Coal-Fired Boiler Fault Prediction using Artificial Neural Networks

Boiler fault is a critical issue in a coal-fired power plant due to its high temperature and high pressure characteristics. The complexity of boiler design increases the difficulty of fault investigation in a quick moment to avoid long duration shut-down. In this paper, a boiler fault prediction model is proposed using artificial neural network. The key influential parameters analysis is carried out to identify its correlation with the performance of the boiler. The prediction model is developed to achieve the least misclassification rate and mean squared error. Artificial neural network is trained using a set of boiler operational parameters. Subsequenlty, the trained model is used to validate its prediction accuracy against actual fault value from a collected real plant data. With reference to the study and test results, two set of initial weights have been tested to verify the repeatability of the correct prediction. The results show that the artificial neural network implemented is able to provide an average of above 92% prediction rate of accuracy

Automated Data Filtering Approach for ANN Modeling of Distributed Energy Systems: Exploring the Application of Machine Learning

To realize the distributed generation and to make the partnership between the dispatchable units and variable renewable resources work efficiently, accurate and flexible monitoring needs to be implemented. Due to digital transformation in the energy industry, a large amount of data is and will be captured every day, but the inability to process them in real time challenges the conventional monitoring and maintenance practices. Access to automated and reliable data-filtering tools seems to be crucial for the monitoring of many distributed generation units, avoiding false warnings and improving the reliability. This study aims to evaluate a machine-learning-based methodology for autodetecting outliers from real data, exploring an interdisciplinary solution to replace the conventional manual approach that was very time-consuming and error-prone. The raw data used in this study was collected from experiments on a 100-kW micro gas turbine test rig in Norway. The proposed method uses Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to detect and filter out the outliers. The filtered datasets are used to develop artificial neural networks (ANNs) as a baseline to predict the normal performance of the system for monitoring applications. Results show that the filtering method presented is reliable and fast, minimizing time and resources for data processing. It was also shown that the proposed method has the potential to enhance the performance of the predictive models and ANN-based monitoring.publishedVersio

Artificial Intelligence driven smart operation of large industrial complexes supporting the net-zero goal: Coal power plants

The true potential of artificial intelligence (AI) is to contribute towards the performance enhancement and informed decision making for the operation of the large industrial complexes like coal power plants. In this paper, AI based modelling and optimization framework is developed and deployed for the smart and efficient operation of a 660 MW supercritical coal power plant. The industrial data under various power generation capacity of the plant is collected, visualized, processed and subsequently, utilized to train artificial neural network (ANN) model for predicting the power generation. The ANN model presents good predictability and generalization performance in external validation test with R2 = 0.99 and RMSE =2.69 MW. The partial derivative of the ANN model is taken with respect to the input variable to evaluate the variable’ sensitivity on the power generation. It is found that main steam flow rate is the most significant variable having percentage significance value of 75.3 %. Nonlinear programming (NLP) technique is applied to maximize the power generation. The NLP-simulated optimized values of the input variables are verified on the power generation operation. The plant-level performance indicators are improved under optimum operating mode of power generation: savings in fuel consumption (3 t/h), improvement in thermal efficiency (1.3 %) and reduction in emissions discharge (50.5 kt/y). It is also investigated that maximum power production capacity of the plant is reduced from 660 MW to 635 MW when the emissions discharge limit is changed from 510 t/h to 470 t/h. It is concluded that the improved plant-level performance indicators and informed decision making present the potential of AI based modelling and optimization analysis to reliably contribute to net-zero goal from the coal power plant

An Intelligent Monitoring Interface for a Coal-Fired Power Plant Boiler Trips

A power plant monitoring system embedded with artificial intelligence can enhance its effectiveness by reducing the time spent in trip analysis and follow up procedures. Experimental results showed that Multilayered perceptron neural network trained with Levenberg-Marquardt (LM) algorithm achieved the least mean squared error of 0.0223 with the misclassification rate of 7.435% for the 10 simulated trip prediction. The proposed method can identify abnormality of operational parameters at the confident level of ±6.3%

Numerical Simulation and Customized DACM Based Design Optimization

PhD thesis in Offshore technologyThe diverse numerical modelling, analysis and simulation tools that have been developed and introduced to markets are intended to perform the virtual design and testing of products and systems without the construction of physical prototypes. Digital prototyping in the form of computer modelling and simulation are important means of numerical model predictions, i.e. design validation and verification. However, as the tools advance to more precise and diverse applications, the operation eventually becomes more complex, computationally expensive and error prone; this is particularly true for complex multi-disciplinary and multidimensional problems; for instance, in multi-body dynamics, Fluid-Structure Interaction (FSI) and high-dimensional numerical simulation problems. On the other hand, integrating design optimization operations into the product and system development processes, through the computer based applications, makes the process even more complex and highly expensive. This thesis analyses and discusses causes of complexity in numerical modelling, simulation and optimization operations and proposes new approaches/frameworks that would help significantly reduce the complexity and the associated computational costs. Proposed approaches mainly integrate, simplify and decompose or approximate complex numerical simulation based optimization problems into simpler, and to metamodel-based optimization problems. Despite advancing computational technologies in continuum mechanics, the design and analysis tools have developed in separate directions with regard to ‘basis functions’ of the technologies until recent developments. Basis functions are the building blocks of every continuous function. Continuous functions in every computational tool are linear combinations of specific basis functions in the function space. Since first introduced, basis functions in the design and modelling tools have developed so rapidly that various complex physical problems can today be designed and modelled to the highest precision. On the other hand, most analysis tools still utilize approximate models of the problems from the latter tools, particularly if the problem involves complex smooth geometric designs. The existing gap between the basis functions of the tools and the increasing precision of models for analysis introduce tremendous computational costs. Moreover, to transfer models from one form of basis function to another, additonal effort is required. The variation of the basis functions also demands extra effort in numerical simulation based optimization processes. This thesis discusses the recently developed integrated modelling and analysis approach that utilizes the state-of-the-art basis function (NURBS function) for both design and analysis. A numerical simulation based shape optimization framework that utilizes the state-of-the-art basis function is also presented in a study in the thesis. One of the common multidisciplinary problem that involves multiple models of domains in a single problem, fluid-structure interaction (FSI) problem, is studied in the thesis. As the name implies, the two models of domains involved in any FSI problems are fluid and structure domain models. In order to solve the FSI problems, usually three mathematical components are needed: namely, i) fluid dynamics model, ii) structural mechanics model and, iii) the FSI model. This thesis presents the challenges in FSI problems and discusses different FSI approaches in numerical analysis. A comparative analysis of computational methods, based on the coupling and temporal discretization schemes, is discussed using a benchmark problem, to give a better understanding of what a multidisciplinary problem is and the challenge for design optimizations that involve such problems. [...

Prediction of power output of a coal-fired power plant by artificial neural network

Accurate modeling of thermal power plant is very useful as well as difficult. Conventional simulation programs based on heat and mass balances represent plant processes with mathematical equations. These are good for understanding the processes but usually complicated and at times limited with large number of parameters needed. On the other hand, artificial neural network (ANN) models could be developed using real plant data, which are already measured and stored. These models are fast in response and easy to be updated with new plant data. Usually, in ANN modeling, energy systems can also be simulated with fewer numbers of parameters compared to mathematical ones. Step-by-step method of the ANN model development of a coal-fired power plant for its base line operation is discussed in this paper. The ultimate objective of the work was to predict power output from a coal-fired plant by using the least number of controllable parameters as inputs. The paper describes two ANN models, one for boiler and one for turbine, which are eventually integrated into a single ANN model representing the real power plant. The two models are connected through main steam properties, which are the predicted parameters from boiler ANN model. Detailed procedure of ANN model development has been discussed along with the expected prediction accuracies and validation of models with real plant data. The interpolation and extrapolation capability of ANN models for the plant has also been studied, and observed results are reported

DESIGN AND IMPLEMENTATION OF INTELLIGENT MONITORING SYSTEMS FOR THERMAL POWER PLANT BOILER TRIPS

Steam boilers represent the main equipment in the power plant. Some boiler trips may lead to an entire shutdown of the plant, which is economically burdensome. An early detection and diagnosis of the boiler trips is crucial to maintain normal and safe operational conditions of the plant. Numbers of methodologies have been proposed in the literature for fault diagnosis of power plants. However, rapid deployment of these methodologies is difficult to be achieved due to certain inherent limitations such as system inability to learn or a dynamically improve the system performance and the brittleness of the system beyond its domain of expertise. As a potential solution to these problems, two artificial intelligent monitoring systems specialized in boiler trips have been proposed and coded within the MA TLAB environment in the present work. The training and validation of the two systems have been performed using real operational data which was captured from the plant integrated acquisition system of JANAMANJUNG coal-fired power plant. An integrated plant data preparation framework for seven boiler trips with related operational variables, has been proposed for the training and validation of the proposed artificial intelligent systems. The feedforward neural network methodology has been adopted as a major computational intelligent tool in both systems. The root mean square error has been widely used as a performance indicator of the proposed systems. The first intelligent monitoring system represents the use of the pure artificial neural network system for boiler trip detection. The final architecture for this system has been explored after investigation of various main neural network topology combinations which include one and two hidden layers, one to ten neurons for each hidden layer, three types of activation function, and four types of multidimensional minimization training algorithms. It has been found that there was no general neural network topology combination that can be applied for all boiler trips. All seven boiler trips under consideration had been detected by the proposed systems before or at the same time as the plant control system. The second intelligent monitoring system represents mergmg of genetic algorithms and artificial neural networks as a hybrid intelligent system. For this hybrid intelligent system, the selection of appropriate variables from hundreds of boiler operation variables with optimal neural network topology combinations to monitor boiler trips was a major concern. The encoding and optimization process using genetic algorithms has been applied successfully. A slightly lower root mean square error was observed in the second system which reveals that the hybrid intelligent system performed better than the pure neural network system. Also, the optimal selection of the most influencing variables was performed successfully by the hybrid intelligent system. The proposed artificial intelligent systems could be adopted on-line as a reliable controller of the thermal power plant boiler

Caracterización energética y desarrollo de modelos predictivos en plantas de cogeneración para la verificación de ahorros en proyectos de eficiencia energética

En este trabajo se desarrolla una metodología para la cuantificación de los ahorros obtenidos de la implantación de medidas de mejora de la eficiencia energética en plantas industriales, con aplicación al caso concreto de una planta de cogeneración de ciclo combinado. El estudio comienza con la revisión de la operación de la planta y de las funciones principales del sistema de control, junto con una evaluación de tipo cualitativo de los cambios acontecidos en la cogeneración tras la aplicación de las mejoras. Posteriormente se procede a la selección del volumen de control del análisis, con el objetivo de minimizar y dirigir los esfuerzos hacia las áreas del sistema efectivamente afectadas por las mejoras. Para la fase de modelado se propone la aplicación de la técnica de las redes neuronales artificiales, que se utilizan para reproducir las generaciones y consumos energéticos del sistema durante el período posterior al “retrofit” bajo su configuración anterior al “retrofit”, simulando el comportamiento de la planta en el caso en que los cambios no hubieran acontecido. Una parte relevante del análisis es dedicada a la selección de las variables a utilizar como predictores en la fase de modelado, así como a la determinación de la influencia relativa de dichas variables de entrada sobre los modelos finales. Por último se calculan los ahorros económicos, como diferencia entre el beneficio de explotación real y el beneficio que la planta generaría en el caso de no haberse implantado las mejoras (“línea base” del beneficio). La aportación principal del estudio realizado reside en la demostración de la validez de una metodología basada en el modelado por redes neuronales artificiales para la cuantificación rigurosa de los ahorros procedentes de la aplicación de medidas de ahorro energético en plantas industriales. El trabajo deja vías abiertas de desarrollo, entre ellas: - La definición de los aspectos técnicos a incluir entre las cláusulas contractuales necesarias para la implementación de la metodología propuesta en los contratos de rendimiento energético estipulados entre los clientes industriales y las empresas de servicios energéticos (ESE). - La utilización de la técnica de modelado propuesta para la generación de líneas de referencia a emplearse para la detección en tiempo real de las desviaciones de la operación del sistema del comportamiento “estándar” o de referencia. - El modelado por redes neuronales artificiales de procesos industriales de elevada complejidad y la utilización de los modelos obtenidos para la optimización de la operación mediante la aplicación del algoritmo genético