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Predictive Modeling of Large-Scale Integrated Refinery Reaction and Fractionation Systems from Plant Data. Part 3: Continuous Catalyst Regeneration (CCR) Reforming Process

机译:基于工厂数据的大型综合炼厂反应和分馏系统的预测模型。第3部分:连续催化剂再生(CCR)重整过程

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This work presents a model for the rating and optimization of an integrated catalytic reforming process with UOP-style continuous catalyst regeneration (CCR) using Aspen HYSYS/Petroleum Refining. The model relies on routinely monitored data, such as American Society for Testing and Materials (ASTM) distillation curves, paraffin-napthene-aromatic (PNA) analysis, and operating conditions. We use a lumped kinetic network with 64 species over a broad Cl-C14 range. This network can represent the key dehydrogenation, dehydrocyclization, isomerization, and hydrocracking reactions that typically occur with petroleum feedstock. The lumped kinetic scheme also allows us to make accurate predictions of benzene, toluene, ethylbenzene, and xylenes (BTEX). In addition, this work accounts for the coke deposited on the catalyst and the associated catalyst regeneration. We implement the hydrogen recycle and product recontacting sections as separate unit operations connected to the CCR reformer model. In addition, we include rigorous tray-by-tray simulation models for primary product recovery. We validate this model using 6 months of plant data from a commercial CCR reforming process handling a feed capacity of 1.4 million tons per year in the Asia Pacific. The validated model predicts key process yields and aromatic yields to within an average absolute deviation (AAD) of 1%. In addition, the model predicts liquid petroleum gas (LPG) composition to within 2.0% AAD. We also present several industrially useful case studies that display common interactions among process variables, such as feed composition, reaction temperature, space velocity, and hydrogen/hydrocarbon ratio (H_2/HC). These case studies accurately quantify the effect of key process variables on the process performance and demonstrate the model applications for improving energy efficiency and optimizing the reformer performance for chemical feedstock production. This work differentiates itself from the reported studies in the literature through the following contributions: (1) detailed kinetic model that accounts for coke generation and catalyst deactivation, (2) complete implementation of a recontactor and primary product fractionation, (3) feed lumping from limited feed information, (4) detailed procedure for kinetic model calibration, (5) industrially relevant case studies that highlight the effects of changes in key process variables, and (6) application of the model to refinery-wide production planning.
机译:这项工作为使用Aspen HYSYS /石油精炼的UOP型连续催化剂再生(CCR)提供了一个综合催化重整过程的评级和优化模型。该模型依赖于常规监测的数据,例如美国材料试验协会(ASTM)的蒸馏曲线,石蜡-萘-芳烃(PNA)分析和操作条件。我们使用具有广泛Cl-C14范围的64种物种的集总动力学网络。该网络可以代表石油原料通常发生的关键脱氢,脱氢环化,异构化和加氢裂化反应。集总动力学方案还使我们能够准确预测苯,甲苯,乙苯和二甲苯(BTEX)。另外,这项工作解释了沉积在催化剂上的焦炭和相关的催化剂再生。我们将氢气再循环和产品再接触部分作为连接到CCR重整器模型的独立单元操作来实施。此外,我们还包括针对主要产品回收的严格的逐托盘模拟模型。我们使用来自商业CCR重整过程的6个月工厂数据验证了该模型,该过程处理了亚太地区每年140万吨的饲料产能。经过验证的模型预测关键工艺的收率和芳烃的收率均在1%的平均绝对偏差(AAD)之内。此外,该模型预测液化石油气(LPG)组成的AAD不得超过2.0%。我们还提供了一些工业上有用的案例研究,这些案例研究显示了工艺变量之间的常见相互作用,例如进料组成,反应温度,空速和氢/烃比(H_2 / HC)。这些案例研究准确地量化了关键过程变量对过程性能的影响,并演示了用于提高能源效率和优化化学原料生产的重整器性能的模型应用。这项工作通过以下贡献与文献报道的研究脱颖而出:(1)详细的动力学模型说明了焦炭的产生和催化剂的失活;(2)完全实施再接触器和初级产品分馏;(3)原料结块有限的进料信息;(4)动力学模型校准的详细程序;(5)突出关键过程变量变化的影响的与工业相关的案例研究;(6)将模型应用于炼厂范围的生产计划。

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