(江西理工大学 材料与化学工程学院，赣州 341000)
摘 要: 针对铝酸钠溶液成分浓度软测量模型的研究现状，为进一步提高软测量精度和命中率，提出多约束条件求解思想, 建立基于BP神经网络的软测量数学模型。该模型以溶液温度和各成分浓度为网络输入变量，对应电导率为输出变量，运用BP网络误差反向传播、权数调整原理实现在多样本约束条件下的网络逆映射求解。实例验证结果表明，该模型能较好地反映铝酸钠溶液电导率与成分浓度、温度间的内在规律，泛化检验散点电导率平均相对误差为1.74%；在多约束条件下，各软测量浓度与实际浓度的相对误差≤2.5%，且浓度适应范围较宽。该研究为实现铝酸钠溶液在线检测奠定了良好的数模基础。
关键字: BP神经网络；逆映射算法；铝酸钠溶液；软测量模型
（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
