Multi-structural fast nonlinear model-based predictive control of a hydronic heating system

Yu, Yuebin; Loftness, Vivian; Yu, Daihong

doi:10.1016/j.buildenv.2013.07.018

Cited by 34 publications

(14 citation statements)

References 20 publications

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“…For simplicity, f is denoted as a mapping from t a to u a (see (7)(8)(9)(10) and Fig. 1 for details).…”

Section: Pls Latent-variable Predictive Modelmentioning

confidence: 99%

“…Although MPC can deal with the multivariable control problem with constraints of input and output variables, linear MPC usually cannot ensure the control performance for processes with explicit nonlinear characteristics because it is impossible to approximate the whole nonlinear process using a linear model. Thus, extensions to nonlinear MPC (NMPC) have been addressed in previous works [2][3][4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

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Latent‐variable Nonlinear Model Predictive Control Strategy for a pH Neutralization Process

Chi¹,

Fei²,

Liu³

et al. 2015

Asian Journal of Control

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Linear model predictive control (MPC) is a widely‐used control strategy in chemical processes. Its extension to nonlinear MPC (NMPC) has drawn increasing attention since many process systems are inherently nonlinear. When implementing the NMPC based on a nonlinear predictive model, a nonlinear dynamic optimization problem must be calculated. For the sake of solving this optimization problem efficiently, a latent‐variable dynamic optimization approach is proposed. Two kinds of constraint formulations, original variable constraint and Hotelling T2 statistic constraint, are also discussed. The proposed method is illustrated in a pH neutralization process. The results demonstrate that the latent‐variable dynamic optimization based the NMPC strategy is efficient and has good control performance.

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“…For simplicity, f is denoted as a mapping from t a to u a (see (7)(8)(9)(10) and Fig. 1 for details).…”

Section: Pls Latent-variable Predictive Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Latent‐variable Nonlinear Model Predictive Control Strategy for a pH Neutralization Process

Chi¹,

Fei²,

Liu³

et al. 2015

Asian Journal of Control

View full text Add to dashboard Cite

show abstract

“…Its controller performance is highly sensitive to the accuracy of the model. Energy savings benefits of MPC for heating, ventilating, and air conditioning (HVAC) systems have been demonstrated in numerous papers [11][12][13][14][15][16][17]. However, up to now, the lack of dynamic simplified model of RCCUV system is preventing the application of MPC on RCCUV system.…”

Section: Introductionmentioning

confidence: 99%

A dynamic simplified model of radiant ceiling cooling integrated with underfloor ventilation system

Zhang

Xia

Cai

2016

Applied Thermal Engineering

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“…Energy use from heating, ventilation, air conditioning, and refrigeration systems (HVAC&R) to offset the heating and cooling load and provide a proper indoor environment for occupants constitutes a large portion of the total energy consumed in buildings [1,2]. Although there have been many studies on the optimal control strategies of air-conditioning systems aiming at energy conservation [3,4], issues, including a problematic or inferior control sequence and set points, equipment performance degradation, and various faults that occur in HVAC&R and building energy systems, further increase the energy use or lead to undesired indoor environmental quality.…”

Section: Introductionmentioning

confidence: 99%

A quantitative comparison of statistical and deterministic methods on virtual in-situ calibration in building systems

Yoon

2017

Building and Environment

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Systematic and random errors of working sensors in building systems could significantly compromise the system's performance and thus indoor environmental quality. An extended virtual in-situ calibration has been suggested to solve problems regarding sensor errors and calibration. This calibration can correct these errors for all critical working sensors in building systems without removing working sensors or adding reference sensors as is done in a conventional calibration. This method is capable of estimating measurands using a parameter estimation technique based on mathematical system models. Deterministic and statistical methods can be used for conducting the estimation. In this study, genetic algorithm (GA)-based optimization is used as a deterministic method and Bayesian Markov Chain Monte Carlo (MCMC) is used as a statistical method to solve the calibration problem formulated by the extended virtual in-situ calibration. A case study of a single-effect LiBr-H 2 O refrigeration system illustrates the problem formulating process and compares the accuracy distributions of calibrations derived from the two different methods.A virtual in-situ sensor calibration (VIC) [8] method has been recently studied in order to solve the practical problems of a conventional sensor calibration in building energy systems; the conventional calibration process and its limitations in building systems were described in [8]. Specifically, the problems addressed are: (1) time and monetary cost; (2) disruption of normal operation; (3) difficulty in accessing various sensors embedded in equipment; and (4) the large quantity of sensors [8,9]. Meanwhile,

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Multi-structural fast nonlinear model-based predictive control of a hydronic heating system

Cited by 34 publications

References 20 publications

Latent‐variable Nonlinear Model Predictive Control Strategy for a pH Neutralization Process

Latent‐variable Nonlinear Model Predictive Control Strategy for a pH Neutralization Process

A dynamic simplified model of radiant ceiling cooling integrated with underfloor ventilation system

A quantitative comparison of statistical and deterministic methods on virtual in-situ calibration in building systems

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