Data-driven soft sensor approach for online quality prediction using state dependent parameter models
Data-driven soft sensor approach for online quality prediction using state dependent parameter models
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DOI：
10.1016/j.chemolab.2017.01.004
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发表时间：
2017-03-15
影响因子：
3.9
通讯作者：
Khalilipour, Mir Mohammad
中科院分区：
文献类型：
--
作者：
Bidar, Bahareh;Sadeghi, Jafar;Khalilipour, Mir Mohammad
本文的目标是设计和实现一种新的数据驱动的软测量，使用状态相关参数（SDP）模型，以提高产品质量监测。SDP模型参数假设为系统状态的函数，采用基于数据的建模思想和SDP方法进行估计。在模拟的连续搅拌釜式反应器和工业脱丁烷塔上验证了该方法的软测量性能。对脱丁烷塔在线监测的不同软测量方法进行了对比研究。结果表明，该建模方法可以解决过程的非线性问题，在观测数据中存在缺失数据时，也能很好地跟踪过程的变化。结果表明，新模型具有更好的鲁棒性和可靠性，模型参数更少，具有工业应用价值。此外，性能指标表明，该模型优于其他传统的软测量方法。
The goal of this paper is to design and implementation of a new data-driven soft sensor that uses state dependent parameter (SDP) models to improve product quality monitoring. The SDP model parameters assumed to be function of the system states, which are estimated by data-based modeling philosophy and SDP method. Soft sensing performance of the proposed method is validated on a simulated continuous stirred tank reactor and an industrial debutanizer column. A comparative study of different soft sensing methods for online monitoring of debutanizer column is also carried out. The results show that the process non-linearity can also be addressed under this modeling method and the change of the process is also well tracked when missing data exist in the observed data. The results indicate that the new model is much more robust and reliable with less model parameters, which make it useful for industrial applications. In addition, the performance indexes show the superiority of the proposed model over other conventional soft sensing methods.