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MINLP Optimization Approach for Managing Integrated Petrochemical Business Planning

机译:MINLP综合石化业务规划管理优化方法

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In this presentation, we describe an optimization approach for managing polyolefin (PE) planning problem with its upstream ethylene plant as the source of its main raw materials. The main challenge it this integrated business planning is to optimize profits based on the allocation of ethylene to be used in various PE product grades. We consider the inventory of PE grades, monthly demands in the production planning and all possible product sequences that can be produced each month. Instead of solving the planning problem as two separate businesses (ethylene and PE), here we solve the entire supply chain from ethane as the ethylene main feedstock. The advantages for solving as integrated business planning, we do not need to use ‘transfer price’ for the ethylene as the main raw material for PE products. The profit optimization is based on true market value for the raw materials and products, with considering all production constraints. The modeling approach used in the planning model is based on mathematical formulation of mass, energy balances, together with nonlinear cracker yield model and PE production sequences. The model describes the entire production material flow from the ethane feedstock to all by products of ethylene, utility and three PE plants. The entire ethylene product with annual capacity of roughly one million ton annually is used for PE production. The main challenge in this model approach is that the upstream unit requires nonlinear model due to component mass balances in mixing, reactor, distillation unit operations and other non-linear constraints; and the downstream requires binary variables to manage production sequence selection for feasible production operation. The resulting model formulation is in the form of large scale Mixed Integer Nonlinear Programming (MINLP) model. This model formulation is a novel approach in petrochemical industry, where the supply chain is usually represented as an LP or NLP. Here, we will discuss the practical usage of optimization of MINLP planning model for a world class ethylene producer with its polymer businesses.
机译:在本介绍中,我们描述了一种用于管理其上游乙烯工厂作为其主要原料来源的聚烯烃(PE)规划问题的优化方法。这一综合业务规划的主要挑战是基于乙烯分配在各种PE产品等级中使用乙烯的利润。我们考虑PE等级的库存,生产计划中的每月需求以及每个月可以生产的所有可能的产品序列。而不是解决一个单独的企业(乙烯和体育),而不是解决乙烯和PE),而是从乙烷作为乙烯主要原料解决整个供应链。作为综合业务规划解决的优势,我们不需要将“转移价格”作为乙烯作为PE产品的主要原料。利润优化是基于原材料和产品的真实市场价值,考虑所有生产限制。规划模型中使用的建模方法是基于质量,能量余量的数学制定,以及非线性裂解产量模型和PE生产序列。该模型描述了通过乙烯,效用和三种PE植物的所有产品流过乙烷原料。整个乙烯产品每年年产量约为100万吨,用于体育生产。这种模型方法中的主要挑战是上游单元由于混合,反应器,蒸馏单元操作和其他非线性约束中的元件质量平衡,因此需要非线性模型;下游需要二进制变量来管理可行生产操作的生产序列选择。得到的模型配方是大规模混合整数非线性编程(MINLP)模型的形式。该模型配方是石油化工业的新方法,供应链通常表示为LP或NLP。在这里,我们将讨论与其聚合物企业的世界级乙烯生产国优化MINLP规划模型的实际使用情况。

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