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Found 7 papers, 0 papers with code
Machine learning for industrial sensing and control: A survey and practical perspective
Soft sensing contains a wealth of industrial applications of statistical and machine learning methods.
Modern Machine Learning Tools for Monitoring and Control of Industrial Processes: A Survey
Over the last ten years, we have seen a significant increase in industrial data, tremendous improvement in computational power, and major theoretical advances in machine learning.
When Bioprocess Engineering Meets Machine Learning: A Survey from the Perspective of Automated Bioprocess Development
ML can be seen as a set of tools that contribute to the automation of the whole experimental cycle, including model building and practical planning, thus allowing human experts to focus on the more demanding and overarching cognitive tasks.
Model predictive control and moving horizon estimation for adaptive optimal bolus feeding in high-throughput cultivation of \textit{E. coli}
We discuss the application of a nonlinear model predictive control (MPC) and a moving horizon estimation (MHE) to achieve an optimal operation of \textit{E. coli} fed-batch cultivations with intermittent bolus feeding.
Fitting nonlinear models to continuous oxygen data with oscillatory signal variations via a loss based on DynamicTime Warping
TheDissolved Oxygen Tension is often the only measurement which is available online, and therefore, a good understanding of the errors in this signal is important for performing a robust estimation. Some of the expected errors will provoke uncertainties in the time-domain of the measurement, and in those cases, the common Weighted Least Squares estimation procedure can fail providing good results.
Model predictive control guided with optimal experimental design for pulse-based parallel cultivation
Optimal experimental design for parameter precision attempts to maximize the information content in experimental data for a most effective identification of parametric model.
Model-plant mismatch learning offset-free model predictive control
By this, we can exploit both the learned model-plant mismatch information and the stabilizing property of the nominal disturbance estimator approach.
