Abstract
Modern large scale processing and manufacturing systems cover a wide array and large number of assets that need to work together to ensure that plant is generating output reliably and at the desired yield rate, such as the viscosity of quench oil in styrene cracking system. However, due to the complexity of the overall process, it is important to consider the entire plant as a network to identify deterioration patterns and forecast condition.
Instead to figure out the prediction from engineering perspective, we propose to leverage deep learning approach to predict the next state based on the historical information. Particularly, recurrent neural network (RNN) is selected in this paper as a basis for temporal forecasting. Considering the fact that there are multiple sub-systems running in parallel whose independence cannot be captured by a normal RNN, we design a LSTM (Long Short Term Memory) network for each sub-system and feed the outputs of LSTMs into a linear neural network layer for predicting viscosity one-hour ahead.
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Shao, D., Zhang, T., Mannar, K., Han, Y. (2016). Time Series Forecasting on Engineering Systems Using Recurrent Neural Networks. In: Li, J., Li, X., Wang, S., Li, J., Sheng, Q. (eds) Advanced Data Mining and Applications. ADMA 2016. Lecture Notes in Computer Science(), vol 10086. Springer, Cham. https://doi.org/10.1007/978-3-319-49586-6_31
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DOI: https://doi.org/10.1007/978-3-319-49586-6_31
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