An integrated approach for model building, maintenance, reduction and updating in complex dynamic chemical systems

过程系统工程涉及动态模型的开发和更新，以及使用这些模型进行监视，控制和优化。复杂系统对标准方法的应用提出了许多挑战。复杂性本身来自许多来源，包括过程的多尺度或分布式性质，它们的网络结构，系统中单元的群体或集合的存在，或者系统中变量的绝对数量。复杂系统提出的具体问题有三种：应用标准分析技术中的计算和数值问题（用于模型更新、估计、监测、优化和控制）对于如此大小和规模的模型，对物理学的理解不完整（和/或化学）导致首先难以构建基本模型，以及从用于这种系统的传感器获得和解释有意义的数据的困难。 在过去，我们的研究已经解决了这些挑战的例子，在各种应用领域，包括开发适合于大规模系统的模型更新和估计的方法，复杂化学系统的混合伪动力学模型的开发，以及替代建模和不确定性传播技术的开发储层建模和预测。在本提案中概述的研究计划中，我们采取了第一步，以开发一个通用的研究框架，以解决为我们拥有不完整知识的系统构建混合或灰箱模型的挑战，以构建基础模型，融合来自多个传感器的信息并将其与开发的模型相协调，以及开发模型简化方法，以降低复杂系统中系统特性分析的难度。 所提出的计划所产生的预期贡献有可能大大推进复杂系统过程系统工程技术发展的最新水平;此外，这些技术将在我的研究中调查的各种应用领域中找到适用性，包括泡沫浮选，煤和石油储层建模和估计，催化剂和催化过程的优化和控制、沥青的热升级、二氧化碳固定和使用微藻的生物燃料生产以及在线光谱数据的化学计量学解释。

Process systems engineering is concerned with the development and updating of dynamic models, and the use of these models for monitoring, control and optimization. Complex systems present many challenges to the application of standard methods for each of objectives. Complexity itself arises from many sources, including the multiscale or distributed nature of processes, their networked structure, the presence of populations or ensembles of units in the system, or the sheer number of variables in the system. The specific problems complex systems pose are of three kinds: computational and numerical issues in applying standard techniques of analysis (for model updating, estimation, monitoring, optimization and control) to models of such size and scale, incomplete understanding of the physics (and/or chemistry) leading to difficulty in constructing fundamental models in the first place, and the difficulty in obtaining and interpreting meaningful data from sensors for such systems. In the past, our research has addressed examples of these challenges in various application areas, including developing methods for model updating and estimation suitable for large-scale systems, the development of hybrid pseudo-kinetic models for complex chemical systems, and the development of techniques for surrogate modeling and uncertainty propagation in reservoir modeling and prediction. In the research program outlined in this proposal, we take the first steps towards developing a general research framework for addressing the challenges of building hybrid or gray-box models for systems where we have incomplete knowledge to build a fundamental model, for fusing information from multiple sensors and reconciling them with the developed models, and the development of model reduction methods for reducing the difficulty of analysis of system characteristics in complex systems. The expected contributions arising from the proposed program have the potential to significantly advance the state of the art in development of process systems engineering techniques for complex systems; in addition, these techniques will find applicability in a wide variety of application areas under investigation in my research, including froth flotation, coal and petroleum reservoir modeling and estimation, optimization and control of catalysts and catalytic processes, thermal upgrading of bitumen, carbon dioxide fixation and biofuel production using microalgae, and chemometric interpretation of on-line spectroscopic data.