本文關(guān)鍵詞：基于軟測量的空調(diào)系統(tǒng)檢測技術(shù)研究及其應(yīng)用　出處：《中國建筑科學(xué)研究院》2014年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 軟測量 污垢熱阻 清洗報警 不確定度 IPLV

【摘要】：隨著科學(xué)技術(shù)的發(fā)展,測試技術(shù)得到了飛速的進(jìn)步,測試手段的發(fā)展日新月異,由傳統(tǒng)的模擬儀器、數(shù)字化儀器、智能化儀器到現(xiàn)今的虛擬儀器時代。然而,在實際測試過程中仍存在許多無法或難以直接用傳感器或過程檢測儀表進(jìn)行測量的重要過程參數(shù),軟測量技術(shù)是解決此類測試問題的有效途徑。 本文主要從冷水機(jī)組冷凝器污垢熱阻和冷水機(jī)組綜合部分性能系數(shù)IPLV兩個方面展開研究,采用軟測量技術(shù)中的基于機(jī)理的傳統(tǒng)建模與基于數(shù)據(jù)驅(qū)動的建模兩種方法,實現(xiàn)冷水機(jī)組冷凝器污垢熱阻清洗報警在線監(jiān)測及綜合部分負(fù)荷性能系數(shù)(IPLV)檢測。 首先從傳熱學(xué)基本原理出發(fā),建立污垢熱阻計算理論模型,找出污垢熱阻的影響因素,并分析了其對污垢系數(shù)的影響度。在此基礎(chǔ)上,對理論模型進(jìn)行改進(jìn),建立了基于不同對數(shù)平均溫差定義的冷水機(jī)組污垢熱阻測試模型。對兩種模型的適用范圍及各影響因素之間的關(guān)系展開討論,并對模型誤差進(jìn)行分析。重點討論了冷凝器傳熱系數(shù)、負(fù)荷率、冷凝器端差、污垢熱阻之間的關(guān)系,為冷凝器污垢熱阻的在線監(jiān)測提供理論支持。 在以上討論的基礎(chǔ)上,建立的基于制冷量衰減幅度冷凝器污垢熱阻清洗報警策略,該模型根據(jù)實際運行工況的不同設(shè)定不同的報警限值限定,對冷水機(jī)組污垢熱阻實現(xiàn)分類報警,避免了由于機(jī)組本身性能及運行條件的改變導(dǎo)致冷凝器端差變化的影響,適用于不同負(fù)荷率、不同機(jī)組的冷凝器污垢熱阻實時在線監(jiān)測。 在LabVIEW2012語言平臺下編寫“冷水機(jī)組冷凝器污垢熱阻清洗報警系統(tǒng)”程序,實現(xiàn)研究成果軟件應(yīng)用的轉(zhuǎn)化。對建立的清洗報警系統(tǒng)進(jìn)行不確定度分析。在10%以內(nèi)的相對不確定度的情況下,反推各測量儀器的允許不確定度,從而對相關(guān)傳感器準(zhǔn)確度進(jìn)行限定,保證監(jiān)測的結(jié)果可靠性。 最后對實際工程監(jiān)測數(shù)據(jù)進(jìn)行處理,比較四種BP模型訓(xùn)練周期,最終確定本文采用自適應(yīng)學(xué)習(xí)率調(diào)整的BP建模方法,建立了結(jié)構(gòu)為5×11×l的IPLV現(xiàn)場檢測模型。通過4個模型的對比校核及實際機(jī)組性能曲線的比較,證明所建立的模型具有較高的預(yù)測精度,為現(xiàn)場獲取機(jī)組的IPLV提供了便捷的途徑。
[Abstract]:With the development of science and technology, testing technology has made rapid progress, the development of test methods by simulation instrument, change rapidly, the traditional digital instrument, intelligent instrument to the era of virtual instrument nowadays. However, there are still many important process parameters are unable or difficult to be directly with the sensor or process measurement instrument were measured in the actual testing process in the soft measurement technology is the effective way to solve such test problems.
This paper mainly launches the research from two aspects of water chiller condenser fouling resistance and water chiller performance comprehensive coefficient IPLV, the soft measurement technology based on traditional modeling mechanism and modeling method based on data driven two, realize the condenser fouling resistance chiller cleaning alarm monitoring and integrated part load value (IPLV) detection.
First, from the basic principle of heat transfer, the establishment of fouling resistance calculation model, find out the factors influencing the fouling resistance, and analyzed its effect on fouling coefficient. On this basis, the theoretical model was improved, established the chiller fouling test model based on the definition of different logarithmic mean temperature difference. The relationship between factors of scope of the two models and the influence is discussed, and the model error is analyzed. The paper discusses the condenser heat transfer coefficient, load rate, condenser terminal difference, the relationship between fouling resistance, provide theoretical support for the on-line monitoring of condenser fouling resistance.
Based on the above discussion, based on the cooling capacity decay rate of fouling in condenser cleaning alarm strategy, the model according to the different setting of the actual operating conditions of different alarm limits defined for the water chiller fouling resistance classification alarm, to avoid the impact caused the condenser end difference unit performance and changes in operating conditions. That is suitable for different loading rate, different units of the real-time online monitoring of fouling in condenser.
Chiller condenser fouling cleaning alarm system "program" in the LabVIEW2012 language platform, realize the transformation of research achievements of software application. Uncertainty analysis is performed to establish the cleaning alarm system. Within 10% relative uncertainty under the condition of the measuring instruments allow estimation uncertainty, and the related sensor the accuracy was limited, ensure the monitoring results of reliability.
At the end of the actual monitoring data processing, comparison of four kinds of BP model training cycle, finally determined by using the adaptive learning rate adjustment method of BP modeling, establish the structure for detection model of 5 * 11 * l IPLV site. By comparing the performance curves of the 4 models and checking the actual units, demonstrate that the prediction accuracy the model has high, provides a convenient way for the field acquisition unit IPLV.

【學(xué)位授予單位】：中國建筑科學(xué)研究院
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TU831

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本文編號：1376155