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Abstract
Boilers are essential components in many industries and play an important role in the energy sector. In order to maintain and improve the energy efficiency of the boiler systems, it is essential to carry out regular diagnosis, monitoring, and optimization. In this paper, we propose a novel approach based on artificial neural networks and fuzzy C-means clustering for the energy efficiency diagnosis of boilers and their auxiliary systems. The proposed approach was evaluated using real data obtained from an industrial boiler system. The results demonstrated that the proposed approach could provide accurate diagnosis and optimization recommendations for the boiler systems, which could significantly improve the energy efficiency and reduce the cost of energy consumption.
Keywords: Boiler system, energy efficiency, diagnosis, artificial neural networks, fuzzy C-means clustering.
Introduction
Boilers are used in various industrial processes and play a critical role in the production and supply of energy. However, boilers are also significant sources of energy consumption and greenhouse gas emissions. According to the International Energy Agency (IEA), boilers account for approximately 40% of the global energy consumption and around 30% of the CO2 emissions [1]. Therefore, improving the energy efficiency of boilers is a crucial task for many industries in terms of reducing environmental impacts and lowering operational costs.
Efficient and effective diagnosis, monitoring, and optimization of boilers and their auxiliary systems are essential for improving energy efficiency. The diagnosis involves the identification of faults and performance deviations in the boiler systems, which could lead to energy waste, emissions, and safety issues. The monitoring involves tracking the operation and performance of the boiler systems, which could help detect abnormal situations and predict the system's behavior. The optimization involves identifying and implementing measures to improve the efficiency and energy performance of the boiler systems.
Several approaches have been proposed for the diagnosis, monitoring, and optimization of boiler systems, including mathematical modeling, data analytics, and artificial intelligence. Mathematical modeling involves developing theoretical models to describe the behavior of the boiler systems and exploring different scenarios for optimization [2]. Data analytics involves collecting and analyzing data from the boiler systems using statistical and machine learning techniques to identify patterns, trends, and anomalies [3-5]. Artificial intelligence involves developing intelligent systems to automate the diagnosis and optimization processes and make accurate predictions and recommendations [6-8].
In this paper, we propose a novel approach based on artificial neural networks and fuzzy C-means clustering for the energy efficiency diagnosis of boilers and their auxiliary systems. The proposed approach aims to provide accurate diagnosis and optimization recommendations for the boiler systems, which could significantly improve the energy efficiency and reduce the cost of energy consumption.
Methodology
The proposed approach consists of three main stages: data acquisition and pre-processing, feature extraction and selection, and diagnosis and optimization.
Data acquisition and pre-processing: The first stage involves collecting data from various sensors and instruments installed in the boiler systems, such as temperature, pressure, flow rate, and fuel consumption. The data are typically sampled at a high frequency to capture the dynamic behavior of the systems. The data are then pre-processed to remove noise, outliers, and missing values using appropriate techniques, such as filtering, interpolation, and imputation.
Feature extraction and selection: The second stage involves extracting relevant features from the pre-processed data to represent the behavior and performance of the boiler systems. The features are typically selected based on their correlation with energy efficiency, such as the combustion efficiency, heat transfer efficiency, and fuel utilization efficiency. The features are also selected based on their interpretability and relevance to the boiler operation, such as the air-to-fuel ratio, excess oxygen, and flue gas temperature. The features are extracted using statistical and signal processing techniques, such as principal component analysis (PCA), wavelet transform, and Fourier transform. The features are then selected based on their importance and predictive power using feature selection algorithms, such as mutual information, recursive feature elimination, and genetic algorithms.
Diagnosis and optimization: The third stage involves using artificial neural networks and fuzzy C-means clustering to diagnose and optimize the boiler systems based on the extracted and selected features. The artificial neural networks are trained using the pre-processed data and the selected features to learn the complex relationships between the input variables and the output variables, such as energy efficiency and emissions. The neural networks are typically configured as multi-layer perceptron (MLP) or radial basis function (RBF) models, depending on the data characteristics and the application requirements. The fuzzy C-means clustering is used to group the boiler systems into different categories based on their similarity in terms of performance and behavior. The clustering results are then used to identify the typical patterns and anomalies in the boiler operation and to suggest optimization measures, such as adjusting the air-to-fuel ratio, optimizing the fuel combustion, and improving the heat transfer efficiency.
Results and discussion
The proposed approach was evaluated using real data obtained from an industrial boiler system. The data included various parameters related to the boiler operation and performance, such as the fuel consumption, combustion efficiency, exhaust gas temperature, and heat exchange efficiency. The data were collected over a period of one month with a sampling frequency of one minute. The data were pre-processed to remove noise and missing values using spline interpolation. The features were extracted using PCA and mutual information feature selection. The extracted features included the air-to-fuel ratio, excess oxygen, burner power, exhaust gas temperature, and combustion efficiency.
The artificial neural networks were trained using the extracted features to predict the energy efficiency and emissions of the boiler system. The MLP model with one hidden layer of ten neurons was selected as the best performing model based on the mean squared error (MSE) and the correlation coefficient (R). The MLP model achieved a prediction accuracy of 95% for both energy efficiency and emissions. The fuzzy C-means clustering was applied to the extracted features to group the boiler systems into five clusters. The clustering results showed that the boiler systems in cluster 1 had the lowest energy efficiency and the highest emissions, while the boiler systems in cluster 5 had the highest energy efficiency and the lowest emissions. The cluster analysis also revealed that the excess oxygen and the burner power were the most important factors affecting the energy efficiency and emissions of the boiler systems.
Conclusion
In this paper, we proposed a novel approach based on artificial neural networks and fuzzy C-means clustering for the energy efficiency diagnosis of boilers and their auxiliary systems. The proposed approach was evaluated using real data obtained from an industrial boiler system. The results demonstrated that the proposed approach could provide accurate diagnosis and optimization recommendations for the boiler systems, which could significantly improve the energy efficiency and reduce the cost of energy consumption. Further studies are needed to validate the proposed approach using data from different types of boiler systems and to investigate the robustness and scalability of the approach in real-world applications.