Combustion machine learning: Principles, progress and prospects
Progress in combustion science and engineering has led to the generation of large amounts
of data from large-scale simulations, high-resolution experiments, and sensors. This corpus …
of data from large-scale simulations, high-resolution experiments, and sensors. This corpus …
Harnessing the power of machine learning for carbon capture, utilisation, and storage (CCUS)–a state-of-the-art review
Carbon capture, utilisation and storage (CCUS) will play a critical role in future
decarbonisation efforts to meet the Paris Agreement targets and mitigate the worst effects of …
decarbonisation efforts to meet the Paris Agreement targets and mitigate the worst effects of …
A survey on deep learning for data-driven soft sensors
Q Sun, Z Ge - IEEE Transactions on Industrial Informatics, 2021 - ieeexplore.ieee.org
Soft sensors are widely constructed in process industry to realize process monitoring, quality
prediction, and many other important applications. With the development of hardware and …
prediction, and many other important applications. With the development of hardware and …
[HTML][HTML] A review on autoencoder based representation learning for fault detection and diagnosis in industrial processes
Process monitoring technologies play a key role in maintaining the steady state of industrial
processes. However, with the increasing complexity of modern industrial processes …
processes. However, with the increasing complexity of modern industrial processes …
[HTML][HTML] Machine learning for combustion
Combustion science is an interdisciplinary study that involves nonlinear physical and
chemical phenomena in time and length scales, including complex chemical reactions and …
chemical phenomena in time and length scales, including complex chemical reactions and …
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 …
[HTML][HTML] Industry 4.0 based process data analytics platform: A waste-to-energy plant case study
JC Kabugo, SL Jämsä-Jounela, R Schiemann… - International journal of …, 2020 - Elsevier
Abstract Industry 4.0 and Industrial Internet of Things (IIoT) technologies are rapidly fueling
data and software solutions driven digitalization in many fields notably in industrial …
data and software solutions driven digitalization in many fields notably in industrial …
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 …
Ensemble deep kernel learning with application to quality prediction in industrial polymerization processes
For predicting the melt index (MI) in industrial polymerization processes, traditional data-
driven empirical models do not utilize the information in a large amount of the unlabeled …
driven empirical models do not utilize the information in a large amount of the unlabeled …
Domain adaptation transfer learning soft sensor for product quality prediction
For multi-grade chemical processes, often, limited labeled data are available, resulting in an
insufficient construction of reliable soft sensors for several modes. Additionally, the current …
insufficient construction of reliable soft sensors for several modes. Additionally, the current …