Comparative performance and machine learning-based optimization of TSA configurations for NH3 removal from NH3 cracking gas

Abstract

To meet the H2 standard used in fuel cells, removing ammonia (NH3) from NH3 cracking gas to less than 0.1 ppm still faces technical challenges. In this study, novel configurations of a three-bed temperature swing adsorption (TSA) process using zeolite 4A and machine learning (ML)-based optimization were developed for effective NH3 removal. Three TSA configurations, consisting of a cooling gas, three-bed TSA, heat exchangers, an expander, and/or a compressor, were designed and evaluated using the TSA model with zeolite 4A pellets after the model validation with a reference. In a techno-economic comparison, the configuration using H2 pressure swing adsorption (PSA) tail gas as the cooling gas and NH3 TSA off-gas from the cooling step as the heating gas for energy recovery (TSA-TGER) performed the best. Dynamic behavior and sensitivity analyses were conducted to elucidate the characteristics of the TSA-TGER configuration. Using five main operating variables selected from the Pearson correlation method, the developed artificial neural network model could precisely predict the results with a reduction in computational cost of 1800 times compared with the process simulation. Finally, at the optimum condition found from the ML-based optimization, the TSA-TGER configuration consumed 2174.8 MJ/tNH3 and 162.33 $/tNH3 to produce an H2 mixture with less than 0.1 ppm NH3, indicating that the NH3 removal cost contributed to only approximately 0.98% of the referred H2 production cost (3580 $/tH2) via the NH3-to-H2 process. These results provide guidelines for designing an effective NH3 removal configuration from NH3 cracking gases. The proposed ML-based optimization approach can also be applied to other purification processes.