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

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.