Speaker: Dr. Sandra Eksioglu, Department of Industrial Engineering, University of Arkansas
Date: Wednesday, November 18, 2020, 4:00 PM – 5:00 PM
Title: Analytical Models for Optimal Process Design in an Integrated Biorefinery
Abstract: Biofuel is a sustainable energy resource which has the potential to meet our needs for liquid fuels. However, the following challenges are impeding the growth of biofuel industry, the variations in feedstock quality (e.g., varied particle size distribution, varied moisture content), the bulky nature of unprocessed feedstock, and the seasonal variations of biomass supply. These inconsistencies have resulted in low/unreliable on-stream times and long start-up times with a consequent loss of revenue. The research proposed here focuses on the development of analytical models to achieve a reliable, cost effective, robust and continuous flow of biomass to the reactor. Specifically, we develop a discrete element model which is used to derive functional relationships between biomass density and particle size distribution, moisture level. We also develop a non-linear optimization model to minimize processing time, minimize system wide costs and maximize system’s reliability subject to feedstock quality and process performance. We develop a case study in order to evaluate the performance of the proposed modeling approach. The data for this case study is collected at Process Demonstration Unit of Idaho National Laboratory.
Bio: Dr. Sandra D. Eksioglu is the Hefley Professor in Logistics and Entrepreneurship in the Department of Industrial Engineering, University of Arkansas. She received a BS in Business Administration from the University of Tirana and a PhD in Industrial Engineering from the University of Florida. Prior to joining the University of Arkansas, she was a faculty member at Mississippi State and Clemson Universities. Dr. Eksioglu’s expertise is in the areas of operations research, network optimization, and algorithmic development. She uses these tools to develop models and solution algorithms for solving large-scale problems that arise in the areas of transportation, logistics, supply chain and health care.
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