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

机译:动力学集模型参数的优化,提高加氢裂化过程中的产品质量

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摘要

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.
机译:摘要在本研究中,具有五个参数的典型连续延长模型已用于热和催化加氢裂化的动力学建模。根据使用粒子群优化(PSO)算法的实验产品分布已经优化了模型参数。已经采用来自加氢裂纹设置的实验数据来验证所提出的模型。在该设置中,从垂直反应器的顶部引入氢气和真空胃进料,并在通过催化剂床后,分离并分析液体和气体产物。反应器的温度在440-470℃的范围内调节热加氢裂化,410-430℃用于催化加氢裂化。液体时间速度(LHSV)在两组实验中的每种催化剂体积为0.5-1.5进料量。优化结果表明,参数仅依赖于温度。模型结果和实验数据之间的比较表明,该模型能够预测最大误差为0.986和0.041的产品产量,分别用于RMSE和AARE值。

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