Deep learning techniques and algorithms are emerging as a disruptive technology with the
potential to transform global economies, environments and societies. They have been …

Wastewater treatment process control optimization is a complex task in a highly nonlinear
environment. Reinforcement learning (RL) is a machine learning technique that stands out …

'Model-free control'and the corresponding 'intelligent'PID controllers (iPIDs), which already
had many successful concrete applications, are presented here for the first time in an unified …

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

Wastewater treatment plants (WWTPs) are designed to eliminate pollutants and alleviate
environmental pollution resulting from human activities. However, the construction and …

This work introduces a novel methodology for real-time optimization (RTO) of process
systems using reinforcement learning (RL), where optimal decisions in response to external …

Urban wastewater sector is being pushed to optimize processes in order to reduce energy
consumption without compromising its quality standards. Energy costs can represent a …

To solve the problem of high-energy consumption in activated sludge wastewater treatment,
a reinforcement learning-based particle swarm optimization (RLPSO) was proposed to …

Hydrological models have been used to understand the transportation of water quantity and
quality in drainage systems, and the stormwater management model (SWMM) is one of the …

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 …