54 citations · 131 across the 14 of their papers we have counts for
5 papers · 1 filter
Application of Soft Actor-Critic Algorithms in Optimizing Wastewater Treatment with Time Delays Integration
Esmaeel Mohammadi, Daniel Ortiz-Arroyo, Aviaja Anna Hansen +4
Wastewater treatment plants face unique challenges for process control due to their complex dynamics, slow time constants, and stochastic delays in observations and actions. These…
Time Series Dataset for Modeling and Forecasting of in Wastewater Treatment
Laura Debel Hansen, Anju Rani, Mikkel Algren Stokholm-Bjerregaard +3
In this paper, we present two years of high-resolution nitrous oxide () measurements for time series modeling and forecasting in wastewater treatment plants (WWTP). The datas…
Improved Long Short-Term Memory-based Wastewater Treatment Simulators for Deep Reinforcement Learning
Esmaeel Mohammadi, Daniel Ortiz-Arroyo, Mikkel Stokholm-Bjerregaard +2
Even though Deep Reinforcement Learning (DRL) showed outstanding results in the fields of Robotics and Games, it is still challenging to implement it in the optimization of industr…
Advancements in Point Cloud-Based 3D Defect Detection and Classification for Industrial Systems: A Comprehensive Survey
Anju Rani, Daniel Ortiz-Arroyo, Petar Durdevic
In recent years, 3D point clouds (PCs) have gained significant attention due to their diverse applications across various fields, such as computer vision (CV), condition monitoring…
Deep Learning Based Simulators for the Phosphorus Removal Process Control in Wastewater Treatment via Deep Reinforcement Learning Algorithms
Esmaeel Mohammadi, Mikkel Stokholm-Bjerregaard, Aviaja Anna Hansen +3
Phosphorus removal is vital in wastewater treatment to reduce reliance on limited resources. Deep reinforcement learning (DRL) is a machine learning technique that can optimize com…