本研究之目的在於利用Kriging代理模型與序列式建模優化生質料焙燒條件，並與田口法結果進行比較。生質料選用棕梠空果串(empty fruit bunch, EFB)與棕梠空果殼(palm kernel shell, PKS)，在四種焙燒條件下取得生質碳(biochar) 並計算PA指標。PA指標是以近似分析所建立的一項指標，其值大小反映出生質物的可燃性。透過代理模型與序列式建模以最少的實驗次數找出最大PA指標的焙燒條件。EFB的案例中表明代理模型對實驗次數的需求確實可以少於田口法所需之次數，但因其PA指標與參數之間呈現線性關係，極值坐落於參數範圍的上下限，以田口法優化EFB案例也可找到全域最佳條件。在PKS的案例中即可看出代理模型的優化結果可保證是全域最佳解，若以田口法依相同實驗次數設計等距的因子水準，即會出現田口法的優化結果是區域最佳解，田口法要找到相似於代理模型的最佳條件則需要多於代理模型所需的實驗次數。

The purpose of this research is to use the Kriging surrogate model and sequential sampling to optimize the torrefaction conditions of biomass, and to compare with the results of the Taguchi method. The empty fruit bunch (EFB) and palm kernel shell (PKS) were selected to torrefy under four torrefaction conditions, and the PA index was calculated. The PA index is an index established by proximate analysis, and its value reflects the flammability of the biochar or biomass. Through the surrogate model and sequential sampling to find the torrefaction condition of the maximal PA index with fewer experiments. The EFB case shows that the number of experiment surrogate model need for optimization can indeed be less than the number required by Taguchi method, but because of the linear relationship between its PA index and parameters, the extreme value is located at the upper and lower limits of the parameter range, so Taguchi method is used to optimize EFB cases can also find the best conditions in the whole parameter domain. In the case of PKS, it can be seen that the optimization result of the surrogate model is guaranteed to be the global solution in the whole parameter domain. If the Taguchi method is used to design factor levels with the same number of experiments, it will result in the optimization result of the Taguchi method is the local solution. The Taguchi method needs more experiments than the surrogate model to find the optimal conditions similar to the result of the surrogate model.

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