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Call to Action for a Truly Sustainable Renewable Future
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-Include high octane/high ethanol Regular Grade fuel in EPA Tier 3 regulations.
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Home » Energy, Federal Agency, Feedstock, Idaho, Logistics, R & D Focus, University/College Programs, Virginia

DOE: Artificial Intelligence Helps Improve Biomass Preprocessing

Submitted by on August 30, 2017 – 12:25 pmNo Comment

(U.S. Department of Energy/Ethanol Producer Magazine)  Researchers at Idaho National Laboratory’s bioenergy program have successfully tested an adaptive, intelligence-based control system that increased the reliability of feedstock preprocessing equipment by more than 50 percent. Before feedstocks can be converted into fuels and products, they undergo various types of preprocessing to meet quality specifications required for conversion processes. Preprocessing may include techniques such as milling, densification, or pelletizing to ensure the feedstock is a uniform format and ready for conversion. There is a huge spectrum of variability in biomass that arises from differences in genetics, relative crop maturity, agronomic practices and harvest methods, soil type, geographic location, and climatic patterns and events. This variability—some of which is avoidable and some of which is not—presents significant cost and performance risks for bioenergy systems.

INL’s control system, developed at the Biomass Feedstock National User Facility’s Process Development Unit, will use biomass sensors, process models, and artificial intelligence to automatically adjust PDU equipment to compensate for moisture and other types of biomass variability—enabling more efficient and reliable processing of biomass feedstocks. The control system’s precision will allow for real-time adjustments of processing equipment in response to inputs from sensors that detect changing biomass characteristics. Researchers anticipate the control system will improve reliability and feedstock consistency compared with human operators alone.

INL staff collaborated with graduate students from Virginia Commonwealth University for a preliminary test of their control system. The students developed a model based on moisture content and feed rates using data from previous PDU operations. The model provides estimates of performance for throughput—how much material is processed per hour, and reliability—how often the system is shut down.

During the preliminary test, researchers included a “human-in-the-loop” to adjust material feed rates. In the artificial intelligence world, “human-in-the-loop” refers to a process that is largely machine run, but still has partial human input.    READ MORE

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