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Diagram of auger pyrolysis reactor. 

Diagram of auger pyrolysis reactor. 

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Conference Paper
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Pyrolysis of soybean cake in an auger reactor was investigated at temperatures 450, 500 and 550C with retaintion time of 4 minutes in reactor under nitrogen flow at 0.2 L/min. The maximum liquid yield 34.24% was obtained at temperature 500C. The gaseous product was seen to increase with increasing pyrolysis temperature while char yield decreased....

Context in source publication

Context 1
... of ground soybean cake was carried out with 1,000 g of sample in an auger reactor as shown in Figure 1. The reactor was 4.5 cm in diameter and 80 cm in length heated by electric furnace with heating zone of 50 cm long. ...


... As a result, such a system could handle various feedstock types and maintain high-quality outputs, this versatility makes the process more viable and robust. Current reactors and researchers have only been able to optimize process parameters for single feedstock, as evidenced in the most published studies [43,92,93]. Publications in circulation do not differentiate available biomass species in the U.S., hence profitability of the process is reliant on being able to process various feedstock materials [3]. ...
Conference Paper
Recent interest in alternative energy sources, particularly biofuels from biomass, is becoming increasingly evident due to energy security and environmental sustainability concerns, such as depletion of conventional energy reserves and carbon footprint effects, respectively. Existing fuels (e.g., biodiesel and ethanol) are neither sustainable nor cost-competitive. There is a need to integrate the recent advanced manufacturing approaches and machine intelligence (MI) techniques (e.g., machine learning and artificial intelligence), targeted on the midstream segment (i.e., pre-/post-conversion processes) of biomass-to-biofuel supply chains (B2BSC). Thus, a comparative review of the existing MI approaches developed in prior studies is performed herein. This review article, additionally, proposes an MI-based framework to enhance productivity and profitability of existing biofuel production processes through intelligent monitoring and control, optimization, and data-driven decision support tools. It is further concluded that a modernized conversion process utilizing MI techniques is essential to seamlessly capture process-level intricacies and enhance techno-economic resilience and socio-ecological integrity of B2BSC.