Hybrid modelling and optimization of process systems

通过模拟预测过程系统行为（制药、化工、精炼或类似工厂）使其能够进行最佳设计和操作。 这些系统由网络建模，其中节点（设备）转换能量或材料，而弧是材料，能量或信息流。 目前对过程系统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非常大的努力来实现期望的精度。 结合第一原理和经验模型（所谓的混合模型）的模型已被探索作为一种替代方案。 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，过程网络的模拟或优化的算法，是固有的限制，是单CPU上的单计算过程，不利用多处理器的计算能力。为特定类型的设备开发典型的混合模型结构-从而为快速设备建模提供手段。一类新的计算体系结构的过程网络-模拟通过节点模型的行为自主，可以同时运行（基于代理的网络计算），andIII。在这种基于代理的系统中实现离散和连续变量优化的算法。 这项研究将为新一代的工艺设计应用和炼油、聚合物、制药或生物应用化学品工艺的优化操作提供基础。

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.