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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. [Display omitted] • A hybrid simulation framework reduced operational costs (77.42% of the total revenue) of biofuel refineries by eliminating overproduction of ethanol due to feedstock loss. • A crop growth model was used to simulate climate change effects on corn yield. • Agent based simulation was used to identify the optimal biofuel production plan. • Feedstock loss could cause the daily overproduction cost between 14% and 24% of the total operational cost at a refinery. [ABSTRACT FROM AUTHOR]

The reduction in the operational cost of a biofuel refinery is vitally important to make biofuel competitive with fossil fuels. The aim of this paper is to find a cost-efficient and sustainable refinery capacity for grain-based ethanol (i.e., corn-based ethanol) production, which will play an important role in promoting the widespread adoption and sustainable use of ethanol, by improving the productivity of the overall refining process. Continuous-event simulation was utilized in this study to model complex operations of a refinery such as the loading, unloading and treatment of feedstock over nine major phases (e.g., feedstock storage and handling, pretreatment and conditioning, fermentation and hydrolysis, and enzyme production) to produce ethanol. To improve the model prediction, the real data of corn yield produced in Tazewell County, Illinois, U.S. were used. The proposed simulation model is implemented in AnyLogic® University 8.6.0 simulation software, Chicago, IL, USA, and the (near) optimal number of reactors for the hydrolysis and fermentation is found via optimization software known as OptQuest®, Boulder, CO, USA, As a result, the proposed approach found that six reactors showed the optimal daily profit from USD 67,500 to 82,217. This information will help engineers and policy makers to modify the capacity of a biofuel refinery for enhancement of the system efficiency and ethanol production. [ABSTRACT FROM AUTHOR]

Switchgrass (Panicum virgatum L.) is a C4, warm season, perennial native grass that has been strongly recommended as an ideal biofuel feedstock. Accurate forecasting of switchgrass yield across a geographically diverse region and under future climate conditions is essential for determining realistic future ethanol production from switchgrass. This study compiled a switchgrass database through reviewing the existing literature from field trials across the U.S. Using observed switchgrass data, a process-based model (ALMANAC) was developed. The ALMANAC simulation results showed that crop management had more effect on yield than location. The ALMANAC model consists of functional relationships that provide a better understanding of interactions among plant physiological processes and environmental factors (water, soil, climate, and nutrients) giving realistic predictions in different climate conditions. This model was used to quantify the impacts of climate change on switchgrass yields. Simulated lowland switchgrass would have more yield increases between Illinois and Ohio in future (2021–2050) under both Representative Concentration Pathway (RCP) 4.5 and 8.5 pathways with low N fertilizer inputs than high N fertilizer inputs. There was no significant effect of climate variability on upland simulated yields, which means that N fertilization is a key factor in controlling upland switchgrass yields under future climate conditions. [ABSTRACT FROM AUTHOR]