Organic catalysts for hydrogen production from noodle wastewater: Machine learning and deep learning-based analysis

Abstract

Hydrogen production from the electrolysis of wastewater is an environmentally friendly and highly efficient process. The performance of this process for instant noodle wastewater is strongly influenced by covering the PVC sheet with different arrangements of antioxidant-containing protein (ACAP) as an organic catalyst. However, analyzing this process through traditional models and experimental studies takes time, money, and effort. In the present research, several machine learning-based models, including the recurrent neural network (RNN), the least absolute shrinkage and selection operator (LASSO), the extreme gradient boosting (XGBoost), the linear regression (LR), and the light gradient-boosting machine (LightGBM) were developed to accurately predict hydrogen production performance from the electrolysis of noodle wastewater. Several materials have been studied in this research, such as Car, Car-Tur, Tur-Car, Tur, Car-Ver-Tur, and Car-Tof-Ver in the 12-V and 24-V states. For each material created, the LASSO regression and the linear regression formula include 12 formulas (six formulas for each state) for hydrogen production. The R-Squared values range between 0.989 and 0.997 for the six formulas by the polynomial form and by the XGBoost and the lightGBM making the six models for the hydrogen production, and the R-Squared values for all models are 0.999 by linear form for the hydrogen production in the 24-V state. For the hydrogen productions in the 12-V state, the values of the R-Squared range between 0.995 and 0.998 by the polynomial form. Using the lightGBM and the XGBoost, six models are made in linear form, and all of those models' R-Squared values are 0.999. Using the RNN algorithm can predict the future of each material's hydrogen production.