Journal article
Modeling and optimization of a wastewater pumping system with data-mining methods
Applied energy, Vol.164, pp.303-311
02/15/2016
DOI: 10.1016/j.apenergy.2015.11.061
Abstract
•Data-mining methods are applied to wastewater pumping system modeling and optimization.•Neural networks are used to model pump energy consumption and wastewater flow rate.•An artificial immune network algorithm is employed to solve the bi-objective optimization problem.•Six to fourteen percent of energy could be saved when balancing the energy cost and wastewater flow rate.
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.
Details
- Title: Subtitle
- Modeling and optimization of a wastewater pumping system with data-mining methods
- Creators
- Zijun Zhang - University of Hong KongAndrew Kusiak - University of IowaYaohui Zeng - University of IowaXiupeng Wei - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Applied energy, Vol.164, pp.303-311
- Publisher
- Elsevier Ltd
- DOI
- 10.1016/j.apenergy.2015.11.061
- ISSN
- 0306-2619
- eISSN
- 1872-9118
- Language
- English
- Date published
- 02/15/2016
- Academic Unit
- Industrial and Systems Engineering; Nursing
- Record Identifier
- 9984187067002771
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