Predictive modeling of a subcritical pulverized-coal power plant for optimization: Parameter estimation, validation, and application

Eslick, John; Zamarripa, Miguel; Ma, Jinliang; Wang, Maojian; Bhattacharya, Indrajit; Rychener, Brian; Pinkston, Philip; Bhattacharyya, Debangsu; Zitney, Stephen E.; Burgard, Anthony P.; Miller, David C.

doi:10.1016/j.apenergy.2022.119226

Cited by 13 publications

(7 citation statements)

References 28 publications

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“…The process model for the primary superheater is developed in the Pyomo-based IDAES software . IAPWS-95 is used for the steam properties . PYOMO.DAE is used for discretizing the differential equations .…”

Section: Results and Discussionmentioning

confidence: 99%

“…50 IAPWS-95 is used for the steam properties. 51 PYOMO.DAE is used for discretizing the differential equations. 52 The fully discretized model yields a set of nonlinear algebraic equations, which is solved using IPOPT.…”

Section: ■ Results and Discussionmentioning

confidence: 99%

“…Figure shows the dynamic simulation of two load changes at ramp rates of 10%/min. The dynamic model of the power plant used for simulating the load change has been presented in our earlier works. , The load is changed from the full load to 50% and maintained at 50% load for 10 min before ramping back up to the full load. Figure also shows the corresponding change in the coal flow rate.…”

Section: Results and Discussionmentioning

confidence: 99%

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Design and Multiobjective Dynamic Optimization of Superheaters for Load-Following Operation in Pulverized Coal Power Plants

Le,

Ma,

Bhattacharyya

et al. 2023

Ind. Eng. Chem. Res.

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Pulverized coal power plants are increasingly participating in aggressive load-following markets, therefore necessitating the design and optimization of primary superheaters for flexible operations. These superheaters play a critical role in maintaining the final steam temperature of the steam turbine, but their high operating temperatures and pressures make them prone to failure. This study focuses on the optimal design of future-generation primary superheaters for a fast load-following operation. To achieve this, a detailed first-principles model of a primary superheater is developed along with submodels for stress and fatigue damage. Two single-objective optimization problems are solved: one for minimizing metal mass as a measure of capital cost and another for minimizing pressure drop on the steam side as a measure of operating cost. Since these objective functions conflict, a multiobjective optimization problem is executed using a weighted metric methodology. Results from these optimization studies show that the base case design can violate stress constraints during the aggressive load-following operation. However, by optimizing the design variables, it is possible to not only satisfy tight stress constraints but also achieve the desired number of allowable cycles and adhere to the steam outlet temperature constraint. In addition, the optimized design reduces either the metal mass or the steam-side pressure drop compared to that of the base case design. Importantly, this approach is not limited to primary superheaters alone but can also be applied to similar high-temperature heat exchangers in other applications.

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Section: Results and Discussionmentioning

confidence: 99%

Section: ■ Results and Discussionmentioning

confidence: 99%

Section: Results and Discussionmentioning

confidence: 99%

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Design and Multiobjective Dynamic Optimization of Superheaters for Load-Following Operation in Pulverized Coal Power Plants

Le,

Ma,

Bhattacharyya

et al. 2023

Ind. Eng. Chem. Res.

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“…Therefore, nonrenewable sources like coal are still expected to be prevalent and be used to ensure the stability of the electric grid in the foreseeable future especially for countries where coal is abundantly available and even subsidized by the government. , The existing coal-fired power plants are generally designed to operate under base-loaded conditions. Under load-following conditions to meet the electricity demand, power plants are subjected to frequent startups and shutdowns and ramping up and down of production. − The rapid fluctuations in operating conditions, especially process temperature, can cause significant damage to the health of the critical components of power plants, causing unplanned outages . While several approaches have been recommended to minimize damage, , it is difficult to avoid excursion in temperature under aggressive cycling .…”

Section: Introductionmentioning

confidence: 99%

Hot Corrosion Monitoring in Boilers Using a Nonlinear Estimator and Electrochemical Noise-Based Corrosion Sensors

Somayajula,

Bhattacharyya,

et al. 2023

Ind. Eng. Chem. Res.

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A kinetic model is developed to calculate the thickness of the corrosion scale formed due to hot corrosion in the waterwall section of a boiler in coal-fired power plants. The proposed model is validated by using the data collected from an operating industrial boiler and using a novel electrochemical noisebased corrosion sensor. As corrosion dynamics are significantly slow compared to the dynamics of temperature and gaseous species concentration, a multiscale model is developed where datadriven models are developed for algebraic states that affect the corrosion rate. The novel corrosion sensors can be costly and may be infeasible to place at all locations, especially due to the high temperature and harsh operating conditions in the waterwall section. This paper investigates opportunities for corrosion estimation by placing sensors for the algebraic state variables, the measurement of which is comparatively more mature and considerably cheaper than that of the corrosion sensors. To this end, a nonlinear estimator is investigated. Unlike many systems that are represented by differential-only or differential algebraic equations, in this particular system, differential equations do not affect the algebraic states but the algebraic states affect the differential states. For nonlinear state estimation of this specific class of differential algebraic equation system, a modified unscented Kalman filter algorithm is developed. The performance of the estimation algorithm is evaluated with different configurations of the sensor network by simulating the boiler with various operating conditions, as expected during load-following operation.

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“…With the rapid development of the power industry, the pulverizers in power plants play a crucial role in ensuring stable power supply and efficient operation [1,2]. To guarantee the high performance and safe operation of pulverizers, researchers have begun to explore accurate predictions of their performance and fault conditions [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Kernel Principal Component Analysis–Bagging–Gaussian Mixture Regression Framework for Pulverizer Soft Sensors Using Reduced Dimensions and Ensemble Learning

Jin,

Si,

Dong

et al. 2023

Energies

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In light of the nonlinearity, high dimensionality, and time-varying nature of the operational conditions of the pulverizer in power plants, as well as the challenge of the real-time monitoring of quality variables in the process, a data-driven KPCA–Bagging–GMR framework for soft sensors using reduced dimensions and ensemble learning is proposed. Firstly, the methodology employs a Kernel Principal Component Analysis to effectively reduce the dimensionality of the collected process data in a nonlinear manner. Secondly, the reduced principal components are then utilized to reconstruct a refined set of input samples, followed by the application of the Bagging algorithm to obtain multiple subsets of the samples and develop corresponding Gaussian Mixture Regression models. Ultimately, the fusion output is achieved by calculating the weights of each local model based on Bayesian posterior probabilities. By conducting simulation experiments on the coal mill, the proposed approach has been validated as demonstrating superior predictive accuracy and excellent generalization capabilities.

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Predictive modeling of a subcritical pulverized-coal power plant for optimization: Parameter estimation, validation, and application

Cited by 13 publications

References 28 publications

Design and Multiobjective Dynamic Optimization of Superheaters for Load-Following Operation in Pulverized Coal Power Plants

Design and Multiobjective Dynamic Optimization of Superheaters for Load-Following Operation in Pulverized Coal Power Plants

Hot Corrosion Monitoring in Boilers Using a Nonlinear Estimator and Electrochemical Noise-Based Corrosion Sensors

A Data-Driven Kernel Principal Component Analysis–Bagging–Gaussian Mixture Regression Framework for Pulverizer Soft Sensors Using Reduced Dimensions and Ensemble Learning

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