78 citations · 126 across the 7 of their papers we have counts for
7 papers
Machine learning for industrial sensing and control: A survey and practical perspective
Nathan P. Lawrence, Seshu Kumar Damarla, Jong Woo Kim +7
With the rise of deep learning, there has been renewed interest within the process industries to utilize data on large-scale nonlinear sensing and control problems. We identify key…
When Bioprocess Engineering Meets Machine Learning: A Survey from the Perspective of Automated Bioprocess Development
Nghia Duong-Trung, Stefan Born, Jong Woo Kim +9
Machine learning (ML) is becoming increasingly crucial in many fields of engineering but has not yet played out its full potential in bioprocess engineering. While experimentation…
Modern Machine Learning Tools for Monitoring and Control of Industrial Processes: A Survey
R. Bhushan Gopaluni, Aditya Tulsyan, Benoit Chachuat +6
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.…
Model predictive control and moving horizon estimation for adaptive optimal bolus feeding in high-throughput cultivation of \textit{E. coli}
Jong Woo Kim, Niels Krausch, Judit Aizpuru +4
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 cultiva…
Fitting nonlinear models to continuous oxygen data with oscillatory signal variations via a loss based on DynamicTime Warping
Judit Aizpuru, Annina Karolin Kemmer, Jong Woo Kim +4
High throughput experimental systems play an important role in bioprocess development, as they provide an efficient way of analysing different experimental conditions and perform s…
Model predictive control guided with optimal experimental design for pulse-based parallel cultivation
Jong Woo Kim, Niels Krausch, Judit Aizpuru +5
Optimal experimental design for parameter precision attempts to maximize the information content in experimental data for a most effective identification of parametric model. With…
Model-plant mismatch learning offset-free model predictive control
Sang Hwan Son, Jong Woo Kim, Tae Hoon Oh +1
We propose model-plant mismatch learning offset-free model predictive control (MPC), which learns and applies the intrinsic model-plant mismatch, to effectively exploit the advanta…