Modeling and optimization of industrial systems: data mining and computational intelligence approach

• Main Author: Zeng, Yaohui
• Other Authors: Kusiak, Andrew
• Format: Others
• Language: English
• Published: University of Iowa 2012
• Subjects: Computational Intelligence; Data mining; Modeling; Optimization; Industrial Engineering
• Online Access: https://ir.uiowa.edu/etd/3557; https://ir.uiowa.edu/cgi/viewcontent.cgi?article=3558&context=etd

Recent years have seen increasingly growing interest in energy conservation. Industrial systems involving large energy consumption are receiving intensive attentions from both academia and industry on optimizing control strategies for potential energy savings. This thesis investigates energy efficiency of two industrial systems, the heating, ventilating and air-conditioning (HVAC) system, and the wastewater pumping system. Both systems are known as dynamic, nonlinear, and multivariate, which are of great challenge for system modeling and performance optimization. Traditional approaches, usually relying on physical equations and mathematical programming, show limited abilities in dealing with complex system modeling and optimization. As an emerging science with an abundance of successful applications in industrial, business, medical areas, data mining has proven its powerful capabilities in nonlinear system modeling and complex pattern recognition. Successful and effective applications of data mining algorithms, such as multilayer perceptron neural network, support vector machine, and boosting tree have been reported in literature and expanded to complex system modeling. Computational intelligence has been an emerging and promising area over these years for its capability of solving difficult optimization problems, for instance, mixed integer nonlinear programing problems. Computational intelligence has been tremendously applied in providing optimal or near-optimal solutions within limited computation time in different kinds of optimization problems. This thesis mainly focuses on employing computational intelligence to generate optimal control strategies in the stated industrial systems. The main contribution of this research lies in utilizing computational intelligence to solve the mixed integer nonlinear programming optimization models built by data mining algorithms. Another strength of this thesis is establishing the unified framework of applying data mining and computational intelligence to real-world system control and optimization.