张伟进论文“Machine learning prediction and optimization of bio-oil production from hydrothermal liquefaction of algae”被Bioresource Technology（IF：9.642）接收发表
发布时间：2021-09-22
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简介：Hydrothermal liquefaction (HTL) of algae is a promising biofuel production technology. However, it is always difficult and time-consuming to identify the best optimal conditions of HTL for different algae by the conventional experimental study. Therefore, machine learning (ML) algorithms were applied to predict and optimize bio-oil production with algae compositions and HTL conditions as inputs, and bio-oil yield (Yield_oil), and the contents of oxygen (O_oil) and nitrogen (N_oil) in bio-oil as outputs. Results indicated that gradient boosting regression (GBR, average test R2 ~0.90) exhibited better performance than random forest (RF) for both single and multi-target tasks prediction.
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