Stochastic techno-economic evaluation model for biomass supply chain: A biomass gasification case study with supply chain uncertainties

Shirleen Lee Yuen Lo, Bing Shen How, Sin Yong Teng, Hon Loong Lam, Chun Hsion Lim, Muhammad Akbar Rhamdhani and Jaka Sunarso

Renewable and Sustainable Energy Reviews, 2021, vol. 152, issue C

Abstract: Sustainable development has been a goal for many industries, which can be achieved via the substitution of non-renewable feedstocks with greener alternatives such as substituting coal with biomass for gasification process. The sustainably produced biomass-derived syngas has a lower carbon emission when utilized as a fuel. Although most studies have reported the possible impact of biomass quality on the feasibility of biomass conversion process, there is still little published works on the incorporation of biomass quality directly into developed mathematical evaluation model. Therefore, this paper aims to perform techno-economic feasibility evaluation on biomass gasification process, with the consideration of various supply chain uncertainties via Monte Carlo simulation (i.e., biomass supply, biomass quality, biomass pricing, transportation fuel price, and syngas sale price). This is achieved via integration of a generic correlation equation relating biomass quality to specific syngas yield into the Monte Carlo model. Upon consideration of the uncertainties, this study revealed that mesocarp fibre has a higher net present value (NPV) in the range of Malaysian Ringgit (MYR) 70 million to greater than MYR 120 million amongst the three types of palm-based biomass (empty fruit bunches (EFB), palm kernel shells (PKS), and mesocarp fibre). Additionally, environmental assessment performed deduced that mesocarp fibre is slightly more preferable in the impact category for HTPI and TTP with a probability of 98 % to achieve the equivalent emissions within the lower range of 0–0.2 kg 1,4-C6H4Cl2,eq whereas EFB and PKS has a probability of 18 % and 28 %, respectively.

Keywords: Biomass gasification; Monte Carlo; Optimization; Aspen plus; Biomass quality; Uncertainty (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (5)

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DOI: 10.1016/j.rser.2021.111644

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