發(fā)布時間：2018-05-06 12:26

  本文選題：轉(zhuǎn)爐煉鋼 + 數(shù)據(jù)解析��； 參考：《東北大學(xué)》2014年碩士論文

【摘要】：如何提高鋼鐵產(chǎn)品質(zhì)量和有效控制產(chǎn)品生產(chǎn)周期是鋼鐵企業(yè)在激烈的市場競爭中面臨的兩大難題。前者可通過操作優(yōu)化、過程控制等手段完善生產(chǎn)工藝；后者則需要對產(chǎn)品的生產(chǎn)周期進(jìn)行有效地預(yù)測判斷。兩者相輔相成,分別從“質(zhì)”和“量”的角度提升企業(yè)效益,提高競爭力。煉鋼是鋼鐵生產(chǎn)中的一道重要工序。在轉(zhuǎn)爐煉鋼過程中,碳與溫度的走勢直接反應(yīng)了轉(zhuǎn)爐內(nèi)的冶煉狀態(tài),決定著氧氣與其他輔料的加入策略,影響著出鋼質(zhì)量。因此,預(yù)測煉鋼過程中鋼水溫度和碳含量對提高煉鋼產(chǎn)品質(zhì)量至關(guān)重要。另一方面,鋼鐵企業(yè)的生產(chǎn)計劃都是圍繞產(chǎn)品合同和生產(chǎn)能力展開,合同生產(chǎn)周期的管理是鋼鐵生產(chǎn)管理中非常重要的一項(xiàng)任務(wù)。預(yù)測合同完成時間有助于從整體上把握合同進(jìn)程,指導(dǎo)生產(chǎn)計劃的排制和調(diào)整。本文利用數(shù)據(jù)解析方法,通過建立企業(yè)實(shí)際生產(chǎn)數(shù)據(jù)的解析模型,分別針對煉鋼過程中鋼水溫度和碳含量以及產(chǎn)品合同生產(chǎn)周期的預(yù)測問題進(jìn)行了解析研究。主要內(nèi)容包括以下幾個部分：(1)以鋼鐵企業(yè)轉(zhuǎn)爐煉鋼為背景,研究鋼水溫度與碳含量的預(yù)測問題。針對該問題,設(shè)計了基于改進(jìn)粒子群算法的最小二乘支持向量機(jī)方法,并分別建立鋼水溫度和碳含量的預(yù)測模型。建模過程中,采用多階段建模方法,實(shí)現(xiàn)對整個過程的動態(tài)預(yù)測。對于實(shí)際數(shù)據(jù)采集不細(xì)致的問題,采用插值算法對數(shù)據(jù)進(jìn)行預(yù)處理。最后通過實(shí)際數(shù)據(jù)進(jìn)行多組實(shí)驗(yàn),驗(yàn)證了該方法的有效性及多階段動態(tài)建模的準(zhǔn)確性。(2)以鋼鐵企業(yè)生產(chǎn)周期管理為背景,研究合同完成時間預(yù)測問題。鋼廠合同類型多,生產(chǎn)工序多且雜,產(chǎn)生了大量的合同數(shù)據(jù)信息。如何根據(jù)合同類型和特征,對合同完成時間進(jìn)行快速準(zhǔn)確的預(yù)測是本文研究重點(diǎn)。針對該問題,建立了基于粒子群算法的最小二乘支持向量機(jī)預(yù)測模型,利用歷史數(shù)據(jù)進(jìn)行解析模型訓(xùn)練,并用實(shí)際生產(chǎn)數(shù)據(jù)進(jìn)行實(shí)驗(yàn)分析,驗(yàn)證了模型的有效性。(3)以某鋼廠的實(shí)際合同管理為背景,設(shè)計開發(fā)了鋼鐵合同管理子系統(tǒng),對合同完成時間進(jìn)行預(yù)測并對當(dāng)前各類合同完成情況進(jìn)行統(tǒng)計。系統(tǒng)不僅實(shí)現(xiàn)了對生產(chǎn)過程中合同數(shù)據(jù)的監(jiān)控和分析功能,還與KPI績效管理系統(tǒng)兼容,將合同管理指標(biāo)作為員工個人的績效評價指標(biāo),實(shí)現(xiàn)績效考評的功能。
[Abstract]:How to improve the quality of iron and steel products and how to effectively control the production cycle are two major problems faced by iron and steel enterprises in the fierce market competition. The former can improve the production process by means of operation optimization and process control, while the latter needs to predict and judge the production cycle of the product effectively. The two supplement each other, from the angle of "quality" and "quantity" to enhance the efficiency of enterprises and enhance their competitiveness. Steelmaking is an important process in steel production. In the process of converter steelmaking, the trend of carbon and temperature directly reflects the smelting state in converter, determines the addition strategy of oxygen and other auxiliary materials, and affects the quality of steel production. Therefore, it is very important to predict the temperature and carbon content of molten steel to improve the quality of steelmaking products. On the other hand, the production planning of iron and steel enterprises revolves around product contract and production capacity, and the management of contract production cycle is a very important task in steel production management. Forecasting the completion time of the contract will help to master the process of the contract as a whole and guide the scheduling and adjustment of the production plan. Based on the method of data analysis, the prediction of temperature and carbon content of molten steel and the production cycle of product contract in the process of steelmaking are studied by establishing the analytical model of actual production data in the enterprise. The main contents are as follows: (1) based on the background of converter steelmaking in iron and steel enterprises, the prediction of molten steel temperature and carbon content is studied. To solve this problem, the least squares support vector machine (LS-SVM) method based on improved particle swarm optimization (PSO) algorithm is designed, and the prediction models of molten steel temperature and carbon content are established respectively. In the process of modeling, the multi-stage modeling method is used to realize the dynamic prediction of the whole process. The interpolation algorithm is used to preprocess the actual data. Finally, the validity of the method and the accuracy of multi-stage dynamic modeling are verified by many experiments based on the actual data. Based on the production cycle management of iron and steel enterprises, the prediction of contract completion time is studied. There are many types of contracts and many production processes in steel mills, which produce a large amount of contract data information. How to predict the contract completion time quickly and accurately according to contract types and characteristics is the focus of this paper. In order to solve this problem, a prediction model of least squares support vector machine based on particle swarm optimization algorithm is established. The analytical model is trained by using historical data, and the experimental analysis is carried out with actual production data. The validity of the model is verified. (3) based on the actual contract management in a steel plant, the steel contract management subsystem is designed and developed, the contract completion time is forecasted and the current contract completion situation is counted. The system not only realizes the function of monitoring and analyzing the contract data in the production process, but also compatible with the KPI performance management system. It regards the contract management index as the performance evaluation index of the individual employees and realizes the function of performance evaluation.
【學(xué)位授予單位】：東北大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：F426.31;F273.2

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