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

数字化正在推动制造业的深度转型，通过应用数字双胞胎进行化学反应系统的设计、控制和实时优化。数字孪生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资，以确定模型结构和精确识别系统特定的动力学参数集。拟议的项目旨在开发一个新的软件框架，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。该框架的具体目标是：1)反应速率表达式的同时识别和动力学参数的精确估计；2)在不确定性和干扰影响下的实验条件稳健设计；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UCL最近开发的反应平台中，允许在催化流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）。该项目将沿着四个主要步骤发展，每个步骤将持续约9个月。机器学习辅助MBDoE软件模块的开发。软件模块将开发和测试在硅实现稳健的最佳实验设计技术。在该模块中，将集成ML模型来模拟影响系统输入和输出的潜在不确定性和干扰。将鲁棒MBDoE技术应用于存在参数失配的设计实验中，用于模型判别和参数精度的提高。最近开发的基于人工神经网络（ann）的动力学模型选择实验设计技术，目前在线下应用，将在线应用，并与标准的MBDoE模型识别技术进行比较。阶段2。异常值检测模块的开发。由于系统生成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。这将实现基于模型的数据挖掘（MBDM）和数据驱动的离群值检测技术。这些技术将通过强制获取的数据中的不确定性进行计算机测试，并进行比较，以验证其在在线应用中的有效性。阶段3。基于物理的机器学习模型识别模块的开发。物理信息神经网络（pinn）已被提出用于求解或发现时变和非线性偏微分方程（PDEs）。pinn是经过训练的神经网络，用于解决监督学习任务，同时尊重特定的物理信息约束。与标准的深度神经网络不同，在训练神经网络时，pinn将物理信息的微分方程直接添加到损失函数中。PINN具有以下优点：1)训练一个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)直观的结果（即，PINN的输出是一组模型函数）。pin将集成在一个模块中，允许识别：i)反应速率表达式（即动力学模型结构）；Ii)反应堆模型（理想、分散模型）；Iii) i)和ii)同时。阶段4。自主反应平台软件模块集成。有了第1、2和3阶段开发的计算模块，将在LabView中开发一个硬件/软件图形用户界面（GUI），用于与硬件通信。

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.