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 localneighborhood 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.

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Residue hydrogenation process (RHP) plays an important role in efficient utilization of heavy oil resources. The high-fidelity model of RHP is so complex that its optimization cost is expensive and even intractable. Furthermore, in the actual industrial processes, due to the low sampling frequencies of some sensors, only a few sampling points can be obtained with some missing values.