文献
J-GLOBAL ID:202002267855878760
整理番号:20A0651985
周期定常状態の加速予測のための実験的に検証された機械学習フレームワークと圧力スイング吸着プロセスの最適化【JST・京大機械翻訳】
Experimentally validated machine learning frameworks for accelerated prediction of cyclic steady state and optimization of pressure swing adsorption processes
著者 (3件):
Pai Kasturi Nagesh
(Department of Chemical and Materials Engineering, University of Alberta, 12th Floor, Donadeo Innovation Centre for Engineering, 9211 - 116 Street, Edmonton, Alberta T6G 1H9, Canada)
,
Prasad Vinay
(Department of Chemical and Materials Engineering, University of Alberta, 12th Floor, Donadeo Innovation Centre for Engineering, 9211 - 116 Street, Edmonton, Alberta T6G 1H9, Canada)
,
Rajendran Arvind
(Department of Chemical and Materials Engineering, University of Alberta, 12th Floor, Donadeo Innovation Centre for Engineering, 9211 - 116 Street, Edmonton, Alberta T6G 1H9, Canada)
資料名:
Separation and Purification Technology
(Separation and Purification Technology)
巻:
241
ページ:
Null
発行年:
2020年
JST資料番号:
T0428B
ISSN:
1383-5866
資料種別:
逐次刊行物 (A)
記事区分:
原著論文
発行国:
オランダ (NLD)
言語:
英語 (EN)