基于改进的FasBack神经模糊系统的4-CBA软测量模型研究

提出一种基于改进的FasBack神经模糊系统的新型对羧基苯甲醛(4-CBA)软测量模型,用Leven-berg-M arquardt算法训练模型中的部分参数,经实际过程数据验证表明,提出的模型学习速度快、预测精度高、鲁棒性强,为实现精对苯二甲酸(PTA)生产过程中4-CBA含量的实时、精确控制提供了一条有效的途径。     

工业异构化反应器建模及仿真

将3个二甲苯异构体集总为一个组分,提出一个新的异构化三角反应网络,建立工业异构化反应器模型.基于多组平衡的工业数据,采用传统的优化方法估计动力学参数,然后对不同操作条件下的大量工业数据进行仿真,结果表明模型估计值与观测值相当吻合.与其他模型相比,文中开发的模型在相对误差、相对均方误差和参数估计时间等性能指标上均体现出优...     

基于FCMISVM的自适应软测量建模算法及其在PX吸附分离过程中的应用

To overcome the problem that soft sensor models cannot be updated with the process changes, a soft sensor modeling algorithm based on hybrid fuzzy c-means (FCM) algorithm and incremental support vector machines (ISVM) is proposed. This hybrid algorithm FCMISVM includes three parts: samples clustering based on FCM algorithm, learning algorithm based on ISVM, and heuristic sample displacement method. In the training process, the training samples are first clustered by the FCM algorithm, and then by training each clustering with the SVM algorithm, a sub-model is built to each clustering. In the predicting process, when an incremental sample that represents new operation information is introduced in the model, the fuzzy membership function of the sample to each clustering is first computed by the FCM algorithm. Then, a corresponding SVM sub-model of the clustering with the largest fuzzy membership function is used to predict and perform incremental learning so the model can be updated on-line. An old sample chosen by heuristic sample displacement method is then discarded from the sub-model to control the size of the working set. The proposed method is applied to predict the p-xylene (PX) purity in the adsorption separation process. Simulation results indicate that the proposed method actually increases the model’s adaptive abilities to various operation conditions and improves its generalization capability.     

一种动态软测量建模方法及其工业应用

针对目前静态软测量建模方法无法反映工业过程动态信息,造成模型预测精度低、鲁棒性差等问题,提出了一种基于模糊曲线和高斯过程的动态软测量建模方法.该方法采用模糊曲线法对输入数据进行处理,并利用处理后的数据构建新的数据集,最后采用高斯过程建立软测量模型.将提出的动态软测量模型应用于PTA氧化过程中4-CBA含量的预测,结果表明,所建模型运算速度快、预测精度高.     

基于模型性能评估的递推PLS建模及应用

为了降低递推部分最小二乘(RPLS)建模方法的模型校正频率,开发了一种基于模型性能评估的RPLS(MPA-RPLS)模型.首先,根据过程的初始特性,自动生成模型的置信限,以均方根误差(RMSEP)为性能指标,评估模型性能;依据模型性能的评估结果,选择性地启动模型校正和置信限校正.然后,引入滑动平均滤波器消除过程变量中的噪声,探讨噪声对模型性能的影响程度.最后,将MPA-RPLS模型应用于一个化学反应过程--C₈芳烃临氢异构化过程,基于大量工业数据,进行仿真验证.仿真结果表明:本文开发的模型仅以微小的精度损失换取了模型计算效率的大幅提高(即模型校正频率大幅下降);滑动平均滤波器可有效地处理变量的噪声,改善模型的预测精度.     

临床大样本下血液黏度与血液电阻率的关系

探讨了临床大样本时血液黏度与血液电阻率之间的关系,通过对大量血液度数据和血液电阻率数据的处理和计算,拟合出血液黏度和血液电阻率之间的定量关系式。基于此种关系,采用电导纳体积描记术即可无损地测量血液黏度。     

一种基于混合建模技术的MIMO软测量建模方法

针对复合肥产品中几种养分含量需要同时预报的一类多输入/多输出(MIMO)软测量建模问题,提出一种基于混合建模方法的复合肥养分含量MIMO软测量模型。该混合模型首先对几个不能实时测量的关键辅助变量采用基于限定记忆部分最小二乘算法的数据驱动建模方法建立自适应软测量模型,然后采用简化机理模型实时计算三种养分含量。基于实际工业过程数据的仿真结果表明,所建模型运算速度快、预测精度高,可以满足复合肥养分含量在线预报的要求。     

基于混合建模技术的复合肥养分含量MIMO软测量模型

In compound fertilizer production, several quality variables need to be monitored and controlled simultaneously. It is very diifficult to measure these variables on-line by existing instruments and sensors. So, soft-sensor technique becomes an indispensable method to implement real-time quality control. In this article, a new model of multi-inputs multi-outputs （MIMO） soft-sensor, which is constructed based on hybrid modeling technique, is proposed for these interactional variables. Data-driven modeling method and simplified first principle modelingmethod are combined in this model. Data-driven modeling method based on limited memory partial least squares（LM-PLS） al.gorithm is used to build soft-senor models for some secondary variables.then, the simplified first principle model is used to compute three primary variables on line. The proposed model has been used in practicalprocess; the results indicate that the proposed model is precise and efficient, and it is possible to realize on line quality control for compound fertilizer process.     

基于混合建模技术的复合肥养分含量MIMO软测量模型

In compound fertilizer production, several quality variables need to be monitored and controlled simultaneously. It is very difficult to measure these variables on-line by existing instruments and sensors. So, soft-sensor technique becomes an indispensable method to implement real-time quality control. In this article, a new model of multi-inputs multi-outputs (MIMO) soft-sensor, which is constructed based on hybrid modeling technique, is proposed for these interactional variables. Data-driven modeling method and simplified first principle modeling method are combined in this model. Data-driven modeling method based on limited memory partial least squares (LM-PLS) algorithm is used to build soft-senor models for some secondary variables; then, the simplified first principle model is used to compute three primary variables on line. The proposed model has been used in practical process; the results indicate that the proposed model is precise and efficient, and it is possible to realize on line quality control for compound fertilizer process.