本文選題：加熱爐 + 過程控制��； 參考：《北京科技大學(xué)》2017年博士論文

【摘要】：加熱爐是連接連鑄和軋線的關(guān)鍵中間設(shè)備,用于加熱板坯使之達(dá)到軋制溫度。作為加熱爐的核心控制系統(tǒng),過程控制系統(tǒng)的主要任務(wù)是根據(jù)生產(chǎn)工藝和相關(guān)數(shù)學(xué)模型來(lái)控制和協(xié)調(diào)生產(chǎn)設(shè)備,通過優(yōu)化設(shè)定獲得符合軋制溫度要求的板坯。由于板坯加熱過程具有時(shí)間長(zhǎng)、變量多和非線性等特點(diǎn),對(duì)其溫度的預(yù)報(bào)較為復(fù)雜,為了建立兼顧精確性和快速性的在線模型,需要在眾多參數(shù)(輻射、對(duì)流、板坯灰度、氧化層、水梁等)中選取對(duì)其影響較大的因素。同時(shí),軋制計(jì)劃的編排很難避免板坯混裝的情況出現(xiàn),這種混裝導(dǎo)致的板坯材料和規(guī)格的復(fù)雜多變性,使得現(xiàn)有研究中對(duì)板坯進(jìn)行批量處理的控制方法較為粗糙。本文針對(duì)這些問題,在熱傳導(dǎo)機(jī)理模型的基礎(chǔ)上,采用數(shù)值建模的方法,構(gòu)建了改進(jìn)的板坯溫度預(yù)報(bào)模型和加熱爐爐溫在線設(shè)定模型。取得了如下創(chuàng)新性成果:(1)通過建立板坯三維溫度場(chǎng)模型,定量分析了氧化層和水梁"黑印"對(duì)板坯溫度計(jì)算的影響,并得到板坯在長(zhǎng)寬高方向的溫度分布。在此基礎(chǔ)上,引入氧化燒損模型,在"黑印"上方、板坯長(zhǎng)高方向上選取計(jì)算域,提出了一種考慮氧化層增長(zhǎng)的、適于在線應(yīng)用的板坯二維溫度預(yù)報(bào)模型,并通過埋偶實(shí)驗(yàn)獲得的板坯表面實(shí)測(cè)值,驗(yàn)證了該模型的精度。(2)為校正板坯內(nèi)部溫度的預(yù)報(bào)偏差,提出板坯內(nèi)部溫度預(yù)報(bào)校正模型。該模型考慮了板坯溫度預(yù)報(bào)模型中參數(shù)的不確定性,并用PCA模型表示這種不確定性下的模型偏差;在對(duì)計(jì)算域中實(shí)驗(yàn)點(diǎn)的單元格進(jìn)行偏差校正后,基于MLS建立單元格參數(shù)與模型偏差的響應(yīng)曲面,以實(shí)現(xiàn)利用有限的實(shí)驗(yàn)數(shù)據(jù)對(duì)計(jì)算域中溫度偏差進(jìn)行近似與校正的功能。實(shí)驗(yàn)結(jié)果顯示,校正后的偏差值比校正前下降了 42%,有效彌補(bǔ)了原始模型的偏差。(3)考慮不同鋼種、規(guī)格板坯混裝的實(shí)際復(fù)雜工業(yè)生產(chǎn)情況,建立加熱爐爐溫在線設(shè)定模型。該模型利用基于改進(jìn)遺傳算法的加熱爐爐溫離線優(yōu)化模型所得到的爐溫值,考慮鋼種等級(jí)、板坯厚度等四個(gè)評(píng)價(jià)指標(biāo),采用改進(jìn)型熵權(quán)-TOPSIS法對(duì)每塊板坯對(duì)應(yīng)的爐溫進(jìn)行賦權(quán),以得到兼顧所有板坯屬性的最優(yōu)爐溫設(shè)定值,提高了板坯溫度的控制精度。將本系統(tǒng)應(yīng)用于某鋼廠加熱爐后,解決了其原有模型預(yù)報(bào)精度低,能耗高等難題;大幅度提高了 RDT預(yù)報(bào)精度,減少了人工操作時(shí)間,在保證加熱質(zhì)量的情況下降低了爐溫;為廠方提高生產(chǎn)效率的同時(shí)節(jié)約了能源。
[Abstract]:Heating furnace is the key intermediate equipment to connect continuous casting and rolling line. It is used to heat slab to reach rolling temperature. As the core control system of the reheating furnace, the main task of the process control system is to control and coordinate the production equipment according to the production process and related mathematical models. Because the slab heating process has the characteristics of long time, many variables and nonlinear, the prediction of the temperature is more complicated. In order to establish the on-line model with both accuracy and rapidity, many parameters (radiation, convection, slab gray scale) are needed. The oxidation layer, water beam, etc. At the same time, the rolling schedule arrangement is difficult to avoid the situation of slab mixed loading, which leads to the complex variability of slab material and specification, which makes the control method of batch processing of slab in the existing research rough. In this paper, based on the heat conduction mechanism model, an improved slab temperature prediction model and a furnace temperature on-line setting model are constructed by numerical modeling. Through the establishment of three-dimensional temperature field model of slab, the influence of oxide layer and "black print" of water beam on slab temperature calculation is quantitatively analyzed, and the temperature distribution of slab in the direction of length, width and height is obtained. On the basis of this, the oxidation burn model is introduced, and the calculation field is selected in the direction of slab length and height above "black print". A two-dimensional temperature prediction model of slab is proposed, which is suitable for on-line application and takes account of the growth of oxide layer. The accuracy of the model is verified by the experimental data obtained from the buried couple experiment, which is used to correct the prediction deviation of the internal temperature of the slab, and a correction model for the prediction of the internal temperature of the slab is put forward. The model takes into account the uncertainty of parameters in the slab temperature prediction model, and uses PCA model to express the model deviation under this uncertainty. The response surface of cell parameter and model deviation is established based on MLS to realize the function of approximating and correcting the temperature deviation in computational domain using limited experimental data. The experimental results show that the deviation value after correction is 42% lower than that before correction, which effectively makes up for the deviation of the original model. (3) considering the actual complex industrial production situation of different steel grades and mixed slab, the on-line setting model of furnace temperature for heating furnace is established. The model uses the furnace temperature value obtained from the off-line optimization model of furnace temperature based on improved genetic algorithm, considering four evaluation indexes such as steel grade and slab thickness, and uses the improved entropy weight TOPSIS method to weight the furnace temperature corresponding to each slab. In order to obtain the optimal furnace temperature setting value which takes all slab attributes into account, the control precision of slab temperature is improved. The system has been applied to a reheating furnace in a steel plant to solve the problems of low prediction precision and high energy consumption of the original model, greatly improve the precision of RDT prediction, reduce the manual operation time, and reduce the furnace temperature under the condition of ensuring the heating quality. Improve the production efficiency for the factory at the same time saving energy.
【學(xué)位授予單位】：北京科技大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2017
【分類號(hào)】：TG307;TP273

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 蘇福永;溫治;;富氧燃燒氣氛中鋼坯的氧化過程[J];工程科學(xué)學(xué)報(bào);2016年09期

2 范國(guó)鋒;彭麗玲;王華;;基于正態(tài)云模型的加熱爐生產(chǎn)過程統(tǒng)計(jì)分析及優(yōu)化控制[J];工業(yè)加熱;2016年04期

3 劉穎;劉義平;陶曙明;溫治;;低負(fù)荷下蓄熱式加熱爐燃燒策略數(shù)值模擬[J];中南大學(xué)學(xué)報(bào)(自然科學(xué)版);2016年06期

4 鄒家民;楊文德;曾偉華;楊文倩;;管式加熱爐熱效率研究[J];青島科技大學(xué)學(xué)報(bào)(自然科學(xué)版);2015年S2期

5 呂志鵬;伍吉倉(cāng);鄭二龍;;利用移動(dòng)最小二乘法進(jìn)行點(diǎn)云變形分析的研究[J];測(cè)繪通報(bào);2015年07期

6 伊智;張衛(wèi)軍;陳海耿;;總括熱吸收率的補(bǔ)償辨識(shí)[J];東北大學(xué)學(xué)報(bào)(自然科學(xué)版);2014年07期

7 肖楠;陳海耿;胡文超;江華;;考慮遮蔽作用的鋼坯水梁黑印數(shù)值模擬[J];軋鋼;2014年03期

8 伊智;張衛(wèi)軍;陳海耿;;總括熱吸收率的基本辨識(shí)[J];東北大學(xué)學(xué)報(bào)(自然科學(xué)版);2014年06期

9 柯華立;董斌;葉波;;板坯加熱爐升溫曲線研究及應(yīng)用[J];冶金自動(dòng)化;2014年03期

10 郭金維;蒲緒強(qiáng);高祥;張永安;;一種改進(jìn)的多目標(biāo)決策指標(biāo)權(quán)重計(jì)算方法[J];西安電子科技大學(xué)學(xué)報(bào);2014年06期

相關(guān)博士學(xué)位論文 前3條

1 楊小玲;多屬性決策分析及其在洪災(zāi)風(fēng)險(xiǎn)評(píng)價(jià)中的應(yīng)用研究[D];華中科技大學(xué);2012年

2 方俊濤;響應(yīng)曲面方法中試驗(yàn)設(shè)計(jì)與模型估計(jì)的比較研究[D];天津大學(xué);2011年

3 任劍;模糊環(huán)境下信息不完全的隨機(jī)多準(zhǔn)則決策方法研究[D];中南大學(xué);2010年

相關(guān)碩士學(xué)位論文 前10條

1 郭春賢;移動(dòng)最小二乘法的快速算法及其應(yīng)用[D];重慶大學(xué);2016年

2 魏亞帥;基于ANFIS的軋鋼加熱爐爐溫智能控制的研究[D];遼寧科技大學(xué);2015年

3 閆曉瑩;基于改進(jìn)粒子群優(yōu)化的軋制力反建模型及自學(xué)習(xí)[D];燕山大學(xué);2015年

4 寧晨陽(yáng);基于模糊PID的步進(jìn)式加熱爐燃燒控制方法研究[D];大連理工大學(xué);2014年

5 張廷玉;加熱爐鋼溫建模與爐溫優(yōu)化設(shè)定研究[D];東北大學(xué);2014年

6 雷杰;富氧燃燒時(shí)加熱爐中鋼坯的加熱及氧化特性研究[D];重慶大學(xué);2014年

7 賀志紅;基于PLC的步進(jìn)梁式加熱爐監(jiān)控系統(tǒng)設(shè)計(jì)[D];燕山大學(xué);2013年

8 楊偉超;加熱爐加熱質(zhì)量?jī)?yōu)化策略研究[D];天津大學(xué);2012年

9 趙麗紅;熱連軋數(shù)學(xué)模型自學(xué)習(xí)研究[D];東北大學(xué);2012年

10 張晶旭;熱帶鋼精軋機(jī)組預(yù)設(shè)定模型的自學(xué)習(xí)研究[D];燕山大學(xué);2012年

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