The Complexities of Biofuels Logistics

Can an intricate modeling system solve the problems of getting biofuels to the pump?
By Luke Geiver | September 08, 2011

The difference between running a biodiesel blend in the 850-unit fleet of Athens-Clarke County, Ga., and not, all came down to roughly the distance of a marathon, or 26.6 miles. In 2006, Steve Hinsch, fleet management superintendent for the Athens-Clarke County fleet, began running a B5 blend as part of a pilot program, eventually increasing the blend to B10. “This program went well, and with very few fueling issues for approximately two years,” Hinsch says. In 2008, however, after two successful years running biodiesel blends in his fleet through that pilot program, Hinsch was forced to stop using biodiesel. What happened is a reminder that, for all of biodiesel's production efficiencies, novel catalysts, feedstock pretreatment and tax credits, there is always one overriding factor that can dwarf everything else. “The cost then became prohibitive,” Hinsch says, “when our local vendor dropped biodiesel due to lack of adequate sales.”


The cost Hinsch refers to wasn’t the price of biodiesel, though, it was the cost to transport the biodiesel he had purchased for his fleet from the production site to his fueling station at fleet headquarters. “Our closest vendor was then about 80 miles,” Hinsch explains, and because of the additional costs related to the added transport distance, the Athens-Clarke County fleet stopped using biodiesel.


Luckily, Hinsch’s fleet, which consists of a multitude of vehicles and equipment including trailers, concrete saws, large trucks and heavy equipment, is running biodiesel again, however, thanks to Down to Earth Energy, a biodiesel producer that just recently came online. More importantly, this producer is only about 26.6 miles away. Hinsch began working with the University of Georgia on a grant to research and use certain diesel particulate filters, and, as Hinsch says, one afternoon at the university, the operators of the Down to Earth Energy facility “happened to be in a meeting that preceded our meetings.” Introductions were made, Hinsch says, and he later met with them to gather preliminary information on their operation. “I was immediately taken by their story, vision and passion,” he explains, but even more so, the dollars and distance were right. Now the two are working together.


While the story of Athens-Clarke’s biodiesel usage may have ended well, thanks to Down to Earth Energy, there are many similar fleets throughout the U.S. that continue to face the same problem Hinsch did: trying to make the price of biofuels transport and logistical issues make sense when it comes time to fill out the yearly budget. Nearly 170 miles away from Hinsch and the Down to Earth Energy facility in Georgia, Professor Jae-Dong Hong of South Carolina State University and his team are getting closer to having the information they need to create a tool that will help fleet managers calculate the costs of biofuels logistics—and include more biodiesel in their budgets—using the most “regret-free” biofuels logistics plans.



The Cost of Getting Biofuel to the Pump


Hong, an industrial and electrical engineering professor at SCSU, will be joined by other economics professors at the university to conduct the three-year study, which will be funded by a $449,921 grant awarded by the USDA’s National Institute for Food and Agriculture. The goal of the research is linked to the larger intent of the James E. Clyburn University Transportation Center at SCSU, which is to establish practical and economical ways to reduce logistics costs of transporting fuels.
Hong estimates that nearly 25 percent of the cost to make biofuel is related to transportation, which he says must be minimized. Yaunchang Xie, a member of Hong’s team, says that biofuels “get a lot of subsidies” that ultimately help lower the cost of biofuels used today.


In recent years, Hong explains, a number of bioenergy studies have been conducted, most of which focus on either the optimization of bioenergy production facility location or feedstock collection, storage or transport operations. Hong’s work sets out to combine the different areas, starting with the investigation of how a biofuels logistics network could be integrated with optimal feedstock areas, collection centers, blending stations, transportation availability and possible inventory quantity strategies.


“In this research,” he says, “we will develop a novel formulation for an integrated, robust design of biomass and biofuels logistics network. The model,” he adds, “will be able to capture location, transportation and inventory decisions in a multi-period planning setting. This model will also be able to consider the uncertainties in biomass supply.”


Although the task to complete the network design might seem daunting, the end result, according to Hong, could be significant. “Our model will be instrumental not only in producing solutions to biomass and biofuels logistics problems, but also in developing and testing various bioenergy policies such as biomass pricing, supply-demand matching and various incentive programs to encourage farmers’ participation.”


When Biodiesel Magazine first spoke with Hong, he was on the campus of Texas A&M working on this research with another member of the team, Halit Uster, an optimization model researcher and industrial and systems engineering associate professor.


Uster will help Hong develop a biofuels logistics model that will feature five major components. The first major feature will focus on the uncertainties involved in biofuels logistics modeling, including the uncertainties in farmer participation and biomass yield, establishing a number to quantify the upper and lower limits of participation levels and biomass yields.


The second area of the network model will essentially model the amount of regret a certain logistical model or approach would bring the user, or, in other words, how successful or unsuccessful a certain approach would be. The regret is defined as the difference between the optimum objective value of a scenario and the objective value for the robust solution, according to Hong. To find the level of regret, Hong says, his team will not only take into account the biofuels being transported, the inventory and storage cost of that particular fuel, but more importantly, the model will look at how those types of factors change from situation to situation.


The third focal point of Hong’s research involves creating a model that can “handle” multiple periods of time. The model, he explains, could potentially generate a robust solution for each period of time. “The solution includes robust assignment of farms and collection centers, vehicle routing and inventory decisions for each period.” The model can be re-optimized when accurate and reliable parameter values such as biomass yield for a future year (or period) is made available. “Our model will be general enough to capture this re-optimization as a special case,” he explains, and in doing so, “it will ensure that many important constraints, such as storage limitations of the collection centers…are not violated.”


Fourth, the modeling will take into consideration the optimization of operational decisions such as inventory control and vehicle routing for feedstock collection and the transportation of biofuel.
And, most importantly, the fifth area of focus will look to design, or minimize, total logistics costs (TLC). Those costs, he says, consist of total fixed costs of locating facilities used for collection of feedstock, production sites or even blending facilities, along with transportation costs of both feedstock and biofuels.


Although the research sounds complex given the modeling strategy and the various factors the team hopes to include in their work, the end result isn’t only about how to potentially improve current logistical problems. Both Xie and Hong voiced their belief that the model could help future investment in bioenergy based on the predictive capabilities and logistical problem-solving ability of the research. “If producing or selling biofuels is not profitable,” Xie says, “the private sector will not come in.” Xie shouldn’t have anything to worry about though, according to Hong. “The mathematical model can be used as a decision-making tool for investors in the biofuels industry,” he says, “as it will estimate the real cost of the business.”


The biofuels logistics study will take a few years, and if the complex modeling system does what they say it will, it could help people like Hinsch understand how the logistics costs, inventory strategies and policy decisions all factor in to the use of biodiesel in his fleet. Until then, Hinsch has his own thoughts on how the difficulties of using biodiesel and the link to getting the fuel from point A to point B, so another user can burn that fuel to get from point C to point D, can be overcome. “As of now,” he explains, “we are paying the same cost of biodiesel as we were for our ultra-low sulfur diesel. This was part of our agreement with our local biodiesel producer. In working with biodiesel and the intricacies of budgets and my customer base, I would first say that the product has to be well developed, and the price has to be right.” Then, he says, “You have to coddle the customers” because most people in his position are skeptics.


When Hong and his team finish the research, the findings will most likely continue to be on display at the NIFA Waterfront Center where the proposal for the work is now. Even though everyone in the entire biofuels landscape may welcome a predictive modeling system that includes an exhaustive list of factors, all of which will be calculated to show which approach will present the least risk—regret—there might be no need for any of it. Unless someone disagrees with Hinsch’s ways to help expand biodiesel use.

Author: Luke Geiver
Associate Editor, Biodiesel Magazine
(701) 738-4944
lgeiver@bbiinternational.com

 
 
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