本研究利用懸掛液滴系統來探討含過氧化氫或水之乳化液滴的微爆分類以及液滴壽命，並且使用CNN模型為乳化液滴的影像進行辨識及分類。研究中，使用柴油及十六烷做為燃料，分別混合過氧化氫(濃度50wt%)以及水，並使用Span80比Tween80為8.5比1.5比例作為乳化劑(3%)，將燃料與過氧化氫或水以體積比8比2混合成乳化液滴，在三種不同溫度環境(300oC、400oC及500oC)測試並觀察乳化液滴的微爆特性；並且利用液滴直徑變化進行微爆的強度分類。研究發現，過氧化氫相較於水，在300oC的環境有較長的液滴壽命；400oC和500oC環境有較高的機率發生高強度微爆。造成如此現象的可能原因是過氧化氫與水本身的物理性質差異，過氧化氫不管在沸點、密度、表面張力或是黏性都略高於水，並且比水更容易與燃料分離，所以在較低溫環境，液滴內部的氣泡不容易破裂並發生噴發或低強度微爆，但當環境溫度升高後，氣泡形成並結合後發生微爆的強度也比較高。而由於高強度微爆消耗掉大量的液滴，因此在高溫環境下的液滴壽命也明顯低於混摻水的液滴。
在影像辨識方面，準確度只在400oC及500oC的訓練或測試中呈現較高的數值，起初認為是由於這兩種液滴圖像較為相近的原因，但透過混淆矩陣檢查各個類別後發現，模型只能夠成功判斷類別零(液滴無變化)，並且其所占之權重為最高，才會導致整體準確率比較高。而類別三之單顆大氣泡把液滴整個撐開來的圖像與類別零的液滴無變化之圖像非常像，導致模型辨識錯誤。僅有少數資料呈現出較為合理的準確度，經檢查發現其氣泡較其他資料清晰且容易判別。

The purpose of this study is to use the suspended droplet method to analyze the classification of micro-explosions and comparisons of droplet characteristics, along with the CNN model to identify droplet images. In this study, diesel and n-hexadecane were used as the fuels. Each of these fuels was mixed with water and hydrogen peroxide at 50 wt% concentration. Span 80 and Tween 80 were used as surfactants at a ratio of 8.5:1.5 (3 vol% of total volume). Fuel, hydrogen peroxide and water were emulsified to synthesize emulsified droplets at a volume ratio of 8:2. Furthermore, we observed the characteristics of micro-explosion and classified the strength by calculating the parameter changes under three different surrounding temperatures (300 oC, 400 oC and 500 oC). We observed a longer life of droplets mixed with hydrogen peroxide as compared with water, under a low surrounding temperature. And the probability of strong micro-explosions was higher under a high surrounding temperature. This result may be due to the differences in physical properties between hydrogen peroxide and water, with hydrogen peroxide being more separable within the fuel.
In image recognition, the high accuracy was only obtained during training or testing at 400 oC and 500 oC. Initially, the reason was thought to involve the similarity of the two droplet images. However, after examining each label with a confusion matrix, the model could only successfully identify the Label 0 (no variation droplet), which weighed the most and led to high accuracy. The images of a single large bubble in Label 3 were very similar to the images of the no variation droplet of Label 0, leading to a systemic model identification error. Image recognition with a reasonable accuracy was only achieved in a few datasets. Overall, the bubbles within droplets were found to be clearer and easier to identify than other droplet features.

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