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Insights from data-driven surrogate models reveal that the Sherwood number characterizes mass transport conditions independent of the convection method, offering design guidelines for scaling up organic electrosynthesis.

 

Electrochemical synthesis of organic chemical commodities provides an alternative to conventional thermochemical manufacturing and enables the direct use of renewable electricity to reduce greenhouse gas emissions from the chemical industry. We discuss electrochemical synthesis approaches that use abundant carbon feedstocks for the production of the largest petrochemical precursors and basic organic chemical products: light olefins, olefin oxidation derivatives, aromatics, and methanol. First, we identify feasible routes for the electrochemical production of each commodity while considering the reaction thermodynamics, available feedstocks, and competing thermochemical processes. Next, we summarize successful catalysis and reaction engineering approaches to overcome technological challenges that prevent electrochemical routes from operating at high production rates, selectivity, stability, and energy conversion efficiency. Finally, we provide an outlook on the strategies that must be implemented to achieve large-scale electrochemical manufacturing of major organic chemical commodities. Expected final online publication date for the Annual Review of Chemical and Biomolecular Engineering, Volume 14 is June 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates. 

Current methods of finding optimal experimental conditions, Edisonian systematic searches, often inefficiently evaluate suboptimal design points and require fine resolution to identify near optimal conditions. For expensive experimental campaigns or those with large design spaces, the shortcomings of the status quo approaches are more significant. Here, we extend Bayesian optimization (BO) and introduce a chemically-informed data-driven optimization (ChIDDO) approach. This approach uses inexpensive and low-fidelity information obtained from physical models of chemical processes and subsequently combines it with expensive and high-fidelity experimental data to optimize a common objective function. Using common optimization benchmark objective functions, we describe scenarios in which the ChIDDO algorithm outperforms the traditional BO approach, and then implement the algorithm on a simulated electrochemical engineering optimization problem. 

Autonomous chemical process development and optimization methods use algorithms to explore the operating parameter space based on feedback from experimentally determined exit stream compositions. Measuring the compositions of multicomponent streams is challenging, requiring multiple analytical techniques to differentiate between similar chemical components in the mixture and determine their concentration. Herein, we describe a universal analytical methodology based on multitarget regression machine learning (ML) models to rapidly determine chemical mixtures' compositions from Fourier transform infrared (FTIR) absorption spectra. Specifically, we used simulated FTIR spectra for up to 6 components in water and tested seven different ML algorithms to develop the methodology. All algorithms resulted in regression models with mean absolute errors (MAE) between 0–0.27 wt%. We validated the methodology with experimental data obtained on mixtures prepared using a network of programmable pumps in line with an FTIR transmission flow cell. ML models were trained using experimental data and evaluated for mixtures of up to 4-components with similar chemical structures, including alcohols ( i.e. , glycerol, isopropanol, and 1-butanol) and nitriles ( i.e. , acrylonitrile, adiponitrile, and propionitrile). Linear regression models predicted concentrations with coefficients of determination, R 2 , between 0.955 and 0.986, while artificial neural network models showed a slightly lower accuracy, with R 2 between 0.854 and 0.977. These R 2 correspond to MAEs of 0.28–0.52 wt% for mixtures with component concentrations between 4–10 wt%. Thus, we demonstrate that ML models can accurately determine the compositions of multicomponent mixtures of similar species, enhancing spectroscopic chemical quantification for use in autonomous, fast process development and optimization. 

Electrolyte ions have a profound impact on the reaction environment of electrochemical systems and can be key drivers in determining the reaction rate and selectivity of electro-organic reactions. We combine experimental and computational approaches to understand the individual effect of the size and concentration of supporting alkali cations, as well as their synergies with other electrolyte ions on the electrosynthesis of adiponitrile (ADN). The size of supporting alkali cations influences the surface charge density, availability of water molecules, and stability of reaction intermediates. Larger alkali cations can help limit hydrogen evolution and the early protonation of intermediates by lowering the availability of water molecules in the near electrode region. A selectivity of 93% towards ADN was achieved at −20 mA cm⁻²in electrolytes containing cesium phosphate salts, ethylenediaminetetraacetic acid, and tetraalkylammonium ions (TAA ions). Electrolytes containing only supporting phosphate salts promote the early hydrogenation of intermediate species leading to low ADN selectivities (i.e., <10%). However, the combined effect of alkali cations and selectivity-directing ions (i.e., TAA ions) is essential in the enhancement of ADN synthesis. The insights gained in this study provide guidelines for the design of aqueous electrolytes that improve selectivity and limit hydrogen evolution in organic electrosynthesis.