| DC 欄位 |
值 |
語言 |
| DC.contributor | 機械工程學系 | zh_TW |
| DC.creator | 王維文 | zh_TW |
| DC.creator | WEI-WUN WANG | en_US |
| dc.date.accessioned | 2025-1-16T07:39:07Z | |
| dc.date.available | 2025-1-16T07:39:07Z | |
| dc.date.issued | 2025 | |
| dc.identifier.uri | http://ir.lib.ncu.edu.tw:444/thesis/view_etd.asp?URN=111323081 | |
| dc.contributor.department | 機械工程學系 | zh_TW |
| DC.description | 國立中央大學 | zh_TW |
| DC.description | National Central University | en_US |
| dc.description.abstract | 本論文使用貧油預混甲烷/空氣燃氣，於固定當量比= 0.65條件下，加入不同體積百分比的氫氣(XH2 = 10%、30%、50%)作為燃料，量測一低NOx多孔性介質輻射燃燒器的表面溫度和其加氫後[NOx]和[CO]的變化。此Ni-Al多孔介質燃燒器是經高溫自我傳遞合成法所製作，具有穹頂中空圓柱形結構，其穹頂中心高度為150 mm、外徑66 mm、多孔介質厚度為9 mm、孔隙率約為0.6。實驗使用三種不同的燃燒功率(P = 10、12、15 kW)，且燃燒器以一內部燃燒模式運行。利用紅外線熱像儀搭配卷積神經網絡來量測和預測溫度，並以熱電偶之溫度數據為標準進行校正，目標為建立智慧化非接觸式溫度量測方法。結果顯示，燃燒器表面溫度隨著混氫比例和功率增加而上升，於功率P = 15 kW及XH2 = 50%條件下，有最高之溫度分佈；[NOx]最高為31 ppm和[CO] = 13.5 ppm (均經3% O2校正)，證明Ni-Al多孔介質燃燒器是一低[NOx]低[CO]之潔淨燃燒器。另外，透過人工智慧，來預測多孔介質燃燒器的溫度，並且減少量測誤差。最後，於應用方面，本研究使用三支以等距三角排列之多孔介質燃燒器，搭配管狀和鰭管式熱交換器，建置一創新型熱水器，並量測其熱效率。此創新熱水器於當量比= 0.8、功率P = 36 kW及XH2 = 0%之條件下，熱效率可達91%，當混氫比例增加至XH2 = 50%時，其熱效率更可高達98%。上述之研究成果，對於綠色能源及智慧化溫度量測方面應有所助益。 | zh_TW |
| dc.description.abstract | This thesis reports the performance of a low-NOx porous radiant burner using lean premixed methane/air mixture at a fixed equivalence ratio of = 0.65, blending with varying hydrogen volume percentages (XH2 = 10%, 30%, 50%). Surface temperature distributions of porous radiant burner and its variations of [NOx] and [CO] after hydrogen addition are measured and analyzed. The Ni-Al porous radiant burner, fabricated through high-temperature self-propagating synthesis method, features a dome-shaped hollow cylindrical structure with a central dome height of 150 mm, an outer diameter of 66 mm, a porous media thickness of 9 mm, and a porosity of approximately 0.6. Experiments are conducted at three different power levels (P = 10, 12, and 15 kW), with the burner operating in an internal combustion mode. Temperature distributions are measured using an infrared thermal imaging camera, predicted by a convolutional neural network and calibrated by thermocouples to develop an intelligent, non-contact temperature measurement method. Results show that the surface temperatures of the burner increase with increasing the hydrogen blending ratio as well as the power levels. At conditions of P = 15 kW and XH2 = 50% with the highest temperature distribution, the highest [NOx] and [CO] are 31 ppm and 13.5 ppm, respectively (both corrected to 3% O2), demonstrating that the Ni-Al porous radiant burner is a clean combustion device having low [NOx] and low [CO] emissions simultaneously. Furthermore, we apply artificial intelligence to predict the temperature distributions of the porous burner and to reduce measurement errors. For the practical application, this work implements three porous media burners arranged in an equilateral triangular configuration together with tubular and finned heat exchangers to construct an innovative water heater of high thermal efficiency. Under conditions of = 0.8, power P = 36 kW, and XH2 = 0%, such a novel water heater achieves 91% thermal efficiency. When blending with the hydrogen at XH2 = 50%, the thermal efficiency increases up to 98%. These results should be useful to the green energy applications and intelligent temperature measurement technologies. | en_US |
| DC.subject | 多孔性介質輻射燃燒器 | zh_TW |
| DC.subject | 表面溫度量測 | zh_TW |
| DC.subject | 低氮氧化物排放 | zh_TW |
| DC.subject | 卷積神經網絡 | zh_TW |
| DC.subject | 創新型多孔介質熱水器 | zh_TW |
| DC.subject | Porous radiant burner | en_US |
| DC.subject | surface temperature measurement | en_US |
| DC.subject | low NOx emissions | en_US |
| DC.subject | convolutional neural network | en_US |
| DC.subject | novel porous media water heater | en_US |
| DC.title | 預混甲烷混氫低碳燃料之多孔性介質燃燒器性能量測及其人工智慧輔助溫度預測 | zh_TW |
| dc.language.iso | zh-TW | zh-TW |
| DC.title | Performance Measurement of A Porous Radiant Burner using Low-Carbon Premixed Methane-Hydrogen/Air Mixtures and Its Artificial Intelligence Assisted Temperature Prediction | en_US |
| DC.type | 博碩士論文 | zh_TW |
| DC.type | thesis | en_US |
| DC.publisher | National Central University | en_US |