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Data-Driven Mathematical Modeling and Global Optimization Framework for Entire Petrochemical Planning Operations

机译:整个石化计划运营的数据驱动数学建模和全局优化框架

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摘要

In this work we develop a novel modeling and global optimization-based planning formulation, which predicts product yields and properties for all of the production units within a highly integrated refinery-petrochemical complex. Distillation is modeled using swing-cut theory, while data-based nonlinear models are developed for other processing units. The parameters of the postulated models are globally optimized based on a large data set of daily production. Property indices in blending units are linearly additive and they are calculated on a weight or volume basis. Binary variables are introduced to denote unit and operation modes selection. The planning model is a large-scale non-convex mixed integer nonlinear optimization model, which is solved to e-global optimality. Computational results for multiple case studies indicate that we achieve a significant profit increase (37-65%) using the proposed data-driven global optimization framework. Finally, a user-friendly interface is presented which enables automated updating of demand, specification, and cost parameters. (C) 2016 American Institute of Chemical Engineers
机译:在这项工作中,我们开发了一种新颖的建模和基于全局优化的计划公式,该公式可以预测高度集成的炼油-石化综合设施内所有生产单元的产品产量和性能。使用摆切理论对蒸馏进行建模,同时为其他处理单元开发基于数据的非线性模型。假定模型的参数是根据每日生产的大量数据进行全局优化的。混合单元中的性能指标是线性加和的,它们是基于重量或体积计算的。引入二进制变量来表示单位和操作模式的选择。该规划模型是一种大规模的非凸混合整数非线性优化模型,可以求解电子全局最优性。多个案例研究的计算结果表明,使用提出的数据驱动的全局优化框架,我们实现了可观的利润增长(37-65%)。最后,提供了一个用户友好的界面,可以自动更新需求,规格和成本参数。 (C)2016美国化学工程师学会

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