發(fā)布時(shí)間：2018-12-19 08:08

【摘要】：選礦企業(yè)作為典型的連續(xù)流程型企業(yè),其最關(guān)鍵生產(chǎn)指標(biāo)通常指精礦品位及作業(yè)回收率,對(duì)于浮選過(guò)程而言,精礦品位的穩(wěn)定與否更是對(duì)企業(yè)的經(jīng)濟(jì)效益起著決定性的作用。傳統(tǒng)的選礦廠(chǎng)通常參考要達(dá)到的經(jīng)濟(jì)目標(biāo)根據(jù)選礦流程機(jī)理和工廠(chǎng)生產(chǎn)積累的經(jīng)驗(yàn)把要達(dá)到的生產(chǎn)指標(biāo)分解為對(duì)應(yīng)的工藝指標(biāo),例如給礦粒度、礦漿濃度和浮選劑添加量等,車(chē)間工作人員的工作則是將這些工藝指標(biāo)控制在在規(guī)定的范圍內(nèi),而生產(chǎn)管理人員則按照工藝指標(biāo)是否在規(guī)定范圍內(nèi)來(lái)判斷生產(chǎn)操作的好壞。本文的所研究的主要是通過(guò)對(duì)浮選生產(chǎn)過(guò)程關(guān)鍵工藝指標(biāo)進(jìn)行優(yōu)化從而實(shí)現(xiàn)對(duì)浮選生產(chǎn)過(guò)程中浮選經(jīng)濟(jì)指標(biāo)最優(yōu)控制的目的。本文以某選礦廠(chǎng)采集的浮選經(jīng)濟(jì)指標(biāo)數(shù)據(jù)為基礎(chǔ),選取給礦品位、給礦粒度、給礦濃度、給礦流量四種數(shù)據(jù)作為RBF神經(jīng)系統(tǒng)的輸入量,精礦品位以及作業(yè)回收率兩種指標(biāo)作為神經(jīng)網(wǎng)絡(luò)的輸出量,利用MATLAB軟件中的simulink工具箱進(jìn)行編譯程序、模擬仿真。對(duì)比實(shí)際曲線(xiàn)與期望曲線(xiàn)之間的誤差,觀(guān)察實(shí)際曲線(xiàn)是否平滑,調(diào)節(jié)神經(jīng)網(wǎng)絡(luò)與混沌時(shí)間序列結(jié)合后的系統(tǒng)精度,考察調(diào)整參數(shù)后系統(tǒng)的擬合程度是否已達(dá)到要求值。實(shí)驗(yàn)結(jié)果分析表明:利用RBF神經(jīng)網(wǎng)絡(luò)可對(duì)混沌系統(tǒng)進(jìn)行預(yù)測(cè)分析,算法簡(jiǎn)便,響應(yīng)速度快,省去許多繁瑣的步驟,提高運(yùn)算效率。同時(shí)通過(guò)對(duì)Mackey-Glass和Lorenz混沌系統(tǒng)的模擬仿真也直接表明了利用神經(jīng)網(wǎng)絡(luò)對(duì)混沌系統(tǒng)的建模及分析可以有效提高系統(tǒng)的的精度。同時(shí),對(duì)于浮選過(guò)程的建模及仿真也充分說(shuō)明了RBF神經(jīng)網(wǎng)絡(luò)可對(duì)混沌時(shí)間序列進(jìn)行有效預(yù)測(cè),也是可以應(yīng)用與生產(chǎn)實(shí)踐的有效方法,也為未來(lái)的研究打好了基礎(chǔ)。
[Abstract]:As a typical continuous process type enterprise, the most key production index usually refers to concentrate grade and operation recovery rate. For flotation process, the stability of concentrate grade plays a decisive role in the economic benefit of the enterprise. The traditional concentrator usually refers to the economic objectives to be achieved. According to the mechanism of the processing process and the experience accumulated in the production of the plant, the production indexes to be achieved are decomposed into corresponding technological indicators, such as the ore size, pulp concentration and the amount of flotation agent, etc. The work of the workshop staff is to control these process indicators within a specified range, while the production management personnel judge the quality of the production operation according to whether the process indicators are within the specified range. The main purpose of this paper is to optimize the key technological parameters of flotation production process so as to achieve the purpose of optimal control of flotation economic indexes in flotation production process. Based on the economic index data of flotation collected from a concentrator, four kinds of data, such as feed grade, ore size, ore concentration and ore flux, are selected as the input amount of RBF neural system. The two indexes of concentrate grade and job recovery are used as the output of neural network. The simulink toolbox in MATLAB software is used to compile the program, and the simulation is carried out. By comparing the error between the actual curve and the expected curve, the paper observes whether the actual curve is smooth, adjusts the precision of the system after the combination of neural network and chaotic time series, and investigates whether the fitting degree of the system has reached the required value after adjusting the parameters. The experimental results show that the chaotic system can be predicted and analyzed by RBF neural network. The algorithm is simple, the response speed is fast, and many tedious steps are eliminated, and the operation efficiency is improved. At the same time, the simulation of Mackey-Glass and Lorenz chaotic system also shows that the modeling and analysis of chaotic system using neural network can effectively improve the accuracy of the system. At the same time, the modeling and simulation of flotation process fully show that RBF neural network can effectively predict chaotic time series, is also an effective method for application and production practice, and provides a good foundation for future research.
【學(xué)位授予單位】：遼寧科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類(lèi)號(hào)】：F426.1;TP183

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