OPTIMIZATION OF KINETIC LUMPING MODEL PARAMETERS TO IMPROVE PRODUCTS QUALITY IN THE HYDROCRACKING PROCESS

Abstract In this study a typical continuous lumping model with five parameters has been used for kinetic modeling of thermal and catalytic hydrocracking. Model parameters have been optimized according to experimental product distributions using a particle swarm optimization (PSO) algorithm. Experimental data from the hydrocracker setup have been employed to validate the proposed model. In this setup hydrogen and vacuum gasoil feed were introduced from the top of a vertical reactor and, after passing through a catalyst bed, the liquid and gas products were separated and analyzed. Temperature of the reactor was adjusted in the range of 440-470°C for thermal hydrocracking, and 410-430°C for catalytic hydrocracking. Liquid hourly space velocities (LHSV) were in the range of 0.5-1.5 feed flow rate per catalyst volume in both sets of experiments. Results of optimization showed that the parameters were only temperature dependent. The comparison between model results and experimental data indicates that the model is capable of predicting product yield with maximum errors of 0.986 and 0.041 for RMSE and AARE values, respectively.

摘要 本研究采用典型的五参数连续集总模型，针对热加氢裂化与催化加氢裂化过程开展动力学建模。模型参数基于实验产物分布，通过粒子群优化（Particle Swarm Optimization, PSO）算法完成优化。本研究借助加氢裂化装置的实验数据对所提模型进行验证。该装置中，氢气与减压瓦斯油进料从立式反应器顶部引入，流经催化剂床层后，对液态和气态产物进行分离与分析。热加氢裂化实验的反应器温度调控范围为440~470℃，催化加氢裂化实验的反应器温度调控范围为410~430℃。两组实验的液时空速（Liquid Hourly Space Velocity, LHSV）均设置为每单位催化剂体积对应0.5~1.5倍的进料流量。优化结果表明，模型参数仅与温度相关。模型预测结果与实验数据的对比显示，该模型可有效预测产物收率，其均方根误差（Root Mean Square Error, RMSE）与平均绝对相对误差（Average Absolute Relative Error, AARE）的最大值分别为0.986和0.041。