Optimizing the Flow Electrooxidation of Glycerol Using Statistical Design of Experiments

Many studies have investigated the conversion of biomass derivatives to value-added products. However, the influence of different factors on the reaction outcomes of these often-complex systems is not well understood. Herein, a statistical design of experiments – specifically, response surface methodology – is applied to the glycerol electrooxidation reaction in a flow electrolyzer. Four operational variables (glycerol concentration, NaOH concentration, flow rate, and catalyst loading) were investigated for their effects on measurable responses of the electrochemical reaction: current density and Faradaic efficiency to a given product. Independent optimizations of current density and Faradaic efficiency, as well as simultaneous optimization of both, were investigated. Each optimization was evaluated using response surface coefficients to analyze sensitivity and simulated runs to visualize the parameter space. These evaluations revealed contradictions in operating conditions required to simultaneously maximize current density and Faradaic efficiency to C3 products glycerate and lactate, leading to low current densities and Faradaic efficiencies. However, simultaneously maximizing current density and Faradaic efficiency to C1 product formate led to high current densities and Faradaic efficiencies. These insights guide tuning GEOR production to maximize overall reactor performance. Furthermore, this study outlines a framework for experimental evaluation and optimization of other electrolysis chemistries.