本文選題：溢流粒度 + 軟測(cè)量�。� 參考：《大連理工大學(xué)》2015年碩士論文

【摘要】：磨礦過程的旋流器溢流粒度是判斷磨礦分級(jí)作業(yè)生產(chǎn)狀況及后續(xù)產(chǎn)品質(zhì)量的重要指標(biāo)。由于影響溢流粒度的因素很多且關(guān)系復(fù)雜,難以建立機(jī)理驅(qū)動(dòng)粒度檢測(cè)模型,因此,工業(yè)現(xiàn)場(chǎng)一般采用離線化驗(yàn)或在線檢測(cè)的方法對(duì)溢流粒度進(jìn)行檢測(cè)。然而,離線化驗(yàn)方法滿足不了實(shí)時(shí)性要求,在線檢測(cè)方法因受噪聲等因素影響測(cè)量精度不高。鑒于磨礦過程積累的大量歷史數(shù)據(jù),可以采用數(shù)據(jù)驅(qū)動(dòng)軟測(cè)量方法對(duì)溢流粒度進(jìn)行估計(jì),進(jìn)而為磨礦過程的控制及決策提供參考信息。針對(duì)溢流粒度檢測(cè)時(shí)存在的建模數(shù)據(jù)含噪聲信號(hào)較高,輔助變量難以確定,對(duì)溢流粒度建立軟測(cè)量模型既要求準(zhǔn)確性又要求穩(wěn)定性等問題,本文提出了一種基于多目標(biāo)分層遺傳算法的溢流粒度模糊建模方法,該方法將模糊模型分為四層：輸入變量層、隸屬度層、規(guī)則庫層和系統(tǒng)集成層。輸入變量層用于獲取軟測(cè)量模型的輔助變量,隸屬度層用于獲取隸屬度函數(shù)類型及相關(guān)參數(shù),對(duì)輔助變量進(jìn)行模糊劃分,規(guī)則庫層用于確定模型的所有規(guī)則,系統(tǒng)集成層將前三層關(guān)聯(lián)起來,代表一個(gè)完整的軟測(cè)量模型。為達(dá)到各層共同進(jìn)化的目的,本文設(shè)計(jì)了遺傳算法各層編碼策略,并構(gòu)建了以平均絕對(duì)百分誤差(Mean Absolute Percentage Error, MAPE)和均方根誤差(Root Mean Square Error, RMSE)為標(biāo)準(zhǔn)的適應(yīng)度函數(shù)來計(jì)算遺傳算法每一層個(gè)體的適應(yīng)度值。鑒于模糊模型訓(xùn)練過程中可能出現(xiàn)異常解,本文將L-M貝葉斯正則化方法融入訓(xùn)練過程。為驗(yàn)證本文方法的有效性,分別選取標(biāo)準(zhǔn)數(shù)據(jù)集和我國某選礦廠實(shí)際生產(chǎn)數(shù)據(jù)進(jìn)行實(shí)驗(yàn),并與已有多種方法進(jìn)行對(duì)比實(shí)驗(yàn),實(shí)驗(yàn)結(jié)果表明本文方法對(duì)含噪聲磨礦數(shù)據(jù)進(jìn)行軟測(cè)量建模具有較好的準(zhǔn)確性和穩(wěn)定性�；诒疚姆椒ㄋ鶎�(shí)現(xiàn)的軟件系統(tǒng)在實(shí)際應(yīng)用中效果顯著。
[Abstract]:The overflow granularity of hydrocyclone in grinding process is an important index to judge the production status of grinding classification operation and the quality of subsequent products.Because there are many factors affecting the overflow particle size and the relationship is complex, it is difficult to establish a mechanism-driven particle size detection model. Therefore, off-line or on-line detection methods are generally used to detect the overflow particle size in industrial field.However, the off-line test method can not meet the real-time requirements, and the measurement accuracy is not high due to noise and other factors.In view of the large amount of historical data accumulated in the grinding process, the method of data-driven soft sensing can be used to estimate the overflow particle size, thus providing reference information for the control and decision-making of the grinding process.In view of the high noise signal in the modeling data of overflow granularity detection, it is difficult to determine the auxiliary variables. The establishment of soft sensor model for overflow granularity requires both accuracy and stability.In this paper, a fuzzy modeling method of overflow granularity based on multi-objective hierarchical genetic algorithm is proposed. The fuzzy model is divided into four layers: input variable layer, membership layer, rule base layer and system integration layer.The input variable layer is used to obtain the auxiliary variables of the soft sensor model, the membership level is used to obtain the membership function types and related parameters, and the auxiliary variables are divided fuzzy, and the rule base layer is used to determine all the rules of the model.The system integration layer associates the first three layers and represents a complete soft sensor model.In order to achieve the goal of coevolution of each layer, the coding strategy of each layer of genetic algorithm is designed in this paper.The fitness function of mean Absolute Percentage error (MAPE) and Root Mean Square error (RMSE) is constructed to calculate the fitness of each layer of genetic algorithm.In this paper, L-M Bayesian regularization method is incorporated into the training process in view of the possible abnormal solutions in the process of fuzzy model training.In order to verify the validity of this method, the standard data set and the actual production data of a concentrator in our country are selected for experiments, and compared with many existing methods.The experimental results show that the proposed method has good accuracy and stability for soft sensor modeling of noisy grinding data.The software system based on this method is effective in practical application.
【學(xué)位授予單位】：大連理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TD921.4;TP18

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本文編號(hào)：1757489