Digitizing sustainable process development: From ex-post to ex-ante LCA using machine-learning to evaluate bio-based process technologies ahead of detailed design
Artikel i vetenskaplig tidskrift, 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.


Artificial neural networks

Machine learning

Clustering and classification

Ex-ante LCA


Paraskevi Karka

Chalmers, Rymd-, geo- och miljövetenskap, Energiteknik

National Technical University of Athens (NTUA)

Stavros Papadokonstantakis

Chalmers, Rymd-, geo- och miljövetenskap, Energiteknik

Technische Universität Wien

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)

Europeiska kommissionen (EU) (EC/H2020/778332), 2018-01-01 -- 2021-12-31.


Rymd- och flygteknik

Övrig annan teknik

Bioinformatik (beräkningsbiologi)



Mer information

Senast uppdaterat