Shortening product development cycles and fully customisable products pose major
challenges for production systems. These not only have to cope with an increased product …

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 …

Deep reinforcement learning (RL) is an optimization-driven framework for producing control
strategies for general dynamical systems without explicit reliance on process models. Good …

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 …

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 …

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 …

Hydrogen can be produced in an environmentally friendly manner through biological
processes using a variety of organic waste and biomass as feedstock. However, the …

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 …

Advanced model-based control methods have been widely used in industrial process
control, but excellent performance requires regular maintenance of its model. Reinforcement …

This paper presents a literature review of reinforcement learning (RL) and its applications to
process control and optimization. These applications were evaluated from a new …