Big Models using Big Data for Simulation-based Design and Operational Optimization

基于模拟的工程科学是一个迅速发展的领域，其中复杂模型的数值模拟被用来做出设计决策。然而，为了让模型代表现实，它们最好是以收集的数据为基础。另一方面，一个新兴的研究活动领域是大数据分析，它使用来自各种信息源的大量实时数据进行决策。此外，建模领域的新发展涉及网络系统，其中多个子模型与给定的拓扑相连接。该方案结合了上述所有领域，通过从大数据中构建大模型，然后将其用于基于模拟的设计和优化操作。 大模型是从大数据派生的不同类型子模型的集群，用于表示给定的系统。大模特继承了大数据的四个V。大模型的体积方面对应于所使用的变量数量，多样性方面对应于共存的不同类型子模型，速度方面对应于动态或时变性质，变异性方面对应于模型不确定性。需要指出的是，建造大型模型需要在开始时采取一种分散的方法，然后是最终的整合。 该提案为构建这种网络模型提出了一种系统的方法。它还开发了使用大数据进行回归的算法。该提案继续涉及流程系统工程的两个方面，即设计和操作。设计决策是使用敏感性分析、混合整数规划和启发式简化来做出的。安全性和灵活性都得到了考虑。运营决策是基于数值优化和实时大数据的组合做出的。对于后者，提出了一种多单元方法，其中一个大模型与系统并行运行，并利用大模型模拟的数据与获得的大数据之间的差异进行适应。 拟议的方法将应用于两个案例研究：(1)飞机环境控制和(2)使用微生物燃料电池的废水处理。它们都有不同的子系统，每个子系统都有完全不同的建模方法。数据也千差万别，从直接测量到间接测量。设计方面将在飞机问题上进行测试，同时将在废水处理案例上进行运行优化。

Simulation-based engineering science is a rapidly growing domain where numerical simulation of complex models are used to make design decisions. However, for the models to represent reality, it is better that they be based on collected data. On the other hand, an emerging area of research activity is the Big Data analytic that uses large volume of data in real-time from a variety of information sources for decision-making. Also, new developments in the modeling field relates to network systems, where a number of sub-models are connected with a given topology. This proposal combines all the above domains, by constructing Big Models from Big Data, which will then be used for simulation-based design and optimal operation. Big Models are clusters of different types of sub-models, derived from Big Data, to represent a given system. Big Models inherit the four Vs of Big Data. The volume aspect of Big Models corresponds to the number of variables used, the variety aspect to the different types of sub-models that co-exist, the velocity aspect to the dynamic or time-varying nature, and the variability aspect to the model uncertainty. It is noted that the construction of Big Models requires a fractured approach in the beginning, followed by an eventual integration. The proposal proposes a systematic methodology for the construction of such a network model. It also develops algorithms for regression with Big Data. The proposal continues with the two aspects of Process Systems Engineering, i.e., the design and operation. Design decisions are made either using a sensitivity analysis, a mixed-integer programing along with a heuristic simplification. Safety and flexibility are taken into consideration. Operational decisions are taken based on a combination of numerical optimization and real-time Big Data. For the latter, a multi-unit approach is proposed, where a Big Model runs in parallel to the system and the difference between data simulated by the Big Model and the obtained Big Data is used for adaptation. The proposed methodology will be applied to two case-studies, (i) the aircraft environmental control and (ii) waste water treatment using microbial fuel cells. Both of them have heterogeneous subsystems, and each of the sub-systems has a completely different modeling methodology. The data are also very varied, from direct to indirect measurements. The design aspect will be tested on the aircraft problem, while operational optimization will be carried out on the waste water treatment case.