Modeling and optimization of a wastewater pumping system with data-mining methods

Zijun Zhang, Andrew Kusiak, Yaohui Zeng and Xiupeng Wei

Applied Energy, 2016, vol. 164, issue C, 303-311

Abstract: In this paper, a data-driven framework for improving the performance of wastewater pumping systems has been developed by fusing knowledge including the data mining, mathematical modeling, and computational intelligence. Modeling pump system performance in terms of the energy consumption and pumped wastewater flow rate based on industrial data with neural networks is examined. A bi-objective optimization model incorporating data-driven components is formulated to minimize the energy consumption and maximize the pumped wastewater flow rate. An adaptive mechanism is developed to automatically determine weights associated with two objectives by considering the wet well level and influent flow rate. The optimization model is solved by an artificial immune network algorithm. A comparative analysis between the optimization results and the observed data is performed to demonstrate the improvement of the pumping system performance. Results indicate that saving energy while maintaining the pumping performance is potentially achievable with the proposed data-driven framework.

Keywords: Neural network; Pumping system; Energy saving; Artificial immune network algorithm; Bi-objective optimization; Data mining (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (19)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:164:y:2016:i:c:p:303-311

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DOI: 10.1016/j.apenergy.2015.11.061

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