本文關鍵詞： 粒徑分布 數(shù)粒平衡方程 狀態(tài)觀測器 低階矩網(wǎng)絡模型 迭代學習控制　出處：《北京化工大學》2015年碩士論文　論文類型：學位論文

【摘要】：近年來,化工行業(yè)和醫(yī)學行業(yè)的飛速發(fā)展,使得結(jié)晶過程面臨著巨大的挑戰(zhàn)和機遇。為了滿足工業(yè)日益發(fā)展的需要,人們對晶體的形貌、粒度和分布的要求也越來越高。因此,實現(xiàn)結(jié)晶過程的在線粒徑分布控制是企業(yè)取勝的關鍵性所在,然而目前對粒徑分布的直接在線測量缺乏有效的手段,如何實現(xiàn)粒徑分布的在線測量并達到優(yōu)化控制的目的成為結(jié)晶過程亟待解決的問題。本文致力于結(jié)晶過程粒徑分布的在線控制研究,來提高終點產(chǎn)品質(zhì)量。本文首先介紹了結(jié)晶過程的反應機理,包括成核速率模型、生長速率模型、溶解度模型、物料衡算模型和數(shù)粒衡算模型的關系和原理。在此基礎上,本文研究了結(jié)晶模型的數(shù)值求解方法、建立結(jié)晶過程的機理模型,分析不同的操作變量對粒徑分布的影響。由于結(jié)晶過程的粒徑分布不可測量或需要花費昂貴的代價,通常采用粒徑分布的矩值代替粒徑分布。針對矩模型不能直接觀測晶體分布的問題,本文在矩模型中應用狀態(tài)估計的方法獲得粒徑分布,通過觀測矩值的變化估計溶液濃度的變化,進而實現(xiàn)直接對粒徑分布的觀測,對整個動態(tài)過程的研究及控制起到了重要的作用。結(jié)晶過程的機理模型通常是在理想情況下建立的,精度有時候難以滿足要求。本文采用數(shù)據(jù)建模的方法,求得結(jié)晶過程分布與矩的關系,建立操作變量與低階矩之間的網(wǎng)絡模型,降低建模難度。然后根據(jù)給定的優(yōu)化性能指標,推導出自適應迭代學習控制率對粒徑分布的低階矩進行控制,從而實現(xiàn)對粒徑分布的間接控制。最后將該方法應用到草酸鈷結(jié)晶過程中,仿真結(jié)果證明了該方法的可行性。
[Abstract]:In recent years, with the rapid development of chemical industry and medical industry, the crystallization process is facing great challenges and opportunities. The key to the success of enterprises is to realize the on-line particle size distribution control in crystallization process. However, the direct on-line measurement of particle size distribution is lack of effective means at present. How to realize the on-line measurement of particle size distribution and achieve the purpose of optimizing control become the urgent problem of crystallization process. This paper is devoted to the research of on-line control of particle size distribution in crystallization process. Firstly, the reaction mechanism of crystallization process was introduced, including nucleation rate model, growth rate model, solubility model, material balance model and particle balance model. In this paper, the numerical solution method of crystallization model is studied, the mechanism model of crystallization process is established, and the influence of different operating variables on particle size distribution is analyzed. The particle size distribution is usually replaced by the moment value of the particle size distribution. In view of the problem that the moment model can not directly observe the crystal distribution, the particle size distribution is obtained by using the method of state estimation in the moment model. The change of solution concentration is estimated by the change of observed moment, and the observation of particle size distribution is realized directly. It plays an important role in the study and control of the whole dynamic process. The mechanism model of the crystallization process is usually established under ideal conditions, and the precision is sometimes difficult to meet the requirements. In this paper, the method of data modeling is used. The relationship between the distribution of the crystallization process and the moment is obtained, and the network model between the operation variables and the lower moments is established to reduce the difficulty of modeling. The adaptive iterative learning control rate is derived to control the lower moment of particle size distribution, and the indirect control of particle size distribution is realized. Finally, the method is applied to the crystallization process of cobalt oxalate, and the simulation results show that the method is feasible.
【學位授予單位】：北京化工大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TQ026.5

【參考文獻】

相關碩士學位論文 前1條

1 何興學;醋酸鈉結(jié)晶動力學研究[D];浙江工業(yè)大學;2012年

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本文編號：1544831