Global optimization of reactive distillation processes using bat algorithm

Reactive distillation (RD) is an important process intensification solution with several advantages. It can improve the reaction selectivity and yield, overcome the thermodynamic restrictions, reduce the cost/energy. However, the optimal design of RD relys on highly nonlinear and multivariable optimization including continuous and integer design variables. Moreover, the objective function is generally non-convex with several constraints. For this problem, the conventional derivative-based optimization algorithms fail to guarantee the global optimal solution. Stochastic optimization algorithms appear to be a better alternative for the optimal design of RD because of the high robustness and efficiency. Bat algorithm (BA), which combines advantages of other existing algorithms, is a potential stochastic optimization algorithms. Moreover, particle swarm optimization (PSO) and harmony search (HS) are the special cases of BA after appropriate simplifications. In this work, the BA, which was implemented in the Matlab and coupled with the Aspen plus, was applied to optimize the reactive distillation for the production of methyl acetate (MeAc). The total annual cost(TAC)was set as the objective function. Product purity constraints were achieved through Aspen plus instead of algorithms to simplify the process. As a result, BA can find the global optimal solution within less computation time, than other stochastic algorithms or sequential optimization. Financial supports from the Natural Science Foundation of China (21276126, 21676141, 61673205) Powered by TCPDF (www.tcpdf.org) PRES17 conference