Data-driven minimization of pump operating and maintenance cost

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

16 Scopus Citations
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Original languageEnglish
Pages (from-to)37-46
Journal / PublicationEngineering Applications of Artificial Intelligence
Online published4 Feb 2015
Publication statusPublished - Apr 2015


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