Hybrid simulation framework for the production management of an ethanol biorefinery

Sojung Kim and Sumin Kim

Renewable and Sustainable Energy Reviews, 2022, vol. 155, issue C

Abstract: To make ethanol cost-competitive with gasoline, it is critical to devise a cost-effective production plan for ethanol refineries. However, unlike with petroleum refineries, the managers of the corn-based ethanol refineries are faced with multiple challenges. Particularly, the refineries should have a consistent production rate despite the uncertain yield of feedstock production that results from a dynamic environment, including climate change. In this study, a hybrid simulation framework was developed for operating biofuel refineries. The framework consisted of two major simulation platforms: (1) Agricultural Land Management Alternative with Numerical Assessment Criteria (ALMANAC) for yield estimation of feedstock considering dynamic environmental factors and (2) simulation-based optimization with agent-based simulation (ABS) to identify the optimal production plan in terms of operational cost based on yield and relevant parameters given by ALMANAC. In ABS, eight major operations (e.g., milling, cooking, liquefaction, and fermentation) of a refinery were modeled via AnyLogic® ABS software to accurately compute operational costs. We used a geographic information system (GIS) map to compute transportation costs between feedstock farms, refineries, and consumers’ facilities. The proposed framework was demonstrated using real data of feedstock and ethanol production in Tazewell County, Illinois, U.S. The experiments identified that the ethanol production planning without considering feedstock loss could cause overproduction at a refinery. The daily overproduction costs with 28% dry matter and 45% dry matter are 23.56% and 14.07% of their total operational costs, respectively. The framework will provide a reliable and practical ethanol production plan considering feedstock supply under dynamic environmental factors.

Keywords: Agent-based modeling; Crop growth model; Corn grain; Renewable energy; Ethanol (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1016/j.rser.2021.111911

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