application of surrogate-assisted gray wolf optimization (sagwo) algorithm for optimization of large-scale process plants with computationally expensive evaluation – gas to liquids (gtl) process case study

Process optimization is necessary in order to decrease energy consumption and production costs# using surrogate models, rather than mathematical modeling or simulator software, is an effective method to decrease the calculations and the time needed for optimization# developing an offline data-based surrogate model for the whole response space requires generating a big data set# this itself involves numerous calculations and, therefore, would be too time-consuming# in this paper, the utilization of an online optimization algorithm is addressed for large-scale processes with a high computational burden# in this algorithm, by using of latin hypercube sampling (lhs) method and the grey wolf meta-heuristic optimization algorithm (gwo) in combination with the support vector machine (svm), a suitable balance between exploration and exploitation abilities is achieved# for comparison, the value of the objective function in the estimated global optimum point (gop) and the number of objective function evaluations (nfes) required to converge to gop are investigated# the large-scale gas to liquids (gtl) process plant is chosen as a case study# the results showed that in the online method, while decreasing nfes to less than one-tenth of the offline method, the gop is found with a relative error of 0#1 percent#