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

This paper reviews dynamic programming (DP), surveys approximate solution methods for it,
and considers their applicability to process control problems. Reinforcement Learning (RL) …

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

Reinforcement learning (RL) has now evolved as a major technique for adaptive optimal
control of nonlinear systems. However, majority of the RL algorithms proposed so far impose …

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 …

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 …

In this work, a combination of a Convolutional Neural Network (CNN) based measurement
sensor and a reinforcement learning (RL) framework that speeds up the control loop is …

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 …