A Novel Framework for Automated Simultaneous Model Identification and Parameter Estimation in Kinetic Studies

动力学研究中自动同步模型识别和参数估计的新框架

• 批准号: 2722453
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2722453
• 关键词: Novel; Framework; Automated; Simultaneous; Model

Digitalisation is driving a deep transformation in manufacturing sectors through the application of digital twins for chemical reaction systems design, control and real time optimisation. Digital twins are based on robust and reliable kinetic models to accurately predict the behaviour of chemical reactions and explore a wide range of operating conditions in the experimental design space. Digital twin models require costly experimentation for model validation and a significant investment of time and analytical resources to identify both model structure and precisely identify the system-specific set of kinetic parameters. The proposed project aims to develop a new software framework based on the integration of physics-informed machine learning (ML) techniques and model-based design of experiments (MBDoE) for the fast identification of kinetic models. The specific goals of this framework are: 1) simultaneous identification of reaction rate expressions and precise estimation of kinetic parameters; 2) robust design of experimental conditions under uncertainty and disturbances affecting a reaction system; 3) minimisation of physical runs for model calibration. The ultimate aim is to integrate this new software framework in reaction platforms recently developed at UCL allowing autonomous experimentation in catalytic flow reactors (a recent video can bee seen in https://www.youtube.com/watch?v=kMCtQqbPixk ). The project will develop along four main steps, each of which will last ~ 9 months:Stage 1. Development of a software module for machine-learning assisted MBDoE. A software module will be developed and tested in-silico implementing robust optimal experimental design techniques. In the module, ML models will be integrated to model potential uncertainty and disturbances affecting inputs and outputs to the system. Robust MBDoE techniques for model discrimination and improvement of parameter precision will be implemented to design experiments in the presence of parametric mismatch. Recently developed experimental design techniques for kinetic model selection using artificial neural networks (ANNs), currently applied offline, will be applied online and compared with standard MBDoE techniques for model identification. Stage 2. Development of a module for outliers detection. As the quality of data generated from a system is essential to identify both the correct kinetic model structure and the set of model parameters precisely, a module will be developed for data mining. This will implement model-based data mining (MBDM) and data-driven outlier detection techniques. These techniques will be tested in-silico by forcing uncertainty in the data acquired and compared to verify their effectiveness in online applications. Stage 3. Development of a module for physics-informed ML model identification. Physics-informed neural networks (PINNs) have been proposed to solve or discover time-dependent and nonlinear partial differential equations (PDEs). PINNs are neural networks trained to solve supervised learning tasks while respecting specific physics-informed constraints. Unlike standard deep neural networks, PINNs add physics-informed differential equations directly into the loss function when training a neural network. PINNs show the following advantages: 1) training a PINN model only requires a small amount of data; 2) the robustness is guaranteed by being forced to follow physical constraints; 3) intuitive results (i.e., the output of a PINN is a set of model functions). PINNs will be integrated in a module allowing to identify: i) reaction rate expressions (i.e. kinetic model structure); ii) reactor model (ideal, dispersion models); iii) both i) and ii) simultaneously. Stage 4. Integration of software modules in autonomous reaction platforms. Armed with the computational modules developed in stage 1, 2 and 3, an hardware/software graphical user interface (GUI) will be developed in LabView for communication with the hardware.

通过在化学反应系统设计、控制和实时优化方面应用数字孪生兄弟，数字化正在推动制造业的深刻变革。数字双胞胎基于稳健可靠的动力学模型，可以准确预测化学反应的行为，并在实验设计空间中探索广泛的操作条件。数字孪生模型需要昂贵的模型验证实验和大量的时间和分析资源投入，以确定模型结构和精确确定系统特定的动力学参数集。该项目旨在开发一种新的软件框架，该框架基于物理信息机器学习(ML)技术和基于模型的实验设计(MBDoE)的集成，用于快速识别动力学模型。这个框架的具体目标是：1)同时识别反应速率表达式和精确估计动力学参数；2)在影响反应系统的不确定性和干扰下稳健地设计实验条件；3)最小化模型校准的物理运行。最终目标是将这个新的软件框架集成到伦敦大学学院最近开发的反应平台中，允许在催化流动反应器中进行自主实验(最近的视频可以在https://www.youtube.com/watch?v=kMCtQqbPixk上看到)。该项目将分四个主要步骤进行，每个步骤将持续约9个月：第一阶段：开发一个用于机器学习辅助MBDoE的软件模块。将开发一个软件模块并在电子计算机上进行测试，以实施稳健的优化实验设计技术。在该模块中，ML模型将被集成以对影响系统输入和输出的潜在不确定性和干扰进行建模。在存在参数失配的情况下，将采用稳健的模型识别和提高参数精度的MBDOE技术来设计实验。最近开发的用于动力学模型选择的人工神经网络(ANN)实验设计技术，目前离线应用，将在线应用，并与标准的MBDOE模型识别技术进行比较。阶段2.开发离群值检测模块。由于系统产生的数据质量对于准确识别正确的动力学模型结构和模型参数集至关重要，因此将开发一个用于数据挖掘的模块。这将实施基于模型的数据挖掘(MBDM)和数据驱动的离群点检测技术。这些技术将通过强制获取的数据中的不确定性进行电子测试，并进行比较，以验证它们在在线应用程序中的有效性。阶段3.开发物理信息最大似然模型识别模块。物理信息神经网络(PINN)被用来求解或发现时间相关的非线性偏微分方程组(PDE)。PINN是一种神经网络，被训练成在尊重特定的物理信息约束的同时解决有监督的学习任务。与标准的深度神经网络不同，PINN在训练神经网络时直接将物理信息的微分方程式加入到损失函数中。PINN具有以下优点：1)训练PINN模型只需要少量的数据；2)通过强制遵循物理约束来保证鲁棒性；3)结果直观(即，PINN的输出是一组模型函数)。PINN将被集成到一个模块中，从而能够识别：i)反应速率表达式(即动力学模型结构)；ii)反应器模型(理想、分散模型)；iii)i)和ii)同时进行。阶段4.自主反应平台中软件模块的集成。在第一阶段、第二阶段和第三阶段开发的计算模块的基础上，将在LabVIEW中开发硬件/软件图形用户界面(GUI)，用于与硬件进行通信。