【摘要】：振動篩是工程中應用最廣泛的篩分設備,主要是通過電機施加振動使物料經(jīng)過篩網(wǎng)選別按物料粒度大小分成若干個等級。實踐證明,篩分機理的突破是促進篩分設備創(chuàng)新與發(fā)展的決定因素。篩分過程主要分為被篩顆粒物的松散、分層、觸篩和透篩四個細節(jié)過程。本文運用離散單元法對平動與擺動復合的新型振動篩分形式下顆粒群的篩分過程進行數(shù)值模擬的研究,分析了不同篩分參數(shù)對分層以及篩分效率的影響,主要內(nèi)容有:1.建立了平-擺復合篩的三維模型,以EDEM軟件為試驗模擬平臺模擬篩分過程,設計試驗,研究分層機理。2.提出了全新的概念——分層沉降系數(shù),定義了平動與擺動復合的新型振動篩分形式下顆粒群分層的特征量(沉降差);尋找顆粒群分層與篩分參數(shù)(振動參數(shù)、結(jié)構(gòu)參數(shù)以及生產(chǎn)工藝參數(shù))之間的關(guān)系,建立了以分層沉降系數(shù)為基礎的沉降差與篩分參數(shù)之間的模型。3.篩分參數(shù)的優(yōu)化:設置振動參數(shù)與結(jié)構(gòu)參數(shù)的正交試驗,考慮振動頻率與擺動頻率的交互作用,分析篩分參數(shù)影響分層的顯著性,并且獲得最佳分層的最優(yōu)篩分參數(shù)組合。4.沉降差的預測:從篩分參數(shù)與沉降差的關(guān)系的角度,應用基于結(jié)構(gòu)最小化準則的支持向量回歸(Support Vector Regression,SVR)方法,建立SVR預測模型來研究預測沉降差問題。在選擇適當?shù)暮撕瘮?shù)和參數(shù)的基礎上,對沉降差進行預測,能獲得較小的誤差,說明了支持向量回歸能夠較好地表示沉降差與篩分參數(shù)之間的非線性映射關(guān)系,用支持向量回歸預測沉降差是合理的,它為預測沉降差提供了一種嶄新的方法。5.在不同的篩分參數(shù)下,通過對比沉降差與篩分效率的關(guān)系,分析分層對篩分效率的影響,豐富分層理論。
[Abstract]:Vibrating screen is the most widely used screening equipment in engineering. The main reason is that the material can be divided into several grades according to the size of the material through the sieve screen selection by the vibration of the motor. Practice shows that the breakthrough of screening mechanism is the decisive factor to promote the innovation and development of screening equipment. The screening process is mainly divided into four detail processes: loose, stratified, contact and permeable particles. In this paper, the discrete element method is used to simulate the sieving process of the particle group in the new type of vibrating screen. The effects of different screening parameters on the stratification and screening efficiency are analyzed. The main contents are: 1: 1. The three-dimensional model of flat-pendulum composite sieve was established. The screening process was simulated with EDEM software, the experiment was designed and the delamination mechanism was studied. In this paper, a new concept, stratified settlement coefficient, is proposed, the characteristic quantity (settlement difference) of particle group stratification is defined under the new type of vibrating screen, and the parameters of particle group stratification and screening (vibration parameter, vibration parameter) are found. The relationship between structural parameters and production process parameters, the model of settlement difference and sieving parameters based on stratified settlement coefficient is established. Optimization of screening parameters: setting the orthogonal test of vibration parameters and structural parameters, considering the interaction between vibration frequency and oscillating frequency, analyzing the significance of sieving parameters affecting stratification, and obtaining the optimal sieving parameter combination. 4. Settlement difference prediction: from the point of view of the relationship between sieving parameters and settlement difference, using the support vector regression (Support Vector Regression,SVR) method based on structural minimization criterion, a SVR prediction model is established to study the problem of predicting settlement difference. On the basis of selecting proper kernel function and parameters, the settlement difference can be predicted and the smaller error can be obtained. It is shown that support vector regression can better represent the nonlinear mapping relationship between settlement difference and sieve parameters. It is reasonable to use support vector regression to predict settlement difference, which provides a new method for predicting settlement difference. Under different screening parameters, the influence of stratification on screening efficiency is analyzed by comparing the relationship between settlement difference and screening efficiency, and the theory of stratification is enriched.
【學位授予單位】：華僑大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TH237.6
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本文編號：2218824