The higher heating value (HHV) provides information about the quantity of energy contained in a fuel such as biomass. Correlations and models can be developed to predict biomass HHV quickly from other analysis data. In this study, a linear regression algorithm (LRA) and stochastic gradient descent (SGD) in a machine‐learning environment were used as novel methods to predict the HHV of biomass. The basis of the model was 78 lines of combined proximate and ultimate analysis data. The LRA model was observed to be more accurate. The testing for both models was done by stratified cross‐validation, stratified shuffle splits, and no sampling tests. The root mean square error (RMSE) of the LRA and SGD models was 8.151 and 21.65 kJ kg⁻¹ and the mean absolute error (MAE) was 6.823 and 13.87 for the stratified shuffle split (ten random samples with 75% data). The coefficient of determination for both models was >0.999 in all cases. The study observed that LRA and SGD are among the most accurate artificial intelligence models for the prediction of biomass HHV. © 2020 Society of Chemical Industry and John Wiley & Sons, Ltd

中文翻译：

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线性回归算法和随机梯度下降法在机器学习环境中预测生物量较高热值的应用

较高的发热量（HHV）提供有关燃料（例如生物质）中所含能量的信息。可以建立相关性和模型，以根据其他分析数据快速预测生物量HHV。在这项研究中，将线性回归算法（LRA）和随机梯度下降（SGD）在机器学习环境中用作预测生物量HHV的新方法。该模型的基础是78条组合的最近和最终分析数据。观察到LRA模型更准确。两种模型的测试都是通过分层交叉验证，分层洗牌分裂和没有抽样测试来完成的。LRA和SGD模型的均方根误差（RMSE）为8.151和21.65 kJ kg ^-1分层混洗拆分的平均绝对误差（MAE）为6.823和13.87（十个随机样本，数据含量为75％）。在所有情况下，两个模型的确定系数均> 0.999。研究发现LRA和SGD是预测生物量HHV的最准确的人工智能模型之一。©2020年化学工业协会和John Wiley＆Sons，Ltd