本文選題：聯(lián)合粉磨系統(tǒng) + 水泥粒度　； 參考：《濟(jì)南大學(xué)》2016年碩士論文

【摘要】：水泥是我國基礎(chǔ)建設(shè)和經(jīng)濟(jì)發(fā)展必不可少的基本原材料,其生產(chǎn)中重要的一個環(huán)節(jié)為水泥聯(lián)合粉磨。該環(huán)節(jié)是由穩(wěn)流倉、輥壓機(jī)、打散機(jī)(V型選粉機(jī))、磨機(jī)、選粉機(jī)以及主排風(fēng)機(jī)等設(shè)備組成的復(fù)雜系統(tǒng),其中穩(wěn)流倉和水泥粒度對水泥生產(chǎn)穩(wěn)定及質(zhì)量有著重要的影響。為此,本文圍繞穩(wěn)流倉和水泥粒度兩個重心,開展水泥粉磨建模與預(yù)測控制研究工作。具體研究工作如下:(1)針對帶有水泥粒度的聯(lián)合粉磨系統(tǒng)建模問題,給出一種分工況的聯(lián)合粉磨粒度建模方法。依據(jù)聯(lián)合粉磨工藝和在線粒度分析儀,分析關(guān)鍵變量之間的相互影響關(guān)系,并通過歷史數(shù)據(jù)劃分了水泥粒度工況模板(兩個典型工況區(qū)間);采用滑動平均濾波方法降低歷史數(shù)據(jù)噪聲對建模的影響;針對典型工況1,采用回歸分析算法建立多輸入單輸出的粒度模型;針對典型工況2,采用最小二乘支持向量機(jī)(LS_SVM)進(jìn)行了相應(yīng)建模;仿真結(jié)果說明基于水泥粒度工況模版所建立的模型能夠較好地描述水泥粒度動態(tài)變化過程。(2)為實現(xiàn)聯(lián)合粉磨穩(wěn)流倉的穩(wěn)定控制,給出了基于神經(jīng)網(wǎng)絡(luò)極限學(xué)習(xí)機(jī)(ELMNN)的建模以及內(nèi)�？刂啤；诼�(lián)合粉磨系統(tǒng)工藝和變量關(guān)系分析,確定了喂料量為影響穩(wěn)流倉料位主要因素;采用滑動平均濾波對數(shù)據(jù)進(jìn)行降噪;利用ELMNN建立穩(wěn)流倉內(nèi)部模型,通過Taylor級數(shù)設(shè)計了穩(wěn)流倉內(nèi)�？刂破�,并分析了閉環(huán)系統(tǒng)的穩(wěn)定性;仿真結(jié)果說明所提出的建模方法和控制器能夠?qū)崿F(xiàn)穩(wěn)流倉料位穩(wěn)定控制。(3)針對帶有非線性的水泥聯(lián)合粉磨粒度(45μm篩余)穩(wěn)定控制問題,給出一種基于模型的廣義預(yù)測粒度控制方法�；�(1)中所建立的典型工況1模型,通過受控自回歸積分滑動平均模型(CARIMA)和長時段的優(yōu)化性能指標(biāo),設(shè)計了廣義預(yù)測粒度控制器;借助粒度閉環(huán)傳遞函數(shù),將粒度閉環(huán)系統(tǒng)轉(zhuǎn)換為粒度內(nèi)模結(jié)構(gòu)形式,并分析了閉環(huán)系統(tǒng)穩(wěn)定性;仿真結(jié)果證明了所提出方法的有效性。(4)在(1)~(3)的研究成果基礎(chǔ)上,將專家系統(tǒng)、Bang-Bang控制、內(nèi)�？刂埔约皬V義預(yù)測控制算法相結(jié)合,構(gòu)建了水泥聯(lián)合粉磨系統(tǒng)的自動控制軟件平臺。工程應(yīng)用說明該平臺具有良好的運(yùn)行效果。
[Abstract]:Cement is an essential raw material for infrastructure construction and economic development in China. Cement grinding is an important link in its production. This link is a complex system composed of steady flow bin, roller press, dispersing machine (V type separator, grinding machine, separator and main exhaust air machine), in which steady flow bin and cement particle size have important influence on the stability and quality of cement production. Therefore, the modeling and predictive control of cement grinding are carried out in this paper, focusing on the two centers of gravity of steady flow bin and cement granularity. The specific research work is as follows: (1) aiming at the modeling problem of combined grinding system with cement particle size, a modeling method of combined grinding particle size is presented. According to the combined grinding process and the on-line particle size analyzer, the interaction between the key variables was analyzed. According to the historical data, the cement granularity working mode template (two typical working conditions) is divided, and the influence of the historical data noise reduction on the modeling is reduced by the sliding average filter method. For typical condition 1, regression analysis algorithm is used to establish the granularity model of multiple input and single output, and for typical condition 2, the corresponding modeling is carried out by using least square support vector machine (LSSVM). The simulation results show that the model based on cement granularity working condition template can better describe the dynamic change process of cement granularity. The modeling and internal model control of ELMNN based on neural network are presented. Based on the analysis of the process and the variable relation of the combined grinding system, the feeding quantity is determined as the main factor affecting the material level of the steady flow silo; the moving average filter is used to reduce the noise of the data; and the internal model of the steady flow bin is established by using ELMNN. The internal model controller of steady flow bin is designed by Taylor series, and the stability of closed loop system is analyzed. The simulation results show that the proposed modeling method and controller can realize the stable control of the material level of the steady flow silo. A generalized predictive granularity control method based on model is presented. A generalized predictive granularity controller is designed based on the model of typical working condition 1, which is based on the controlled autoregressive integral moving average model (CARIMA) and the optimization performance index for a long period of time, with the aid of the granularity closed-loop transfer function. The closed-loop system is transformed into a granular internal model structure, and the stability of the closed-loop system is analyzed. The simulation results show that the proposed method is effective, and the expert system Bang-Bang is controlled on the basis of the research results. Combined with internal model control and generalized predictive control algorithm, the automatic control software platform of cement combined grinding system is constructed. The engineering application shows that the platform has good running effect.
【學(xué)位授予單位】：濟(jì)南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TQ172.63;TP273

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