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Optimal daily scheduling of mobile machines to transport cellulosic biomass from satellite storage locations to a bioenergy plant

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  • An, Heungjo

Abstract

Effective machine operations in recently established bioenergy companies can contribute significantly to improving economic viability. However, few studies have addressed the complex daily operations of machines supplying biomass to bioenergy plants. This study develops a mixed integer linear programming model to minimize the total cost of transporting biomass from satellite storage locations to a bioenergy plant by determining optimal daily schedules for trucks and mobile loaders by considering multiple trips and visits as well as their synchronized loading operations at satellite storage locations. The present case study analyzes the impact of the number of satellite storage locations supplying corn stover on both cost and the required number of transport machines. Moreover, it investigates the potential effectiveness of employing an on-site loader. This study concludes that transporting biomass from a mid-range number (between 3 and 4) of satellite storage locations using an on-site loader at the most distant satellite storage location may represent the desired structure for realizing the most economical and stable utilization of transport machines. Moreover, the present study estimates the optimal transportation cost at 8.88–9.50 USD per dry Mg to transport 200–240 dry Mg of corn stover per day for the studied context.

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  • An, Heungjo, 2019. "Optimal daily scheduling of mobile machines to transport cellulosic biomass from satellite storage locations to a bioenergy plant," Applied Energy, Elsevier, vol. 236(C), pages 231-243.
    • Handle: RePEc:eee:appene:v:236:y:2019:i:c:p:231-243
    DOI: 10.1016/j.apenergy.2018.11.073
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    1. Lo, Shirleen Lee Yuen & How, Bing Shen & Leong, Wei Dong & Teng, Sin Yong & Rhamdhani, Muhammad Akbar & Sunarso, Jaka, 2021. "Techno-economic analysis for biomass supply chain: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).

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