本文選題：浮選過程 + 軟測量��； 參考：《遼寧科技大學(xué)》2015年碩士論文

【摘要】：由于浮選生產(chǎn)過程是一個復(fù)雜的物理化學(xué)綜合反應(yīng)過程,它具有強(qiáng)非線性、強(qiáng)耦合性等特點,因此精礦品位和浮選回收率很難在線實時的獲取。本文提出采用人工神經(jīng)網(wǎng)絡(luò)和軟測量建模相結(jié)合的方法對精礦品位和浮選回收率進(jìn)行預(yù)測。本文具體工作主要有以下幾個方面:以浮選過程的精礦品位和浮選回收率預(yù)測為目標(biāo),提出了一種基于PSO-GSA算法優(yōu)化的浮選過程前饋神經(jīng)網(wǎng)絡(luò)軟測量模型。萬有引力算法雖然具有較好的尋優(yōu)能力,但是其收斂速度較慢,并且容易陷入局部最優(yōu)。本章利用粒子群算法優(yōu)化萬有引力算法中的速度和位置,從而提高收斂速度和預(yù)測精度。最后運用所提算法優(yōu)化前饋神經(jīng)網(wǎng)絡(luò)軟測量模型參數(shù),并對浮選過程的關(guān)鍵工藝技術(shù)指標(biāo)進(jìn)行預(yù)測和仿真。其次提出了一種基于浮選泡沫圖像特征提取的混洗布谷鳥搜索算法優(yōu)化BP神經(jīng)網(wǎng)絡(luò)軟測量模型。由于浮選泡沫圖像中包含大量有關(guān)浮選過程的信息,因此針對浮選泡沫圖像的顏色、視覺和形狀共14個參數(shù)進(jìn)行特征提取,以作為精礦品位軟測量模型的輸入變量;并采用等距映射方法對高維輸入向量進(jìn)行降維,降低BP神經(jīng)網(wǎng)絡(luò)的輸入維數(shù)和網(wǎng)絡(luò)規(guī)模;最后提出一種自適應(yīng)步長的混洗布谷鳥搜索算法優(yōu)化BP神經(jīng)網(wǎng)絡(luò)軟測量模型,并對該模型進(jìn)行仿真驗證。最后提出一種基于改進(jìn)螢火蟲優(yōu)化算法的浮選過程回聲狀態(tài)網(wǎng)絡(luò)軟測量模型。將浮選過程數(shù)據(jù)和從浮選圖像數(shù)據(jù)提取的圖像特征信息共同作為軟測量模型的輔助變量,并采用核主元分析方法對高維輸入向量進(jìn)行降維,提取非線性主元,以降低ESN的目標(biāo)維數(shù)和網(wǎng)絡(luò)規(guī)模;然后采用基于擁擠度因子的GSO算法對浮選過程ESN軟測量模型進(jìn)行優(yōu)化,并對精礦品位和浮選回收率進(jìn)行預(yù)測仿真�？傊�,通過仿真結(jié)果表明了三種神經(jīng)網(wǎng)絡(luò)軟測量模型均能夠取得較好的預(yù)測效果,能夠提高浮選過程中精礦品位和浮選回收率的預(yù)測精度,可以滿足浮選生產(chǎn)過程的控制要求。
[Abstract]:Because the flotation process is a complex physical and chemical synthesis reaction process, it has the characteristics of strong nonlinearity and strong coupling, so the concentrate grade and the flotation recovery rate are difficult to obtain on line. In this paper, a combination of artificial neural network and soft measurement modeling is proposed to predict the grade of concentrate and the recovery rate of flotation. The main work of this paper is as follows: in order to predict the concentrate grade of flotation process and the recovery rate of flotation, a soft sensing model of feedforward neural network based on the optimization of PSO-GSA algorithm is proposed. Although the universal gravitational algorithm has good optimization ability, its convergence speed is slow and it is easy to fall into the process. In this chapter, the particle swarm optimization is used to optimize the speed and position of the gravitational force algorithm, thus improving the convergence speed and prediction accuracy. Finally, the proposed algorithm optimizes the parameters of the soft sensing model of the feedforward neural network, and pretests and simulate the key technological parameters of the flotation process. Secondly, a flotation bubble is proposed. The BP neural network soft measurement model is optimized by the mixed washing cuckoo search algorithm. The flotation foam image contains a lot of information about the flotation process, so the color, the vision and the shape of the floatation foam image are extracted with 14 parameters to make the input variable for the soft measurement model of the concentrate grade. An isometric mapping method is used to reduce the dimension of high dimensional input vector and reduce the input dimension and network size of BP neural network. Finally, a kind of adaptive step size mixed washing cuckoo search algorithm is proposed to optimize the soft sensing model of BP neural network, and the model is verified by simulation. Finally, a new algorithm based on improved firefly optimization algorithm is proposed. The soft measurement model of the process echo state network is selected. The flotation process data and the image feature information extracted from the floatation image data are used as auxiliary variables of the soft measurement model, and the kernel principal component analysis method is used to reduce the dimension of the high-dimensional input vector, and the nonlinear principal element is extracted to reduce the target dimension and network size of ESN; then, it is used to reduce the dimension of the target and the network size. Based on the GSO algorithm of crowding degree factor, the ESN soft measurement model of flotation process is optimized, and the concentrate grade and flotation recovery rate are predicted and simulated. In conclusion, the simulation results show that three neural network soft sensing models can achieve better prediction effect, and can improve the concentrate grade and flotation recovery rate in the process of high flotation. The prediction accuracy can meet the control requirements of flotation production process.
【學(xué)位授予單位】：遼寧科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TD923;TP18;TP391.41

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本文編號：2004750