This work proposes a new blended stacked ensemble machine-learning model (BEM) to predict biomass’s higher heating value (HHV) from the ultimate analysis. Gorilla troop optimization (GTO) is utilized to estimate the hyperparameter values of BEM, leading to GBEM. In GBEM, support vector regression (SUVR), Gaussian process regression (GAPR), and Decision Tree (DETR) are used as the base learner, whereas adaptive linear neural network (ADALINE) is used as a meta-learner, respectively. Furthermore, Linear Regression (LIR), generalized additive model (GEAM), and bagging of regression trees (BAGG) are also designed for comparison purposes. Results reveal that GBEM predicts the HHV with a lower AARD% (2.959%) value than other designed ML predictive models. In addition to this, a predictive equation that gives the relationship between HHV and the ultimate analysis parameters C, H, O, N, and S is also derived using GTO.

中文翻译：

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基于最终分析的生物质 HHV 预测的混合系综模型

这项工作提出了一种新的混合堆叠集成机​​器学习模型（BEM），用于根据最终分析预测生物质的较高热值（HHV）。大猩猩部队优化（GTO）用于估计 BEM 的超参数值，从而产生 GBEM。在GBEM中，分别使用支持向量回归（SUVR）、高斯过程回归（GAPR）和决策树（DETR）作为基学习器，而自适应线性神经网络（ADALINE）分别用作元学习器。此外，线性回归（LIR）、广义加性模型（GEAM）和回归树装袋（BAGG）也被设计用于比较目的。结果表明，GBEM 预测 HHV 的 AARD% (2.959%) 值低于其他设计的 ML 预测模型。除此之外，还使用 ​​GTO 导出了给出 HHV 与极限分析参数 C、H、O、N 和 S 之间关系的预测方程。