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Timely and accurate three-dimensional (3-D) NOx concentration distribution prediction is essential for achieving low-emission and efficient operation in power plants. This study proposed a theory-guided data-driven prediction method for the 3-D NOx concentration distribution prediction. Firstly, the method created a foundational dataset by fusing numerical simulation data from the computational fluid dynamics (CFD) with operational data from the distributed control system (DCS). Then, the data was classified into three load condition categories, and the center operating conditions for each category were computed separately. Subsequently, the K-means algorithm was employed to extract representative data to address the computational challenges associated with big data. Finally, a Theory-Guided Deep Neural Network model (TG-DNN) was established leveraging the principle of carbon element mass conservation and deep neural network. Experimental results demonstrate that the method effectively monitors the 3-D NOx concentration distribution, potentially facilitating efficient production processes. • Develop a framework for 3-D NOx distribution prediction online with high-precision. • Propose a Theory-Guided DNN merging conservation theory for interpretability. • Utilize the K-Means algorithm for resampling preserving data characteristics. • The proposed model outperformed other comparison models. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) reconstructions of temperature distributions can be used to effectively design power plants and ensure production safety. Typically, 3D temperature reconstruction based on the flame image processing technology and finite element calculation of furnace combustion using computational fluid dynamics (CFD) simulation are performed to obtain the furnace temperature field. In this study, a novel online method that overcomes the defects of image detection devices was proposed for reconstructing the temperature field with improved evaluation accuracy. Numerical simulations were used to perform numerous calculations. In this method, deep neural network (DNN) models were used for reconstructing the 3D temperature distribution. The training set was derived from offline CFD simulations that were set for a specific boiler and a series of typical working conditions. Based on established DNN models, the online calculation of 3D temperature distribution was realized for current operating conditions. The result revealed that the furnace temperature field could be accurately reconstructed online in a 350-MW tangentially fired boiler. Compared with the numerical simulation results, the mean absolute percent error under the tilt angles of 0°, 10°, and −10° were 3.61 %, 4.25 %, and 4.52 %. The proposed integrated method was applied to actual boilers with average error 3.448 % and achieved feasible solutions within 20 s. • A novel method was used to reconstruct the furnace temperature field. • The boundary condition of the method was defined from actual operation data. • The measuring point, CFD simulation, and proposed reconstruction errors were compared. • Multiple hyperparameter configurations were tested and optimal model was found. • The combustion process was monitored using a three-dimensional visualization system. [ABSTRACT FROM AUTHOR]

[Display omitted] • A 3D combustion temperature prediction method, GDNN, uses CFD simulations to achieve online reconstructions was proposed. • A temperature field correction method employs actual measurement points was proposed to correct the prediction results. • Optimal parameters for the proposed prediction and correction method were selected through a parameter analysis. • The proposed GDNN and correction method jointly yield the lowest error, demonstrating their real-world effectiveness. Predicting furnace temperature distribution is vital for coal-fired boiler safety. Existing methods, including finite element calculations and three-dimensional (3D) reconstruction still face limitations. A 3D combustion temperature field prediction method, GDNN, which utilizes offline-computed computational fluid dynamics (CFD) simulation results for online reconstructions of entire boiler, was proposed. GDNN method leverages the knowledge of the temperature field acquired by the base neural network model and Gaussian processes. Furthermore, a temperature field correction method is introduced, which employs intermediate variables of the GDNN model and measured values from temperature sensors to establish a correction model for the entire predicted temperature field. We compared GDNN's effectiveness with four well-established algorithms: Extreme Learning Machine (ELM), Least Absolute Shrinkage and Selection Operator (LASSO), Deep Neural Network(DNN), and Radial Basis Function (RBF) network, by also substituting these algorithms as the base model in our proposed method. The experimental results demonstrate that the proposed prediction method exhibits the highest performance, and the correction method effectively improves the overall results. The optimal parameters for predicting and correcting 3D furnace temperature field results were determined through experimental comparison, and the proposed method was applied to a 350 MW boiler, achieving an error of 2.41%, proving its real-world effectiveness. [ABSTRACT FROM AUTHOR]