Data-driven minimization of pump operating and maintenance cost
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
Author(s)
Detail(s)
Original language | English |
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Pages (from-to) | 37-46 |
Journal / Publication | Engineering Applications of Artificial Intelligence |
Volume | 40 |
Online published | 4 Feb 2015 |
Publication status | Published - Apr 2015 |
Link(s)
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.
Research Area(s)
- Cost optimization, Data mining, Energy savings, Particle swarm optimization, Pump system, Scheduling
Citation Format(s)
Data-driven minimization of pump operating and maintenance cost. / Zhang, Zijun; He, Xiaofei; Kusiak, Andrew.
In: Engineering Applications of Artificial Intelligence, Vol. 40, 04.2015, p. 37-46.
In: Engineering Applications of Artificial Intelligence, Vol. 40, 04.2015, p. 37-46.
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review