Optimization of coordinated control system considering on-line correction of coal quality

Boyu, Ping; Zeng, Deliang; Gongqing, Liu; Sun, Pengfei; Shuo, Kong; Li, Gengda; Chen, Baowei

doi:10.23919/ccc50068.2020.9189644

Cited by 1 publication

(1 citation statement)

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“…Differences in coal quality and insufficient combustion processes can lead to changes in these parameters [33,34]. Once these unobservable parameters change, it will inevitably influence the prediction accuracy of the DT model, and then corrupt subsequent control and optimization decisions by more or less [35,36]. In addition, because of the modeling and calculation errors, the DT model can hardly be the same as the real system [37].…”

Section: Introductionmentioning

confidence: 99%

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

Zhao,

Jiang,

Cai

et al. 2024

Meas. Sci. Technol.

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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 in 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 of DT for sophisticated process industry. First, combining Stein variational gradient descent (SVGD) 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 the filtering residual containing time-varying noise, we propose a neural network and modified wavelet-based model error compensation (NNMW-MEC) block. Based on modified wavelet technique, the filtering residual denoising (FRD) 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 (NNSC) 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 model and uncertain noise statistics.

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