Hybrid modelling and optimization of process systems

过程系统的混合建模和优化

• 批准号: 341228-2007
• 负责人: Mahalec, Vladimir
• 金额: $ 1.8万
• 依托单位: McMaster University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2007
• 资助国家: 加拿大
• 起止时间: 2007-01-01 至 2008-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=363960
• 关键词: Hybrid; modelling; optimization; process; systems

Prediction of a process system behaviour (pharmaceutical, chemical, refining or similar plants) via simulation has enabled their optimal design and operation.  These systems are modeled by networks where nodes (equipment) transform energy or material, while arcs are material, energy, or information flows.  Current abilities to model process systems are limited by the accuracy of equipment models, as well as the algorithms for solving the entire network.  First principles equipment models often require a very large effort to achieve desired accuracy.  Models combining the first principles and empirical models (so called hybrid models) have been explored as an alternative.   At present there are no well defined methods that determine what types of equations to include in each part of a hybrid model.   Currently, process networks are simulated or optimized by algorithms that are inherently limited by being single computational processes on single CPUs and do not exploit multi-processor computing capabilities.Goal of the research is to enable an order of magnitude improvement in simulation speed for large process systems and a significant reduction of the effort required to build accurate plant equipment models by:I. Development of typical hybrid model structures for specific types of equipments - thereby providing means for rapid equipment modeling.II. A new class of computational architecture for a process network - simulation via node models that behave autonomously and can run simultaneously (agent-based network computations),  andIII. Algorithms enabling optimization of discrete & continuous variables in such agent-based systems.  The research will provide a basis for a new generation of applications in process design and optimal operation of processes in refining, polymers, pharmaceuticals, or chemicals for bio applications.

通过模拟预测过程系统行为（药物，化学，炼油厂或类似植物）的预测已使其最佳设计和运行。这些系统是通过网络建模的，该网络（设备）会转换能量或材料，而电弧是材料，能量或信息流。当前建模过程系统的能力受设备模型的准确性以及解决整个网络的算法的限制。第一原理设备模型通常需要大量努力才能达到所需的准确性。结合了第一原理和经验模型（所谓的混合模型）的模型已被探索为替代方案。目前，尚无定义的方法来确定在混合模型的每个部分中都包含哪些方程式。当前，通过算法对过程网络进行了模拟或优化，这些算法固有地限制了单个CPU上的单个计算过程，并且不利用多处理器计算能力。该研究的目标是使大小工艺速度的大小提高大小工艺系统，并大大降低了精确的工厂设备模型所需的大量降低。开发特定类型设备的典型混合模型结构 - 从而为快速设备建模提供手段。一个新的流程网络计算体系结构 - 通过自主行为并可以类似运行的节点模型模拟（基于代理的网络计算）和III。在此类基于代理的系统中实现离散和连续变量的优化算法。这项研究将为生物应用中的精炼，聚合物，药品或化学品中的过程设计和过程的最佳运作提供新一代应用程序的基础。