在這項研究中，使用熱力學分析和吉布斯能量最小化方法分析了氨的燃燒。此外，還使用熱力學分析研究了在高爐條件下將新型氨添加到氧化鐵還原中，並進一步開發了神經網絡來預測熱力學分析的結果。因此，本研究分為兩部分，如下所述。
本研究的第一部分旨在研究發動機條件下氨燃燒的熱力學和隨後的產物選擇性非催化還原 (SNCR)，重點是通過吉布斯自由能最小化產生氮氧化物 (NOX)。結果顯示，與現有的關於氨燃燒和 SNCR 的研究具有良好的相關性。 NOX 產量隨溫度增加而增加，並隨著氨當量比增加而減少，且與壓力較無關。在 NOX 產量最高的條件下，NOX 濃度達到 21,963 ppm 。氨燃燒的反應在高溫下為放熱反應，焓會隨著溫度升高而降低，且隨著氨當量比的增加而升高。此外，SNCR 模擬顯示，通過選擇性非催化還原，氨燃燒期間產生的大量 NOX 濃度的降低在熱力學上高達 98% 以上是有利的。熱力學分析的某些部分，例如 SNCR 對一氧化二氮 (N2O) 濃度的影響，顯示與現有研究不一致，表明吉布斯最小化方法存在一些局限性。
本研究的第二部分旨在模擬添加氨以還原氧化鐵的熱力學，並利用熱力學分析的結果開發神經網絡模型，根據輸入的參數預測氧化鐵還原性能。結果顯示，當按比例減去碳和空氣時，氨的添加可以增加鐵 (Fe) 的還原。然而，由氨輔助的氧化鐵還原反應會更吸熱，這結果顯示氨可能沒有足夠的放熱活性來維持氧化鐵的吸熱還原。此外，模擬結果顯示添加氨可以限制使用焦炭的傳統氧化鐵還原反應產生的二氧化碳 (CO2)。此外，添加氨可以減少氮氧化物 (NOX) 和一氧化二氮 (N2O) 的排放，具體取決於添加的方法。本研究開發了一個神經網路模型，其中包含 一個隱藏層與10 個神經元，並根據溫度、氨添加百分比和氨添加方法預測系統的熱焓量、氧化鐵還原的程度、二氧化碳生成量、NOX 濃度和 CO 濃度。經過 43 個 epoch 的訓練後，神經網絡的 R2 值為 0.9985。儘管本研究中採用的 Gibbs 最小化方法存在一些局限性，但結果顯示氨有可能在煉鐵中補充焦炭，因此需要更多的研究來確定氨可用於減少煉鐵過程中溫室氣體排放的程度。

In this study, ammonia combustion was analyzed using a thermodynamic analysis and the Gibbs energy minimization method. In addition, novel ammonia addition to iron oxide reduction under blast furnace conditions was also investigated using a thermodynamic analysis, with a neural network further developed to simulate the results of the thermodynamic analysis. Therefore, this study is divided into two parts, as described below.
The first part of this study aimed to investigate the thermodynamics of ammonia combustion at engine conditions and subsequent selective non-catalytic reduction (SNCR) of the products with an emphasis on the production of nitrogen oxides (NOX) through the minimization of Gibbs free energy. The results showed a good correlation with existing research about ammonia combustion and SNCR. NOX production increased with temperature, decreased with increasing ammonia equivalence ratio, and was largely independent of pressure. At the conditions with the highest NOX production, the NOX concentration peaked at 21,963 ppm. The ammonia combustion reaction was exothermic at high temperatures and had a reaction enthalpy that decreased with temperature and increased with increasing ammonia equivalence ratio. Further, the SNCR simulation indicated that the reduction of large NOX concentrations produced during ammonia combustion could be thermodynamically favorable up to over 98% through selective non-catalytic reduction. Some portions of the thermodynamic analysis, such as the effect of SNCR on nitrous oxide (N2O) concentration, showed inconsistency with existing research, indicating some limitations associated with the Gibbs minimization method.
The second part of his study aimed to simulate the thermodynamics of ammonia addition to iron oxide reduction and to use the results of the thermodynamic analysis to develop a neural network that predicts iron oxide reduction performance from input parameters. The results showed that ammonia addition can increase iron (Fe) reduction when carbon and air are proportionally subtracted. However, iron oxide reduction assisted by ammonia is more endothermic, indicating ammonia may have insufficient exothermic activity to maintain the endothermic reduction of iron oxides. Additionally, the simulation suggests ammonia addition can limit the carbon dioxide (CO2) produced by conventional iron oxide reduction using coke. Further, ammonia addition can reduce oxides of nitrogen (NOX) and nitrous oxide (N2O) emissions, depending on the addition method. A neural network was developed using one hidden layer with 10 neurons to predict the system enthalpy, the extent of iron oxide reduction, carbon dioxide production, NOX concentration, and CO concentration from the temperature, ammonia addition percent, and method of ammonia addition. After training for 43 epochs, the neural network achieved an R2 value of 0.9985. Although there are some limitations to the Gibbs minimization method employed in this study, the results indicate ammonia has the potential to supplement coke in ironmaking. More research is required to determine the extent ammonia could be employed to reduce greenhouse gas emissions from ironmaking processes.

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