Prediction of sorption enhanced steam methane reforming products from machine learning based soft-sensor models

Prediction of sorption enhanced steam methane reforming products from machine learning based soft-sensor models

Carbon dioxide-abated hydrogen can be synthesised via various processes, one of which is sorption enhanced steam methane reforming (SE-SMR), which produces separated streams of high purity H2 and CO2. Properties of hydrogen and the sorbent material hinder the ability to rapidly upscale SE-SMR, there...

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Bibliographic Details
Main Authors: Paula Nkulikiyinka, Yongliang Yan, Fatih Güleç, Vasilije Manovic, Peter T. Clough
Format: Article
Language:English
Published: Elsevier 2020-11-01
Series:Energy and AI
Subjects:
Machine learning
Artificial neural network
Soft sensor
Sorption enhanced steam methane reforming
Calcium looping
Online Access:http://www.sciencedirect.com/science/article/pii/S2666546820300379

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http://www.sciencedirect.com/science/article/pii/S2666546820300379

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