S
Shahab Sokhansanj
Researcher at University of British Columbia
Publications - 369
Citations - 13055
Shahab Sokhansanj is an academic researcher from University of British Columbia. The author has contributed to research in topics: Pellets & Moisture. The author has an hindex of 54, co-authored 355 publications receiving 11677 citations. Previous affiliations of Shahab Sokhansanj include University of Saskatchewan & Oak Ridge National Laboratory.
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Performance study of a re-circulating cabinet dryer using a household dehumidifier
TL;DR: In this paper, the performance and operating characteristics of a low temperature re-circulating cabinet dryer using a dehumidifier loop were studied using alfalfa, initially at 70% moisture content, was dried to 10% moisture in the dryer.
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Knife grid size reduction to pre-process packed beds of high- and low-moisture switchgrass.
TL;DR: Tests of a linear knife grid with switchgrass quantified the combined effect of shearing stresses, packed bed consolidation, and frictional resistance to flow through a knife grid, finding that knife grid cutting energy was much less than energy values published for rotary equipment.
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Measurements of wood pellets self-heating kinetic parameters using isothermal calorimetry
TL;DR: In this paper, a TAM air isothermal calorimeter was used to measure the rate of heat release by bulk wood pellets at temperatures ranging from 30 to 50 degrees C. The results showed that self-heating rate at the tested temperature range strongly depended on the reaction temperature and the age of pellets.
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Compression of fractionated sun-cured and dehydrated alfalfa chops into cubes--specific energy models.
TL;DR: Empirical equations were fitted to the data relating specific energy for cube making to pressure, residence time, and leaf content to determine the specific energy requirements for the compression of fractionated sun-cured and dehydrated alfalfa chops, when subjected to different pressures and holding times.
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A quantile-based scenario analysis approach to biomass supply chain optimization under uncertainty
David S. Zamar,R. Bhushan Gopaluni,Shahab Sokhansanj,Shahab Sokhansanj,Nathaniel K. Newlands +4 more
TL;DR: This paper develops a statistically robust quantile-based approach for stochastic optimization under uncertainty, which builds upon scenario analysis and was found to outperform alternative methods in terms of computational efficiency and ability to meet the Stochastic problem requirements.