本文選題：篦冷機(jī) + 預(yù)測(cè)控制　； 參考：《濟(jì)南大學(xué)》2017年碩士論文

【摘要】：篦冷機(jī)作為熟料冷卻系統(tǒng)的關(guān)鍵設(shè)備,冷卻效果的好壞不僅關(guān)系到水泥熟料的質(zhì)量,還會(huì)對(duì)水泥生產(chǎn)的其他環(huán)節(jié)產(chǎn)生間接影響。當(dāng)今國(guó)內(nèi)水泥廠(chǎng)篦冷機(jī)的控制主要以人工控制和半自動(dòng)控制為主,人工控制主觀(guān)性強(qiáng)、隨意性大,而且篦冷機(jī)自身存在非線(xiàn)性、大滯后、工況波動(dòng)頻繁等諸多問(wèn)題,對(duì)篦冷機(jī)的控制研究正由傳統(tǒng)控制算法向先進(jìn)智能控制算法研究轉(zhuǎn)變,因此,本課題對(duì)水篦冷機(jī)進(jìn)行預(yù)測(cè)控制研究是很有必要的。本文在深入了解水泥篦冷機(jī)工藝的基礎(chǔ)上,結(jié)合操作員的操作經(jīng)驗(yàn),以典型工況為切入點(diǎn),采用動(dòng)態(tài)矩陣控制算法,提出了基于典型工況下的篦冷機(jī)預(yù)測(cè)控制方案,最后采用模塊化思想對(duì)篦冷機(jī)預(yù)測(cè)控制軟件進(jìn)行設(shè)計(jì)與開(kāi)發(fā),經(jīng)現(xiàn)場(chǎng)調(diào)試后,應(yīng)用于現(xiàn)場(chǎng)實(shí)際控制中,通過(guò)實(shí)際應(yīng)用效果表明,該控制軟件取得了不錯(cuò)的控制效果,具有較強(qiáng)的實(shí)用性。本文的詳細(xì)工作內(nèi)容如下:(1)建立水泥篦冷機(jī)典型工況模板。針對(duì)篦冷機(jī)自身存在的非線(xiàn)性、工況波動(dòng)頻繁等問(wèn)題,采用K-means聚類(lèi)方法對(duì)歷史數(shù)據(jù)進(jìn)行聚類(lèi)分析,劃分出三種典型工況,并根據(jù)主導(dǎo)變量(篦壓)進(jìn)行劃分得到其他參變量的工況基準(zhǔn)值點(diǎn),所劃分的典型工況占到了整個(gè)篦冷機(jī)工況的90%左右,通過(guò)實(shí)際應(yīng)用也驗(yàn)證了該方法劃分工況的合理性和正確性。本課題是在典型工況背景下對(duì)篦冷機(jī)進(jìn)行研究,該研究也為下一步的研究奠定基礎(chǔ)。(2)典型工況下篦冷機(jī)預(yù)測(cè)模型研究。針對(duì)所劃分出的典型工況,選取現(xiàn)場(chǎng)典型歷史數(shù)據(jù),考慮到現(xiàn)場(chǎng)干擾眾多,數(shù)據(jù)難免會(huì)有誤差,因此,選取均值濾波方法對(duì)典型歷史數(shù)據(jù)進(jìn)行均值濾波處理,然后采用最小二乘法建立篦冷機(jī)預(yù)測(cè)模型。由于篦冷機(jī)長(zhǎng)時(shí)間運(yùn)行過(guò)程中存在工況的切換,當(dāng)工況變化時(shí),勢(shì)必會(huì)牽扯到模型的切換。針對(duì)不同工況間模型的切換,采用模糊加權(quán)的方式進(jìn)行平滑切換,以此來(lái)保證切換過(guò)程的平穩(wěn)性。(3)典型工況下篦冷機(jī)DMC預(yù)測(cè)控制器的研究。根據(jù)建立的預(yù)測(cè)模型設(shè)計(jì)篦冷機(jī)DMC控制器,通過(guò)MATLAB仿真確立了各典型工況下最佳的控制參數(shù),考慮到現(xiàn)場(chǎng)實(shí)際應(yīng)用,針對(duì)控制器之間的切換,采用模糊加權(quán)的方式實(shí)現(xiàn)平滑切換,并對(duì)所設(shè)計(jì)的控制器穩(wěn)定性性能進(jìn)行分析。(4)篦冷機(jī)預(yù)測(cè)控制軟件的工程實(shí)現(xiàn)。根據(jù)以上研究?jī)?nèi)容,采用模塊化思想開(kāi)發(fā)篦冷機(jī)預(yù)測(cè)控制軟件,該軟件主要由數(shù)據(jù)采集系統(tǒng)、數(shù)據(jù)庫(kù)存儲(chǔ)系統(tǒng)、控制算法系統(tǒng)等三部分組成。在實(shí)際生產(chǎn)現(xiàn)場(chǎng),該軟件以外掛包的方式安裝在工程師站,通過(guò)現(xiàn)場(chǎng)的實(shí)際運(yùn)行,取得了不錯(cuò)的控制效果,具有較強(qiáng)的實(shí)用性,同時(shí)也驗(yàn)證了本課題的研究意義所在。
[Abstract]:Grate cooler as the key equipment of clinker cooling system, the cooling effect is not only related to the quality of cement clinker, but also has an indirect effect on other aspects of cement production. At present, the control of grate cooler in domestic cement plant is mainly controlled by manual control and semi-automatic control. Manual control is subjective and random, and the grate cooler itself has many problems, such as nonlinear, large lag, frequent fluctuation of working conditions, and so on. The control research of grate cooler is changing from traditional control algorithm to advanced intelligent control algorithm. Therefore, it is necessary to study the predictive control of grate cooler in this paper. On the basis of deep understanding of cement grate cooler technology, combined with operator's operation experience, taking typical working condition as the breakthrough point and adopting dynamic matrix control algorithm, the predictive control scheme of grate cooler based on typical working condition is put forward in this paper. Finally, the predictive control software of grate cooler is designed and developed by modularization idea. After debugging, it is applied to the actual control on the spot. The result of practical application shows that the control software has achieved good control effect. It has strong practicability. The detailed work of this paper is as follows: (1) the typical working condition template of cement grate cooler is established. Aiming at the nonlinearity and frequent fluctuation of the grate cooler itself, K-means clustering method is used to cluster the historical data, and three typical working conditions are divided. According to the main variable (grate pressure), the reference points of other parameters are obtained. The typical working conditions are about 90% of the total grate cooler working conditions. The rationality and correctness of the method are verified by practical application. In this paper, the grate cooler is studied under the background of typical working conditions, which also lays a foundation for the next research. (2) the prediction model of grate cooler under typical working conditions. In view of the typical working conditions, the typical historical data is selected. Considering the numerous field interference, the data will inevitably have errors. Therefore, the mean value filtering method is selected to process the typical historical data. Then the prediction model of grate cooler is established by least square method. Due to the switching of the operating conditions during the long time operation of grate cooling units, the model switching will be involved when the operating conditions change. In order to ensure the smoothness of the switching process, the fuzzy weighting method is used to smooth the model switching between different working conditions. (3) the research of DMC predictive controller for grate cooler under typical working conditions. The DMC controller of grate cooler is designed according to the established prediction model. The optimal control parameters under typical working conditions are established by MATLAB simulation. Considering the practical application in the field, the switching between controllers is considered. The fuzzy weighting method is used to realize smooth switching and the stability performance of the controller is analyzed. (4) the engineering implementation of predictive control software for grate cooler. According to the above research contents, the predictive control software of grate cooler is developed by modularization. The software consists of three parts: data acquisition system, database storage system and control algorithm system. In the actual production site, the software is installed in the engineer station by the way of external package. Through the actual operation of the field, the software has achieved good control effect and has strong practicability. At the same time, it has verified the significance of the research of this subject.
【學(xué)位授予單位】：濟(jì)南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TQ172.622.4;TP273

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