Ali Dolatshahi Zand, Kaveh Khalili-Damghani, Sadigh Raissi. Designing an Intelligent Control Philosophy in Reservoirs of Water Transfer Networks in Supervisory Control and Data Acquisition System Stations. International Journal of Automation and Computing, vol. 18, no. 5, pp.694-717, 2021. https://doi.org/10.1007/s11633-021-1284-1
Citation: Ali Dolatshahi Zand, Kaveh Khalili-Damghani, Sadigh Raissi. Designing an Intelligent Control Philosophy in Reservoirs of Water Transfer Networks in Supervisory Control and Data Acquisition System Stations. International Journal of Automation and Computing, vol. 18, no. 5, pp.694-717, 2021. https://doi.org/10.1007/s11633-021-1284-1

Designing an Intelligent Control Philosophy in Reservoirs of Water Transfer Networks in Supervisory Control and Data Acquisition System Stations

doi: 10.1007/s11633-021-1284-1
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  • Author Bio:

    Ali Dolatshahi Zand received the B.Sc. degree in electrical engineering from Shahed University, Iran in 2003, and received the M. Sc. degree in industrial engineering from Islamic Azad University, South Tehran Branch, Iran in 2014. He is a Ph. D. degree candidate in industrial engineering at the Islamic Azad University, South Tehran Branch, Iran. He has been working in designing SCADA, system and industrial automation for more than 16 years. His research interests include SCADA systems, industrial automation, soft computing, meta-heuristic methods, artificial neural network, reliability engineering and demand forecasting. E-mail: adolatshahi@yahoo.com ORCID iD: 0000-0002-9095-0493

    Kaveh Khalili-Damghani received the M. Sc. degree from Islamic Azad University, South Tehran Branch, Iran in 2005, received the Ph. D. degree in industrial engineering from Islamic Azad University, South Tehran Branch, Iran in 2008, and received the Ph. D. degree in industrial management from Allameh Tabatabei University, Iran in 2012. He has published more than 200 papers in high quality journals such as Information Sciences, Quality and Reliability Engineering International, Annals of Operations Research, Expert Systems with Applications, Computers and Industrial Engineering, International Journal of Advanced Manufacturing Technology, Applied Soft Computing, Reliability Engineering and System Safety, Measurement, Journal of Industrial Engineering International, Journal of Industrial Engineering: Theory, Application, and Practice, Project Management Journal, TOP, and Applied Mathematics and Computations. He is associate editor of six international journals indexed by Scopus. His research interests include soft computing, fuzzy sets and systems, meta-heuristic methods, multi-criteria decision making, data envelopment analysis, reliability optimization, quantitative modelling of supply chain, and applied operations research. E-mail: k_khalili@azad.ac.ir (Corresponding author) ORCID iD: 0000-0002-2338-1673

    Sadigh Raissi received the B. Sc, M. Sc and Ph. D. degrees in industrial engineering from Islamic Azad University, Science and Research Branch, Iran in 2002. He is an associate professor at School of Industrial Engineering, Islamic Azad University, South Tehran Branch (IAU-STB), Iran. He has been engaged in industrial systems engineering technology development and the technical consultant from 1988 up to the present. He has worked in different management positions, both in the private and public sectors; the last one was deputy of research and planning at IAU-STB. By his attempts, more than 10 scientific journals initiated and research activities facilitated. Currently, he is also acts as Editor-in- Chief of the Journal of Industrial Engineering International. He has published more than 180 research papers. His research interests include quality & reliability engineering, system simulation, and statistical methods in engineering. E-mail: raissi@azad.ac.ir

  • Received Date: 2020-06-26
  • Accepted Date: 2021-01-28
  • Publish Online: 2021-03-24
  • Publish Date: 2021-10-01
  • In this paper, a hybrid neural-genetic fuzzy system is proposed to control the flow and height of water in the reservoirs of water transfer networks. These controls will avoid probable water wastes in the reservoirs and pressure drops in water distribution networks. The proposed approach combines the artificial neural network, genetic algorithm, and fuzzy inference system to improve the performance of the supervisory control and data acquisition stations through a new control philosophy for instruments and control valves in the reservoirs of the water transfer networks. First, a multi-core artificial neural network model, including a multi-layer perceptron and radial based function, is proposed to forecast the daily consumption of the water in a reservoir. A genetic algorithm is proposed to optimize the parameters of the artificial neural networks. Then, the online height of water in the reservoir and the output of artificial neural networks are used as inputs of a fuzzy inference system to estimate the flow rate of the reservoir inlet. Finally, the estimated inlet flow is translated into the input valve position using a transform control unit supported by a nonlinear autoregressive exogenous model. The proposed approach is applied in the Tehran water transfer network. The results of this study show that the usage of the proposed approach significantly reduces the deviation of the reservoir height from the desired levels.

     

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