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Published on: June 15, 2014
Amaranthus hybridus waste solid biofuel: comparative and machine learning studies
Abayomi Bamisaye1, Ayodeji Rapheal Ige2, Kayode Adesina Adegoke3
1Department of Chemistry, Faculty of Natural and Applied Sciences, Lead City University Ibadan Oyo State Nigeria abayomibamisaye@gmail.com.
Developing sustainable bio-briquettes from Amaranthus hybridus waste, with cassava starch binder, offers a renewable energy alternative. Delignification improved combustion and physicochemical properties, making these agro-waste briquettes environmentally friendly and efficient solid biofuels.
Area of Science:
- Agricultural Science
- Renewable Energy
- Materials Science
Background:
- Fossil fuel depletion and environmental concerns necessitate sustainable energy alternatives.
- Agro-waste management poses challenges, requiring innovative disposal and utilization strategies.
- Bio-briquettes offer a promising renewable energy source derived from biomass.
Purpose of the Study:
- To develop and evaluate bio-briquettes from Amaranthus hybridus waste using cassava starch as a binder.
- To assess the impact of delignification on the combustion and physicochemical properties of the bio-briquettes.
- To optimize the bio-briquette production process using machine learning models.
Main Methods:
- Preparation of alkali-treated (TAHB) and untreated (UAHB) Amaranthus hybridus bio-briquettes with cassava starch binder.
- Characterization using Fourier Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscopy (SEM), and Energy-Dispersive X-ray Fluorescence (EDXRF).
- Combustion and physicochemical parameter evaluation, alongside optimization using Adaptive Neuro-Fuzzy Inference System (ANFIS) and Fuzzy C-Means (FCM) clustering.
Main Results:
- Delignification significantly reduced lignin content in TAHB (11.47%) compared to UAHB (12.31%).
- Calorific value increased notably in TAHB (12.53 MJ kg⁻¹) versus UAHB (10.43 MJ kg⁻¹).
- FTIR and SEM confirmed morphological and structural changes, while EDXRF indicated low Potential Toxic Elements (PTEs). Machine learning models demonstrated high prediction accuracy (RMSE, MAE, MAPE).
Conclusions:
- Alkali treatment and delignification of Amaranthus hybridus waste enhance bio-briquette quality for cleaner combustion.
- The developed bio-briquettes are environmentally friendly and possess improved energy content, suitable as sustainable solid biofuels.
- Machine learning models effectively predict bio-briquette efficiency, aiding process optimization for renewable energy production.
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