無碳燃料燃燒研究聚焦於氫氣與氨氣在工業燃燒中的分段噴注、混合—化學耦合與排放控制，目標是建立可驗證的條件與限制。Carbon-free fuel-combustion research focuses on staged hydrogen and ammonia injection, mixing–chemistry coupling, and emissions control in industrial-combustion contexts, with the goal of defining verifiable conditions and limits.
將氫氣與氨氣等低碳燃料導入甲烷或工業燃燒背景時，混燃比例、噴注位置、噴流動量與停留時間共同決定火焰結構與排放結果。本方向已累積氫氣槍注（hydrogen lancing）、氨氣分段噴注等具體噴注策略的實驗與模擬資料，逐步釐清各策略的有效條件與適用範圍。When low-carbon fuels such as hydrogen and ammonia are introduced into methane or industrial-combustion backgrounds, the co-firing ratio, injection location, jet momentum, and residence time jointly determine flame structure and emissions outcomes. The work has accumulated experimental and computational data on specific injection strategies including hydrogen lancing and ammonia staged injection, progressively clarifying the effective conditions and applicable range of each strategy.
氫氣與氨氣在燃燒過程中各自帶來不同的污染風險：氫燃燒可能因高溫提高熱型 NOx，氨燃燒則可能引入燃料型 NOx、N₂O 與未反應 NH₃ 等排放。本方向同時量測多種排放物與局部火焰條件，建立排放化學與燃燒參數之間的對應關係。Hydrogen and ammonia each introduce distinctive pollution risks: hydrogen combustion can elevate thermal NOx through higher temperatures, while ammonia combustion can introduce fuel-NOx, N₂O, and unreacted NH₃. The work simultaneously measures multiple emissions and local flame conditions, building correspondences between emissions chemistry and combustion parameters.
本方向以氫氣、氨氣混燒實驗與模擬累積的反應—混合—排放資料為基礎，發展可應用於工業燃燒系統的監測與診斷技術。未來將進一步整合線上火焰光譜量測與機器學習模型，建立可即時推估局部燃燒條件與排放指標的監診系統。Building on reaction–mixing–emissions data accumulated from hydrogen and ammonia co-firing experiments and simulations, the work develops monitoring and diagnostic techniques applicable to industrial combustion systems. Going forward, in-situ flame-spectrum measurements will be further integrated with machine-learning models to build monitoring-and-diagnostic systems capable of real-time estimation of local combustion conditions and emission indices.
使用可調式燃燒器與噴注模組，系統改變主燃料／氫氣／氨氣比例、噴注高度與深度（如槍注或分段噴注）、噴流動量、當量比、停留時間，以及基準火焰與爐內幾何條件，建立可橫向比較的實驗資料。Using configurable burners and injection modules, the work systematically varies main-fuel / hydrogen / ammonia ratios, injection height and depth (e.g., lancing or staged injection), jet momentum, equivalence ratio, residence time, as well as base flame and furnace geometry, to build comparable experimental datasets.
燃燒區量測整合高速火焰影像、化學發光成像、可調式二極體雷射吸收光譜（tunable diode laser absorption spectroscopy, TDLAS）、煙氣多組分分析儀（如 FTIR、化學發光氮氧化物分析儀），以及必要時的雷射診斷，連結局部反應區條件與整體排放趨勢。Combustion-zone measurements integrate high-speed flame imaging, chemiluminescence imaging, tunable diode laser absorption spectroscopy (TDLAS), multicomponent flue-gas analyzers (e.g., FTIR, chemiluminescent NOx analyzers), and laser diagnostics where needed, connecting local reaction-zone conditions to global emissions trends.
採用詳細反應機理（Cantera）與 PSR／PFR／CRN 等低階模型分析溫度、氧濃度、混合時間與噴注位置對 NO、N₂O 與燃料轉化路徑的影響，並將模型結果與量測排放及局部火焰條件對照以識別主導機制。本方向同時發展火焰光譜機器學習模型，建立從光譜特徵直接推估局部當量比、溫度與排放指標的能力。Detailed kinetic mechanisms (Cantera) together with reduced-order models such as PSR, PFR, and CRN (chemical reactor network) are used to analyze how temperature, oxygen level, mixing time, and injection location affect NO, N₂O, and fuel-conversion pathways, cross-checking model results against measured emissions and local flame conditions to identify the dominant mechanisms. The work simultaneously develops flame-spectrum machine-learning models, building the capability to estimate local equivalence ratio, temperature, and emission indices directly from spectral features.