Hydrogen generation from anaerobic co-digestion and statistical evaluation using machine learning algorithms Online publication date: Wed, 17-May-2023
by Chinmay Deheri; Saroj Kumar Acharya
International Journal of Global Warming (IJGW), Vol. 30, No. 2, 2023
Abstract: Hydrogen generation from anaerobic co-digestion of food waste (FW) and cow dung (CD) was statistically predicted using machine learning (ML) models. Laboratory scale experiments were performed using CaO2 and CaCO3 as additives. Maximum hydrogen generation of 115.28 and 109.47 mL g-1 TS was obtained using CaO2 and CaCO3. Further, the Pearson correlation matrix evaluated the correlation between the operational parameters such as inoculum to substrate (I/S) ratio, pH, and reactor temperature with the output parameter (hydrogen generation). I/S ratio showed the highest correlation of 0.94 with hydrogen generation compared to the other parameters. Moreover, four regression models were created using ML algorithms such as linear regression (LR), decision tree regression (DTR), random forest regression (RFR), and support vector regression (SVR) to predict hydrogen production. Hydrogen generation was accurately predicted by the ML models with an r2 score greater than 0.9 and an RMSE value less than 1.
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