The vacuum distillation unit (VDU) is the key unit to produce vacuum gas oil and vacuum residue, which has a very important impact on the downstream secondary processing units. The optimization of deep-cut vacuum distillation seeks to improve the yield of heavy vacuum gas oil (HVGO) and its dry point temperature, which is related to the economic benefits of the refinery. In this study, we first established a simple model of a VDU by using the Aspen HYSYS Process simulation software. Then, we built a rigorous model with fast convergence by using the initial values obtained by the simple model. The rigorous model can accurately reflect the refinery’s operation and can make predictions. Then, based on the rigorous model, we increased the flash section temperature (FST) to 420 °C and the steam flow rate (SFR) of the stripping to 26 t/h. We eventually increased the yield of HVGO by 6.3 percentage points to 43.4%, while increasing its D86 95%-point temperature by 31.9 °C to 570.9 °C. In this way, the refinery can effectively optimize the deep-cut vacuum distillation and obtain greater economic benefits.

Aiming at the problems of low precision, low search efficiency, and being easy to fall into local optimization of the multi-objective harris hawk optimization algorithm (MOHHO) based on grid method, a MOHHO based on blank angle region enhanced search (BARESMOHHO) is proposed. The main changes of the algorithm are as follows: firstly, chaotic mapping is used to initialize the population, which is beneficial to speed up the search. Then, in order to find low-density regions faster, the algorithm adjusts the classification level according to the number of individuals in the external archive. In order to make the distribution of individuals in the target space more uniform, inspired by the idea of symmetrical segmentation, the number of archives at different levels are symmetrically distributed. Finally, it strengthens the search for the non-individual region (blank angle region) in the process of division. The effectiveness of the proposed algorithm is verified by comparing it with some known classical functions on test functions.