Soft sensing of lpg processes using deep learning
This study investigates the integration of soft sensors and deep learning in the oil-refinery
industry to improve monitoring efficiency and predictive accuracy in complex industrial …
industry to improve monitoring efficiency and predictive accuracy in complex industrial …
Techno-economic risk-constrained optimization for sustainable green hydrogen energy storage in solar/wind-powered reverse osmosis systems
Hydrogen energy storage systems (HESSs) are vital for enhancing the resilience of energy
systems and coping with the intermittency of renewable energy sources. However, their …
systems and coping with the intermittency of renewable energy sources. However, their …
Application of machine learning algorithms and feature selection methods for better prediction of sludge production in a real advanced biological wastewater treatment …
Although the management of sewage sludge is an important and challenging task of
wastewater treatment, there is a scarcity of studies on the prediction of waste sludge. To …
wastewater treatment, there is a scarcity of studies on the prediction of waste sludge. To …
A deep semi-supervised learning framework towards multi-output soft sensors development and applications in wastewater treatment processes
Soft sensors have emerged as a powerful tool for predicting quality-related but hard-to-
measured variables in the wastewater treatment plants (WWTPs). However, due to high data …
measured variables in the wastewater treatment plants (WWTPs). However, due to high data …
Coordinated operation for a resilient and green energy-water supply system: A co-optimization approach with flexible strategies
Sustainable operation of green renewable energy (RE)-powered reverse osmosis (RO)
desalination (RE-RO) systems requires co-optimization and smart strategies to reduce costs …
desalination (RE-RO) systems requires co-optimization and smart strategies to reduce costs …
Chemical-guided screening of top-performing metal–organic frameworks for hydrogen storage: An explainable deep attention convolutional model
AH Ba-Alawi, S Palla, SR Ambati, HT Nguyen… - Chemical Engineering …, 2024 - Elsevier
Abstract Metal-organic framework (MOF)-based adsorptive hydrogen storage holds promise
for enhancing the sustainable design of hydrogen storages by enhancing the usable …
for enhancing the sustainable design of hydrogen storages by enhancing the usable …
A novel framework for high resolution air quality index prediction with interpretable artificial intelligence and uncertainties estimation
Accurate air quality index (AQI) prediction is essential in environmental monitoring and
management. Given that previous studies neglect the importance of uncertainty estimation …
management. Given that previous studies neglect the importance of uncertainty estimation …
Adaptive self-calibrated soft sensor for reliable nutrient measurement in rivers: Two-stage stacked autoencoder with densely connected fusion network
A soft sensor effectively estimates concentrations of total nitrogen (TN) and total phosphorus
(TP) in rivers by utilizing easily measurable variables. However, in practical applications, the …
(TP) in rivers by utilizing easily measurable variables. However, in practical applications, the …
Integrating Fusion Autoencoder with Multi-Agent Reinforcement Learning for Optimal Energy Dispatch Under Uncertainties
Energy hub concept has been applied to energy management systems to enhance system
flexibility. However, energy management studies face various challenges due to demand …
flexibility. However, energy management studies face various challenges due to demand …