首页|Study Data from Minjiang University Update Understanding of Machine Learning (Recent Advances In the Production Processes of Hydrothermal Liquefaction Biocrude and Aid-in Investigation Techniques)
Study Data from Minjiang University Update Understanding of Machine Learning (Recent Advances In the Production Processes of Hydrothermal Liquefaction Biocrude and Aid-in Investigation Techniques)
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NETL
NSTL
Elsevier
Current study results on Machine Learning have been published. According to news reporting originating in Fuzhou, People’s Republic of China, by NewsRx journalists, research stated, “This review provides an overview of recent advances in hydrothermal liquefaction (HTL) biocrude production processes using plastics as feedstock, seawater as the processing medium, and microwave irradiation as a process intensification method. Additionally, the review examines the application of aid-in investigation tools such as kinetics, machine learning, and feasibility analysis to HTL research.” Financial supporters for this research include Natural Science Foundation of Fujian Province, Fashu Research Foundation, Seed Industry Innovation and Industrializa- tion Project of Fujian Province, Minjiang University. The news reporters obtained a quote from the research from Minjiang University, “All these aspects have been underexplored in review literature compared to process optimization, biocrude upgrading, continuous HTL, and aqueous phase reutilization. The potential of HTL as an effective method for the depolymerization of plastics is initially evaluated. The ease of plastic depolymerization follows the order of polycarbonate (300 degrees C) ＞polystyrene (350 degrees C) ＞polyethylene = polypropylene (420 degrees C) ＞polyethylene terephthalate (＞450 degrees C). Both synergism and antagonism are observed for co-HTL of plastics with biomass, ranging from-48.3% to 79.2%. Using seawater as an alternative HTL processing medium shows promising potential, while the effect of sea salts on biocrude yield/ quality is still controversial especially when carbohydrate-rich feedstocks are utilized, necessitating more comprehensive examination. Microwave irradiation has been shown to increase biocrude yield from lipid, produce comparable yields from protein and lignin, and decrease yield from carbohydrate compared to conventional heating. As for the aid-in investigation tools, limited efforts have been made to apply kinetic modeling to the HTL of plastics, which could be particularly useful when synergism or antagonism is observed during coHTL of plastics and biomass. Machine learning-enabled predictions of product yield and quality have been found to be more accurate than traditional mathematical models. Future research could focus on using machine learning algorithms to elucidate product formation mechanisms. The techno-economic and life cycle assessment reveal that the commercialization of HTL technology remains a distant prospect, further improvements in product yield, quality, and process energy efficiency are essential.”
FuzhouPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningTechnologyMinjiang University
NETL
NSTL
Elsevier
