A predictive model is developed using an artificial neural network (ANN) to calculate the solid-liquid, and gas yields (wt %) from the torrefaction of olives stones, based on the material and process parameters. These parameters are average olive stone particle size, reaction temperature and reaction time. Ordinary Kriging interpolation is coupled with ANN to improve the experimental data resolution by increasing the data points used in building the ANN models. This coupling improved the ANN prediction accuracy (R²) by 11.1%, 13.5%, and 1.0% in training and 27.3%, 8.5%, and 14.8% in validation of the solid, liquid and gas yields, respectively. Also, the mean absolute deviations of the models significantly improved after the coupling. The prediction profiles show a linear relationship between the solid and liquid yields and a nonlinear relation for the gas yields in terms of the material and process parameters. Average olive stone particle size showed the highest effect on the yields due to the improvement in heat transfer with the exposed surface area of the olive stones leading to a faster reaction rate.

基于材料和工艺参数，使用人工神经网络 (ANN) 开发了一个预测模型，以计算橄榄核烘焙的固-液和气体产量 (wt %)。这些参数是平均橄榄石粒度、反应温度和反应时间。普通克里金插值与 ANN 相结合，通过增加用于构建 ANN 模型的数据点来提高实验数据分辨率。这种耦合提高了 ANN 预测精度 (R ²) 在训练中分别提高了 11.1%、13.5% 和 1.0%，在固体、液体和气体产率的验证中分别提高了 27.3%、8.5% 和 14.8%。此外，耦合后模型的平均绝对偏差显着改善。预测曲线显示了固体和液体产率之间的线性关系以及气体产率在材料和工艺参数方面的非线性关系。橄榄核的平均粒径对产率的影响最大，因为橄榄核的暴露表面积提高了热传递，从而加快了反应速度。