The manufacturing world is subject to ever-increasing cost optimization pressures.
Maintenance adds to cost and disrupts production; optimized maintenance is therefore of …

In metal additive manufacturing (AM), the material microstructure and part geometry are
formed incrementally. Consequently, the resulting part could be defect-and anomaly-free if …

This review article explores how emerging generative artificial intelligence (GenAI) models,
such as large language models (LLMs), can enhance solution methodologies within process …

Iron making processes and automation systems are mostly controlled by logical rules and
PID controllers. The dynamic behavior of these processes varies due to factors such as raw …

The proper tuning of proportional–integral–derivative (PID) control is critical for satisfactory
control performance. However, existing tuning methods are often time-consuming and …

The operation of modern wastewater treatment facilities is a balancing act in which a
multitude of variables are controlled to achieve a wide range of objectives, many of which …

This work addresses recent developments for solving problems in process systems
engineering based on machine learning algorithms. A general description of most popular …

This article addresses the prediction stability, prediction accuracy, and control capability of
the current probabilistic model-based reinforcement learning (MBRL) built on neural …

Meta-learning is a branch of machine learning which trains neural network models to
synthesize a wide variety of data in order to rapidly solve new problems. In process control …

Due to their complex nonlinear dynamics and batch-to-batch variability, batch processes
pose a challenge for process control. Due to the absence of accurate models and resulting …