Process planning in a fuzzy environment |
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| Institution: | 1. Department of Information Technology, Mepco Schlenk Engineering College (Autonomous), Sivakasi, India;2. Department of Computer Science and Engineering, Mepco Schlenk Engineering College (Autonomous), Sivakasi, India;1. School of Chemistry and Chemical Engineering/Key Lab of Low-carbon Chemistry & Energy Conservation of Guangdong Province, Sun Yat-Sen University, Guangzhou 510275, China;2. Chemical and Biomolecular Engineering Department, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong;1. Department of Industrial & Systems Engineering, National University of Singapore, and IPS Research Center, Waseda University, Japan;2. College of Mathematics & Computer Science, Hebei University, China;1. Shaanxi Key Laboratory of Energy Chemical Process Intensification, School of Chemical Engineering and Technology, Xi''an Jiaotong University, Xi''an, Shaanxi 710049, China;2. Key Laboratory of Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Ministry of Education, Shanghai 200237, China |
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| Abstract: | Mixed-integer optimization models for chemical process planning typically assume that model parameters can be accurately predicted. As precise forecasts are difficult to obtain, process planning usually involves uncertainty and ambiguity in the data. This paper presents an application of fuzzy programming to process planning. The forecast parameters are assumed to be fuzzy with a linear or triangular membership function. The process planning problem is then formulated in terms of decision making in a fuzzy environment with fuzzy constraints and fuzzy net present value goals. The model is transformed to a deterministic mixed-integer linear program or mixed-integer nonlinear program depending on the type of uncertainty involved in the problem. For the nonlinear case, a global optimization algorithm is developed for its solution. This algorithm is applicable to general possibilistic programs and can be used as an alternative to the commonly used bisection method. Illustrative examples and computational results for a petrochemical complex with 38 processes and 24 products illustrate the applicability of the developed models and algorithms. |
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