Logo of Xiamen UniversityAuthors: Qingyin Jiang · Yi Cai · Jia Shi · Zhikai Cao · Binghui Chen · Hua Zhou

Affiliation: Department of Chemical and Biochemical Engineering, National Engineering Laboratory for Green Chemical Productions of Alcohols−Ethers−Esters, College of Chemistry & Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China

Journal: Industrial & Engineering Chemistry Research, 2015, 54 (17), pp 4805–4814

DOI: 10.1021/ie5050617


Steady-state and dynamic simulations of a hydrocracking fractionator are carried out using the process simulator ASPEN PLUS with industrial process data. The main products of fractionation include naphtha, diesel, and tail oil. To obtain more economic benefits, more naphtha must be produced in a refinery because naphtha is more profitable than other products. Thus, optimizing the hydrocracking process is important. Optimization is often challenging to implement because the product quality of naphtha (dry point) is difficult to measure on-line by sensors. The product quality of naphtha is sampled and analyzed by experimental ASTM D86 curves in laboratory, so the measured value will be delayed. To solve this problem, a model of naphtha dry point (NDP) is established by artificial neural networks using simulation results. This NDP model is then used as a soft sensor and applied in an optimal quality control strategy. The on-line soft sensor and optimal quality control strategy are integrated by MATLAB CAPE-OPEN and ASPEN PLUS with an Ole for Process Control server. The increase of naphtha yield is obvious with the use of the proposed method. Several key factors influencing naphtha yield are investigated using the optimal quality control strategy by dynamic simulation and results show excellent system robustness.