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

To meet the H-2 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 H-2 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/ tNH(3) and 162.33 $/tNH(3) to produce an H-2 mixture with less than 0.1 ppm NH3, indicating that the NH3 removal cost contributed to only approximately 0.98% of the referred H-2 production cost (3580 $/tH(2)) 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.

Automated summary

In this study, a novel configuration of a three-bed temperature swing adsorption (TSA) process using zeolite 4A and machine learning-based optimization was developed for effective NH3 removal. The proposed configuration performed the best in a techno-economic comparison and consumed a small amount of cost for NH3 removal.