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

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.

Automated summary

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.