A generalized data assimilation architecture of digital twin for complex process industrial systems

Abstract

Abstract As one of the critical cores of digital twin (DT), data assimilation (DA) can maintain consistency and synchronization between DT and physical system. Kalman filtering is a common DA method, but its estimation performance is deteriorated by factors such as model inaccuracy and time-varying noise covariance in practical applications. The errors caused by these multiple uncertainties are all coupled to the measurements, which augments the difficulty for DT to obtain physical system information. In order to tackle the DA problem with multiple uncertainties, this paper proposes a generalized DA architecture for DT in sophisticated process industry. First, by combining Stein variational gradient descent and nonlinear Bayesian filtering paradigm, a recursive estimation framework is established, which has higher accuracy in estimating the noise covariance compared to traditional methods. Second, to effectively deal with model inaccuracy by using filtering residuals containing time-varying noise, we propose a neural network and modified wavelet-based model error compensation (NNMW-MEC) block. Based on the modified wavelet technique, the filtering residual denoising built in NNMW-MEC can better cope with time-varying noise compared to existing wavelets, and extract the low-frequency signal involving model error information from noisy residual smoothly. In addition, because of the neural network-based state-compensation subblock, NNMW-MEC has more outstanding ability in compensating the state deviations with large changing range. Finally, we take the boiler system in a coal-fired power plant as an example to verify the effectiveness of our architecture. Experimental results show that the DA architecture proposed in this paper can improve the estimation performance of DT under inaccurate models and uncertain noise statistics.