Thermal Cracking Furnace Optimal Modeling Based on Enriched Kumar Model by Free-Radical Reactions

Abstract

The Kumar model as a molecular model has achieved successful application. However, only 22 reactions limit its veracity and adaptability for feedstocks. A series of models with different degrees of integration of the free radical model and the molecular model has been proposed to enhance feedstock adaptability and simulation accuracy. An improved search engine algorithm, namely Improved PageRank (IPR), is provided and applied to calculate the importance of substances in Kumar model to screen the free-radical reaction network for efficient model selection. A methodology of optimal structure and model parameters chosen is applied to the target to improve the adaptability of the material and the accuracy of the model. Then, two cases with different feedstocks are demonstrated with industrial data to verify the correctness of the proposed approach and its wide feedstock adaptability. The proposed model demonstrates good performance: (1) The mean relative errors (MRE) of the K-R (Kumar and free-radical) model have reached an order of magnitude less than 0.1% compared with 5% in the Kumar model. Further, (2) the K-R model can be implemented to model some feedstocks which Kumar model can’t simulate successfully. The K-R model can be applied in simulation of extensive feedstocks with high accuracy.

Optimization of a Thermal Cracking Reactor Using Genetic Algorithm and Water Cycle Algorithm

“…Mu and Gu 23 studied the optimal modeling of thermal cracking furnaces based on the enriched Kumar model by considering free-radical reactions. An improved search engine algorithm (IPR) was developed to compute the importance of substances in the Kumar model for efficient model selection.…”