發(fā)布時間：2018-10-10 07:43

【摘要】：近十多年來,我國的工業(yè)發(fā)展取得了長足的進步,其中冶金工業(yè)的發(fā)展,對國家經(jīng)濟、社會的快速成長和國防科技建設(shè)的提升起到了極大的促進作用。氧化鋁作為生產(chǎn)金屬鋁的原料,在鋁冶煉工業(yè)中具有舉足輕重的地位。目前,拜耳法是我國生產(chǎn)氧化鋁所采用的主要方法之一,在該工藝過程中,氧化鋁焙燒過程是影響氧化鋁質(zhì)量、生產(chǎn)能耗和生產(chǎn)成本的重要工段之一。利用智能化方法對焙燒過程進行建模,利用合適的算法進行焙燒的參數(shù)優(yōu)化和控制研究是氧化鋁生產(chǎn)工業(yè)技術(shù)創(chuàng)新的一個方向,是提高氧化鋁質(zhì)量的有效途徑。本文以氣態(tài)懸浮焙燒爐工藝為基礎(chǔ),采用改進粒子群(PSO)優(yōu)化極限學(xué)習(xí)機算法(ELM)對氧化鋁焙燒進行預(yù)測建模,利用遺傳算法(GA)完成氧化鋁焙燒工況參數(shù)的優(yōu)化,設(shè)計基于DCS的氧化鋁焙燒過程控制系統(tǒng),通過BP神經(jīng)網(wǎng)絡(luò)PID控制器實現(xiàn)焙燒關(guān)鍵參數(shù)的精確控制,主要內(nèi)容有:(1)針對焙燒過程建模困難的問題,分別采用BP神經(jīng)網(wǎng)絡(luò)、標準ELM和改進PSO優(yōu)化ELM建立焙燒溫度預(yù)測模型,對比發(fā)現(xiàn),采用改進PSO優(yōu)化方法相較于BPNN和標準ELM方法,在預(yù)測精度和泛化性能方面均有明顯優(yōu)勢。(2)針對焙燒過程參數(shù)耦合嚴重,工況波動頻繁的問題,利用遺傳算法,建立氧化鋁焙燒工況優(yōu)化模型。以實際生產(chǎn)正常工況狀態(tài)下焙燒溫度穩(wěn)定值(1070℃)為控制目標,尋找對焙燒溫度影響較大的操作參數(shù)在技術(shù)指標范圍內(nèi)的最優(yōu)組合,并以此為基礎(chǔ),建立優(yōu)化工況數(shù)據(jù)庫,在生產(chǎn)過程中,控制系統(tǒng)根據(jù)監(jiān)控到的焙燒溫度與設(shè)定值之間的偏差,從優(yōu)化工況數(shù)據(jù)庫中尋找最優(yōu)工況組合,指導(dǎo)對應(yīng)控制變量的實時調(diào)整,使得生產(chǎn)過程處于最優(yōu)狀態(tài),避免人工設(shè)定的主觀性和生產(chǎn)過程的誤操作,減少不必要的能耗,穩(wěn)定焙燒溫度,提高氧化鋁質(zhì)量。(3)針對氧化鋁焙燒過程自動化水平不足、生產(chǎn)和管理工作不完善的現(xiàn)狀,設(shè)計基于DCS的氧化鋁焙燒過程控制系統(tǒng)。采用BP神經(jīng)網(wǎng)絡(luò)PID控制器實現(xiàn)過程操作參數(shù)控制,以及生產(chǎn)過程的監(jiān)控,合理配置生產(chǎn)資料,以提高生產(chǎn)效率,降低企業(yè)生產(chǎn)成本。(4)以氧化鋁焙燒溫度為例,設(shè)計氧化鋁焙燒過程優(yōu)化控制系統(tǒng)。在高級過程控制系統(tǒng)仿真平臺上構(gòu)建對象模型、虛擬執(zhí)行機構(gòu)、基礎(chǔ)控制回路,進行仿真實驗,結(jié)果表明系統(tǒng)可以很好的跟蹤焙燒溫度的設(shè)定值,驗證了控制系統(tǒng)的可行性。
[Abstract]:In recent ten years, great progress has been made in China's industrial development, among which the development of metallurgical industry has played a great role in promoting the rapid growth of national economy and society and the promotion of national defense science and technology construction. Alumina, as the raw material of aluminum production, plays an important role in aluminum smelting industry. At present, Bayer process is one of the main methods of alumina production in China. In this process, alumina roasting process is one of the important sections which affect the quality, energy consumption and production cost of alumina. It is a direction of technological innovation in alumina production industry to model the roasting process by intelligent method and to optimize and control the roasting parameters by using the appropriate algorithm. It is an effective way to improve the quality of alumina. Based on the technology of gaseous suspension roaster, the prediction modeling of alumina roasting is carried out by using the improved particle swarm (PSO) optimization extreme learning machine (ELM) algorithm, and the optimization of operating conditions parameters of alumina roasting is accomplished by genetic algorithm (GA). The control system of alumina roasting process based on DCS is designed, and the precise control of the key parameters of roasting is realized by BP neural network PID controller. The main contents are as follows: (1) aiming at the difficult modeling problem of roasting process, BP neural network is adopted respectively. Standard ELM and modified PSO optimization ELM are used to establish the calcination temperature prediction model. The comparison of the improved PSO optimization method with BPNN and standard ELM method is made. It has obvious advantages in prediction accuracy and generalization performance. (2) aiming at the problems of severe coupling of calcination process parameters and frequent fluctuation of operating conditions, the optimization model of alumina roasting condition is established by genetic algorithm. Taking the stable value of calcination temperature (1070 鈩，

本文編號：2261205