论文部分内容阅读
优化技术广泛用于化工生产中“最佳”工艺条件的确定,工程师常需在无先验信息情况下,从若干工艺条件中确定同时能满足多方需求的最佳方案,实现效益最大化。枚举法只能在较简单的情况下使用,随着生产实际复杂程度的增加,枚举法显得无能为力。近来提出的元启发式蚁群优化算法无论计算时间,还是优化质量,都能满足复杂体系的优化。本研究采用Pareto蚁群算法,对间歇自由基聚合反应器进行了多目标优化,结果表明,该算法具有较强的鲁棒性,可用于间歇自由基聚合反应器的设计。
Optimization techniques are widely used in the chemical industry to determine the “best” process conditions. Engineers often need to determine the best solution to meet the needs of multiple parties from a number of process conditions without prior information, and maximize the benefits . Enumeration method can only be used in relatively simple circumstances, with the actual increase in the complexity of the production, the enumeration seems powerless. The recently proposed meta-heuristic ant colony optimization algorithm can satisfy the optimization of complex system, regardless of the computation time or the optimization quality. In this study, the Pareto ant colony optimization algorithm was used to optimize the batchwise radical polymerization reactor. The results show that the algorithm is robust and can be used in the design of batch free radical polymerization reactor.