氫能因高質量能量密度與燃燒零碳排特性，被視為淨零轉型之關鍵能源載體，而液態氫（LH₂）為大規模氫能儲運之主要型態之一。然而現行氫液化製程之比能耗（SEC）普遍達10-15kWh/kgLH₂，遠高於理論值，且規模放大後之能耗與經濟性變化仍缺乏系統性評估。
本研究以 Song 等人提出之LN₂預冷結合氦氣Joule-Brayton 深冷循環為基礎，使用Aspen Plus V14.0 搭配 REFPROP高精度狀態方程式重建基準製程（0.5 t/d），並導入Donaubauer一階動力學模型，以RPlug模組取代傳統平衡轉換假設，使五級正-仲氫轉換器之模擬具備物理意義。透過靈敏度分析篩選關鍵操作變數後，結合遺傳演算法（GA）進行多變數優化，使SEC由12.03降至8.72 kWh/kgLH₂，較 Song 等人之9.82 kWh/kgLH₂低約 11.2%，㶲效率則由29.1%提升至45.0%。
進一步建立放大分析架構，產能由0.5 t/d放大至5、25及50 t/d，依文獻調整各規模之設備等熵效率與壓降，並於每一規模重新執行GA優化。結果顯示無壓降模型下SEC由8.72降至6.65 kWh/kgLH₂、㶲效率由45.0%提升至58.9%；加入壓降修正後SEC則由9.23降至6.79 kWh/kgLH₂。㶲破壞分布亦呈現結構性轉變，壓縮機佔比由47.5%降至34.6%，換熱器則由19.2%升至37.1%並於50 t/d規模超越壓縮機。最後建立初步技術經濟分析（TEA）模型，SLC由0.5 t/d之3.14 USD/kgLH₂降至50 t/d之 1.60 USD/kgLH₂，敏感度分析顯示外購LN₂價格為單位液化成本之主要制約因素。

Liquid hydrogen is a leading carrier for large-scale hydrogen storage, yet current liquefiers consume 10–15 kWh/kg LH₂, and how this penalty and its cost evolve with plant scale has not been assessed consistently. This work rebuilds the LN₂-precooled helium Joule–Brayton process of Song et al. at 0.5 t/d in Aspen Plus V14.0 with REFPROP, optimizes it by genetic algorithm (GA), and scales it to 50 t/d.
The five ortho–para converters are modeled as RPlug reactors driven by the first order kinetics of Donaubauer et al. rather than assumed to reach equilibrium; the model matches the reference data to 2.5% NRMSE, with all stages within 0.4 percentage points of equilibrium. A GA coupled to Aspen Plus then minimized SEC at four capacities under literature-based isentropic efficiencies and pressure drops.
At 0.5 t/d the GA lowers SEC from 12.03 to 8.72 kWh/kg LH₂, 11.2% below the reference value, and raises exergy efficiency from 29.1% to 45.0%. At 50 t/d, SEC reaches 6.65 kWh/kg LH₂ (6.79 with pressure drop) and exergy efficiency 58.9%, 85% of the gain coming from equipment efficiency and re-optimization. The exergy bottleneck shifts from compression (47.5% to 34.6%) to cryogenic heat exchange (19.2% to 37.1%). Liquefaction cost falls from 3.14 to 1.60 USD/kg LH₂, of which purchased LN₂ is about 60% and the dominant sensitivity.

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