Characterization and Artificial Neural Networks Modelling of methylene blue adsorption of biochar derived from agricultural residues: Effect of biomass type, pyrolysis temperature, particle size

Abstract

Biochar has been explored as a sorbent for contaminants, soil amendment and climate change mitigation tool through carbon sequestration. Through the optimization of the pyrolysis process, biochar can be designed with qualities to suit the intended uses. Biochar samples were prepared from four particle sizes (100–2000 µm) of three different feedstocks (oak acorn shells, jift and deseeded carob pods) at different pyrolysis temperatures (300–600 °C). The effect of these combinations on the properties of the produced biochar was studied. Biochar yield decreased with increasing pyrolysis temperature for all particle sizes of the three feedstocks. Ash content, fixed carbon, thermal stability, pH, electrical conductivity (EC), specific surface area (SSA) of biochar increased with increasing pyrolysis temperature. Volatile matter and pH value at the point of zero charge (pHpzc) of biochar decreased with increasing pyrolysis temperature. Fourier-transform infrared spectroscopy (FTIR) analysis indicated that the surface of the biochar was rich with hydroxyl, phenolic, carbonyl and aliphatic groups. Methylene blue (MB) adsorption capacity was used as an indicator of the quality of the biochar. Artificial neural networks (ANN) model was developed to predict the quality of the biochar based on operational conditions of biochar production (parent biomass type, particle size, pyrolysis temperature). The model successfully predicted the MB adsorption capacity of the biochar. The model is a very useful tool to predict the performance of biochar for water treatment purposes or assessing the general quality of a design biochar for specific application.

Keywords

• Biochar
• Slow pyrolysis
• Characterization
• Agricultural wastes
• Pyrolysis temperature
• Artificial Neural Networks