Deep reinforcement learning in production systems: a systematic literature review
Shortening product development cycles and fully customisable products pose major
challenges for production systems. These not only have to cope with an increased product …
challenges for production systems. These not only have to cope with an increased product …
Machine learning in fermentative biohydrogen production: advantages, challenges, and applications
Hydrogen can be produced in an environmentally friendly manner through biological
processes using a variety of organic waste and biomass as feedstock. However, the …
processes using a variety of organic waste and biomass as feedstock. However, the …
Deep reinforcement learning with shallow controllers: An experimental application to PID tuning
NP Lawrence, MG Forbes, PD Loewen… - Control Engineering …, 2022 - Elsevier
Deep reinforcement learning (RL) is an optimization-driven framework for producing control
strategies for general dynamical systems without explicit reliance on process models. Good …
strategies for general dynamical systems without explicit reliance on process models. Good …
Reinforcement learning based optimal control of batch processes using Monte-Carlo deep deterministic policy gradient with phase segmentation
Batch process control represents a challenge given its dynamic operation over a large
operating envelope. Nonlinear model predictive control (NMPC) is the current standard for …
operating envelope. Nonlinear model predictive control (NMPC) is the current standard for …
Physics-informed neural networks for hybrid modeling of lab-scale batch fermentation for β-carotene production using Saccharomyces cerevisiae
Abstract β-Carotene has a positive impact on human health as a precursor of vitamin A.
Building a kinetic model for its production using Saccharomyces cerevisiae in a batch …
Building a kinetic model for its production using Saccharomyces cerevisiae in a batch …
Deep hybrid model‐based predictive control with guarantees on domain of applicability
A hybrid model integrates a first‐principles model with a data‐driven model which predicts
certain unknown dynamics of the process, resulting in higher accuracy than first‐principles …
certain unknown dynamics of the process, resulting in higher accuracy than first‐principles …
A deep reinforcement learning approach to improve the learning performance in process control
Y Bao, Y Zhu, F Qian - Industrial & Engineering Chemistry …, 2021 - ACS Publications
Advanced model-based control methods have been widely used in industrial process
control, but excellent performance requires regular maintenance of its model. Reinforcement …
control, but excellent performance requires regular maintenance of its model. Reinforcement …
Real-time optimization meets Bayesian optimization and derivative-free optimization: A tale of modifier adaptation
EA del Rio Chanona, P Petsagkourakis… - Computers & Chemical …, 2021 - Elsevier
This paper investigates a new class of modifier-adaptation schemes to overcome plant-
model mismatch in real-time optimization of uncertain processes. The main contribution lies …
model mismatch in real-time optimization of uncertain processes. The main contribution lies …
Machine learning in process systems engineering: Challenges and opportunities
This “white paper” is a concise perspective of the potential of machine learning in the
process systems engineering (PSE) domain, based on a session during FIPSE 5, held in …
process systems engineering (PSE) domain, based on a session during FIPSE 5, held in …
Deep reinforcement learning control of hydraulic fracturing
Hydraulic fracturing is a technique to extract oil and gas from shale formations, and
obtaining a uniform proppant concentration along the fracture is key to its productivity …
obtaining a uniform proppant concentration along the fracture is key to its productivity …