Multi‐stage intelligent operation optimization for a hydrocracking fractionation system with a multi‐fractionator series‐parallel structure

Abstract

A fractionation system is an essential unit in the hydrocracking process. Its optimal operation is challenging because of the complexity in the structure of the distillation tower and composition of the stream. In addition, the seriesparallel structure between the distillation towers of different techniques aggravates the coupling and complexity of the hydrocracking fractionation system (HFS). This, in turn, increases the time complexity of the optimization problem. In this paper, a rigorous mechanism model of an actual HFS is first applied to describe the operating conditions of the HFS. Then, an improved state transition algorithm (STA) with a staged evaluation strategy is proposed to solve the above problem. To overcome problems caused by the series-parallel structure of HFS, the model is divided into multiple stages for evaluation by mechanism analysis. Furthermore, several typical convergence estimation criteria are introduced to reduce unnecessary model calculations. To solve time-consuming problems associated with HFS optimization, the adaptive change operator is used to improve the search function of the original algorithm and two performance criteria are presented to reduce the optimization time. The proposed algorithm is successfully applied to the operational parameter optimization problem of HFS with a multi-fractionator series-parallel structure. The experimental results indicated that the staged evaluation strategy improved the fast convergence probability of the HFS mechanism model and reduced unnecessary calculations, whereas the improved algorithm increased accuracy and significantly decreased optimization time.

Real‐time dynamic energy efficiency evaluation and analysis of industrial processes based on multi‐objective state transition algorithm with reference vector

“…Hydrocracking is an improvement of catalytic cracking technology and is an important process in the petrochemical industry with the advantage of high product yield and high product quality. [32][33][34][35] In the hydrocracking process, hydrogen converts heavy oil into light oil by a cracking reaction under high pressure and temperature condition with the help of catalysts. Figure 10 shows a simplified flowchart of the hydrocracking process.…”

Chemical Automation Control Based on Intelligent Optimization Algorithm in the Background of “Internet+”

“…In the field of chemical automation control, intelligent optimization algorithms are widely used in the monitoring, control, and optimization of production processes, which include but are not limited to genetic algorithms, neural networks, fuzzy logic, machine learning, etc., which can deal with complex industrial data and achieve the optimal scheduling of the production process, fault prediction, and risk assessment [5][6][7].In the field of chemical automation control, the intelligent optimization algorithms have the following specific applications: The production process is optimized through algorithms to improve the utilization of raw materials and product quality, reduce energy consumption, use machine learning technology for equipment condition monitoring, predict potential failures, reduce downtime [8], use intelligent algorithms for real-time monitoring of potential safety risks and early warning to ensure production safety, analyze a large amount of production data, to provide a scientific basis for management decision-making [9][10].…”