This study explored the predictive modelling of the pyrolysis of rice husk to determine the thermal degradation mechanism of rice husk. The study can ensure proper modelling and design of the system, towards optimising the industrial processes. The pyrolysis of rice husk was studied at 10, 15 and 20 °C min⁻¹ heating rates in the presence of nitrogen using thermogravimetric analysis technique between room temperature and 800 °C. The thermal decomposition shows the presence of hemicellulose and some part of cellulose at 225–337 °C, the remaining cellulose and some part of lignin were degraded at 332–380 °C, and lignin was degraded completely at 480 °C. The predictive capability of artificial neural network model was studied using different architecture by varying the number of hidden neurone node, learning algorithm, hidden and output layer transfer functions. The residual mass, initial degradation temperature and thermal degradation rate at the end of the experiment increased with an increase in the heating rate. Levenberg–Marquardt algorithm performed better than scaled conjugate gradient learning algorithm. This result shows that rice husk degradation is best described using nonlinear model rather than linear model. For hidden and output layer transfer functions, ‘log-sigmoid and tan-sigmoid', and ‘tan-sigmoid and tan-sigmoid' transfer functions showed remarkable results based on the coefficient of determination and root mean square error values. The accuracy of the results increases with an increasing number of hidden neurone. This result validates the suitability of an artificial neural network model in predicting the devolatilisation behaviour of biomass.

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稻壳的热分解：全面的人工智能预测模型

本研究探索了稻壳热解的预测模型，以确定稻壳的热降解机理。该研究可以确保对系统进行适当的建模和设计，以优化工业流程。在10、15和20°C min ^-1下研究了稻壳的热解在室温和800°C之间使用热重分析技术在氮气存在下加热速率。热分解表明在225–337°C下存在半纤维素和部分纤维素，剩余的纤维素和部分木质素在332–380°C下降解，而木质素在480°C完全降解。通过改变隐藏神经元节点的数量，学习算法，隐藏层和输出层传递函数，使用不同的体系结构研究了人工神经网络模型的预测能力。随着加热速率的增加，实验结束时的残余质量，初始降解温度和热降解速率会增加。Levenberg–Marquardt算法的性能优于比例共轭梯度学习算法。该结果表明，使用非线性模型而不是线性模型可以最好地描述稻壳的降解。对于隐藏层和输出层传递函数，基于确定系数和均方根误差值，“对数-S型和棕褐色-S型”传递函数和“ tan-S型和棕褐色-S型”传递函数显示了显着的结果。结果的准确性随隐藏神经元数量的增加而增加。该结果证实了人工神经网络模型在预测生物质的脱挥发分行为中的适用性。基于确定系数和均方根误差值，传递函数显示出显着结果。结果的准确性随隐藏神经元数量的增加而增加。该结果证实了人工神经网络模型在预测生物质的脱挥发分行为中的适用性。基于确定系数和均方根误差值，传递函数显示出显着结果。结果的准确性随隐藏神经元数量的增加而增加。该结果证实了人工神经网络模型在预测生物质的脱挥发分行为中的适用性。