This project aims to address the main challenges related to developing optimization tools for the process industries, which are: 1. Lack of good models that can be used in optimization algorithms (offline model development) 2. Wrong value of model parameters used in the optimization problem (online model update) 3. Numerical robustness including computational issues By leveraging big data and machine-learning algorithms, we can develop decision-support tools for complex cyber-physical systems that can be a part of the industrial internet-of-things. In this project, we will utilize process data to develop machine-learning based models (also known as digital-twins), that can be used for developing optimization tools. This will enable us to address the challenges with respect to developing models for optimization. Reinforcement learning approaches will also be used to operate the processes in new operating conditions other than the conditions used to train the model. To address the computational robustness issues of solving optimization problems, we also aim to approximate computationally intensive optimization problems using machine-learning algorithms. Instead of developing surrogate models that will be used in the optimizer, we plan to build surrogate optimizers or AI optimizers that approximate the numerical optimization solvers. This project aims to restructure existing process industries by utilizing data more efficiently and it will also open new industrial applications that could benefit from data-driven decision-support tools. At the same time, this project will also develop novel algorithms for machine-learning-based optimization and thus move the research front and develop new subject areas in the field of online process optimization and autonomous decision-making. Therefore, this project will address challenges in the short to medium time horizon.
Project leader: Sigurd Skogestad
Institution: Institutt for kjemisk prosessteknologi