(45b) Accelerating the Scaleup Process for Complex Electrochemical Reactors Using a Smart Manufacturing Inspired Multiscale Modeling Approach

As renewably sourced electricity becomes cost competitive with fossil fuel sources, the transition to electrified chemical synthesis pathways becomes increasingly desirable. Existing technologies like fuel cells and water electrolyzers are leading the way, but these systems have taken decades to reach profitable commercial scale due to minimal cost benefit and lengthy scaleup periods. The development of industrial scale electrochemical reactors is key to developing more complete and integrated synthesis pathways, but scaleup is limited by a lack of understanding about how heat and mass transport effects can change with scale.

To overcome this hang-up, a method is presented by which scaling complex lab scale electrochemical processes to industrial scale reactors can be accelerated using multiscale modeling techniques inspired by smart manufacturing methods. This method is split into 4 thrusts as (1) lab scale reactor design and fabrication (2) integration of intelligent control systems using machine learning models and model predictive control (3) development of a multiscale model and (4) utilization of multiscale modeling for next-scale reactor design and digital verification. To demonstrate the utility of this method, the scaleup of an electrochemical CO₂ reduction reactor is used as a case study. For this process it is shown how machine learning was utilized to process large amount of data to provide intelligent control of the reactor in tandem with automation systems. In parallel to this a multiscale CFD model was used to develop a universal model of the reactor that could be utilized to inform the development of and digitally verify the performance of a larger scale design. A final discussion of choke points and limitations of this method as well as preferential conditions for application to other processes will be presented.