.11.005Ho, W. (2008). Integrated analytic hierarchy process and its applications – A literature review. European Journal of Operational Research, 186(1), 211-228. doi:10.1016/j.ejor.2007.01.004Jowitt, P. W., & Germanopoulos, G. (1992). Optimal Pump Scheduling in Water‐Supply Networks. Journal of Water Resources Planning and Management, 118(4), 406-422. doi:10.1061/(asce)0733-9496(1992)118:4(406)Jowitt, P. W., & Xu, C. (1990). Optimal Valve Control in Water‐Distribution Networks. Journal of Water Resources Planning and Management, 116(4), 455-472. doi:10.1061/(asce)0733-9496(1990)116:4(455)Kurek, W., & Ostfeld, A. (2013). Multi-objective optimization of water quality, pumps operation, and storage sizing of water distribution systems. Journal of Environmental Management, 115, 189-197. doi:10.1016/j.jenvman.2012.11.030Lima, G. M., Luvizotto, E., & Brentan, B. M. (2017). Selection and location of Pumps as Turbines substituting pressure reducing valves. Renewable Energy, 109, 392-405. doi:10.1016/j.renene.2017.03.056Mala-Jetmarova, H., Sultanova, N., & Savic, D. (2017). Lost in optimisation of water distribution systems? A literature review of system operation. Environmental Modelling & Software, 93, 209-254. doi:10.1016/j.envsoft.2017.02.009Montalvo, I., Izquierdo, J., Pérez-García, R., & Herrera, M. (2014). Water Distribution System Computer-Aided Design by Agent Swarm Optimization. Computer-Aided Civil and Infrastructure Engineering, 29(6), 433-448. doi:10.1111/mice.12062Odan, F. K., Ribeiro Reis, L. F., & Kapelan, Z. (2015). Real-Time Multiobjective Optimization of Operation of Water Supply Systems. Journal of Water Resources Planning and Management, 141(9), 04015011. doi:10.1061/(asce)wr.1943-5452.0000515Ostfeld, A., Uber, J. G., Salomons, E., Berry, J. W., Hart, W. E., Phillips, C. A., … Walski, T. (2008). The Battle of the Water Sensor Networks (BWSN): A Design Challenge for Engineers and Algorithms. Journal of Water Resources Planning and Management, 134(6), 556-568. doi:10.1061/(asce)0733-9496(2008)134:6(556)Todini, E. (2000). Looped water distribution networks design using a resilience index based heuristic approach. Urban Water, 2(2), 115-122. doi:10.1016/s1462-0758(00)00049-2Zaidan, A. A., Zaidan, B. B., Al-Haiqi, A., Kiah, M. L. M., Hussain, M., & Abdulnabi, M. (2015). Evaluation and selection of open-source EMR software packages based on integrated AHP and TOPSIS. Journal of Biomedical Informatics, 53, 390-404. doi:10.1016/j.jbi.2014.11.012Żak, J., & Kruszyński, M. (2015). Application of AHP and ELECTRE III/IV Methods to Multiple Level, Multiple Criteria Evaluation of Urban Transportation Projects. Transportation Research Procedia, 10, 820-830. doi:10.1016/j.trpro.2015.09.03

Control of a hybrid electric vehicle with predictive journey estimation

Battery energy management plays a crucial role in fuel economy improvement of charge-sustaining parallel hybrid electric vehicles. Currently available control strategies consider battery state of charge (SOC) and driver’s request through the pedal input in decision-making. This method does not achieve an optimal performance for saving fuel or maintaining appropriate SOC level, especially during the operation in extreme driving conditions or hilly terrain. The objective of this thesis is to develop a control algorithm using forthcoming traffic condition and road elevation, which could be fed from navigation systems. This would enable the controller to predict potential of regenerative charging to capture cost-free energy and intentionally depleting battery energy to assist an engine at high power demand. The starting point for this research is the modelling of a small sport-utility vehicle by the analysis of the vehicles currently available in the market. The result of the analysis is used in order to establish a generic mild hybrid powertrain model, which is subsequently examined to compare the performance of controllers. A baseline is established with a conventional powertrain equipped with a spark ignition direct injection engine and a continuously variable transmission. Hybridisation of this vehicle with an integrated starter alternator and a traditional rule-based control strategy is presented. Parameter optimisation in four standard driving cycles is explained, followed by a detailed energy flow analysis. An additional potential improvement is presented by dynamic programming (DP), which shows a benefit of a predictive control. Based on these results, a predictive control algorithm using fuzzy logic is introduced. The main tools of the controller design are the DP, adaptive-network-based fuzzy inference system with subtractive clustering and design of experiment. Using a quasi-static backward simulation model, the performance of the controller is compared with the result from the instantaneous control and the DP. The focus is fuel saving and SOC control at the end of journeys, especially in aggressive driving conditions and a hilly road. The controller shows a good potential to improve fuel economy and tight SOC control in long journey and hilly terrain. Fuel economy improvement and SOC correction are close to the optimal solution by the DP, especially in long trips on steep road where there is a large gap between the baseline controller and the DP. However, there is little benefit in short trips and flat road. It is caused by the low improvement margin of the mild hybrid powertrain and the limited future journey information. To provide a further step to implementation, a software-in-the-loop simulation model is developed. A fully dynamic model of the powertrain and the control algorithm are implemented in AMESim-Simulink co-simulation environment. This shows small deterioration of the control performance by driver’s pedal action, powertrain dynamics and limited computational precision on the controller performance

Identification of Water Hammering for Centrifugal Pump Drive Systems

Water hammering is a significant problem in pumping systems. It damages the pipelines of the pump drastically and needs to identify with an intelligent method. Various conventional methods such as the method of characteristics and wave attenuation methods are available to identify water hammering problems, and the predictive control method is one of the finest and time-saving methods that can identify the anomalies in the system at an early stage such that the device can be saved from total damage and reduce energy loss. In this research, a machine learning (ML) algorithm has used for a predictive control method for the identification of water hammering problems in a pumping system with the help of simulations and experimental-based works. A linear regression algorithm has been used in this work to predict water hammering problems. The efficiency of the algorithm is almost 90% compared to other ML algorithms. Through a Vib Sensor app-based device at different pressures and flow rates, the velocity of the pumping system, a fluctuation between healthy and faulty conditions, and acceleration value at different times have been collected for experimental analysis. A fault created to analyze a water hammering problem in a pumping system by the sudden closing and opening of the valve. When the valve suddenly closed, the kinetic energy in the system changed to elastic resilience, which created a series of positive and negative wave vibrations in the pipe. The present work concentrates on the water hammering problem of centrifugal pumping AC drive systems. The problem is mainly a pressure surge that occurs in the fluid, due to sudden or forced stops of valves or changes in the direction and momentum of the fluid. Various experimental results based on ML tool and fast Fourier transformation (FFT) analysis are obtained with a Vib Sensor testbed set-up to prove that linear regression analysis is the less time-consuming algorithm for fault detection, irrespective of data size

A data-driven methodology to support pump performance analysis and energy effiency optimization in wastewater treatment plants

Studies and publications from the past ten years demonstrate that generally the energy efficiency of Waste Water Treatment Plants (WWTPs) is unsatisfactory. In this domain, efficient pump energy management can generate economic and environmental benefits. Although the availability of on-line sensors can provide high-frequency information about pump systems, at best, energy assessment is carried out a few times a year using aggregated data. Consequently, pump inefficiencies are normally detected late and the comprehension of pump system dynamics is often not satisfactory. In this paper, a data-driven methodology to support the daily energy decision-making is presented. This innovative approach, based on fuzzy logic, supports plant managers with detailed information about pump performance, and provides case-based suggestions to reduce the pump system energy consumption and extend pump life spans. A case study, performed on a WWTP in Germany, shows that it is possible to identify energy inefficiencies and case-based solutions to reduce the pump energy consumption by 18.5%

Operational optimisation of water supply networks using a fuzzy system

This paper presents a fuzzy system to control the pressure in a water distribution network, by using valves and controlling the rotor speed of the pumping systems. The variable frequency drive tracks the minimum head of the pumping system, while the control valves have the function of eliminating the excess pressure at various points of the network. The control system can track any reference pressure value and there is no limit for the number of monitored points. Experiments were carried out to demonstrate the fuzzy system’s efficiency. By extrapolating the results achieved in the experimental setup to a real hydraulic network with leakages and no pressure control, the volumetric losses could be reduced by more than 56%. The experiments showed that the system is robust enough to control the pressure of an experimental setup of water distribution. Besides, the proposed system can be easily applied to similar water supply systems and would help to reduce the consumption of water and electricity, as well as to reduce the maintenance costs