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

复杂动态化学系统中模型构建、维护、简化和更新的综合方法

• 批准号: RGPIN-2014-06634
• 负责人: Prasad, Vinay
• 金额: $ 1.82万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=589131
• 关键词: integrated; approach; model; building; maintenance

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.

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。复杂的系统对每个目标的标准方法的应用提出了许多挑战。复杂性本身有很多来源，包括过程的多尺度或分布式性质、其网络结构、系统中群体或单元集合的存在，或者系统中变量的绝对数量。复杂系统提出的具体问题分为三类：将标准分析技术（用于模型更新、估计、监测、优化和控制）应用于如此大小和规模的模型时的计算和数值问题，对物理的不完整理解（和/或化学）首先导致构建基本模型的困难，以及从此类系统的传感器获取和解释有意义的数据的困难。 过去，我们的研究已经解决了各个应用领域中这些挑战的例子，包括开发适用于大型系统的模型更新和估计方法、复杂化学系统的混合拟动力学模型的开发以及技术的开发用于油藏建模和预测中的替代建模和不确定性传播。在本提案概述的研究计划中，我们迈出了开发通用研究框架的第一步，以解决为系统构建混合或灰盒模型的挑战，在这些系统中，我们不完全了解构建基本模型的知识，以融合来自多个系统的信息。传感器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。 拟议计划的预期贡献有可能显着推进复杂系统过程系统工程技术开发的最新技术水平；此外，这些技术将适用于我研究中的各种应用领域，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、二氧化碳固定使用微藻生产生物燃料，以及在线光谱数据的化学计量解释。