Artificial intelligence technologies revolutionizing wastewater treatment: Current trends and future prospective
Integration of the Internet of Things (IoT) into the fields of wastewater treatment and water
quality prediction has the potential to revolutionize traditional approaches and address …
quality prediction has the potential to revolutionize traditional approaches and address …
Deep learning with spatiotemporal attention-based LSTM for industrial soft sensor model development
Industrial process data are naturally complex time series with high nonlinearities and
dynamics. To model nonlinear dynamic processes, a long short-term memory (LSTM) …
dynamics. To model nonlinear dynamic processes, a long short-term memory (LSTM) …
Nonlinear dynamic soft sensor modeling with supervised long short-term memory network
X Yuan, L Li, Y Wang - IEEE transactions on industrial …, 2019 - ieeexplore.ieee.org
Soft sensor has been extensively utilized in industrial processes for prediction of key quality
variables. To build an accurate virtual sensor model, it is very significant to model the …
variables. To build an accurate virtual sensor model, it is very significant to model the …
A novel deep learning based fault diagnosis approach for chemical process with extended deep belief network
Deep learning networks have been recently utilized for fault detection and diagnosis (FDD)
due to its effectiveness in handling industrial process data, which are often with high …
due to its effectiveness in handling industrial process data, which are often with high …
Variable correlation analysis-based convolutional neural network for far topological feature extraction and industrial predictive modeling
In process industries, accurate prediction of critical quality variables is particularly important
for process control and optimization. Usually, soft sensors have been developed to estimate …
for process control and optimization. Usually, soft sensors have been developed to estimate …
Deep learning-based feature representation and its application for soft sensor modeling with variable-wise weighted SAE
In modern industrial processes, soft sensors have played an important role for effective
process control, optimization, and monitoring. Feature representation is one of the core …
process control, optimization, and monitoring. Feature representation is one of the core …
An overview of artificial intelligence application for optimal control of municipal solid waste incineration process
Artificial intelligence (AI) has found widespread application across diverse domains,
including residential life and product manufacturing. Municipal solid waste incineration …
including residential life and product manufacturing. Municipal solid waste incineration …
Data-driven monitoring and safety control of industrial cyber-physical systems: Basics and beyond
Industrial cyber-physical systems (ICPSs) are the backbones of Industry 4.0 and as such,
have become a core transdisciplinary area of research, both in industry and academia. New …
have become a core transdisciplinary area of research, both in industry and academia. New …
A layer-wise data augmentation strategy for deep learning networks and its soft sensor application in an industrial hydrocracking process
In industrial processes, inferential sensors have been extensively applied for prediction of
quality variables that are difficult to measure online directly by hard sensors. Deep learning …
quality variables that are difficult to measure online directly by hard sensors. Deep learning …
Deep learning for fault-relevant feature extraction and fault classification with stacked supervised auto-encoder
Abstract Stacked auto-encoder (SAE)-based deep learning has been introduced for fault
classification in recent years, which has the potential to extract deep abstract features from …
classification in recent years, which has the potential to extract deep abstract features from …