小球藻的催化热解：用于优化的深度神经网络。
Catalytic thermal degradation of Chlorella vulgaris: Evolving deep neural networks for optimization.
机构信息
Brno University of Technology, Institute of Process Engineering & NETME Centre, Technicka 2896/2, 616 69 Brno, Czech Republic.
National HiCoE Thermochemical Conversion of Biomass, Centre for Biofuel and Biochemical Research, Institute of Sustainable Building, Chemical Engineering Department, Universiti Teknologi PETRONAS, Seri Iskandar, Perak 32610, Malaysia.
出版信息
Bioresour Technol. 2019 Nov;292:121971. doi: 10.1016/j.biortech.2019.121971. Epub 2019 Aug 8.
The aim of this study is to identify the optimum thermal conversion of Chlorella vulgaris with neuro-evolutionary approach. A Progressive Depth Swarm-Evolution (PDSE) neuro-evolutionary approach is proposed to model the Thermogravimetric analysis (TGA) data of catalytic thermal degradation of Chlorella vulgaris. Results showed that the proposed method can generate predictions which are more accurate compared to other conventional approaches (>90% lower in Root Mean Square Error (RMSE) and Mean Bias Error (MBE)). In addition, Simulated Annealing is proposed to determine the optimal operating conditions for microalgae conversion from multiple trained ANN. The predicted optimum conditions were reaction temperature of 900.0 °C, heating rate of 5.0 °C/min with the presence of HZSM-5 zeolite catalyst to obtain 88.3% of Chlorella vulgaris conversion.
本研究旨在利用神经进化方法确定小球藻的最佳热转化。提出了一种渐进深度群进化（PDSE）神经进化方法来模拟小球藻催化热降解的热重分析（TGA）数据。结果表明，与其他传统方法相比（RMSE 和 MBE 分别低>90%），所提出的方法可以生成更准确的预测。此外，还提出了模拟退火法来从多个训练好的 ANN 中确定微藻转化的最佳操作条件。预测的最佳条件为反应温度 900.0°C，加热速率 5.0°C/min，HZSM-5 沸石催化剂存在下，小球藻转化率为 88.3%。
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