Abstract Machine learning (ML) has recently gained in popularity, spurred by well-publicized
advances like deep learning and widespread commercial interest in big data analytics …

This paper provides a review of model predictive control (MPC) methods with active
uncertainty learning. System uncertainty poses a key theoretical and practical challenge in …

Advanced model‐based controllers are well established in process industries. However,
such controllers require regular maintenance to maintain acceptable performance. It is a …

Abstract 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 …

In recent years, researchers have explored the application of Reinforcement Learning (RL)
and Artificial Neural Networks (ANNs) to the control of complex nonlinear and time varying …

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 …

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 …

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 …

Advanced model-based controllers are well established in process industries. However,
such controllers require regular maintenance to maintain acceptable performance. It is a …

Hydraulic fracturing has played a crucial role in enhancing the extraction of oil and gas from
deep underground sources. The two main objectives of hydraulic fracturing are to produce …