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，过程网络由算法模拟或优化，所述算法固有地受限于在单个CPU上的单个计算过程，并且不利用多处理器计算能力。

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.