Journal article
Data-driven minimization of pump operating and maintenance cost
Engineering applications of artificial intelligence, Vol.40, pp.37-46
04/2015
DOI: 10.1016/j.engappai.2015.01.003
Abstract
A data-driven model for scheduling pumps in a wastewater treatment process is proposed. The objective is to minimize the cost of pump operations and maintenance. A neural network algorithm is applied to model performance of the pumps using the data collected at a municipal wastewater treatment plant. The discrete-state Markov process is utilized to develop a model of maintenance decisions. The developed pump performance and maintenance models are integrated into a scheduling model. A hierarchical particle swarm optimization algorithm is designed to solve the proposed scheduling model. The concepts developed in this paper are illustrated with two case studies.
Details
- Title: Subtitle
- Data-driven minimization of pump operating and maintenance cost
- Creators
- Zijun Zhang - University of Hong KongXiaofei He - University of IowaAndrew Kusiak - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Engineering applications of artificial intelligence, Vol.40, pp.37-46
- Publisher
- Elsevier Ltd
- DOI
- 10.1016/j.engappai.2015.01.003
- ISSN
- 0952-1976
- eISSN
- 1873-6769
- Grant note
- name: Early Career Scheme Grant of the Hong Kong Research Grant Council, award: CityU-138313
- Language
- English
- Date published
- 04/2015
- Academic Unit
- Nursing; Industrial and Systems Engineering
- Record Identifier
- 9984187071902771
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