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

Due to its efficient operation and environment-friendly characteristic, gas-fired unit (GFU)
plays a more and more important role in electric power systems and natural gas systems. To …

Actor‐critic (AC) methods have emerged as an important class of reinforcement learning
(RL) paradigm that enables model‐free control by acting on a process and learning from the …

The increasing penetration of inverter-based resources (IBRs) calls for an advanced active
and reactive power (PQ) control strategy in microgrids. To enhance the controllability and …

Deep reinforcement learning (DRL) agents are trained through trial-and-error interactions
with the environment. This leads to a long training time for dense neural networks to achieve …

This article studies a dynamic operation optimization problem for a steelmaking process.
The problem is defined to determine optimal operation parameters that bring smelting …

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

Reinforcement Learning (RL) controllers have generated excitement within the control
community. The primary advantage of RL controllers relative to existing methods is their …

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 …