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IKTPLUSS-IKT og digital innovasjon

Intelligent use of data to build optimization tools for cyber-physical systems in the process industry

Alternative title: Intelligent bruk av data for utvikling av optimaliseringsverktøyer for kompleke cyper-fysiske systemer

Awarded: NOK 11.4 mill.

The process industry is one of the biggest producers of data, where a typical process generates thousands of data points each second. The process industry has used real-time data for decision making for more than 50 years and has up to now been the leading industry in terms of intelligent use of data. However, in spite of many successes, there is still a large potential for further improvements in developing decision support tools to address future challenges with industry 4.0. By decision-support tools, we mean computer-based algorithms that determine how to operate a system in order to achieve a desired objective, e.g. minimize operational costs and energy consumption. The three main challenges of developing optimization tools for complex cyber-physical systems are (in order of importance): 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 So far we have identified three main decision-making paradigms for real time process optimization based on what should be the input, and what should be output for the machine learning models. This helps us understand on what paradigm to use based on what kind of data is available from the process. In addition, we are also developing optimization algorithms to significantly reduce the computational cost of training. We proposed a new framework where optimal operation is achieved directly using real-time process measurements as feedback, which we call the primal-dual feedback optimizing control. Learning for expert demonstrations requires quality training data set. Lack of rich training data set can lead to poor learning. To address the issue of rich training data sets, this project proposed a novel data augmentation framework where we can augment several additional training data samples from existing samples.

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.

Publications from Cristin

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IKTPLUSS-IKT og digital innovasjon