Open AccessElucidating the non-linear effect of process parameters on hydrogen production by catalytic methane reforming: an artificial intelligence approach
Author(s) -
May Ali Alsaffar,
Alyaa K. Mageed,
Mohamed Abdel Rahman Abdel Ghany,
Bamidele Victor Ayodele,
Siti Indati Mustapa
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/991/1/012078
Subject(s) - methane , space velocity , hydrogen production , yield (engineering) , hydrogen , catalysis , carbon dioxide reforming , artificial neural network , biological system , production (economics) , thermodynamics , syngas , materials science , mathematics , chemistry , computer science , machine learning , physics , selectivity , organic chemistry , macroeconomics , biology , economics
This study focuses on the non-linear effect of gas hourly space velocity (GHSV), oxygen (O 2 ) concentration in the feed, the reaction temperature, and the CH 4 /CO 2 ratio on hydrogen production by catalytic methane dry reforming using artificial neural networks (ANN). Ten different ANN models were configured by varying the hidden neurons from 1 to 10. The various ANN model architecture was tested using 30 datasets. The ANN model with the topology of 4-9-2 resulted in the best performance with the sum of square error (SSE) of 0.076 and coefficient of determination (R 2 ) greater than 0.9. The predicted hydrogen yield and the CH 4 conversions by the optimized ANN model were in close agreement with the observed values obtained from the experimental runs. The level of importance analysis revealed that all the parameters significantly influenced the hydrogen yield and the CH 4 conversion. However, the reaction temperature with the highest level of importance was adjudged the parameter with the highest level of influence on the methane dry reforming. The study demonstrated that ANN is a robust tool that can be employed to investigate predictive modeling and determine the level of importance of parameters on methane dry reforming.