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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]

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]

Advancing methods for modeling combustion systems and optimizing their operations is beneficial to improve the combustion performance. This paper develops a deep data-driven framework for the optimization of combustion system operations. First, a deep belief network based method is developed to model both of the combustion efficiency and the NOx emission. Next, a multi-objective optimization model is developed by integrating the deep belief network based models, the considered operational constraints, and the control variable constraints. Two objectives, maximizing the combustion efficiency and minimizing the NOx emission, are considered in the optimization. Due to the nonlinearity and complexity of the optimization model, traditional exact solution methods are not applicable to solve it. A recently presented swarm intelligence method, the JAYA algorithm, is applied to obtain the optimal solutions of the developed optimization model. Advantages of using JAYA are proved by benchmarking against well-known computational intelligence methods. The feasibility and effectiveness of the developed framework for optimizing the combustion process using industrial data is validated by computational experiments. Results demonstrate the potential of further improving both of the combustion efficiency and NOx emission by optimizing control settings of the combustion system. • The multi-objective optimization of combustion system operations using deep data-driven models is studied. • A deep belief network based method is developed to model the combustion efficiency and NOx emission. • A data-driven multi-objective optimization model of combustion process operations is developed. • A JAYA algorithm is firstly applied to solve the developed optimization model. • Computational results show the potential of further improving the combustion performance based on the developed model. [ABSTRACT FROM AUTHOR]

The robust generation self-scheduling problem under electricity price uncertainty is usually solved by the commercial solver, which is limited in computation time and memory requirement. This paper proposes an improved Lagrangian relaxation algorithm for the robust generation self-scheduling problem where the quadratic fuel cost and the time-dependent exponential startup cost are considered. By using the optimal duality theory, the robust generation self-scheduling problem, which has a max-min structure, is reformulated as a minimization mixed integer nonlinear programming (MINLP) problem. Upon the reformulation, the Lagrangian relaxation algorithm is developed. To obtain a solvable relaxed problem, the variable splitting technique is introduced before the relaxation. The obtained relaxed problem is decomposed into a linear programming-type subproblem and multiple single-unit subproblems. Each single-unit subproblem is solved optimally by a two-stage backward dynamic programming procedure. The special cases of the problem are discussed and a two-stage algorithm is proposed. The proposed algorithms are tested on test cases of different sizes and the numerical results show that the algorithms can find near-optimal solutions in a reasonable time. [ABSTRACT FROM AUTHOR]

To control the furnace temperature of a power plant boiler precisely, a dual-optimized adaptive model predictive control (DoAMPC) method is designed based on the data analytics. In the proposed DoAMPC, an accurate predictive model is constructed adaptively by the hybrid algorithm of the least squares support vector machine and differential evolution method. Then, an optimization problem is constructed based on the predictive model and many constraint conditions. To control the boiler furnace temperature, the differential evolution method is utilized to decide the control variables by solving the optimization problem. The proposed method can adapt to the time-varying situation by updating the sample data. The experimental results based on practical data illustrate that the DoAMPC can control the boiler furnace temperature with errors of less than 1.5% which can meet the requirements of the real production process. [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]

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. [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]

To schedule wind power efficiently and to mitigate the adverse effects caused by wind's intermittency and variability, an advanced wind power prediction model is proposed in this paper. This model is a combined model via switching different data-driven chaotic time series models. First, inputs of this model come from the reconstructed data based on the chaotic characteristics of wind power time series. Second, three different data mining algorithms are used to construct wind power prediction models individually. To obtain a regime for switching optimal models, a Markov chain is trained. Then, weights of different data-driven modes are calculated by the Markov chain switching regime, and used in the final combined model for wind power prediction. The industrial data from actual wind farms is studied. Results of the proposed model are compared with that of non-reconstructed input data, traditional data-driven models and two typical combined models. These results validate the superiority of proposed model on improving wind power prediction accuracy. • Wind power prediction is realized by chaotic time series and switching regime. • Chaotic wind power time series is analyzed and reconstructed as model inputs. • Different data-driven models are trained as prediction modes. • A regime constructed by Markov chain is proposed for modes switching. [ABSTRACT FROM AUTHOR]

This paper proposes a dynamic analytics method based on the least squares support vector machine with a hybrid kernel to address real-time prediction problems in the converter steelmaking process. The hybrid kernel function is used to enhance the performance of the existing kernels. To improve the model’s accuracy, the internal parameters are optimized by a differential evolution algorithm. In light of the complex mechanisms of the converter steelmaking process, a multistage modeling strategy is designed instead of the traditional single-stage modeling method. Owing to the dynamic nature of the practical production process, great effort has been made to construct a dynamic model that uses the prediction error information based on the static model. The validity of the proposed method is verified through experiments on real-world data collected from a basic oxygen furnace steelmaking process. The results indicate that the proposed method can successfully solve dynamic prediction problems and outperforms other state-of-the-art methods in terms of prediction accuracy. Note to Practitioners—With the development of cyber-physical systems, abundant real-time data have been collected from the converter steelmaking process. These data provide an opportunity to solve product quality prediction problems using data-driven models. This paper proposes a dynamic analytics method based on the least squares support vector machine with a hybrid kernel to address this challenging issue. To improve the model’s performance, a differential evolution algorithm is used to optimize its parameters. Because of the fierce physicochemical reaction in the converter furnace, it is difficult for a single-stage model to achieve accurate predictions. Thus, a multistage modeling strategy is proposed to address this difficulty, and a dynamic model based on feedback error is developed to realize real-time prediction. We verify the effectiveness of the proposed method using real data from a basic oxygen furnace (BOF) steelmaking process. The computational results reveal that the proposed method has a higher prediction accuracy than other methods, making it helpful in guaranteeing the specified product quality and in maintaining stable BOF operation. [ABSTRACT FROM AUTHOR]

Abstract Wind power ramps cause large-amplitude power fluctuation which harmfully affects the stability of power system's operation. As a new issue in wind power integration, the existing ramp forecasting methods still has some imperfection, e.g., harmonization on long-term trend and short-term precision. Therefore, an advanced method is proposed in this paper, mainly focus on improving the performance of wind power ramp prediction. This method utilizes wind power curve to build a primary model which can capture the trend of wind power variation. Then, prediction residual of the primary model is corrected by a MSAR (Markov-Switching-Auto-Regression) model which combining the advantages of AR models and Markov chain. Finally, an improved swinging door algorithm is applied to extract linear segments, and ramp definitions are used to detect ramp events. Actual wind farm data is used to test the proposed method. Comparison with traditional methods are presented, the numerical results validate that the proposed approach has improved performance not only on wind power prediction but also on ramp prediction. Highlights • An advanced approach is proposed to improve ramp prediction. • The approach contains wind power prediction, residual correction, ramp detection. • Primary models capture trend of wind power variation. • Details precision are corrected by MSAR correction prediction residual. [ABSTRACT FROM AUTHOR]