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Modeling and optimization of a catalytic naphtha reformer.

机译：催化石脑油重整器的建模和优化。

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Catalytic naphtha reforming is one of the key processes in petroleum refining, converting gasoline boiling range low-octane hydrocarbons to high octane compounds which can be blended into gasoline. Other valuable byproducts include hydrogen and cracked light gases.; Modeling of a typical semi-regenerative catalytic reformer has been carried out involving most its key constituent units. Kinetic modeling of the reactions occurring in the fixed bed reactors connected in series formed the most significant part of the overall simulation effort. A reaction scheme involving 36 pseudocomponents connected together by a network of 35 reactions for components in the C{dollar}sb5{dollar}-C{dollar}sb{lcub}10{rcub}{dollar} range has been modeled. The Hougen-Watson Langmuir-Hinshelwood type reaction rate expressions are used to represent rate of each reaction. Deactivation of the catalyst was modeled by including the corresponding equations for coking kinetics.; The kinetic model was parameterized by bench-marking the kinetic model against plant data. A feed characterization procedure was developed to infer the composition of chemical species in the feed and reformate from the given ASTM distillation data. A non-linear regression procedure was carried out to calculate the rate parameters that provided the best match between the model and the plant data.; The key to most optimum reformer operation lies in choosing the four catalyst bed inlet temperatures, and recycle ratio. Sometimes, in practice, four beds are operated at same inlet temperatures and varying influence of each bed behavior on the outlet properties is not taken into account. This leads to a sub-optimal operation. It is also important to consider the objective function over the entire catalyst run-length, which has been predetermined for optimization analysis. The optimization analysis was conducted in two stages. In the first stage, the decision variables are optimized but held constant throughout the life of the catalyst. This time-invariant mode of operation showed a significant improvement in objective function over the base case. In the second stage of the analysis, the problem was expanded to optimize the path of the decision variables over the run-length. This time-optimal problem also showed a substantial improvement in profits over the time-invariant case. Finally, sensitivity of objective function to uncertainties in model parameters was examined.
机译：催化石脑油重整是石油精制的关键过程之一，将汽油沸程低的辛烷烃转化为可以混合到汽油中的高辛烷值化合物。其他有价值的副产物包括氢气和裂解的轻质气体。典型的半蓄热式催化重整器的建模已涉及其大多数关键组成单元。在串联连接的固定床反应器中发生的反应的动力学建模是整个模拟工作中最重要的部分。已建立了一个反应方案，该方案涉及36个假组分，这些假组分由C {dollar} sb5 {dollar} -C {dollar} sb {lcub} 10 {rcub} {dollar}范围内35个反应的网络连接在一起。 Hougen-Watson Langmuir-Hinshelwood型反应速率表达式用于表示每个反应的速率。通过包括相应的焦化动力学方程，对催化剂的失活进行建模。通过对照植物数据对动力学模型进行基准标记来对动力学模型进行参数化。开发了进料表征程序，以根据给定的ASTM蒸馏数据推断进料和重整产品中化学物质的组成。进行了非线性回归程序来计算速率参数，该参数提供了模型与工厂数据之间的最佳匹配。最佳重整器操作的关键在于选择四个催化剂床的入口温度和循环比。有时，实际上，四个床在相同的进口温度下运行，而没有考虑每个床行为对出口性能的不同影响。这导致次优操作。同样重要的是要考虑整个催化剂运行长度的目标函数，该目标函数已为优化分析预先确定。优化分析分两个阶段进行。在第一阶段，决策变量得到了优化，但在整个催化剂寿命中保持不变。与基本情况相比，这种时不变的操作模式显示出目标函数的显着改善。在分析的第二阶段，问题被扩展以优化整个运行过程中决策变量的路径。与时间不变的情况相比，这个时间最优的问题也显示出利润的实质性改善。最后，检验了目标函数对模型参数不确定性的敏感性。

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