Cloud-based Decision Support System to Optimize the Production of Second-generation Biofuels
Uncertainty and risk are two of the main challenges faced by biorefineries. A proper assessment of the uncertainties and risks related to emerging technologies, supply availability and quality, and opportunity costs when making long-term decisions is vital for the profitability and sustainability of the future bioeconomy.
Consequently, Dr. Krystel Castillo, Director of Texas Sustainable Research Institute and Associate Professor of Mechanical Engineering Department, The University of Texas at San Antonio, collaborated with Drs. Sandra Eksioglu (Clemson University), Mohammad Roni (Idaho National Laboratory), and Erin Webb (Oak Ridge National Laboratory) to develop a cloud-based decision support system (DSS) that integrates novel biomass quality control principles, uncertainty, and risk measures in bioenergy logistics systems optimization. This DSS aimed to facilitate the adoption of practices that use swtichgrass and woody biomass as sources of bioenergy.
“We developed two-stage stochastic programming models that capture the variability in biomass quality properties. Firstly, a hub-and-spoke model introduced variability in the biomass moisture and ash contents, to design a biofuel supply chain. Based on our case study in Texas, our results showed an impact on the investment and operation cost of approximately 8.31% due to the quality-related characteristics,” Castillo said.
“Secondly, we developed a comprehensive model using other considerations about biomass characteristics that affect the overall performance of the supply chain. The model accounts for seasonality in the biomass supply and links the dry matter loss with the time period between harvesting and preprocessing to get more accurate measure of the biomass degradation. Inventories at all levels were established to provide biomass supply during the year,” Brewer added.
Through extensive computational experimentation, researchers show that the incorporation of moisture, ash, and dry matter loss results in a 44.44% increase in the number of depots required in the network to densify the biomass and minimize the quality-related costs.
“The DSS that we developed allows us to compare different types of biomass conversion technology, so that a standardized and robust supply chain design (value stream) can be found. Additionally, new conversion technologies can be evaluated before being implemented in the supply chain,” Castillo said.
The cloud-based DSS is a web-based portal that allows analysis and visualization of the total value stream of a biomass-based project from a dynamic and stochastic point of view. This online DSS is available to the public through the following website: https://bioenergy.texasenergy.utsa.edu/
The models developed have the potential to positively impact society by supporting commercial scale biofuel production. The DSS will help researchers and decision makers undertake sound actions based on a holistic approach. These critical actions will impact stakeholders in the short, medium, and long-term periods. Determining economic sizing and feasibility of large-scale bioenergy projects will be possible by considering the biomass quality requirements, supply uncertainty, and risk measures.
Funding of this project was provided by the U.S. Department of Agriculture-National Institute of Food and Agriculture (USDA-NIFA) through the South Central Sun Grant Program.