A neighborhood‐adaptive state transition algorithm for operational optimization of residue hydrogenation fractionation process

Abstract

Residual hydrogenation fractionation process (RHFP) is significant for extracting valuable fractions from heavy oil with a severe energy consumption. The operational optimization of RHPF for comprehensively improving product profits and reducing energy consumption costs has been studied in this paper. Considering the complex nonlinear dynamic constraints, a novel neighborhood-adaptive state transition algorithm (NaSTA) is proposed. Specifically, a variable local-neighborhood strategy is designed to adaptively change the size of the neighborhood and speed up the initial search. Then, randomly generating large-scope candidate solutions aids the algorithm to jump out of the local optimal solution. In addition, an out-of-bound candidate solution processing mechanism is proposed to reasonably allocate candidate solutions that exceed the predefined neighborhood boundaries. The statistical research manifests the superiority of the proposed method compared with other optimization algorithms. The experimental results verify that the energy consumption is considerably reduced by approximately 10.06% so that the overall profit is improved.