【作者】 邱添；

【导师】 王煤；

【作者基本信息】 四川大学 ， 化学工程， 2007， 硕士

【摘要】 化工生产过程中，产品质量指标和生产过程中参数的预测和控制，对于保证生产系统的连续运行、提高产量和质量起着十分重要的作用。氢氮比是合成氨生产中一个极为重要的控制指标。针对氢氮比控制大滞后、非线性和时变性的特点，运用非线性主成分分析方法与神经网络相结合，在大量实测生产数据的基础上，建立了氢氮比预测模型。建模时，首先对变量即影响因素进行降维处理，用2个主成分反映5个变量所包含的94.85％的信息。以降维所得的主成分作为模型输入变量，经过网络训练、回想及数据预测等大量的比较计算，确定了模型结构，建立了神经网络氢氮比预测模型。与实测氢氮比相比，预测氢氮比的绝对误差的平均值为0.0352，平均相对误差为1.6926％，能够满足对合成塔入口氢氮比进行实时控制的要求，且具有训练速度快、预测迅速的特点。磷铵产品的水分含量是重要的质量指标。针对生产过程中影响因素众多，水分含量较难预测和控制的现状，首先用非线性主成分分析方法处理原始数据样本以减少变量数目。采用3个主成分代表9个变量所包含的96.29％的信息。用所得主成分作为输入变量，建立了神经网络磷铵水分预测模型。与磷铵产品的实测水分相比，模型预测水分的绝对误差的平均值为0.2019，平均相对误差为7.1％。更多还原

【Abstract】 In chemical processes, the prediction and control of product quality indexes and production process parameters play a very important role to steady the whole factory running production system and increase the output and quality.H₂/N₂ ratio is an important control index for ammonia synthesis. According to the characteristic of H₂/N₂ ratio such as big-lag, non-linear and variety-by-time, non-linear principal component analysis and the neural network are combined, the prediction model of H₂/N₂ ratio is confirmed base on large numbers of the measured data. In the process of modeling, the first step is to decrese the dimensions of variables that are the influential factors, and the result is that the two principal components has contained the information that is 94.85% of the information of the five variables. The principal components are chosen as input variables of the network model. After performing a large number of computing experiment and comparison like training, recalling and predicting data by the network, the model structure of it is confirmed, and the neural network model for predicting H₂/N₂ ratio is established. The result show that the average absolute value of the absolute error between the predicting data and the surveying data is 0.0352, and the average relative error is 1.6926%, these can satisfy the requirement of predicting H₂/N₂ ratio timely, and has training quickly, predicting rapidly characteristic.The water content of ammonium phosphate is an important quality index. According to the actuality of ammonium phosphate production such as multi -influential factors, predicting and controlling hardly of the water content, firstly, original data is treated by non-linear principal component analysis to lower the number of variables. The result is that the three principal components have contained the information that is 96.29% of the information of the nine variables. The principal components are chosen as input variables of the network model, the neural network model for predicting water content is established. The result show that the average absolute value of the absolute error between the predicting data and the surveying data is 0.2019, and the average relative error is 7.1%.更多还原