Search

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]

The development of an accurate nitrogen oxide (NO x) prediction model is difficult because of multiple parameters, strong coupling, and long delay time of selective catalytic reduction (SCR) systems. In this study, a modeling scheme based on combined feature selection, the JAYA optimization algorithm, and deep neural network (DNN) was developed. First, historical operating data preprocessing, including eliminating outlier points and normalization, was completed. Next, a combined feature selection algorithm based on classification and regression tree, random forest, extreme gradient boosting, and maximal information coefficient (MIC) was developed to select critical input variables. Subsequently, the delay time of the input variables was calculated based on the MIC and JAYA algorithm, and the modeling data were reconstructed. Finally, the real-time dynamic prediction of the SCR outlet NO x concentration was realized based on the DNN model. Experimental results based on operation data of 1000 MW ultra-supercritical boiler revealed that the prediction errors of the established models were less than 5%. Thus, could accurately predict the NO x emission concentration at the outlet of SCR system. • A combined feature selection algorithm based on CART, RF, XGBoost, and MIC is developed. • A JAYA-based method for calculating delay time between NOx emission and input variables is developed. • A real-time dynamic prediction model of the SCR outlet NOx concentration is developed. • Case study on NOx emissions at a 1000 MW coal-fired power plant. • The proposed model outperformed other comparable models. [ABSTRACT FROM AUTHOR]

Accurate and reliable wind direction prediction is important for improving wind power conversion efficiency and operation safety. In this paper, a two-phase deep learning model is proposed and constructed for high-performance short-term wind direction forecasting. In the first phase, a hybrid data processing strategy, including data reconstruction, outlier deletion, dimension reduction, and sequence decomposition, is proposed to extract the most meaningful information from practical data. Then, in the second phase, a robust echo state network is developed for wind direction forecasting. In addition, its hyper-parameters are optimized using an improved flower pollination algorithm (IFPA) to achieve high efficiency. Experiments conducted on data from real wind farms validate the proposed hybrid data processing method. Finally, comparisons with benchmark prediction models show that the proposed network achieves superior performance. • A two-phase short-term wind direction prediction model is proposed. • Hybrid data processing method is used to extract data's most meaningful information. • Improved echo state network (ESN) is developed in prediction modeling. • An improved flower pollination algorithm is proposed to optimize ESN's parameters. • The proposed model is validated efficient and effective in wind direction prediction. [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]

An automatic encoder (AE) extreme learning machine (ELM)-AE-ELM model is proposed to predict the NOx emission concentration based on the combination of mutual information algorithm (MI), AE, and ELM. First, the importance of practical variables is computed by the MI algorithm, and the mechanism is analyzed to determine the variables related to the NOx emission concentration. Then, the time delay correlations between the selected variables and NOx emission concentration are further analyzed to reconstruct the modeling data. Subsequently, the AE is applied to extract hidden features within the input variables. Finally, an ELM algorithm establishes the relationship between the NOx emission concentration and deep features. The experimental results on practical data indicate that the proposed model shows promising performance compared to state-of-art models. • A MI-based method for calculating delay time between NOx emission and input variables is developed. • A new feature extraction method based on auto-encoder is developed. • An AE-ELM-based method for predicting NOx emissions is developed. • Case study on NOx emissions at a 1000 MW coal-fired power plant. • The proposed model outperformed other comparable models. [ABSTRACT FROM AUTHOR]

C–H activation provides a straightforward approach to construct new chemical bonds and privileged heterocycles. However, due to common deficiencies, C–H activation still faces a great challenge towards sustainability and applications. Described herein is a robust and convenient rhodium-catalyzed regioselective C–H activation/[4 + 2] annulation for the synthesis of isoquinolones with salient features including a recyclable catalytic reaction system, unique regioselectivity, gram-scale synthesis with low catalyst loading (e.g., 0.5 mol%), ethanol as the green solvent, air and water compatibility, and simple purification processes. The roles of the hydroxy group and the pivaloyl part in both substrates, the additional pivotal function of propargyl alcohol partners, and the origin of the unique chemoselectivity are clarified based on integrated experimental and DFT studies. Additionally, a propargyl alcohol-connected distinctive hydrogen bonding network with the hydroxy group and the pivaloyl part is also rationally deduced for conferring the observed reverse regioselectivity by subsequent IGMH analysis. [ABSTRACT FROM AUTHOR]