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Cloud-Based Decision Support System Integrating Biomass Quality, Uncertainty and Risk to Optimize the Production of Second-Generation Biofuels

2016 USDA-NIFA Integrated Award


Principal Investigators and affiliations:
PI: Dr. Krystel Castillo (University of Texas at San Antonio)
Co-PI:  Dr. Sandra Eksioglu (Clemson University)

Funded: $150,000

Start Date: 9/01/2016

End Date: 8/31/2018


The long-term goal of this project is to enable the conversion of large quantities of biomass (seen as a commodity) into a marketable product and identifying a dominant biomass conversion technology for the bioenergy industry.


Specific goals:

  1. Development of a unified computational and theoretical scheme that links novel biomass quality control principles, uncertainty, and risk measures in bioenergy logistics systems optimization
  2. Design of robust biofuel SCs that holistically embrace an understanding of the biomass quality variability (which is variable and is defined as moisture, ash, and sugar contents, among others), supply uncertainty and biomass conversion technology risk
  3. the development of a cloud-based biofuels decision support system that incorporates the set of mathematical programming models and tractable here and now algorithms.



  1. Develop novel mathematical models based on stochastic programming to design large-scale bioenergy systems which incorporate the concepts of biomass quality and supply uncertainties, and their impact on biomass conversion technology selection.
  2. Integrate computational methods and mathematical programming formulations proposed into a Cloud-based Decision Support System (DSS).
  3. Conduct student internships at INL and ORNL and develop seminars/workshops as well as instructional material.


Expected Outcomes

This project contributes to the displacement of foreign oil through the enhancement of a biobased economy by: (1) reducing feedstock quality uncertainty and quantifying feedstock quality impact on technology selection and SC design, which overcome feedstock quality barriers faced by bioenergy industry; thus, increasing yields and operational performance; and (2) encouraging investment, which translates into industry growth by providing an expected profit that includes uncertainty and risk in the feedstock.

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