Compromising adjustment solution of primary reaction coefficients in ethylene cracking furnace modeling

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

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.

Multi-AGV path planning with double-path constraints by using an improved genetic algorithm

This paper investigates an improved genetic algorithm on multiple automated guided vehicle (multi-AGV) path planning. The innovations embody in two aspects. First, three-exchange crossover heuristic operators are used to produce more optimal offsprings for getting more information than with the traditional two-exchange crossover heuristic operators in the improved genetic algorithm. Second, double-path constraints of both minimizing the total path distance of all AGVs and minimizing single path distances of each AGV are exerted, gaining the optimal shortest total path distance. The simulation results show that the total path distance of all AGVs and the longest single AGV path distance are shortened by using the improved genetic algorithm.