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  1. Rehan M, Gardy J, Demirbas A, Rashid U, Budzianowski WM, Pant D, et al.
    Bioresour Technol, 2018 Feb;250:17-25.
    PMID: 29153646 DOI: 10.1016/j.biortech.2017.11.024
    This study presents a preliminary assessment of biodiesel production from waste sources available in the Kingdom of Saudi Arabia (KSA) for energy generation and solution for waste disposal issues. A case study was developed under three different scenarios: (S1) KSA population only in 2017, (S2) KSA population and pilgrims in 2017, and (S3) KSA population and pilgrims by 2030 using the fat fraction of the municipal solid waste. It was estimated that S1, S2, and S3 scenarios could produce around 1.08, 1.10 and 1.41 million tons of biodiesel with the energy potential of 43423, 43949 and 56493 TJ respectively. Furthermore, annual savings of US $55.89, 56.56 and 72.71 million can be generated from landfill diversion of food waste and added to the country's economy. However, there are challenges in commercialization of waste to biodiesel facilities in KSA, including waste collection and separation, impurities, reactor design and biodiesel quality.
  2. Chen WH, Hsu HJ, Kumar G, Budzianowski WM, Ong HC
    Bioresour Technol, 2017 Dec;246:12-19.
    PMID: 28803060 DOI: 10.1016/j.biortech.2017.07.184
    This study focuses on the biochar formation and torrefaction performance of sugarcane bagasse, and they are predicted using the bilinear interpolation (BLI), inverse distance weighting (IDW) interpolation, and regression analysis. It is found that the biomass torrefied at 275°C for 60min or at 300°C for 30min or longer is appropriate to produce biochar as alternative fuel to coal with low carbon footprint, but the energy yield from the torrefaction at 300°C is too low. From the biochar yield, enhancement factor of HHV, and energy yield, the results suggest that the three methods are all feasible for predicting the performance, especially for the enhancement factor. The power parameter of unity in the IDW method provides the best predictions and the error is below 5%. The second order in regression analysis gives a more reasonable approach than the first order, and is recommended for the predictions.
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