Eco-friendly de-lignified and raw Celosia argentea waste solid biofuel: Comparative studies and machine learning modelling

Abstract

This study produced and compared the combustion properties of alkaline treated Celosia argentea (TCAB) and untreated Celosia argentea (UCAB) waste bio-briquettes using starch as binder. Heat energy of TCAB and UCAB were determined. Potential toxic elements, surface modification and bond orientation of briquettes were monitored using EDXRF, SEM and FTIR, respectively. Levenberg-Marquardt Back-Propagation based Artificial Neural Network (LMBP-ANN) and Fuzzy C-means Clustered Adaptive Neuro-Fuzzy Inference System (FCM-ANFIS) models were adopted for the bio-briquettes. Calorific values of 11.38 ± 0.20 MJ/Kg (UCAB) and 12.79 ± 0.25 MJ/Kg (TCAB) at p < 0.05 were recorded. EDXRF showed percentage reduction in Pb content of TCAB (1.425 %) as against 3.253 % in UCAB. FTIR recorded a CO stretch difference of 4 cm−1, while SEM shows morphological restructuring. FCM-ANFIS outperformed LMBP-ANN with root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute deviation (MAD) and R2-values of 0.0748, 2.4756, 0.0687 and 0.9323, respectively at the testing phase for heat content, suggesting strong agreement between the experimental data and predicted values. The study shows that alkaline pretreatment enhanced the combustionproperties and eco-friendliness of the solid-biofuel.