Modeling and optimization of industrial systems with data mining

Thesis Committee and providing invaluable suggestions and feedback on my research. I thank members of Intelligent System Laboratory who have worked with me. Special thanks to my colleagues: Yaohui Zeng, who worked with me on HVAC project and introduced me to this research area; Dr. Xiupeng Wei, who worked with me on wastewater project and shared his research experiences with me; Guanglin Xu, who discussed me with implementation of the HVAC optimization framework. Finally and most importantly, I want to express thanks to my parents for their continuous, unconditional love and support. iv ABSTRACT 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.