Biomass higher heating value (HHV) is the maximum energy released by its complete oxidation. The aim of this mini-review was to synthesize the early efforts of researchers in the prediction of biomass HHV by artificial neural network (ANN) models. This was conducted to evaluate the progress of research, identify knowledge gaps and synthesize future perspectives in the research area. Multi-layer perceptron artificial neural network (MLP-ANN) was observed to be the most accurate ANN model for the prediction of biomass HHV. Model accuracy was more dependent on the ANN architecture than on the data size. Investigations based on ultimate analysis data (either singularly or combined with proximate analysis data) gave more accurate models. Evaluating more intricate and sophisticated ANN architectures could yield better models for biomass HHV prediction. ANN models based on chemical analysis and physical properties data are unreported and could be explored in future studies. There is likely to be a paradigm shift in biomass HHV prediction as soon as the more accurate ANN models become more popular with researchers in biomass energy.

Abbreviations AI

Artificial Intelligence; ANFIS-PSO: Adaptive Neuro-Fuzzy Inference System with Particle Swamp Optimization; ANN: Artificial Neural Networks; FNPLS: Network-based Fuzzy Partial Least Squares; HHV: Higher Heating Value; IFNPLS: Iterative Network-based Fuzzy Partial Least Squares; INNPLS: Iterative Neural Network Adapted Partial Least Squares; LHV: Lower Heating Value; MLP-ANN: Multi-Layer Perceptron Artificial Neural Network; NNPLS: Neural Network Adapted Partial Least Squares; PCA-ANN: Principal Component Analysis with ANN Paradigm; PCA-IFNPLS: Principal Component Analysis with Iterative Neural Network Adapted PLS; PLS: Partial Least Squares; R²: Coefficient of Determination; RMSE: Root Mean Square Error

中文翻译：

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人工神经网络在预测生物质较高热值中的应用：早期评估

生物质较高的热值（HHV）是其完全氧化释放的最大能量。这项小型综述的目的是综合研究人员在通过人工神经网络（ANN）模型预测生物量HHV的早期努力。这样做是为了评估研究进展，发现知识差距并综合研究领域的未来观点。多层感知器人工神经网络（MLP-ANN）被认为是预测生物量HHV的最准确的ANN模型。模型的准确性更依赖于ANN体系结构，而不是数据大小。基于最终分析数据（单独或与最近的分析数据组合）的调查给出了更准确的模型。评估更复杂，更复杂的人工神经网络架构可以为生物质HHV预测提供更好的模型。基于化学分析和物理性质数据的人工神经网络模型尚未报道，可以在未来的研究中探索。一旦更精确的人工神经网络模型在生物质能源研究人员中变得越来越流行，生物质HHV预测就可能发生范例转变。

缩写AI

人工智能; ANFIS-PSO：具有粒子沼泽优化功能的自适应神经模糊推理系统；人工神经网络：人工神经网络；FNPLS：基于网络的模糊偏最小二乘；HHV：更高的发热量；IFNPLS：基于迭代网络的模糊偏最小二乘；INNPLS：迭代神经网络改编的偏最小二乘；LHV：较低的发热量；MLP-ANN：多层感知器人工神经网络；NNPLS：神经网络自适应偏最小二乘；PCA-ANN：使用ANN范例进行主成分分析；PCA-IFNPLS：采用迭代神经网络改编的PLS进行主成分分析；PLS：偏最小二乘；R ²：测定系数；RMSE：均方根误差