在2023年，台灣的咖啡渣產量已達3.384萬公噸；同時，台灣進口蓖麻粕達1.1萬公噸，若將兩者進行共裂解，不但可開創新的咖啡渣再利用途徑，也可拓展蓖麻粕的再利用價值。在本次研究，創新地結合國內生產的咖啡渣(SCG)和國外進口的蓖麻粕(CW)，進行流體化床的快速共熱裂解的研究。
在化學反應動力學方面，首度提出一套集成多模型的組合動力學分析策略，結合等轉換率法、主圖法、改良Šesták & Berggren模型與全域優化演算法，以克服單一傳統方法在異質生質物熱裂解機制刻畫上的不足。結果顯示，Coats & Redfern法僅能提供粗略活化能範圍，且忽略不同階段的機制變化；等轉換法搭配主圖法只能在既有經典模型範疇內進行對比，無法揭示生質物中未包含的隱蔽或複合反應機制。相較之下，本研究透過基因演算法進行全域搜尋，對Šesták & Berggren模型的m、n與p參數進行擬合，成功在不同升溫速率(10~30°C/min)與混摻比條件下，擬合出實驗曲線並量化五大機制(擴散、冪次、階數、幾何收縮、成核)的耦合作用。研究發現，純SCG與純CW在中等速率區間內動力學行為高度一致，而混摻樣品則隨CW含量提升，展現從階數(Fn)主導轉向擴散(Dn)與成核機制(An)主導；BR:80%樣品尤具最強降活化能與反應促進效果。
此外，本研究利用20kWth級鼓泡式流體化床進行共熱裂解實驗，將通過Taguchi法設計與S/N分析，決定最大液態產率(wt.%)與最佳吲哚產率(Area%)的製程參數：先以最大液態產率為例，在熱解溫度400°C、混摻比BR：80wt.%、N₂流量10 SLPM、冷凝溫度-5°C條件下，理論S/N = 27.48 dB、液產率23.66wt.%，與實驗值23.33wt.%誤差僅1.39%，彰顯Taguchi法在多因子優化中之精準度；再以最佳吲哚產率為例，在熱解溫度450°C、混摻比BR：80wt.%、N₂流量10 SLPM、冷凝溫度0°C時，吲哚產率達最佳，理論S/N = 40.42 dB，預測選擇性近100 Area%，顯示低流量與適當冷凝溫度對蛋白質前驅體(例如：色胺酸)裂解生成含氮雜環，具有決定性影響。此優化策略提高液態產率與高價值化學品的生產效率。

In 2023, Taiwan produced approximately 33,840 t of spent coffee grounds (SCG) and imported around 11,000 t of castor waste (CW). This study combines domestically produced SCG and imported CW to conduct research on rapid co-pyrolysis in a 20 kWth bubbling fluidized bed.
For the first time, we propose an integrated multi model kinetic analysis strategy that combines isoconversional methods, master‐plot techniques, a modified Šesták & Berggren model, and a global optimization algorithm. Under different heating rates (10~30°C/min) and blend ratios, this approach successfully fits experimental thermogravimetric curves and quantifies the coupled contributions of five principal mechanisms (diffusion, power law, reaction order, geometric contraction, and nucleation). We find that pure SCG and pure CW exhibit highly consistent kinetic behavior at moderate heating rates, whereas increasing CW content in the blends shifts the dominant mechanism from reaction order (Fₙ) toward diffusion (Dₙ) and nucleation (Aₙ). Notably, the 80 wt.% CW blend shows the greatest reduction in activation energy and the most pronounced reaction enhancement.
In addition, we optimize both maximum liquid yield (wt.%) and indole yield (100 Area%) using a Taguchi method and signal to noise (S/N) analysis. For maximum liquid yield, at 400 °C, BR = 80 wt.%, N₂ flow = 10 SLPM, and condenser temperature = −5 °C, the predicted S/N ratio is 27.48 dB and the theoretical liquid yield is 23.66 wt.%, which agrees with the measured value of 23.33 wt.% (error = 1.39 %). For optimal indole yield, at 450 °C, BR = 80 wt.%, N₂ flow = 10 SLPM, and condenser temperature = 0 °C, the predicted S/N ratio is 40.42 dB, with a selectivity approaching 100 Area%.

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