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.

中文翻译：

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使用深度置信网络测量二烯产量的软传感方法研究

乙烯作为化工行业的重要原料，是衡量一个国家发展水平最可靠的指标之一。二烯收率是乙烯装置重要的生产质量指标参数，对其进行实时检测和控制非常重要。由于在线分析仪器技术的限制，二烯收率难以在线测量。受此启发，本文研究了测量二烯收率的软传感技术。提出了一种基于深度置信网络算法网络的二烯产量预测方法，并通过该方法充分挖掘历史二烯产量数据的规律性。首先，数据特征向量被融合和归一化。然后，将数据输入由两层受限玻尔兹曼机组成的 DBN 进行无监督训练，最后使用 DBN 模型预测二烯产率。实验结果表明，测试集与历史数据的均方误差为1.15%，实测数据的平均绝对百分比误差为2.79%。实验结果表明了所提出方法的有效性。