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: 2010
• 资助国家: 加拿大
• 起止时间: 2010-01-01 至 2011-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=458286
• 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.

通过模拟预测过程系统行为（制药、化工、炼油或类似工厂），使其能够实现最佳设计和操作。 这些系统通过网络建模，其中节点（设备）转换能量或物质，而弧则是物质、能量或信息流。 当前对过程系统进行建模的能力受到设备模型的准确性以及求解整个网络的算法的限制。 第一原理设备模型通常需要付出很大的努力才能达到所需的精度。 作为替代方案，人们已经探索了结合第一原理和经验模型（所谓的混合模型）的模型。 目前，还没有明确定义的方法来确定混合模型的每个部分中包含哪些类型的方程。 目前，过程网络是通过算法来模拟或优化的，这些算法本质上受到单个 CPU 上的单个计算过程的限制，并且不利用多处理器计算能力。