连续竞争反应装置的效益优化方法与应用研究
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摘要
连续竞争反应(Consecutive-competitive Reaction)指的是反应过程中,中间产物会参与生成其他产物的反应,通常可以用A→B→C的形式进行表示。连续竞争反应是化学中最基本的复杂反应之一,也是化工生产中一类重要的生产工艺。目前,石油及化工行业中许多重要的产品,如异丙醇胺、乙醇胺等的生产过程,都属于连续竞争反应。对于此类反应,其动力学参数虽然可以通过理论计算得到,然而由于反应的复杂性与实际生产工艺条件的影响,理论的计算结果往往与实际生产过程存在较大的差异,导致很难对实际的生产过程进行准确的控制；同时,连续竞争反应中各反应步骤的同步性决定了其反应产物必然是多种产品的混合物,生产企业无法通过单独生产某种产品的方式获得最大利润。由于产品价格、原料价格、能源价格及其消耗量、市场需求等因素在内的市场条件的不断变化,要求生产企业能够根据市场信息对产品组合进行优化,得到满足生产约束条件的最大盈利产品组合,同时及时地对操作条件进行调整,实现生产的高度灵活化与企业效益的最大化。
本文首先对化工生产过程中的建模与产品组合优化中的研究难点及其应用现状进行了深入而全面的总结,将遗传算法与反应机理相结合,提出了一种可用于复杂化工过程建模的灰箱建模算法；在此基础上,从系统的角度出发,将操作条件、产品组合、企业利润结合起来,提出了一种基于遗传算法的产品组合优化算法,以实现商务与生产的集成,并通过典型的连续竞争反应验证该算法的有效性；最后,将本研究所介绍的算法应用于乙醇胺生产过程,基于生产数据的仿真研究取得了良好的效果。本文的工作主要包括：
(1)针对复杂化工过程的特点,在结合机理模型与统计模型各自优势的基础上,提出了一种基于遗传算法的灰箱模型,该模型以反应机理为模型框架,通过遗传算法对模型参数进行辨识估计,在保证模型可靠性的同时提高了模型的预测精度。最后,通过典型连续竞争反应过程的仿真研究,验证了该算法的有效性。
(2)从产品组合优化及操作条件优化两个方面着手,以产品组合为中间变量,建立了操作条件、产品组合以及企业利润三者之间的关系。并以此为基础,采用遗传算法对基于企业生产利润最大化的产品组合与相关操作条件进行优化。同时在第二章研究工作的基础上,采用上述方法计算最大化利润下的产品组合以及相关操作条件,取得了良好的效果。
(3)将本文所提算法应用到乙醇胺生产过程中,根据实际生产的历史数据建立了反映乙醇胺产品分布与操作条件之间关系的过程模型,随后根据企业的实际情况,建立了描述产品组合与企业利润之间关系的经济模型。并将上述两个模型进行联立,利用遗传算法对该企业最大利润下的产品组合与相关操作条件进行了优化求解。结果表明,本文所提算法可有效地对连续竞争反应的生产过程进行优化控制,具有较好的实际应用价值。
Consecutive-competitive reaction (CCR) is one of popular chemical reaction forms in chemical industry, for example, the production of ethanolamines and isopropanolamines. The path of CCR can be described by the form A→B→C, where its intermediate products would be processed into multiple finished products through further reaction steps. Finally the product-mix of CCR can be separated through a distillation operation. From business point of view, in order to maximize the enterprise's profits, it is critical to determine and manipulate a desired finished-product-mix under an uncertain market place with the changes of demand of finished-products as well as the prices of feedstock and energy. Consequently, the tasks of an integrated business and production system are twofold: determine an optimal product-mix and the relevant control strategy to meet the business goal subject to both business and production constraints.
This thesis starts with a general review of the research progresses on system modeling and product-mix optimization for chemical processes. By combining first principles with genetic algorithm (GA), a grey-box modeling approach to complex chemical reaction processes was developed. This study also proposes a GA-based product-mix optimization solution with a bridge between profits, product-mix and operation optimization, and then the proposed solution is applied to the production of ethanolamines, a typical consecutive-competitive reaction system. The production data based simulation results show that the proposed solution could be applied in CCR processes to implement the business and production integration. The main topics studied in this thesis are summarized as follows:
(1) This thesis proposes a GA based grey-box model which is fully utilizing the strengths of first-principles model and statistical model. This proposed grey-box model is developed with first-principles oriented model structure and unknown model parameters. The later could be estimated by using GA algorithms. The effectiveness of the proposed grey-box model is proven by the simulation results for a typical consecutive-competitive reaction.
(2) This thesis also proposes an integrated solution that combines product-mix optimization with operation optimization. The proposed optimization solution is tested with a production data based grey-box model for an example consecutive-competitive reaction process.
(3) The proposed solutions are further applied to a production-scale ethanolamine production line. In this study, a process model presenting the relationship between operation conditions and product-mix, and a profits model presenting the relationship between product-mix and the resulting profits are developed respectively. On the basis of the combining these two models, GA is used to optimize the production profit for the ethanolamine production line.
The proposed integrated solutions have great potentials in the optimization control for many other consecutive-competitive chemical reaction processes.
本文首先对化工生产过程中的建模与产品组合优化中的研究难点及其应用现状进行了深入而全面的总结,将遗传算法与反应机理相结合,提出了一种可用于复杂化工过程建模的灰箱建模算法；在此基础上,从系统的角度出发,将操作条件、产品组合、企业利润结合起来,提出了一种基于遗传算法的产品组合优化算法,以实现商务与生产的集成,并通过典型的连续竞争反应验证该算法的有效性；最后,将本研究所介绍的算法应用于乙醇胺生产过程,基于生产数据的仿真研究取得了良好的效果。本文的工作主要包括：
(1)针对复杂化工过程的特点,在结合机理模型与统计模型各自优势的基础上,提出了一种基于遗传算法的灰箱模型,该模型以反应机理为模型框架,通过遗传算法对模型参数进行辨识估计,在保证模型可靠性的同时提高了模型的预测精度。最后,通过典型连续竞争反应过程的仿真研究,验证了该算法的有效性。
(2)从产品组合优化及操作条件优化两个方面着手,以产品组合为中间变量,建立了操作条件、产品组合以及企业利润三者之间的关系。并以此为基础,采用遗传算法对基于企业生产利润最大化的产品组合与相关操作条件进行优化。同时在第二章研究工作的基础上,采用上述方法计算最大化利润下的产品组合以及相关操作条件,取得了良好的效果。
(3)将本文所提算法应用到乙醇胺生产过程中,根据实际生产的历史数据建立了反映乙醇胺产品分布与操作条件之间关系的过程模型,随后根据企业的实际情况,建立了描述产品组合与企业利润之间关系的经济模型。并将上述两个模型进行联立,利用遗传算法对该企业最大利润下的产品组合与相关操作条件进行了优化求解。结果表明,本文所提算法可有效地对连续竞争反应的生产过程进行优化控制,具有较好的实际应用价值。
Consecutive-competitive reaction (CCR) is one of popular chemical reaction forms in chemical industry, for example, the production of ethanolamines and isopropanolamines. The path of CCR can be described by the form A→B→C, where its intermediate products would be processed into multiple finished products through further reaction steps. Finally the product-mix of CCR can be separated through a distillation operation. From business point of view, in order to maximize the enterprise's profits, it is critical to determine and manipulate a desired finished-product-mix under an uncertain market place with the changes of demand of finished-products as well as the prices of feedstock and energy. Consequently, the tasks of an integrated business and production system are twofold: determine an optimal product-mix and the relevant control strategy to meet the business goal subject to both business and production constraints.
This thesis starts with a general review of the research progresses on system modeling and product-mix optimization for chemical processes. By combining first principles with genetic algorithm (GA), a grey-box modeling approach to complex chemical reaction processes was developed. This study also proposes a GA-based product-mix optimization solution with a bridge between profits, product-mix and operation optimization, and then the proposed solution is applied to the production of ethanolamines, a typical consecutive-competitive reaction system. The production data based simulation results show that the proposed solution could be applied in CCR processes to implement the business and production integration. The main topics studied in this thesis are summarized as follows:
(1) This thesis proposes a GA based grey-box model which is fully utilizing the strengths of first-principles model and statistical model. This proposed grey-box model is developed with first-principles oriented model structure and unknown model parameters. The later could be estimated by using GA algorithms. The effectiveness of the proposed grey-box model is proven by the simulation results for a typical consecutive-competitive reaction.
(2) This thesis also proposes an integrated solution that combines product-mix optimization with operation optimization. The proposed optimization solution is tested with a production data based grey-box model for an example consecutive-competitive reaction process.
(3) The proposed solutions are further applied to a production-scale ethanolamine production line. In this study, a process model presenting the relationship between operation conditions and product-mix, and a profits model presenting the relationship between product-mix and the resulting profits are developed respectively. On the basis of the combining these two models, GA is used to optimize the production profit for the ethanolamine production line.
The proposed integrated solutions have great potentials in the optimization control for many other consecutive-competitive chemical reaction processes.
引文
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