Georgiadis, M.C., Papageorgiou, L.G., and Mac-chietto, S., Optimal Cleaning Policies in Heat Exchanger Networks under Rapid Fouling, Industrial & Engineering Chemistry Research, 39, 2000, pp. Smaïli, F., Vassiliadis, V.S., and Wilson, D.I., Mitigation of Fouling in Refinery Heat Exchanger Networks by Optimal Management of Cleaning, Energy and Fuels, 15, 2001, pp. Yeap, B.L., Design of Heat Exchanger Networks with Fouling Mitigation, in CGPS Dissertation: University of Cambridge, UK, 2001. Metaheuristic algorithm always provide a solution at the end of optimization’s iteration and it can be run continuesly in order to get more optimal solution.ĮSDU, Heat Exchanger Fouling in the Preheat Train of a Crude Oil Distillation Unit, ESDU, London 2000. The results showed that efficiency of HEN 23% increased which can be translated in IDR 14.1 Billion of fuel saving. In this paper, the GA will be used to solve the optimization of cleaning schedule of Heat Exchanger Network (HEN) in a refinery Crude Preheat Train (CPT). One of metaheuristic algorithms is Genetic Algorithm (GA). This method works without influenced by the previous optimization results. Another method is metaheuristic method that simple and promising global optimum solution without introducing any approximations or simplifying assumptions. This method sometime provide unconvergen solution. Deterministic method requires enough knowledge to determine areas that can provide global optimum solution. Solving this problem can use two methods, namely deterministic and metaheuristic. The resulting MINLP problem is very complex and finding the global optimum is a challenging task. MINLP, Metaheuristic, Genetic Algorithm, Heat Exchanger Network. Fakultas Teknologi Industri, Jurusan Teknik Fisika, Institut Teknologi Sepuluh Nopember, Kampus ITS Sukolilo Surabaya 60111
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