Ni Peng, Liu Bin, He Ge
China University of Petroleum Beijing 102249 China.
Lanzhou Petro of PetroChina Company Limited Lanzhou 730060 China
RSC Adv. 2021 Aug 24;11(46):28557-28564. doi: 10.1039/d1ra03228c. eCollection 2021 Aug 23.
Rigorous mechanistic models of refining processes are often too complex, which results in long modeling times, low model computational efficiencies, and poor convergence, limiting the application of mechanistic-model-based process optimization and advanced control in complex refining production processes. To address this problem and take advantage of big data technology, this study used case-based reasoning (CBR) for process optimization. The proposed method makes full use of previous process cases and reuses previous process cases to solve production optimization problems. The proposed process optimization method was applied to an actual fluid catalytic cracking maximizing iso-paraffins (MIP) production process for industrial validation. The results showed that the CBR method can be used to obtain optimization results under different optimization objectives, with a solution time not exceeding 1 s. The CBR method based on big data technology proposed in this study provides a feasible solution for fluid catalytic cracking to achieve online process optimization.
炼油过程的严格机理模型通常过于复杂，这导致建模时间长、模型计算效率低以及收敛性差，限制了基于机理模型的过程优化和先进控制在复杂炼油生产过程中的应用。为了解决这一问题并利用大数据技术，本研究采用基于案例推理（CBR）进行过程优化。所提出的方法充分利用以前的过程案例，并重用以前的过程案例来解决生产优化问题。将所提出的过程优化方法应用于实际的最大化异构烷烃（MIP）生产的流化催化裂化过程进行工业验证。结果表明，CBR方法可用于在不同优化目标下获得优化结果，求解时间不超过1秒。本研究提出的基于大数据技术的CBR方法为流化催化裂化实现在线过程优化提供了一种可行的解决方案。
