本文選題：連鑄　切入點：粘結(jié)漏鋼　出處：《大連理工大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：連鑄是現(xiàn)代煉鋼企業(yè)鑄造鋼坯最主要的方法,它具有簡化生產(chǎn)工序、鋼水利用率高、耗能低、鋼坯質(zhì)量好、改善勞動條件、生產(chǎn)過程機(jī)械化和自動化程度高等優(yōu)點。連鑄技術(shù)的應(yīng)用徹底改變了鑄造車間的生產(chǎn)工藝流程、人員分配和物流控制,為鋼鐵生產(chǎn)的自動化和信息化技術(shù)的應(yīng)用,從而大幅改善環(huán)境和提高產(chǎn)品質(zhì)量提供了有力的物質(zhì)條件保障。漏鋼是連鑄過程中極具危害性的重大生產(chǎn)事故,為避免連鑄漏鋼事故的發(fā)生,國際上通常從兩個方向上著手研究解決,一方面是改善連鑄設(shè)備和工藝條件,從漏鋼形成的機(jī)理上杜絕事故的發(fā)生；另一方面是及時檢測識別出漏鋼的特征,采取減速拉坯等有效措施避免漏鋼。本文對粘結(jié)漏鋼的形成機(jī)理,漏鋼模式識別原理進(jìn)行了分析,理解溫度的動態(tài)傳遞是漏鋼過程的物理特性的基本映射。把熱電偶的動態(tài)數(shù)據(jù)轉(zhuǎn)化為靜態(tài)數(shù)據(jù),組合空間網(wǎng)絡(luò)結(jié)構(gòu)以便于區(qū)域判別法的實現(xiàn)。在此基礎(chǔ)上尋求一種有效的基于GA-BP神經(jīng)網(wǎng)絡(luò)模式識別的連鑄機(jī)漏鋼預(yù)報模型。本文對結(jié)晶器粘結(jié)漏鋼時熱電偶溫度的變化進(jìn)行了分析,將熱電偶的溫度隨時間變化規(guī)律作為模式識別的特征,從而進(jìn)行是否漏鋼的判定。本模型既建立了單個電偶的單偶時序網(wǎng)絡(luò),又建立了多個電偶的組偶空間網(wǎng)絡(luò),在組偶空間模型的建立中,區(qū)別之前的“1”字型二電偶空間網(wǎng)絡(luò)結(jié)構(gòu)和“⊥”字型的四電偶空間網(wǎng)絡(luò)結(jié)構(gòu),建立了“T”字型的四電偶空間網(wǎng)絡(luò)結(jié)構(gòu),并對兩種四電偶空間網(wǎng)絡(luò)結(jié)構(gòu)進(jìn)行了比對,因而連鑄機(jī)漏鋼模式的識別將更準(zhǔn)確、更及時。本文對BP神經(jīng)網(wǎng)絡(luò)和遺傳算法進(jìn)行了探討,將遺傳算法與BP神經(jīng)網(wǎng)絡(luò)結(jié)合,利用遺傳算法對BP神經(jīng)網(wǎng)絡(luò)的初始權(quán)值進(jìn)行全局最優(yōu)搜索,其中使用了利用導(dǎo)數(shù)修正種群中個體數(shù)的參數(shù),從而改善了遺傳算法局部搜索能力不強(qiáng)的缺點,加速了遺傳算法的收斂速度,搜索到條件最優(yōu)解后,將最優(yōu)解賦予BP網(wǎng)絡(luò)進(jìn)行精確求解。這樣既避免了局部極小的問題,又達(dá)到了快速高效和全局尋優(yōu)的目的,使訓(xùn)練結(jié)果得到極大的改善。BP神經(jīng)網(wǎng)絡(luò)以最速下降法為學(xué)習(xí)準(zhǔn)則,以誤差反向傳播算法進(jìn)行連接權(quán)值的調(diào)整,將大量訓(xùn)練樣本數(shù)據(jù)輸入到神經(jīng)網(wǎng)絡(luò)中,得到最優(yōu)的權(quán)值解,進(jìn)而獲得相對較優(yōu)的連鑄機(jī)漏鋼預(yù)報模型神經(jīng)網(wǎng)絡(luò)。利用Visual Studio 2010集成開發(fā)環(huán)境,用C++語言完成程序的開發(fā)和界面設(shè)計,界面部分采用MFC設(shè)計完成。在軟件開發(fā)中使用了具有跨語言、跨平臺的專業(yè)圖形程序接口功能的OpenGL完成熱電偶波形的顯示。訓(xùn)練并離線測試了單偶時序網(wǎng)絡(luò)和組偶空間網(wǎng)絡(luò)預(yù)報模型,測試結(jié)果表明,基于GA-BP神經(jīng)網(wǎng)絡(luò)模式識別的連鑄機(jī)漏鋼預(yù)報模型具有很好的非線性映射精度和分類識別能力。
[Abstract]:Continuous casting is the most important method for casting billets in modern steelmaking enterprises. It has the advantages of simplifying production process, high utilization ratio of molten steel, low energy consumption, good quality of billets and improving working conditions. The application of continuous casting technology has completely changed the production process, the distribution of personnel and the control of logistics in the foundry workshop, for the application of automation and information technology in iron and steel production. Thus greatly improving the environment and improving the quality of products provide a strong material condition guarantee. Steel breakout is a very harmful production accident in the continuous casting process, in order to avoid the occurrence of continuous casting breakout accidents, The international studies and solutions are usually carried out in two directions. On the one hand, it is to improve the equipment and technological conditions of continuous casting, to prevent the occurrence of accidents from the mechanism of steel breakout formation; on the other hand, to detect and identify the characteristics of steel breakout in time. In this paper, the forming mechanism of bond breakout and the principle of pattern recognition of steel breakout are analyzed. Understand that the dynamic transfer of temperature is the basic mapping of the physical properties of the breakout process. The combined spatial network structure is convenient for the realization of zone discriminant method. On this basis, an effective model for predicting steel breakout in continuous casting machine based on GA-BP neural network pattern recognition is sought. The thermocouple temperature of mould bonding breakout is studied in this paper. Has been analyzed, The temperature variation of thermocouple with time is taken as the characteristic of pattern recognition, so as to determine whether the steel breakout is broken. This model not only establishes the single pair sequential network of single couple, but also establishes the even space network of multiple pairs. In the establishment of the model of even space, the network structure of "1" type of two-electric couple space and "Karabakh" type of four-pair space is distinguished, and the "T" type of four-pair space network structure is established. Two kinds of four-couple spatial network structure are compared, so the recognition of the breakout pattern of continuous casting machine will be more accurate and timely. This paper discusses BP neural network and genetic algorithm, and combines genetic algorithm with BP neural network. The genetic algorithm is used to search the initial weights of BP neural network, in which the parameters of individual number in the population are modified by using the derivative, which improves the weak local search ability of the genetic algorithm. The convergence speed of genetic algorithm is accelerated. After searching the conditional optimal solution, the optimal solution is assigned to the BP network to solve the problem accurately. This not only avoids the problem of local minima, but also achieves the purpose of fast and high efficiency and global optimization. The training results are greatly improved. BP neural network takes the steepest descent method as the learning criterion, adjusts the link weight by the error back-propagation algorithm, and inputs a large number of training sample data into the neural network to obtain the optimal weight solution. Finally, a relatively good neural network for predicting steel breakout of continuous casting machine is obtained. Using Visual Studio 2010 integrated development environment, C language is used to complete the program development and interface design. The interface is designed by MFC. In software development, cross-language is used. The display of thermocouple waveform is accomplished by OpenGL with the function of professional graphic program interface across platforms. The prediction models of single and even sequence networks and even space networks are trained and tested offline. The test results show that, The failure prediction model of continuous casting machine based on GA-BP neural network pattern recognition has good nonlinear mapping accuracy and classification recognition ability.
【學(xué)位授予單位】：大連理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TF777

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本文編號：1597454