胺洗滌製程是一種具有潛力的燃燒後碳捕捉方法，然而大量操作能耗限制其商業化可能性。為分析及改良製程性能，熱力學及製程層面之工程問題顯然需要更進階之解決方案。在實際工程應用框架中採用代理模型已被證明可有效減輕高保真模型之高計算成本問題。然而，由於嚴謹胺洗滌製程模型收斂速度十分緩慢，使得透過模擬收集足量建模樣本仍所費不貲。另一方面，由於摻配不同醇胺溶劑是提升碳捕捉效率之有效方法，因此精準預測二氧化碳於醇胺水溶液之汽液平衡行為係分析摻配配方之技術發展重點。然而，建立可靠模型亦需收集大量平衡溶解度實驗數據，且傳統模型之參數適用範圍一般較為侷限，故有必要開發可減輕數據量或提升模型性能之方法。
本研究開發多種基於類神經網絡漸進式學習之熱力學或製程建模策略，以期可有效減少所需樣本數量並提升模型預測性能。首先，漸進合成法係透過整合「程序合成」及「漸進式學習」之概念，簡化高保真模擬流程以減少計算時間；結果顯示該法可減少約23至64%數據收集時間，而模型預測誤差則減少14至40%。多步擴展法則將既有模型進行多步參數擴充；數值實驗結果顯示，總數據收集時間可以節省超過47%，且模型具物理可解釋性。此外，聯合增量學習法則透過多個單溶劑模型之聯合增量學習，可以顯著減少二氧化碳於醇胺混合物中之平衡溶解度之預測誤差達68%。

Although amine scrubbing process is a promising option for post-combustion carbon capture, the high energy consumption for operation still hinders its commercialization. To analyze and improve the process performance of this technology, thermodynamic and process-level engineering issues must be addressed more thoroughly. From the macroscopic view, the first-principle models have usually adopted for rigorous simulations. However, the computational burdens of using these high-fidelity models in realistic engineering applications are often overwhelming. This shortcoming inevitably leads to construct simpler surrogate models to alleviate the computation efforts. However, since the simulation of the amine scrubbing processes are complex and the convergence speed is slow, high computational cost is still inevitable for acquisition of sufficient modeling samples through the first-principles simulation runs. On the other hand, on the thermodynamic level, blending alkanolamine solvents is an effective method to improve carbon capture efficiency, and the vapor-liquid equilibrium (VLE) behavior of carbon dioxide in aqueous alkanolamine solution is critical for analyzing the blending formula. However, constructing reliable models requires collecting a large amount of experimental data of equilibrium solubility, and the applicable range of parameters of conventional models is generally limited. Therefore, it is necessary to develop methods that can reduce the amount of data or improve model performance.
In this study, several thermodynamic or process modeling strategies based on progressive learning of artificial neural networks were developed to effectively reduce the required number of samples and enhance the model prediction performances. The progressive synthesis method integrates the concepts of process synthesis and progressive learning by simplifying the flowsheets of high-fidelity simulations for effective reduction of computation efforts. The results showed that the data acquisition time can be reduced by 23 to 64%, and the model prediction errors can be reduced by 14 to 40%. The multi-step expansion approach expands the architectures of existing models and capacity of parameters in step-wise manners. The experimental results show that the total data acquisition time can be saved by more than 47%, and the model prediction performance can also be significantly improved. The joint incremental learning method significantly improves the prediction performance of the equilibrium solubility of carbon dioxide in alkanolamine mixtures through joint incremental learning of single-solvent models.

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