A stochastic approach to global optimization of chemical processes

Chemical process optimization often leads to large nonconvex nonlinear programming problems that have many nonlinear equality constraints. Since the global optimization of such a problem is one of the toughest NP-hard problems, large problems in many cases cannot be solved in a reasonable time span if we rely solely on deterministic algorithms that are theoretically guaranteed to find the global optimum. Generally, stochastic algorithms, which do not guarantee the global optimality of the obtained solution, are suitable for large problems, but not efficient when there are too many equality constraints. Therefore, an algorithm suitable for general chemical process optimization problems is proposed in this paper, which is based on a feasible point strategy and combination of a stochastic global algorithm and a deterministic local algorithm.