A stochastic approach to global optimization of chemical processes

Chemical process optimization often leads to large nonconvex nonlinear prog ramming problems that have many nonlinear equality constraints. Since the g lobal optimization of such a problem is one of the toughest NP-hard problem s, large problems in many cases cannot be solved in a reasonable time span if we rely solely on deterministic algorithms that are theoretically guaran teed 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 con straints. Therefore, an algorithm suitable for general chemical process opt imization problems is proposed in this paper, which is based on a feasible point strategy and combination of a stochastic global algorithm and a deter ministic local algorithm. (C) 1999 Elsevier Science Ltd. All rights reserve d.