Combining Fundamental Models with Data

将基本模型与数据相结合

• 批准号: RGPIN-2020-03901
• 负责人: McAuley, Kim
• 金额: $ 3.35万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=716711
• 关键词: Combining; Fundamental; Models; Data

New methods will be developed for combining fundamental models and data, leading to accurate chemical-process models with reduced experimental costs and shorter development times. In the longer-term, the research will contribute to development of digital twins as advanced tools for industrial operation. Students will receive valuable training in modeling and data analysis, preparing them for careers that contribute to process digitalization and development of greener processes. The proposed research focuses on two persistent gaps that make it difficult to develop effective fundamental models of chemical and biochemical processes. The first is that it is difficult to use prior knowledge to select optimal experimental conditions that result in reliable parameter estimates and accurate model predictions. Although routine process data may be freely available, informative new experiments required for fundamental-model building can be expensive. Companies need better tools to plan experiments so that accurate models can be built in a shorter time with less cost. The second is is that it is difficult to tune dynamic models for use in on-line monitoring and control. State estimators are available for updating states and parameters in models as new data become available. However, tuning of state estimators (based on measurement and model uncertainties) remains a difficult problem. We will address the first gap by developing improved sequential methods for Model-Based Design of Experiments (MBDOE). The new methods will permit MBDOE calculations even when limited data (or a large number of model parameters) results in a non-invertible Fisher information matrix. The non-invertibility problem will be addressed using Bayesian terms in the MBDOE objective functions. The proposed approach will be compared with current methods using case studies on i) production of bio-sourced polyethers, ii) CO2 hydrogenation for renewable fuels production and iii) acrylate/methacrylate copolymerization for low-solvent automotive coatings. We will address the second gap using new simultaneous methods to estimate model parameters and obtain uncertainty information for state-estimator tuning. Fundamental models will be augmented with new empirical stochastic terms and state equations to account for disturbances and model imperfections. Fundamental model parameters, model uncertainties and measurement-noise variances will be estimated from old batches of dynamic data using efficient maximum-likelihood and Bayesian algorithms. The proposed simultaneous method will be tested against existing techniques for tuning extended Kalman filters (EKFs) and related estimators wherein model parameters and noise covariances are estimated in separate steps. We hypothesize that more-reliable parameter and state estimates will result from the proposed simultaneous approach, leading to improved on-line model predictions. An industrial polyethylene reactor model will be used as a case study.

将开发新的方法来结合基本模型和数据，从而降低实验成本和缩短开发时间，从而获得准确的化学过程模型。从长远来看，这项研究将有助于数字孪生的发展，作为工业运营的先进工具。学生将接受建模和数据分析方面的宝贵培训，为他们的职业生涯做好准备，为过程数字化和绿色过程的发展做出贡献。拟议的研究重点是两个持续的差距，使其难以开发有效的基本模型的化学和生物化学过程。首先，很难使用先验知识来选择最佳实验条件，从而得到可靠的参数估计和准确的模型预测。虽然常规过程数据可以免费获得，但基础模型构建所需的信息丰富的新实验可能是昂贵的。公司需要更好的工具来规划实验，以便在更短的时间内以更低的成本建立准确的模型。第二个问题是很难调整动态模型用于在线监测和控制。状态估计器可用于在新数据可用时更新模型中的状态和参数。然而，调整状态估计（基于测量和模型的不确定性）仍然是一个困难的问题。 我们将通过开发改进的基于模型的实验设计（MBDOE）的顺序方法来解决第一个差距。新的方法将允许MBDOE计算，即使有限的数据（或大量的模型参数）的结果在一个不可逆的Fisher信息矩阵。将使用MBDOE目标函数中的贝叶斯项来解决不可逆性问题。所提出的方法将使用以下案例研究与当前方法进行比较：i）生物源聚醚的生产，ii）用于可再生燃料生产的CO2加氢，iii）用于低溶剂汽车涂料的丙烯酸酯/甲基丙烯酸酯共聚。 我们将使用新的同步方法来估计模型参数，并获得状态估计器调整的不确定性信息，以解决第二个差距。基本模型将增加新的经验随机项和状态方程，以考虑干扰和模型缺陷。基本模型参数、模型不确定性和测量噪声方差将使用有效的最大似然法和贝叶斯算法从旧批次的动态数据中估计。建议的同时进行的方法将测试对现有的技术调整扩展卡尔曼滤波器（EKFs）和相关的估计，其中模型参数和噪声协方差估计在单独的步骤。我们假设，更可靠的参数和状态估计将导致从建议的同时进行的方法，从而改善在线模型预测。一个工业聚乙烯反应器模型将被用作案例研究。