A dynamic mathematical model of an ultra-supercritical coal fired once-through boiler-turbine unit

He, Fan; Zhang, Yufei; Su, Zhi‐gang; Wang, Ben

doi:10.1016/j.apenergy.2016.11.074

Cited by 66 publications

(16 citation statements)

References 45 publications

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“…Based on this model, a sliding mode predictive controller was proposed in [19] to achieve excellent load tracking ability under wide-range operation. In [20], this model was further improved with added closed-loop validations and more reasonable structure.…”

Section: Introductionmentioning

confidence: 99%

Stacked Auto-Encoder Modeling of an Ultra-Supercritical Boiler-Turbine System

et al. 2019

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The ultra-supercritical (USC) coal-fired boiler-turbine unit has been widely used in modern power plants due to its high efficiency and low emissions. Since it is a typical multivariable system with large inertia, severe nonlinearity, and strong coupling, building an accurate model of the system using traditional identification methods are almost impossible. In this paper, a deep neural network framework using stacked auto-encoders (SAEs) is presented as an effective way to model the USC unit. In the training process of SAE, maximum correntropy is chosen as the loss function, since it can effectively alleviate the influence of the outliers existing in USC unit data. The SAE model is trained and validated using the real-time measurement data generated in the USC unit, and then compared with the traditional multilayer perceptron network. The results show that SAE has superiority both in forecasting the dynamic behavior as well as eliminating the influence of outliers. Therefore, it can be applicable for the simulation analysis of a 1000 MW USC unit.

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Section: Introductionmentioning

confidence: 99%

Stacked Auto-Encoder Modeling of an Ultra-Supercritical Boiler-Turbine System

et al. 2019

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“…Accordingly, employing these models in modern control methods is difficult. Several statistics-based models have been proposed for boiler combustion efficiency prediction in the literature [5][6][7]. In Reference [5], an autoregressive integrated moving average (ARIMA) model has been proposed, based on simulation data to predict the boiler combustion efficiency.…”

Section: Introductionmentioning

confidence: 99%

“…In Reference [5], an autoregressive integrated moving average (ARIMA) model has been proposed, based on simulation data to predict the boiler combustion efficiency. The thermodynamic principles have been employed to develop a dynamic model for an ultra-supercritical coal-fired once-through boiler-turbine unit [6]. In Reference [7], the prediction accuracy of the boiler combustion efficiency has been verified through a partial least squares (PLS) model.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Nonlinear Model Predictive Control of the Combustion Efficiency under the NOx Emissions and Load Constraints

Tang

Cao

2019

Energies

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A data-driven modeling method with feature selection capability is proposed for the combustion process of a station boiler under multi-working conditions to derive a nonlinear optimization model for the boiler combustion efficiency under various working conditions. In this approach, the principal component analysis method is employed to reconstruct new variables as the input of the predictive model, reduce the over-fitting of data and improve modeling accuracy. Then, a k-nearest neighbors algorithm is used to classify the samples to distinguish the data by the different operating conditions. Based on the classified data, a least square support vector machine optimized by the differential evolution algorithm is established. Based on the boiler key parameter model, the proposed model attempts to maximize the combustion efficiency under the boiler load constraints, the nitrogen oxide (NOx) emissions constraints and the boundary constraints. The experimental results based on the actual production data, as well as the comparative analysis demonstrate: (1) The predictive model can accurately predict the boiler key parameters and meet the demands of boiler combustion process control and optimization; (2) The model predictive control algorithm can effectively control the boiler combustion efficiency, the average errors of simulation are less than 5%. The proposed model predictive control method can improve the quality of production, reduce energy consumption, and lay the foundation for enterprises to achieve high efficiency and low emission.

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“…Fan et al . have claimed that different studies can be carried out on their proposed model . However, individual behavior of different components is lumped to derive a generic equation.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive dynamic modeling, simulation, and validation for an industrial boiler incident investigation

Taimoor

Alghamdi

Farooqi

et al. 2019

Process Safety Progress

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Industrial boilers simulation can serve number of purposes like training, optimization, HAZOP studies, and incident investigation. Simulation including trip logics, interlockings, safety devices, start‐up and shutdown procedure is presented in this work. Simulation is directly accomplished in dynamic mode to benefit from the holdup approach to avoid anomalies because of recycling. The industrial DCS/PLC diagrams are replicated to imitate the boiler‐integrated behavior. Trip logics, start‐up and shutdown procedures are programmed using event scheduler. The inbuilt pressure relief module validates the simulated PSVs and rupture disc capacities. The model is then verified at three stages including ‘steady state’ achievement in dynamic mode, controller validation by start‐up and shut down procedures and finally by direct industrial data under deviant conditions. Where an unfortunate event is a concern, it also provides an opportunity to ponder and to evolve at various levels. Operational simulation of any such incident may help in getting a deep insight and thus to take accurate countermeasures. One such attempt is done in this work. A boiler accident has been reported. The complete response of the controllers and interlockings/trip system is imitated. The obtained results were compared with the chain of events that took place. © 2019 American Institute of Chemical Engineers Process Saf Prog: e12040 2019

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A dynamic mathematical model of an ultra-supercritical coal fired once-through boiler-turbine unit

Cited by 66 publications

References 45 publications

Stacked Auto-Encoder Modeling of an Ultra-Supercritical Boiler-Turbine System

Stacked Auto-Encoder Modeling of an Ultra-Supercritical Boiler-Turbine System

Adaptive Nonlinear Model Predictive Control of the Combustion Efficiency under the NOx Emissions and Load Constraints

Comprehensive dynamic modeling, simulation, and validation for an industrial boiler incident investigation

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