基于元模型的过程系统优化

Optimization Methodology of Process System Based on Meta-modeling

应用均匀实验设计和支持向量机方法构建复杂过程系统的经验模型(元模型),并将其作为适应度函数与遗传算法结合,建立了该系统的优化方法.该方法只需采用少量仿真模型计算数据便可建立复杂过程系统的元模型,可显著降低复杂过程系统模型的计算过程,便于复杂过程系统的优化.将该方法用于普光高含硫天然气净化装置全流程操作参数优化,在操作参数优化空间内均匀选取10个实验点,建立了净化装置全流程元模型,其预测值的相对误差小于4%.优化结果表明,在优化操作点,净化装置有效能效率提高了6.6%.

A general methodology for optimization of complex process system via empirical meta-modeling is described. The uniform design and support vector machine are used to build a meta-model of the system, and the meta-rnodel as fitness function is incorporated into genetic algorithm. This optimization methodology involves data collection from the process simulation model or real operation, and fitting to less complex surrogates： meta-model, which is more readily optimized. The use of empirical meta-model allows the optimization to complex process while requiring only a few of solutions to be obtained from the process model. The effectiveness of proposed optimization methodology of complex process system has been proved by optimizing the operating parameters of Puguang high acid natural gas purification plant. The meta-model of whole process has been built by selecting 10 experimental points within the optimizing space of operating parameters. The relative error of predicted value by the meta-model is less than 4%. The optimization results show that the exergy efficiency of purification plant could be increased by 6.6% under the optimum operating point.