Digitizing sustainable process development: From ex-post to ex-ante LCA using machine-learning to evaluate bio-based process technologies ahead of detailed design
Journal article, 2022

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.

Biorefineries

Artificial neural networks

Machine learning

Clustering and classification

Ex-ante LCA

Author

Paraskevi Karka

Chalmers, Space, Earth and Environment, Energy Technology

National Technical University of Athens (NTUA)

Stavros Papadokonstantakis

Chalmers, Space, Earth and Environment, Energy Technology

Vienna University of Technology

A. Kokossis

National Technical University of Athens (NTUA)

Chemical Engineering Science

0009-2509 (ISSN)

Vol. 250 117339

Renewable systems engineering for waste valorisation ΙΙ (RENESENG II)

European Commission (EC) (EC/H2020/778332), 2018-01-01 -- 2021-12-31.

Subject Categories

Aerospace Engineering

Other Engineering and Technologies not elsewhere specified

Bioinformatics (Computational Biology)

DOI

10.1016/j.ces.2021.117339

More information

Latest update

2/25/2022