【摘要】：為了降低生產(chǎn)成本、提高氧化鋁產(chǎn)品質(zhì)量,提出了一種氧化鋁焙燒爐內(nèi)溫度智能優(yōu)化控制方法。首先,基于機(jī)理分析和灰關(guān)聯(lián)分析法確定影響氧化鋁焙燒爐內(nèi)溫度的關(guān)鍵參數(shù),建立爐內(nèi)溫度的ELM網(wǎng)絡(luò)預(yù)測(cè)模型,然后采用改進(jìn)PSO對(duì)預(yù)測(cè)模型的參數(shù)進(jìn)行優(yōu)化;最后利用遺傳算法的全局尋優(yōu)能力尋找得到不同爐內(nèi)溫度下對(duì)應(yīng)的最優(yōu)操作參數(shù)值。通過(guò)實(shí)際生產(chǎn)數(shù)據(jù)進(jìn)行仿真,仿真結(jié)果表明,優(yōu)化控制方法可以較準(zhǔn)確地預(yù)測(cè)焙燒爐內(nèi)溫度,同時(shí)得到預(yù)期指標(biāo)范圍內(nèi)的操作參數(shù)值。
[Abstract]:In order to reduce the production cost and improve the quality of alumina products, an intelligent optimization control method for temperature in alumina roaster was proposed. Firstly, based on the mechanism analysis and grey correlation analysis, the key parameters affecting the temperature of alumina roaster are determined, and the ELM network prediction model of the furnace temperature is established. Then, the parameters of the prediction model are optimized by improved PSO. Finally, the global optimization ability of genetic algorithm is used to find the corresponding optimal operating parameters at different furnace temperatures. The simulation results show that the optimal control method can accurately predict the temperature in the calciner and obtain the operating parameters in the range of the expected index.
【作者單位】： 廣西大學(xué)電氣工程學(xué)院;
【基金】：國(guó)家自然科學(xué)基金項(xiàng)目(61364007) 廣西自然科學(xué)基金項(xiàng)目(2014GXNSFAA118391)
【分類號(hào)】：TP18;TQ133.1

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本文編號(hào)：2267315