Early-Stage Evaluation of Catalyst Using Machine Learning Based Modeling and Simulation of Catalytic Systems: Hydrogen Production via Water–Gas Shift over Pt Catalysts

Abstract

The goal of this research is to create a new machine learning (ML)-centric methodology for process modeling, designing, and evaluating hydrogen production based on the water–gas shift reaction (WGSR). The approach evaluates the one-pass conversion of catalyst and process overall conversion, as well as the economics of the catalytic conversion process, without the use of kinetics and a process model that requires much trial and error. To accomplish this, an ML model was developed to predict the catalyst performance based on critical catalysis descriptors like catalyst composition, operating conditions, and feed composition. We developed a surrogate model for the hydrogen production process based on the predicted results to determine the mass and energy information on the process, which includes multiple unit operations and recycling. Finally, we assessed the hydrogen production process using different technical and economic metrics such as hydrogen amount, energy consumption, and unit energy cost. The approach can perform a kinetics-free simulation of hydrogen production processes using predicted catalyst performances and evaluate early-state catalysts from an industrial perspective by identifying the optimal operating conditions and the catalyst structure for economic and energy-efficient hydrogen production. As a result, the processes over Pt/Co(10 wt %)/Al2O3, Pt/Co(20 wt %)/Al2O3, and Pt/Ce(5 wt %)/TiO2 show the best performance to produce high-purity hydrogen.

CO2 Hydrogenation to Gasoline and Aromatics: Mechanistic and Predictive Insights from DFT, DRIFTS and Machine Learning

“…In attempts to improve chemical manufacturing, molecular‐level ML‐assisted modeling of large catalytic systems has been reported in systems such as water gas shift reaction, [83] steam reforming, [84] dry reforming, [85] etc. In a recent investigation by Kim et al., [86] ANN and surrogate models were used to evaluate the performance of 30 types of Pt catalysts for H 2 production via water gas shift reaction (WGSR). The approach helped identified optimal operating conditions and catalysts for achieving optimized unit energy cost and energy consumption.…”

Hybrid Quantum Neural Network Model with Catalyst Experimental Validation: Application for the Dry Reforming of Methane

“…Recently, the application and potential of ML for developing catalysts for the methane reforming reaction, 13,14 electrocatalysis, 15,16 water gas shift reaction, 17,18 oxidative coupling of methane reaction, 19−21 and selective catalytic reduction reaction have been reported. 22−24 In the field of catalysis research, the application of ML is gaining significant attention.…”

“…This trend involves the strategic use of density functional theory calculation data in tandem with ANNs or tree-based ensemble algorithms. These methodologies enable researchers to conduct extensive screening studies, aiming to predict energy changes related to adsorption and surface energies. − ,,, Additionally, the application of ML facilitates the assessment of the economic efficiency of various catalysts . This is achieved through detailed analysis of experimental data sourced from the existing literature.…”

“…[14][15][16]19,23,24 Additionally, the application of ML facilitates the assessment of the economic efficiency of various catalysts. 17 This is achieved through detailed analysis of experimental data sourced from the existing literature. These approaches are instrumental in identifying optimal catalyst compositions and reaction conditions.…”

“…Over the past decade, ML techniques, including artificial neural networks (ANNs), have been successfully applied in various research fields, − including chemical engineering. Recently, the application and potential of ML for developing catalysts for the methane reforming reaction, , electrocatalysis, , water gas shift reaction, , oxidative coupling of methane reaction, − and selective catalytic reduction reaction have been reported. − …”