Simultaneous Data Reconciliation and Gross Error Detection Based on Denoising Autoencoder

Industrial process data is usually affected by random and gross errors leading to deviation from the true value and violation of process constraints. Traditional data reconciliation methods rely on accurate process model, which can lead to incorrect reconciliation results if the model is not accurate enough. This paper proposes a deep learning approach based on denoising autoencoder (DAE) for dealing with simultaneous data reconciliation and gross error detection(SDR & GED) in steady state systems without directly using the process models/constraints. Reconciled values that satisfy the process constraints can be gradually obtained by iterative training. In addition, a pre-training method is proposed to accelerate the training procedure. The proposed DAE framework is equipped with a one dimensional convolutional neural network, which can reduce the model complexity. Furthermore, DAE is capable of handling a variety of noises, so that data with different types of noise distributions can still be reconciled effectively. Finally, loop analysis and process constraints construction for the hot section of an olefin plant are performed, and the effectiveness of the proposed method is verified by the study of this system.