APPROPRIATE Life Cycle Assessment: <b>A</b> <b>PRO</b>cess-Specific, <b>PR</b>edictive <b>I</b>mpact <b>A</b>ssessmen<b>T</b> Method for Emerging Chemical Processes
Johanna Kleinekorte, Jonas Kleppich, Lorenz Fleitmann, Verena Beckert, Luise Blodau, André Bardow
Abstract
High Resolution Image Download MS PowerPoint Slide A sustainable chemical industry needs to quantify its emissions and resource consumption by life cycle assessment (LCA). However, LCA requires detailed mass and energy balances, which are usually not available at early process development stages. Here, we introduce a framework ( A PRO cess-specific, PR edictive impact A ssessmen T method for E merging chemical processes, APPROPRIATE) to provide a fully automated, predictive LCA framework for the early phases of process development. Based on Gaussian Process Regression, the framework is already applicable at Technology Readiness Level 2. To overcome the limited LCA data availability, we employ an encoder–decoder network in combination with transfer learning to achieve a latent representation as a condensed molecular descriptor. We further propose to integrate not only molecular but also process descriptors, e.g., the stoichiometric sum of the reactants’ impacts. Thereby, we can distinguish between process alternatives and incorporate changes in the background systems. The framework is compared to state-of-the-art predictive LCA approaches and shows increased prediction accuracy in terms of the coefficient of determination of R 2 = 0.61 for the global warming impact compared to an R 2 = 0.3 in former studies. Highly relevant features are the stoichiometric sum of the reactants’ impacts and the condensed molecular descriptors. APPROPRIATE supports decision making in early process development stages by allowing the distinction between process alternatives and quantifying predictions’ uncertainty.