Statistical Modeling of industrial Problems: Statistical Optimum Design of Plate Heat Exchangers Networks

Statistical Modeling of industrial Problems. This text book presents the application of Nondominated Sorting Genetic Algorithm II (NSGA-II) and Markov Chain Monte Carlo (MCMC) methods in designing the optimal plate heat exchanger networks. Heat exchangers are devices that are used to cool or heat a fluid by exchanging thermal energy with another fluid entering at a different temperature. Depending on the application, different types and geometries of heat exchangers are available on the market. Among them plate heat exchangers composed of several plates separated by empty spaces called duct are considered in this text book. NSGA-II as a multiobjective optimization method was considered due to the fact that the goals set is to minimize the Life Cycle Cost (LCC) function which represents the price of saved energy, to minimize the heat exchanger network area together with maximizing the momentary heat recovery output at the same time, after that, MCMC methods are used on top of optimization results to take into account the uncertainty in the models.