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Data-Driven Modeling Based on Two-Stream λ Gated Recurrent Unit Network With Soft Sensor Application
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DOI：10.1109/TIE.2019.2927197.png)
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Abstract
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Data-driven soft sensors, estimating the pivotal quality variables, have been widely employed in industrial process. This paper proposes a novel soft sensor modeling approach based on a two-stream. gated recurrent unit (TS - lambda GRU) network. First, factors lambda(1) and lambda(2) are implemented to alter the linear constraint existing in the original GRU unit, enriching the information passing through. Then, a two-stream network structure is designed, equipped with some advanced network parameter adjustment techniques, such as batch normalization and dropout rate, to learn diverse features of the various process data. Finally, the learned features from the two streams are fused and a supervised learning regression layer is employed to decrease the error between the output and label. The application inmelt viscosity index estimation for a real polymerization industrial process has demonstrated that the proposed TS - lambda GRUs algorithm for soft sensor modeling is more accurate and promising than other existing methods.
Keywords:
lambda factor
gated recurrent unit (GRU)
melt viscosity index (MVI)
soft sensor modeling
two streams
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