精细搜索策略应用于质量交换网络综合

Fine search strategy applied to mass exchange network synthesis

质量交换网络是化工过程系统的重要组成部分,其优化设计对降低污染排放具有重要意义。采用启发式算法优化质量交换网络时,存在难以兼顾全局搜索和局部搜索的问题。通过分析不同精度优化参数下的优化结果,揭示了该问题的成因,并提出一种精细搜索策略用于基础算法所得结构的深度优化。该策略包含两种方法,方法1采用具有个体回代与分化的高精度强制进化随机游走算法,可保留个体结构变异能力;方法2采用确定性方法依次对多维目标函数中的每个变量进行一维搜索,具有精度高收敛快的优点。将该策略应用于焦炉气脱硫和空气除氨算例,得到的结果分别为407308 USD·a^(-1)和127807 USD·a^(-1),经济性优于现有文献中的结果,验证了本策略的有效性。

Mass exchange network is an important part of chemical process system,and its optimal design is of great significance to reduce pollution emissions.When the heuristic algorithm is used to optimize the mass exchange network,there is a problem that it is difficult to take into account both the global search and the local search.By analyzing the optimization results under different precision optimization parameters,this paper reveals the causes of the problem,and proposes a fine search strategy for the in-depth optimization of the structure obtained by the basic algorithm.The strategy includes two methods.Method 1 adopted a high-precision random walk algorithm with compulsive evolution(RWCE)with individual back substitution and differentiation,which can retain the ability of individual structure variation.Method 2 used the deterministic approach to perform a one-dimensional search for each variable in the multidimensional objective function in turn,which has the advantages of high precision and fast convergence.Applying this strategy to coke oven gas sweetening and ammonia removal problem,the results are 407308 USD·a^(-1)and 127807 USD·a^(-1),which are better than the best results in the current literature.The effectiveness of the proposed strategy is verified.