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首页> 外文期刊>Biofuels, bioproducts & biorefining: Biofpr >Artificial neural network based modeling for the prediction of yield and surface area of activated carbon from biomass
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Artificial neural network based modeling for the prediction of yield and surface area of activated carbon from biomass

机译：基于人工网络基于生物量预测活性炭产量和表面积的模型

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Activated carbon (AC) is an adsorbent material with broad industrial applications. Understanding and predicting the yield and quality of AC produced from different feedstock is critical for biomass screening and process design. In this study, multi-layer feedforward artificial neural network (ANN) models were developed to predict the total yield and surface area of AC produced from various biomass feedstock using pyrolysis and steam activation. In total, 168 data samples identified from experiments in literature were used to train, validate, and test the ANN models. The trained ANN models showed high accuracy (R-2 > 0.9) and demonstrated good alignment with the independent experimental data. The impacts of using datasets based on different biomass characterization methods (i.e., ultimate analysis and proximate analysis) were evaluated and compared. Finally, a contribution analysis was conducted to understand the impact of different process factors on AC yield and surface area. (c) 2019 Society of Chemical Industry and John Wiley & Sons, Ltd
机译：活性炭（AC）是具有广泛工业应用的吸附材料。理解和预测由不同原料产生的AC的产量和质量对于生物质筛选和工艺设计至关重要。在该研究中，开发了多层前馈人工神经网络（ANN）模型以预测使用热解和蒸汽活化从各种生物质原料产生的AC的总产率和表面积。总共，从文献实验中识别的168个数据样本用于培训，验证和测试ANN模型。培训的ANN模型显示出高精度（R-2> 0.9），并与独立实验数据显示出良好的对准。使用基于不同的生物量表征方法的使用数据集（即，最终分析和近分析）的影响进行了评估并进行比较。最后，进行了贡献分析以了解不同处理因素对交流产量和表面积的影响。 （c）2019年化学工业协会和约翰瓦里和儿子有限公司

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