Assessing titanium dioxide nanoparticles transport models by Bayesian uncertainty analysis

Liu, Jin; Wu, Jichun; Liang, Xiuyu; Sun, Yuanyuan; Zhan, Hongbin

doi:10.1007/s00477-018-1617-y

Cited by 4 publications

(2 citation statements)

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“…Since u r (k) is unknown at the current instant k, the nominal system prediction output x 0 (k + 1) must be biased from the target x r (k + 1). To alleviate this issue, historical nominal state error x c (k) − x 0 (k) is used to compensate the future nominal state value in (24), i.e., both left and right side of Formulation (23) times a compensation coefficient a (0 ≤ a ≤ 1) and then sum (24), we have,…”

Section: The Online Implementation For Offline Designed Rmpc Control Lawmentioning

confidence: 99%

“…Uncertainties of model structure and parameters result from uncertain system identification approach. Compared with Monte Carlo uncertainty analysis [21,22] and Bayesian uncertainty analysis [23,24] which give a probabilistic description of uncertainty, the set-membership identification (SMI) [25][26][27][28][29][30][31] aims to achieve a deterministic model uncertainty by bounding the error of uncertain model. There are two kinds of SMI methods according to the uncertain description.…”

Section: Introductionmentioning

confidence: 99%

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An Efficient Robust Predictive Control of Main Steam Temperature of Coal-Fired Power Plant

Wang¹,

Wu²,

Shen³

2020

Energies

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Regulating performance of the main steam temperature (MST) system concerns the economy and safety of the coal-fired power plant (CFPP). This paper develops an offset-free offline robust model predictive control (RMPC) strategy for the MST system of CFPP. Zonotope-type uncertain model is utilized as the prediction model in the proposed RMPC design owing to its features of higher accuracy, compactness of representation and less complexity. An offline RMPC aiming at the system robustness and computational efficiency is then developed to maintain the desired steam temperature in case of wide operating condition change. The proposed RMPC is realized by two stages: in the first stage, the RMPC law set, which is the piecewise affine (PWA) of the MST system state is designed offline; then in the second stage, the explicit control law is selected online according to the current state. To achieve an offset-free tracking performance, a manipulated variable target observer is employed to update the chosen RMPC law. The control simulations using on-site operating data of a 1000 MW ultra-supercritical power plant show that the proposed approach can achieve satisfactory control performance and online computation efficiency even under complicated operating conditions.

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Section: The Online Implementation For Offline Designed Rmpc Control Lawmentioning

confidence: 99%