Abstract:

Soft sensor technology has attracted wide attention as an important method for the acquisition of critical quality variables in chemical processes.The researches on soft sensor nowadays mainly focus on the modeling technique.However, due to the complexity and diversity of chemical processes, there are always unsatisfactory results, such as unstable estimations, large random mistakes and so on, when using soft sensor models to directly estimate the critical quality variables.Aimed at this problem, several ameliorative algorithms have been reported, but they still have drawbacks of heavy calculation burden and poor applicability.Thus the authors propose a new soft sensor method, the output data fusion soft sensor design method based on adaptive extended Kalman filter (EKF)algorithm, which fuses the model estimations and field measurements to calibrate the deviations in modeling results by Kalman filtering.And a noise statistics estimator with attenuation factors is also developed under the condition of data fusion soft sensor.By integrating the noise estimator with EKF algorithm, an adaptive extended Kalman filter is constructed, which can effectively improve the accuracy and anti-interference capability of the EKF-based data fusion soft sensor.The effectiveness of the proposed algorithm is analyzed in depth through simulations.The algorithm is also used in a lab experiment to validate its practicability and applicability.

Key words:

软仪表, 数据融合, 扩展Kalman滤波, 噪声统计估计器, 自适应

摘要：

在化工过程中，作为观测关键质量参数的重要手段，软仪表技术受到了广泛的关注。目前，关于软仪表的研究主要集中在建模技术上。然而，化工过程复杂多样，仅使用软测量模型进行质量变量的估计易出现预估效果不稳定、随机偏差大等现象。为此，文献提出了一系列的改进算法，但仍存在计算复杂、算法抗干扰能力差等问题。本文提出一种基于自适应扩展Kalman滤波(EKF)的输出融合软仪表设计方法，利用Kalman滤波算法对软测量模型预估数据和现场观测进行数据融合，校正软测量模型预估偏差;并在输出融合软仪表背景下，设计了一种含衰减因子的观测噪声统计估计器，将其与滤波算法相结合，构成自适应EKF算法，以提高融合软仪表的输出精度及抗干扰性能。通过仿真实验对所提出的算法进行了全面分析，并将该算法应用于小型实验装置，验证了算法的实用性及有效性。

关键词:

软仪表, 数据融合, 扩展Kalman滤波, 噪声统计估计器, 自适应