(江西理工大學(xué) 材料與化學(xué)工程學(xué)院，贛州 341000)
摘 要: 針對鋁酸鈉溶液成分濃度軟測量模型的研究現(xiàn)狀，為進(jìn)一步提高軟測量精度和命中率，提出多約束條件求解思想, 建立基于BP神經(jīng)網(wǎng)絡(luò)的軟測量數(shù)學(xué)模型。該模型以溶液溫度和各成分濃度為網(wǎng)絡(luò)輸入變量，對應(yīng)電導(dǎo)率為輸出變量，運(yùn)用BP網(wǎng)絡(luò)誤差反向傳播、權(quán)數(shù)調(diào)整原理實(shí)現(xiàn)在多樣本約束條件下的網(wǎng)絡(luò)逆映射求解。實(shí)例驗(yàn)證結(jié)果表明，該模型能較好地反映鋁酸鈉溶液電導(dǎo)率與成分濃度、溫度間的內(nèi)在規(guī)律，泛化檢驗(yàn)散點(diǎn)電導(dǎo)率平均相對誤差為1.74%；在多約束條件下，各軟測量濃度與實(shí)際濃度的相對誤差≤2.5%，且濃度適應(yīng)范圍較寬。該研究為實(shí)現(xiàn)鋁酸鈉溶液在線檢測奠定了良好的數(shù)模基礎(chǔ)。
關(guān)鍵字: BP神經(jīng)網(wǎng)絡(luò)；逆映射算法；鋁酸鈉溶液；軟測量模型
（Faculty of Materials Science and Chemistry Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China）
Abstract:Considering present soft sensor model research of sodium aluminate solution composition concentration, the multiple restrictive conditions solving thinking was presented to improve its soft-sensing precision and hit rate, and the soft-sensing mathematic model was established based on BP neural network. The sodium aluminate solution temperature and the component concentration were selected as the input nodes, and the corresponding electrical conductivity as the output node. Inverse mapping solution was achieved by combining organically the back-propagation principle and the weights values adjustment principle. Results show that the model can reflect the laws among electrical conductivity, composition concentration and temperature, and the average relative error of electrical conductivity at the generalization test is 1.74%; the relative error between the soft-sensing concentration and the real composition concentration is not more than 2.5%; the model is effective to soft measure the sodium aluminate solution compositon concentration waving in wider range, and thus the research lays a better foundation to achieve measure on-line in sodium aluminate solution.
Key words: BP neural network; inverse mapping algorithm; sodium aluminate solution; soft-sensing model
