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Pyrolysis of soybean cake in an auger reactor was investigated at temperatures 450, 500 and 550C 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 500C. The gaseous product was seen to increase with increasing pyrolysis temperature while char yield decreased....
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... amount of organic phase is found to decrease from 10.02% to 8.60% with the increasing pyrolysis temperature. The chemical components of organic phase characterized by GC-MS is shown in Table 2 which exhibits the major component of 38.04% carboxylic acid. The heating value of the organic phase is 7,890 kcal/kg and none for the aqueous phase. ...Citations
... 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]. ...
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.
