| 研究生: |
何柏成 He, Bo-Cheng |
|---|---|
| 論文名稱: |
多級膜式直接空氣捕獲系統之建模與超結構最佳化 Modeling and Superstructure Optimization of a Multistage Membrane-Based Direct Air Capture System |
| 指導教授: |
吳煒
Wu, Wei |
| 學位類別: |
碩士 Master |
| 系所名稱: |
工學院 - 化學工程學系 Department of Chemical Engineering |
| 論文出版年: | 2026 |
| 畢業學年度: | 114 |
| 語文別: | 中文 |
| 論文頁數: | 132 |
| 中文關鍵詞: | 多級膜分離 、直接空氣捕獲 、二氧化碳捕獲 、遺傳演算法 、TOPSIS 、超結構最佳化 、逆向流 、年化總成本 |
| 外文關鍵詞: | membrane-based direct air capture, superstructure optimisation;, genetic algorithm, TOPSIS, counter-current flow, specific capture cost, siloxane nanomembrane |
| 相關次數: | 點閱:34 下載:0 |
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面對日益嚴峻的氣候變遷,直接空氣捕獲(Direct Air Capture, DAC)被視為達成負碳排放的關鍵技術之一。然而,大氣中的 CO2濃度僅約 400 ppm,遠低於燃燒後煙道氣,使得捕獲程序在熱力學驅動力、能耗與設備規模上皆面臨顯著挑戰。有鑑於膜分離技術具有結構簡單、模組化、免除吸收劑再生能耗等優勢,本研究提出系統性評估一套多級膜式直接空氣捕獲(membrane-based DAC, m-DAC)程序,藉由建模、經濟分析與最佳化的整合流程,探討其應用於超低濃度 CO₂ 捕獲之技術與經濟可行性。
本研究於 Aspen Custom Modeler(ACM)中,依據質量守恆與滲透傳輸方程,分別建構同向流、交叉流與逆向流三種膜分離單元模型,並以500個離散單元進行數值求解。模型於等溫、穩態、忽略壓降與固定滲透係數等假設下建立,並經網格獨立性測試確認其收斂性;同時以文獻數據交叉驗證,膜面積及兩側 CO2濃度之誤差均控制於5%以內。在此基礎上,本研究以Python建置遺傳演算法(Genetic Algorithm, GA),透過COM介面與Aspen Plus進行資料交換,並以年化總成本(TAC)最小化與CO2回收率最大化為雙目標,建立多級串聯之超結構最佳化架構;最終導入TOPSIS多準則決策法,自帕雷托前緣中篩選兼顧效能與經濟性之最適操作點。
分析結果顯示,流場配置為影響 m-DAC 效能的決定性因素:逆向流因能於膜模組軸向維持較高的局部濃度差,僅需5級串聯與約8bar 操作壓力即可達成目標純度,而交叉流與同向流則分別須提升至約12.7 bar 與30 bar。於商業膜材Polaris™ 的最佳化中,逆向流可同時使CO2出口流量提升6.63%、出口濃度提升5.92%,並使TAC 降低2.18%,展現效能與成本兼容的優勢。此外,本研究亦評估高透氣性Siloxane奈米薄膜之潛力,發現其雖能有效縮減膜面積並提高捕獲流量,卻因 N2、O2的伴隨穿透而推升後端壓縮能耗,凸顯膜材設計須在滲透率與選擇率間取得權衡。整體而言,本研究所建立之建模—最佳化整合方法,可作為未來 m-DAC 程序設計、膜材篩選與工業化評估之參考依據。
Direct air capture must contend with a CO2 concentration of only 400 ppm, at which a single membrane stage cannot generate a useful driving force, so a multistage cascade is unavoidable— and its stage pressures and areas form a large, strongly coupled design space.
This work couples rigorous membrane models to a genetic algorithm to optimise that space.Co-current, cross-flow and counter-current unit models were built in Aspen Custom Modeler,verified grid-independent at 500 cells and validated to within 5 %, then embedded in an Aspen Plus flowsheet and driven through a COM interface by a Python genetic algorithm using ratio-encoded chromosomes and a lexicographic purity constraint. TOPSIS selects one design from each Pareto front.
Flow configuration dominates: counter-current reaches the target purity in five stages at 8bar, whereas cross-flow needs 12.7 bar and co-current nearly 30 bar and a sixth stage. Cost is governed by the module frame (73.5–84.8 %), not the membrane (under 2 %). A high-permeance siloxane nanomembrane cuts the specific cost by 37–38 % in cross-flow and counter-current but roughly doubles the compression and cooling duty. The siloxane counter-current design strictly dominates the other five, at 20,738 USD per tonne CO2, 80.3 % recovery and 18.19 mol% purity.
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