An AI-Based Modelling of a Sorption Enhanced Chemical-Looping Methane Reforming Unit

Hydrogen as a green fuel has attracted enormous attention recently. Although hydrogen combustion produces no harmful by-products, hydrogen production can be almost disastrous. Hydrogen production mainly originates from fossil fuels, and more than ۸۰% of hydrogen production is produced using fossil fuel reformation with CO۲ formation as a by-product. Light hydrocarbon gases, predominantly methane, are extensively used for hydrogen production. While methane reforming is an economical and efficient process, decarburization of flue gas can be a challenge. Processes involving chemical looping can be used to mitigate these challenges, and they are favorable for simultaneous CO۲ capture during hydrogen generation. Intelligent models can help have accurate monitoring of such plants. The aim of this paper is to provide an Artificial Intelligence (AI) based approach to model a Sorption-Enhanced Chemical-Looping Reforming (SECLR) unit. To this end first, a SECLR unit was simulated using ASPEN Plus version ۱۱. Then the simulation results were validated by experimental data, and the SECLR unit went through ۳۱۰۰۰ different scenarios. The derived data from ASPEN Plus was modeled and simulated with machine learning methods to estimate the CH۴ conversion, H۲ Purity, and CO۲ removal in the SECLR process. Artificial neural networks, ensemble learning, and support vector machine methods were developed to predict the CH۴ conversion, H۲ Purity, and CO۲ removal in a SECLR unit. All three models could provide satisfactory results for predicting CH۴ conversion, CO۲ removal, and H۲ Purity. According to statistical evaluations, Artificial Neural Network (ANN) outperformed Support Vector Machine (SVM) and ensemble learning in producing results with lower error values and higher accuracy with an average ۵.۲۳e-۵ of error and R۲ of ۰.۹۸۶۴.

کلیدواژه ها:

Machine Learning ، methane reforming ، Artificial neural network ، Chemical-looping reforming ، Ensemble Learning