张伟进论文“Machine learning aided production of bio-oil with high energy recovery and lownitrogen content from hydrothermal liquefaction of biomass with experiment verification”被Chemical Engineering Journal（IF：10.652）接收发表

Release time:2021-06-01

Description of Publication:Hydrothermal liquefaction (HTL) of biomass with high moisture (e.g., algae, sludge,manure, and food waste) is a promising and sustainable approach to producerenewable energy (bio-oil) and protect the environment. However, the production ofbio-oil with high yield and preferable properties such as low nitrogen content (N_oil) istime/labor-consuming using the traditional HTL experimental method. To this context,machine learning (ML) algorithms were employed to aid the bio-oil production with theconsideration of related factors in HTL, including biochemical and elementalcompositions of biomass, process parameters, and solvents. Results showed that therandom forest (RF) algorithm was the best one (average R2 =0.80) for the multi-taskprediction of bio-oil yield (Yield_oil), N_oil, and energy recovery (ER_oil), henceemployed for post feature interpretation and optimization.

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