Pre-treatment of Malaysian agricultural wastes toward biofuel production

Various renewable energy technologies are under considerable interest due to the projected depletion of our primary sources of energy and global warming associated with their utilizations. One of the alternatives under focus is renewable fuels produced from agricultural wastes. Malaysia, being one of the largest producers of palm oil, generates abundant agricultural wastes such as fibers, shells, fronds, and trunks with the potential to be converted to biofuels. However, prior to conversion of these materials to useful products, pre-treatment of biomass is essential as it influences the energy utilization in the conversion process and feedstock quality. This chapter focuses on pre-treatment technology of palm-based agriculture waste prior to conversion to solid, liquid, and gas fuel. Pre-treatment methods can be classified into physical, thermal, biological, and chemicals or any combination of these methods. Selecting the most suitable pre-treatment method could be very challenging due to complexities of biomass properties. Physical treatment involves grinding and sieving of biomass into various particle sizes whereas thermal treatment consists of pyrolysis and torrefaction processes. Additionally biological and chemical treatment using enzymes and chemicals to derive lignin from biomass are also discussed

A unified correlation for estimating HHV of solid, liquid and gaseous fuels

A unified correlation for computation of higher heating value (HHV) from elemental analysis of fuels is proposed in this paper. This correlation has been derived using 225 data points and validated for additional 50 data points. The entire spectrum of fuels ranging from gaseous, liquid, coals, biomass material, char to residue-derived fuels has been considered in derivation of present correlation. The validity of this correlation has been established for fuels having wide range of elemental composition, i.e. C - 0.00-92.25%, H - 0.43-2,5.15%, 0 - 0.00-50.00%, N - 0.00-5.60%, S - 0.00-94.08% and Ash - 0.00-71.4%. The correlation offers an average absolute error of 1.45% and bias error as 0.00% and thereby establishes its versatility. Complete details of few salient data points, the methodology used for derivation of the correlation and the base assumptions made for derivation are the important constituents of this work. A summary of published correlations along with their basis also forms an important component of present work. (C) 2001