Document Type


Date of Degree

Spring 2014

Degree Name

MS (Master of Science)

Degree In

Industrial Engineering

First Advisor

Kusiak, Andrew

First Committee Member

Kusiak, Andrew

Second Committee Member

Chen, Yong

Third Committee Member

Krokhmal, Pavlo


Energy efficiency of industrial systems is of great concern to many. Modeling and optimization of industrial systems has been an active research area aiming at improvement of energy efficiency of these systems. Traditional analytical and physics-based methods, reported in literature, limit modeling industrial systems, which are complex, nonlinear, and dynamic.

Due to progress in data collection techniques, large volume of data has been collected and stored for analysis. Although much valuable information is contained in such data, utilization of the data in modeling industrial systems is lagging. Data mining is a novel science, providing a platform and techniques to model complex systems and processes. Data mining techniques have been widely applied in modeling various systems.

In this Thesis, two energy intensive industrial systems are investigated, a pump system in wastewater treatment plants, and an HVAC system in commercial buildings. Data mining is utilized to derive models describing the relationship between target, operational cost of systems, and system control variables. An optimization model is constructed to minimize operational cost of a system, and intelligent algorithms are employed to solve the optimization models. The study demonstrates a considerable energy saving by applying the proposed control strategy.

The approach developed in this Thesis can be applied to industrial systems other than the pump and HVAC systems.


Data Mining, HVAC, Pump


ix, 72 pages


Includes bibliographical references (pages 69-72).


Copyright 2014 Xiaofei HE