Prediction of hydrogen generation from perhydro-dibenzyltoluene empowered with machine learning

초록

The perhydrodibenzyltoluene (H18-DBT) exhibits promising potential as a viable option for hydrogen pro-duction purposes. There are several important features for hydrogen generation predictions including dosage of H18-DBT, temperature, concentration of catalyst, and stirring speed. This study presents the Hydrogen Pro-duction Prediction System Empowered with Machine Learning (HPPSML), which employs the Scaled Conjugate Approach (SCG) to predict the quantity of hydrogen generated from H18-DBT. The dataset is classified into three categories based on the percentage of produced hydrogen: low class, medium class, and high class. The results elucidate that the accuracy of the proposed HPPSML is higher (96.65 %) for the high class whereas it is 93.20 % and 89.80 % for the low and medium class respectively. The overall performance of the proposed HPPSML using the SCG approach was found to have an accuracy of 89.80 % and a misclassification rate of 10.2 %.

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