A data‐based optimal setting method for the coking flue gas denitration process

A Local Density-Based Abnormal Case Removal Method for Industrial Operational Optimization under the CBR Framework

Operational optimization is essential in modern industry and unsuitable operations will deteriorate the performance of industrial processes. Since measuring error and multiple working conditions are inevitable in practice, it is necessary to reduce their negative impacts on operational optimization under the case-based reasoning (CBR) framework. In this paper, a local density-based abnormal case removal method is proposed to remove the abnormal cases in a case retrieval step, so as to prevent performance deterioration in industrial operational optimization. More specifically, the reasons as to why classic CBR would retrieve abnormal cases are analyzed from the perspective of case retrieval in industry. Then, a local density-based abnormal case removal algorithm is designed based on the Local Outlier Factor (LOF), and properly integrated into the traditional case retrieval step. Finally, the effectiveness and the superiority of the local density-based abnormal case removal method was tested by a numerical simulation and an industrial case study of the cut-made process of cigarette production. The results show that the proposed method improved the operational optimization performance of an industrial cut-made process by 23.5% compared with classic CBR, and by 13.3% compared with case-based fuzzy reasoning.

An online optimization strategy for a fluid catalytic cracking process using a case-based reasoning method based on big data technology

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.

The case-based reasoning (CBR) method based on big data technology proposed in this study provides a feasible solution for fluid catalytic cracking to achieve online process optimization.