ZSM-22分子筛上长链烷的烃裂化和异构化反应

用盐酸对ZSM-22进行脱铝改性,考察脱铝对催化剂反应的影响,脱铝能使催化剂酸强度有所增强。在连续微反应装置中,氢分压为1MP,质量空速为1.3h-1条件下研究改性后的ZSM-22的反应性能。以正十六烷为探针反应物考察了改性后的ZSM-22的长链烷烃反应性能,结果表明改性后ZSM-22对十六烷有很好的异构化选择性和很好的裂化选择性。     

Modelling of a vacuum residue hydrocracking in an industrial slurry phase reactor

The upgradation of the bottom of the barrel has gained much interest across the refineries due to severe environmental rules, limitations of conventional oil reserves, and its flexibility to produce light end products which benefit end users. Slurry phase hydrocracking is one of the growing technologies to fulfil the increasing demand for light cut. Modelling of an industrial slurry phase reactor (SPR) for vacuum residue hydrocracking using different kinetic models is proposed. The axial dispersion model (ADM) is used for modelling an industrial SPR. The mathematical model of the reactors is incorporated for the three different lump kinetic models. This study deals with the continuous stirred tank reactor (CSTR) and SPR modelling, followed by industrial SPR modelling. The small lab-scale reactor models are validated with the experimental data reported in the literature. The study's objective was to investigate the one-dimensional and two-dimensional concentration dynamics of each lump along the axial and radial positions of industrial SPR. The vacuum residue conversion into the light fractions was obtained by more than 73% in industrial SPR. Also, the yield of vacuum gas oil and resins were evaluated as 49% and 63%, respectively. The sensitivity analysis was performed to explain the effect of process variables. The optimum range was found as a length of 15 m, liquid hourly space velocity (LHSV) of 0.2 h⁻¹, 1% catalyst concentration, and 420°C reaction temperature to enhance the throughput of the reactor.     

Multiple input self-organizing-map ResNet model for optimization of petroleum refinery conversion units

This work introduces a deep-learning network, i.e., multi-input self-organizing-map ResNet (MISR), for modeling refining units comprised of two reactors and a separation train. The model is comprised of self-organizing-map and the neural network parts. The self-organizing-map part maps the input data into multiple two-dimensional planes and sends them to the neural network part. In the neural network part, residual blocks enhance the convergence and accuracy, ensuring that the structure will not be overfitted easily. Development of the MISR model of hydrocracking unit also benefits from the utilization of prior knowledge of the importance of the input variables for predicting properties of the products. The results show that the proposed MISR structure predicts more accurately the product yields and properties than the previously introduced self-organizing-map convolutional neural network model, thus leading to more accurate optimization of the hydrocracker operation. Moreover, the MISR model has smoother error convergence than the previous model. Optimal operating conditions have been determined via multi-round-particle-swarm and differential evolution algorithms. Numerical experiments show that the MISR model is suitable for modeling nonlinear conversion units which are often encountered in refining and petrochemical plants.