Ten recommendations for literature data generation for chemical process optimization in renewable feedstock conversion

Abstract

Process development of recyclable and renewable feedstocks entails intensive and tedious catalyst and solvent screening and multiparameter process exploration. The solid nature of these feedstocks necessitates highly time-consuming, batch experiments, leading to limited published datasets. Our ability to rapidly advance process development requires a systematic approach to the simultaneous optimization of catalysts, solvents, and processing conditions. One approach toward this goal is to harness and analyze accumulated literature data to develop performance-process correlations. With this goal, we study a prototype biomass reaction, the fructose dehydration to 5-hydroxymethyl furfural (HMF) over zeolite catalysts. We explore literature data to optimize the vast descriptor space of zeolite characteristics, solvents, and process conditions using correlation analysis. We assess the quality of reported literature data and identify where provenance is lacking. We develop apparent kinetic analysis to correlate performance with solvent and catalyst properties. We showcase that solvents and to an extent processing conditions dominate HMF yield; in contrast, zeolites play a secondary role for this chemistry. We provide ten recommendations for improving this methodology to extract further knowledge and improve process optimization.