Research on Soft-Sensing Methods for Measuring Diene Yields Using Deep Belief Networks. (29th April 2022)

• Record Type: Journal Article
• Authors: Deng, Xiangwu; Peng, Zhiping; Cui, Delong
• Other Names: Kumar Jitendra Academic Editor.
• Is Part Of: International journal of chemical engineering. Volume 2022(2022)
• Publication Date: 2022-04-29
• Subjects: Chemical engineering -- Periodicals; Chemical engineering; Electronic journals; Periodicals; 660
• DOI: 10.1155/2022/4133703
• Languages: English
• ISSNs: 1687-806X

Abstract

Abstract : As an important raw material for the chemical industry, ethylene is one of the surest indicators that measure the development level of a country. The diene yield is an important production quality index parameter of ethylene units, and it is very important to detect and control them in real time. Due to the limitations of online analytical instrumentation technology, diene yields are difficult to measure online. Motivated by this, this article has studied soft-sensing technology for measuring diene yields. A diene yield prediction method based on a deep belief network algorithm network is proposed, and the regularity of historical diene yield data is fully explored by the method. First, the data feature vectors are fused and normalized. Then, the data are fed into a DBN consisting of two layers of restricted Boltzmann machines for unsupervised training, and finally, a DBN model is used to predict the diene yield. The experimental results show that the mean squared error of the test set with historical data is 1.15%, and the mean absolute percentage error of the measured data is 2.79%. The experimental results are provided to show the effectiveness of the proposed method.