期刊
CHEMICAL ENGINEERING SCIENCE
卷 250, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ces.2021.117339
关键词
Ex-ante LCA; Biorefineries; Machine learning; Artificial neural networks; Clustering and classification
资金
- Marie Curie Grant RENESENG II [778332]
- Marie Curie Actions (MSCA) [778332] Funding Source: Marie Curie Actions (MSCA)
This research aims to develop a data-science based framework for estimating life cycle assessment (LCA) metrics of bio-based and biorefinery processes in the early design stages. The framework applies advanced analytics such as classification trees and artificial neural networks to improve the robustness and efficiency of LCA estimations.
Life Cycle Assessment is a data-intensive process holding great promise to benefit from advanced analytics and machine learning technologies. The present research aims at the development of a data-science based framework with capabilities to estimate LCA metrics of bio-based and biorefinery processes in early design phases. Life cycle inventories may combine experimental (pilot and lab scale) data, property and thermodynamic databases, and model-derived data from simulations and design studies. The framework applies advanced analytics such as classification trees and artificial neural networks (ANN) with a scope to produce input-output relationships through predictor variables that refer to the molecular structure of bio-chemical or bio-fuel products of interest, the feedstocks used, and the process technologies characteristics. The combined use of ANNs and trees demonstrates a coordinated level of complementarity between the approaches, while it improves robustness and streamlines LCA estimations in the early-stage design. (c) 2021 Elsevier Ltd. All rights reserved.
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