Data-driven minimization of pump operating and maintenance cost

被引：15

作者：

Zhang, Zijun ^{[1
]}

He, Xiaofei ^{[2
]}

Kusiak, Andrew ^{[2
]}

机构：

[1] City Univ Hong Kong, Dept Syst Engn & Engn Management, Hong Kong, Hong Kong, Peoples R China

[2] Univ Iowa, Dept Mech & Ind Engn, Iowa City, IA 52242 USA

来源：

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE | 2015年 / 40卷

关键词：

Pump system; Scheduling; Energy savings; Data mining; Particle swarm optimization; Cost optimization; WASTE-WATER; OPTIMIZATION; ENERGY;

D O I：

10.1016/j.engappai.2015.01.003

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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. (C) 2015 Elsevier Ltd. All rights reserved.

引用

页码：37 / 46

页数：10

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