【摘要】：水泥粉磨是水泥生產的最后一個環(huán)節(jié),直接決定了水泥生產線最終的產量及質量。近年來,半終粉磨工藝的應用在一定程度上增加了水泥產量,降低了能源消耗;在線激光粒度分析儀在水泥粉磨環(huán)節(jié)上的應用,顯現(xiàn)了其在提質、節(jié)能、降耗和增產方面的巨大潛力。本文以在線激光粒度分析儀為檢測手段,結合半終粉磨工藝機理,圍繞水泥粉磨環(huán)節(jié)的磨機負荷和水泥粒度,開展半終粉磨系統(tǒng)建模及數(shù)據(jù)驅動控制研究。本文研究內容為山東省重大專項“智能化工廠關鍵技術研究與應用示范(2015ZDXX010F01)”和國際合作項目“面向節(jié)能減排的水泥生產過程集成控制系統(tǒng)研究(SQ2013ZOC600)”兩個項目的核心內容之一,主要研究工作如下:(1)針對水泥半終粉磨系統(tǒng)的磨機負荷和水泥粒度兩個關鍵參數(shù),在分析其影響因素的基礎上,建立了其各自數(shù)學模型。選取循環(huán)風機轉速和磨機電流分別作為神經網絡的輸入和輸出參量,采用極限學習機神經網絡(ELM)算法建立了磨機負荷的數(shù)學模型,其輸入層與隱含層的權值和隱含層神經元的閾值在辨識過程中隨機產生且保持不變,隱含層神經元的數(shù)量被確定之后便可得到唯一最優(yōu)解,并進行了仿真驗證;選取后選粉機轉速為模型輸入,小于45?m顆粒含量為模型輸出,采用最小二乘法建立了水泥粒度的數(shù)學模型,仿真驗證顯現(xiàn)了該模型與水泥粒度的動態(tài)變化具有良好的一致性,為后續(xù)水泥粒度控制算法的研究奠定了基礎。(2)為提高半終粉磨粒度控制的穩(wěn)定性和魯棒性,給出了一種基于數(shù)據(jù)驅動技術的水泥粒度自適應PID控制方法,解決了控制方法對模型的依賴問題。在建立水泥粒度系統(tǒng)緊格式動態(tài)線性化數(shù)據(jù)模型的基礎上,應用粒度控制系統(tǒng)的I/O數(shù)據(jù)(后選粉機轉速和小于45?m顆粒含量)估計其偽偏導數(shù)(PPD),調整PID控制器的參數(shù);仿真結果驗證了該控制算法的有效性。(3)提出了包括系統(tǒng)軟硬件架構、數(shù)據(jù)庫設計、Bang-Bang與數(shù)據(jù)驅動自適應PID控制相結合的半終粉磨粒度優(yōu)化控制方案,研發(fā)了半終粉磨粒度優(yōu)化控制系統(tǒng),進行了工程應用,取得良好運行效果。
[Abstract]:Cement grinding is the last link in cement production, which directly determines the final output and quality of cement production line. In recent years, the application of semi-finished grinding technology has increased cement production to a certain extent and reduced energy consumption. The application of on-line laser particle size analyzer in cement grinding shows its great potential in improving quality, saving energy, reducing consumption and increasing production. In this paper, based on on-line laser particle size analyzer and combined with the mechanism of semi-finish grinding, the modeling and data-driven control of semi-finished grinding system are studied around the mill load and cement particle size of cement grinding link. The research contents of this paper are "key Technology Research and Application demonstration (2015ZDXX010F01) of Intelligent Factory" and "Research on Integrated Control system of cement production process oriented to Energy Saving and Emission reduction (SQ2013ZOC600)" and International Cooperation Project "Research on cement production process Integrated Control system for Energy Saving and Emission reduction (SQ2013ZOC600)" One of the core elements of the project, The main research work is as follows: (1) aiming at the two key parameters of grinding machine load and cement particle size of the cement semi-finished grinding system, based on the analysis of its influencing factors, the respective mathematical models are established. The rotational speed of circulating fan and the current of mill are selected as the input and output parameters of neural network, and the mathematical model of mill load is established by using the (ELM) algorithm of extreme learning machine neural network. The weights of the input layer and the hidden layer and the threshold value of the neuron in the hidden layer are generated randomly and remain unchanged during the identification process. The number of neurons in the hidden layer is determined and the unique optimal solution is obtained. Selecting the rotational speed of the separator as the model input and the particle content less than 45 m as the model output, the mathematical model of cement particle size is established by using the least square method. The simulation results show that the model is in good agreement with the dynamic change of cement particle size, which lays a foundation for the subsequent research of cement granularity control algorithm. (2) to improve the stability and robustness of semi-finished grinding granularity control. An adaptive PID control method for cement granularity based on data-driven technology is presented, and the dependence of the control method on the model is solved. Based on the dynamic linearization data model of cement granularity system compact format, the pseudo-partial derivative (PPD), is estimated by using the I / O data of the granularity control system (the speed of the later separator and the particle content less than 45m). Adjust the parameters of PID controller; Simulation results verify the effectiveness of the control algorithm. (3) A semi-finish-grinding granularity optimization control scheme, which includes system hardware and software architecture, database design, Bang-Bang and data-driven adaptive PID control, is proposed. The particle size optimization control system of semi-finish grinding has been developed and applied in engineering, and good operation effect has been obtained.
【學位授予單位】：濟南大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TQ172.632;TP273

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