Nonlinear state estimation of a power plant superheater by using the extended Kalman filter for differential algebraic equation systems

Saini, Vivek; Bhattacharyya, Debangsu; Purdy, Daniel; Parker, Jonathan; Boohaker, Charles

doi:10.1016/j.applthermaleng.2024.123471

Cited by 4 publications

(3 citation statements)

References 51 publications

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“…Obviously, a larger number of discretization grid points may be considered for higher spatial resolution, if needed. 46 The flow pattern as well as the input and output variables to the ECNN model remain the same for both flue gas and steam as compared to the lumped model. However, for both training and forward problems while developing ECNN for the distributed model, the enthalpy balance constraint eq (eq 13) has to be exactly satisfied for each of the individual control volumes in spatial directions, along with the boundaries of the system.…”

Section: Case Studies and Corresponding Mass−energy Balance Constraintsmentioning

confidence: 99%

“…Therefore, in this work, three nonlinear processes with different levels of complexity for mass and energy transfer within the system have been chosen for modeling. While the first case study considers the development of both lumped and distributed models of an adiabatic superheater/reheater system , involving no generation/loss/conversion of energy within the system, the second and third case studies, respectively, consider developing MECNNs to model the widely used nonisothermal lumped (i.e., CSTR) van de Vusse reactor system , involving an exothermic reaction (hence heat generation), as well as an electrically heated distributed tubular (plug-flow) reactor , involving vapor-phase cracking of acetone during the production of acetic anhydride, where electric energy gets transformed to heat to facilitate the endothermic reaction. Although, the mass balance constraints are trivially satisfied for the superheater/reheater system (Case Study 1), both mass and energy conservation laws have been simultaneously incorporated as equality constraints for the other two case studies considered in this work.…”

Section: Case Studies and Corresponding Mass–energy Balance Constraintsmentioning

confidence: 99%

“…For simplicity, the superheater/reheater model, as shown in Figure a, has been discretized into two control volumes each in the horizontal and vertical directions, thus leading to a 2 × 2 grid structure as shown in Figure b. Obviously, a larger number of discretization grid points may be considered for higher spatial resolution, if needed . The flow pattern as well as the input and output variables to the ECNN model remain the same for both flue gas and steam as compared to the lumped model.…”

Section: Case Studies and Corresponding Mass–energy Balance Constraintsmentioning

confidence: 99%

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Development of Steady-State and Dynamic Mass and Energy Constrained Neural Networks for Distributed Chemical Systems Using Noisy Transient Data

Mukherjee,

Bhattacharyya

2024

Ind. Eng. Chem. Res.

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The paper presents the development of algorithms for mass and energy constrained neural network models that can exactly conserve the overall mass and energy of distributed chemical process systems, even though the noisy transient data used for optimal model training violate the same. In contrast to approximately satisfying mass and energy balance constraints of a system by soft penalization of objective function, algorithms have been developed for solving equality-constrained nonlinear optimization problems, thus providing the guarantee of exactly satisfying the system mass and energy conservation laws. For developing dynamic mass-energy constrained network models for distributed systems, hybrid series and parallel dynamic-static neural networks have been leveraged. The developed algorithms for solving both the training and forward problems are validated using both steady-state and dynamic data in the presence of various noise characteristics. The developed data-driven algorithms are flexible to exactly satisfy mass and energy balance constraints for dynamic chemical processes if the system holdup information is available. The proposed network structures and algorithms are applied to the development of data-driven lumped and distributed models of an adiabatic superheater/reheater system, a nonisothermal continuous stirred tank reactor, as well as an electrically heated plug-flow reactor system where one form of energy gets transformed to another. It has been observed that the mass-energy constrained neural networks yield a root mean squared error of <1% with respect to the system truth for the case studies evaluated in this work.

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Section: Case Studies and Corresponding Mass−energy Balance Constraintsmentioning

confidence: 99%

Section: Case Studies and Corresponding Mass–energy Balance Constraintsmentioning

confidence: 99%

Section: Case Studies and Corresponding Mass–energy Balance Constraintsmentioning

confidence: 99%

See 1 more Smart Citation

Development of Steady-State and Dynamic Mass and Energy Constrained Neural Networks for Distributed Chemical Systems Using Noisy Transient Data

Mukherjee,

Bhattacharyya

2024

Ind. Eng. Chem. Res.

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A data-driven regression model for predicting thermal plant performance under load fluctuations

Prokhorskii,

Rudra,

Preißinger

et al. 2024

Carb Neutrality

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The global energy demand is still primarily reliant on fossil-fueled thermal power plants. With the growing share of renewables, these plants must frequently adjust their loads. Maintaining, or ideally increasing operational efficiency under these conditions is crucial. Increasing the efficiency of such systems directly reduces associated greenhouse gas emissions, but it requires sophisticated models and monitoring systems. Data-driven models have proven their value here, as they can be used for monitoring, operational state estimation, and prediction. However, they are also sensitive to (1) the training approach, (2) the selected feature set, (3) and the algorithm used. Using operational data, we comprehensively investigate these model parameters for performance prediction in a thermal plant for process steam generation. Specifically, four regression algorithms are evaluated for the prediction of the highly fluctuating live steam flow with two training approaches and three feature subsets of the raw dataset. Furthermore, manual and automatic clustering methods are used to identify different states of operation regarding the fuel amounts used in the combustion chamber. Our results show that the live steam flow is predicted with excellent accuracy for a testing period of one month (R2=0.994 and NMAE=0.55%) when using a dynamic training approach and a comprehensive feature set comprised of 48 features representing the combustion process. It is also seen that the statically trained model predicts various load changes with strong accuracy and that the accuracy of the dynamically trained model can be approached by incorporating the cluster information into the static model. These models reflect the plant’s physical intricacies under varying loads, where deviations from the predicted live steam flow indicate unwanted long-term drifts. They can be directly implemented to help operators detect inefficiencies and optimize plant performance.

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Development of hybrid first principles – artificial intelligence models for transient modeling of power plant superheaters under load-following operation

Mukherjee,

Saini,

Adeyemo

et al. 2025

Applied Thermal Engineering

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Nonlinear state estimation of a power plant superheater by using the extended Kalman filter for differential algebraic equation systems

Cited by 4 publications

References 51 publications

Development of Steady-State and Dynamic Mass and Energy Constrained Neural Networks for Distributed Chemical Systems Using Noisy Transient Data

Development of Steady-State and Dynamic Mass and Energy Constrained Neural Networks for Distributed Chemical Systems Using Noisy Transient Data

A data-driven regression model for predicting thermal plant performance under load fluctuations

Development of hybrid first principles – artificial intelligence models for transient modeling of power plant superheaters under load-following operation

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