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Harnessing the power of machine learning for carbon capture, utilisation, and storage (CCUS) – A state-of-the-art review

Yongliang (Harry) Yan, Tohid Borhani, Gokul Subraveti, Nagesh Pai, Vinay Prasad, Arvind Rajendran, Paula Nkulikiyinka, Jude Odianosen Asibor, Zhien Zhang, Ding Shao, Lijuan Wang, Wenbiao Zhang (+7 others)
2021 Energy & Environmental Science  
This approach allowed them to synthesise different PSA cycles for post-combustion CO 2 capture and calculate their performances based on the neural network models underpinning each step.  ...  Xie et al. 141 compared the performance of RBF and BPNNs on the prediction of TG curves of oxy-co-combustion of textile dyeing sludge and pomelo peel, with the mixing ratio, heating rates, combustion  ... 
doi:10.1039/d1ee02395k fatcat:oherbaerwfcarc744bn77pu77y

Methods and tools in CAD – selected issues

Bogusław Butryło
2021
Depending on the purpose of the attack on local wireless networks, OSI models can be divided into several categories [10] These attacks are based on the use of vulnerable wireless networks represented  ...  The classification model is built based on the signatures of the training sample and then used to classify real network activity.  ...  CONCLUSIONS This work presents very simple and efficient filtering techniques to improve the accuracy of the localization procedure based on gradient increments.  ... 
doi:10.24427/978-83-66391-87-1 fatcat:im7ow5462nh7rgsgnoj7ykuuci