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Model based approach to synthesize spare-supported cleaning schedules for existing heat exchanger networks
Affiliation:1. State University of Maringá, Av. Colombo 5790, Maringá – PR – 87020900, Brazil;1. University of Tuzla, Faculty of Technology, Univerzitetska 8, Tuzla 75000, Bosnia and Herzegovina;2. University of Maribor, Faculty of Chemistry and Chemical Engineering, Smetanova ul. 17, Maribor 2000, Slovenia;1. Institute of Chemical Process Systems Engineering, School of Chemical Engineering, Dalian University of Technology, Dalian 116024, China;2. Key Laboratory of Industrial Ecology and Environmental Engineering, Ministry of Education, School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China;1. Department of Chemical Engineering, University of Cape Town, Private Bag X3, Rondebosch 7701, Cape Town, South Africa
Abstract:Almost every modern chemical process is equipped with a heat-exchanger network (HEN) for optimal energy recovery. However, as time goes on after startup, fouling on the heat-transfer surface in an industrial environment is unavoidable. If the heat exchangers in an operating plant are not cleaned regularly, the targeted thermal efficiency of HEN can only be sustained for a short period of time. To address this practical issue, several mathematical programming models have already been developed to synthesize online cleaning schedules. Although the total utility cost of a HEN could be effectively reduced accordingly, any defouling operation still results in unnecessary energy loss due to the obvious need to temporarily take the unit to be cleaned out of service. The objective of the present study is thus to modify the available model so as to appropriately assign spares to replace them. Specifically, two binary variables are adopted to respectively represent distinct decisions concerning each online exchanger in a particular time interval, i.e., whether it should be cleaned and, if so, whether it should be substituted with a spare. The optimal solution thus includes not only the cleaning schedule but also the total number of spares, their capacities and the substitution schedule. Finally, the optimization results of a series of case studies are also presented to verify the feasibility of the proposed approach.
Keywords:Heat-exchanger network  Spare  Cleaning schedule
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