本文選題：濕法煙氣脫硫 + PH值��； 參考：《西南石油大學》2015年碩士論文

【摘要】：火力發(fā)電廠煙氣脫硫系統(tǒng)中,吸收塔漿液PH值的大小直接關系到脫硫效率的高低。脫硫效率是評價火電廠S02排放是否達標的唯一指標,因此,對吸收塔漿液PH值進行實時、精確的檢測顯得尤為重要。在工程實踐中,吸收塔漿液PH值代表吸收塔溶液的酸堿度,通常是采用PH值測試儀進行測量,但PH測試儀失效的情況時有發(fā)生。當PH值測試儀失效時,易造成吸收塔漿液酸堿度的失衡。酸堿度失衡的吸收塔漿液會加速脫硫系統(tǒng)設備的損壞,嚴重時會造成整個脫硫系統(tǒng)癱瘓。為了將PH測試儀失效對整個脫硫系統(tǒng)的損害降到最小,脫硫系統(tǒng)在吸收塔漿液PH測試儀失效時會采用臨時方法對吸收塔漿液PH值進行測量。一般情況下,常用的臨時方法是采用冗余設備或現(xiàn)場取樣對吸收塔漿液PH值進行測量。鑒于傳統(tǒng)的臨時方法存在著經濟成本高、時滯性大的缺點,研發(fā)一種新型的PH值檢測方法就變得尤為重要。本文針對石灰石-石膏濕法煙氣脫硫工藝進行研究,提出一種將人工智能模型應用于吸收塔漿液PH值檢測的方法。首先,將濕法脫硫系統(tǒng)中吸收塔漿液PH值的影響因素煙氣流量、S02濃度、02含量、粉塵含量、煙氣溫度、吸收塔漿液密度、石灰石漿液密度等多個主要運行指標作為輸入變量,吸收塔漿液的PH值作為輸出變量,分別建立偏最小二乘回歸(PLS)、粒子群優(yōu)化的BP神經網絡(PSO-BP)、模擬退火優(yōu)化的支持向量機(SA-SVM)以及遺傳優(yōu)化的最小二乘支持向量機(GALS-SVM)等檢測模型。其次,取西南地區(qū)某裝機容量為600MW的火力發(fā)電廠的實時檢測數據,用已建立四種模型對脫硫系統(tǒng)中的吸收塔漿液PH值進行檢測,確定人工智能模型的可行性。第三,選取同一組樣本數據對上述四種人工智能模型進行檢驗,比較幾個模型的精確度。經研究分析發(fā)現(xiàn)：相對于其他三種人工智能檢測模型,PSO-BP神經網絡檢測模型的相對誤差最小,具有更好的檢測性能。第四,為了得到更加精確的結果,引入裁剪平均法對四種人工智能模型檢測結果進行處理。最后,將優(yōu)化后的人工智能模型應用于該電廠,證實了使用該人工智能模型對脫硫系統(tǒng)運行成本的控制有顯著的效果。研究結果表明,人工智能檢測模型能夠用于石灰石-石膏濕法煙氣脫硫系統(tǒng)吸收塔漿液PH值檢測的研究,并為脫硫系統(tǒng)的安全生產、節(jié)能減排與成本控制提供更加可靠的保障。
[Abstract]:In the flue gas desulfurization system of thermal power plant, the PH value of absorber slurry is directly related to the desulfurization efficiency. Desulfurization efficiency is the only index to evaluate whether S02 emission is up to standard, so it is very important to measure the PH value of absorber slurry in real time and accurately. In engineering practice, the pH value of the slurry of the absorber represents the pH of the solution of the absorber, which is usually measured by using the PH tester, but the failure of the PH tester occurs from time to time. When the PH tester fails, it is easy to cause the imbalance of pH and alkalinity of the absorber slurry. The slurry of absorber with unbalanced acidity and alkalinity will accelerate the damage of desulfurization system equipment and cause the whole desulfurization system to be paralyzed. In order to minimize the damage to the whole desulfurization system caused by the failure of the PH tester, a temporary method will be used to measure the PH value of the slurry in the absorber during the failure of the PH tester of the absorber slurry. In general, the commonly used temporary method is to measure the PH value of the absorber slurry by using redundant equipment or field sampling. In view of the disadvantages of the traditional temporary method, such as high economic cost and large delay, it is very important to develop a new PH detection method. Based on the study of limestone gypsum wet flue gas desulphurization process, an artificial intelligence model is proposed to detect PH value of slurry in absorber. Firstly, the main operating parameters, such as S02 concentration, dust content, flue gas temperature, absorption tower slurry density, limestone slurry density and so on, are taken as input variables, which are the influencing factors of PH value of absorber slurry in wet desulphurization system, such as the content of S02, dust content, flue gas temperature, limestone slurry density and so on. The PH value of the slurry of the absorber is taken as the output variable, and the detection models such as partial least square regression, PSO BP neural network, simulated annealing optimized support vector machine (SA-SVM) and genetic optimization least squares support vector machine (GALS-SVM) are established, respectively. Secondly, taking the real-time detection data of a 600MW thermal power plant in southwest China, the PH value of absorber slurry in desulfurization system is detected by using four established models, and the feasibility of artificial intelligence model is determined. Thirdly, four artificial intelligence models mentioned above are tested with the same set of sample data, and the accuracy of several models is compared. It is found that compared with the other three artificial intelligence detection models, the PSO-BP neural network has the least relative error and has better detection performance. Fourthly, in order to obtain more accurate results, clipping average method is introduced to deal with the results of four artificial intelligence models. Finally, the optimized artificial intelligence model is applied to the power plant, and it is proved that the artificial intelligence model has a remarkable effect on the operation cost control of desulfurization system. The results show that the artificial intelligence detection model can be used to detect the PH value of slurry in the absorption tower of limestone gypsum wet flue gas desulfurization system, and provide more reliable guarantee for the safe production, energy saving and emission reduction and cost control of the desulfurization system.
【學位授予單位】：西南石油大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：X773;X831

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