本文選題：燒結(jié)工藝　切入點(diǎn)：神經(jīng)網(wǎng)絡(luò)　出處：《東華大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：鋼鐵企業(yè)的能源消耗問題以及工業(yè)流程中的動(dòng)態(tài)監(jiān)控問題始終是鋼鐵行業(yè)關(guān)注的問題。燒結(jié)是鐵前三工藝之一,其生產(chǎn)過程是典型的物質(zhì)和能量的連續(xù)、非連續(xù)變化的復(fù)雜過程,且一些流程參數(shù)存在滯后性、測量不準(zhǔn)確等特點(diǎn),因此加強(qiáng)燒結(jié)過程的監(jiān)督與控制能夠有效地控制能源消耗以及燒結(jié)礦的質(zhì)量。目前工業(yè)中運(yùn)用的三維監(jiān)控還比較少,大多運(yùn)用二維平面監(jiān)控,為了更真實(shí)的展現(xiàn)燒結(jié)工藝,本文利用三維技術(shù)進(jìn)行監(jiān)控系統(tǒng)的設(shè)計(jì)。整體思路為:針對燒結(jié)工藝的動(dòng)態(tài)流程,利用神經(jīng)網(wǎng)絡(luò)建立模型,完成工藝流程的動(dòng)態(tài)預(yù)測;同時(shí)利用三維建模技術(shù)對流程進(jìn)行三維模型的建立完成監(jiān)控部分的設(shè)計(jì)。具體得到研究與設(shè)計(jì)結(jié)果如下:1.參數(shù)選取:針對燒結(jié)工藝流程進(jìn)行詳細(xì)分析與了解,依據(jù)流程特點(diǎn)以及涉及到的各類工業(yè)參數(shù),選取出對最終能耗影響較大的9個(gè)參數(shù),進(jìn)行數(shù)據(jù)收集。2.系統(tǒng)總體設(shè)計(jì)與數(shù)據(jù)庫設(shè)計(jì):基于整個(gè)系統(tǒng)需求,進(jìn)行系統(tǒng)的總體設(shè)計(jì),并且對數(shù)據(jù)庫進(jìn)行了設(shè)計(jì)。通過進(jìn)行數(shù)據(jù)庫的需求分析,完成基礎(chǔ)表功能的確定,再進(jìn)行概念結(jié)構(gòu)的設(shè)計(jì),轉(zhuǎn)化為E-R圖,最終根據(jù)E-R圖進(jìn)行用戶表的設(shè)計(jì)。3.能耗預(yù)測:針對燒結(jié)工藝流程特點(diǎn)以及參數(shù)特點(diǎn),運(yùn)用小波神經(jīng)網(wǎng)絡(luò)進(jìn)行能耗的預(yù)測。針對參數(shù)的數(shù)據(jù)特點(diǎn),進(jìn)行神經(jīng)網(wǎng)絡(luò)的學(xué)習(xí)與訓(xùn)練,完成能耗的靜態(tài)預(yù)測。此處的靜態(tài)預(yù)測是預(yù)測一天當(dāng)中平均的能耗(kgce/t),由于燒結(jié)工藝的連續(xù)性與時(shí)滯性,在靜態(tài)預(yù)測的基礎(chǔ)上進(jìn)行動(dòng)態(tài)的預(yù)測,即預(yù)測某采樣期的能耗值。動(dòng)態(tài)預(yù)測中,神經(jīng)網(wǎng)絡(luò)的輸入為要預(yù)測的當(dāng)前采樣點(diǎn)的影響參數(shù)以及前幾采樣點(diǎn)的影響參數(shù),輸出為當(dāng)前采樣期的能耗值。這樣能夠充分考慮到工藝的時(shí)滯性,使能耗預(yù)測更為及時(shí)準(zhǔn)確。4.針對監(jiān)控環(huán)節(jié)進(jìn)行了三維監(jiān)控系統(tǒng)的研究與設(shè)計(jì)。功能包括流程顯示、數(shù)據(jù)的顯示、場景的切換、報(bào)警提示等。它涉及的主要模塊有:1)3D模型設(shè)計(jì)模塊:實(shí)現(xiàn)工業(yè)流程中各器件的三維再現(xiàn),依據(jù)流程進(jìn)行三維模型的建立,包括燒結(jié)機(jī)、環(huán)冷機(jī)等模型的設(shè)計(jì)與創(chuàng)建;2)動(dòng)畫及界面顯示模塊:將創(chuàng)建好的3D模型導(dǎo)入U(xiǎn)nity3D中,運(yùn)用自帶的粒子系統(tǒng)以及GUI系統(tǒng)進(jìn)行三維動(dòng)畫特效和顯示界面的添加與制作,最終完成初步的顯示界面;3)報(bào)警模塊:依據(jù)顯示框中的能耗預(yù)測值與真實(shí)值進(jìn)行比較,最終實(shí)現(xiàn)當(dāng)兩者差值較大時(shí),報(bào)警燈閃爍,提示操作者進(jìn)行參數(shù)調(diào)整;4)數(shù)據(jù)顯示模塊:將Unity3D和數(shù)據(jù)庫進(jìn)行連接,實(shí)現(xiàn)兩者之間的數(shù)據(jù)的交互。本論文最終實(shí)現(xiàn)了設(shè)計(jì)的三維監(jiān)控系統(tǒng),為燒結(jié)工業(yè)流程的監(jiān)控提供較好的依據(jù)。
[Abstract]:The problem of energy consumption in iron and steel enterprises and the problem of dynamic monitoring in industrial process are always concerned by the iron and steel industry. Sintering is one of the first three iron processes, and its production process is a typical continuous process of material and energy. The complex process of discontinuous change, and some process parameters have the characteristics of lag, inaccurate measurement and so on. Therefore, strengthening the supervision and control of sintering process can effectively control the energy consumption and the quality of sinter. The whole idea is: according to the dynamic process of sintering process, the neural network is used to establish the model to complete the dynamic prediction of the process flow. At the same time, the 3D modeling technology is used to build the 3D model of the process to complete the design of the monitoring part. The specific research and design results are as follows: 1. Parameter selection: detailed analysis and understanding of the sintering process, According to the process characteristics and all kinds of industrial parameters involved, 9 parameters which have a great impact on the final energy consumption are selected for data collection .2.The overall design and database design of the system: based on the requirements of the whole system, the overall design of the system is carried out. And the database is designed. By analyzing the requirements of the database, the function of the basic table is determined, and then the conceptual structure is designed, which is transformed into the E-R diagram. Finally, according to E-R diagram, the user table is designed .3.Energy consumption prediction: according to the characteristics of sintering process and parameters, wavelet neural network is used to predict energy consumption. According to the data characteristics of parameters, the study and training of neural network are carried out. The static prediction is to predict the average energy consumption during the day. Because of the continuity and time delay of the sintering process, the static prediction is based on the static prediction. In dynamic prediction, the input of the neural network is the influence parameters of the current sampling points to be predicted and the influence parameters of the previous sampling points. The output is the energy consumption value of the current sampling period. In this way, the time-delay of the process can be fully taken into account, and the energy consumption prediction can be more timely and accurate .4. the research and design of the three-dimensional monitoring system for the monitoring link is carried out. Data display, scene switching, alarm warning and so on. The main modules involved in it are: 1: 1 / 1 3D model design module: realize 3D reproduction of each device in the industrial process, build 3D model according to the process, including sintering machine, Design and creation of models such as ring cooling machine) Animation and Interface display Module: the created 3D model is imported into Unity3D, and the special effects of 3D animation and the adding and making of display interface are carried out by using the particle system and GUI system. Finally, the initial display interface (3) alarm module is completed: according to the energy consumption prediction value in the display box and the real value, the alarm lamp flashes when the difference between the two values is large. The data display module: connecting Unity3D and database to realize the data interaction between the two. Finally, this paper realizes the design of a three-dimensional monitoring system. It provides a good basis for the monitoring of sintering process.
【學(xué)位授予單位】：東華大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP277

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