Artificial Neural Network and its Applications in the Energy Sector – An Overview

In order to realize the goal of optimal use of energy sources and cleaner environment at a minimal cost, researchers; field professionals; and industrialists have identified the expediency of harnessing the computational benefits provided by artificial intelligence (AI) techniques. This article provides an overview of AI, chronological blueprints of the emergence of artificial neural networks (ANNs) and some of its applications in the energy sector. This short survey reveals that despite the initial hiccups at the developmental stages of ANNs, ANN has tremendously evolved, is still evolving and have been found to be effective in handling highly complex problems even in the areas of modeling, control, and optimization, to mention a few

Application of Intelligent Computational Techniques in Power Plants:A Review

Growing worldwide demand for energy leads to increasing the levels of challenge in power plants management. These challenges include but are not limited to complex equipment maintenance, power estimation under uncertainty, and energy optimisation. Therefore, efficient power plant management is required to increase the power plant’s operational efficiency. Conventional optimisation tools in power plants are not reliable as it is challenging to monitor, model and analyse individual and combined components within power systems in a plant. However, intelligent computational tools such as artificial neural networks (ANN), nature-inspired computations and meta-heuristics are becoming more reliable, offering a better understanding of the behaviour of the power systems, which eventually leads to better energy efficiency. This paper aims to provide an overview of the development and application of intelligent computational tools such as ANN in managing power plants. Also, to present several applications of intelligent computational tools in power plants operations management. The literature review technique is used to demonstrate intelligent computational tools in various power plants applications. The reviewed literature shows that ANN has the greatest potential to be the most reliable power plant management tool

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

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

Nowadays mathematical modeling and numerical simulations play an important role in life and natural science. Numerous researchers are working in developing different methods and techniques to help understand the behavior of very complex systems, from the brain activity with real importance in medicine to the turbulent flows with important applications in physics and engineering. This book presents an overview of some models, methods, and numerical computations that are useful for the applied research scientists and mathematicians, fluid tech engineers, and postgraduate students

Study of power plant, carbon capture and transport network through dynamic modelling and simulation

The unfavourable role of CO₂ in stimulating climate change has generated concerns as CO₂ levels in the atmosphere continue to increase. As a result, it has been recommended that coal-fired power plants which are major CO₂ emitters should be operated with a carbon capture and storage (CCS) system to reduce CO₂ emission levels from the plant. Studies on CCS chain have been limited except a few high profile projects. Majority of previous studies focused on individual components of the CCS chain which are insufficient to understand how the components of the CCS chain interact dynamically during operation. In this thesis, model-based study of the CCS chain including coal-fired subcritical power plant, post-combustion CO₂ capture (PCC) and pipeline transport components is presented. The component models of the CCS chain are dynamic and were derived from first principles. A separate model involving only the drum-boiler of a typical coal-fired subcritical power plant was also developed using neural networks.The power plant model was validated at steady state conditions for different load levels (70-100%). Analysis with the power plant model show that load change by ramping cause less disturbance than step changes. Rate-based PCC model obtained from Lawal et al. (2010) was used in this thesis. The PCC model was subsequently simplified to reduce the CPU time requirement. The CPU time was reduced by about 60% after simplification and the predictions compared to the detailed model had less than 5% relative difference. The results show that the numerous non-linear algebraic equations and external property calls in the detailed model are the reason for the high CPU time requirement of the detailed PCC model. The pipeline model is distributed and includes elevation profile and heat transfer with the environment. The pipeline model was used to assess the planned Yorkshire and Humber CO₂ pipeline network.Analysis with the CCS chain model indicates that actual changes in CO₂ flowrate entering the pipeline transport system in response to small load changes (about 10%) is very small (<5%). It is therefore concluded that small changes in load will have minimal impact on the transport component of the CCS chain when the capture plant is PCC

Plantwide Control and Simulation of Sulfur-Iodine Thermochemical Cycle Process for Hydrogen Production

A PWC structure has developed for an industrial scale SITC plant. Based on the performance evaluation, it has been shown that the SITC plant developed via the proposed modified SOC structure can produce satisfactory performance – smooth and reliable operation. The SITC plant is capable of achieving a thermal efficiency of 69%, which is the highest attainable value so far. It is worth noting that the proposed SITC design is viable on the grounds of economic and controllability

Modelling and predictive control of a drum-type boiler

Boilers generate steam continuously and on a large scale. Controlling the boiler process is extremely difficult - it is a highly nonlinear process, its dynamics vary with load and it is strongly multivariable. It is also inherently unstable due to the integrator effect of the drum. In addition, boilers are commonly used in situations where the load can change suddenly and without prior warning. Traditionally, boilers have been controlled by Single-Input, Single-Output (SISO) Proportional plus Integral (PI) controllers. This strategy does not take into account the interaction of the controlled variables or the effect of load on boiler dynamics. This work investigates whether boiler control can be improved by applying multivariable or nonlinear predictive control strategies. Predictive control is a model-based control strategy which is chosen for its ability to handle nonlinear, constrained and multivariable systems. Two nonlinear controllers are developed - a fuzzified linear predictive controller which is based upon several linearised models of the plant and and a nonlinear predictive controller, based upon a single nonlinear plant model. These controllers are compared both with each other and with the conventional PI control strategy. A detailed first-principles model of the boiler is developed for this work. This is used to simulate a boiler plant for controller testing. It is also used to derive a linear state-space model for the linear predictive controller. The nonlinear predictive controllers uses a neural network model

Simulation of power plants steam generators and cooling towers with artificial neural network

A modelagem da operação de equipamentos é uma opção metodológica importante para a melhoria da eficiência de usinas geradoras de energia. Uma dessas metodologias é a rede neural artificial (RNA), que vem ganhando espaço devido à sua capacidade de modelar problemas complexos com base em comportamentos registrados de sistemas reais. O objetivo do presente estudo é desenvolver modelos de RNA capazes de reproduzir o funcionamento do gerador de vapor e da torre úmida de arrefecimento da planta termoelétrica a carvão de PECÉM, no estado do Ceará, Brasil. O modelo de RNA para o gerador de vapor superaquecido a carvão estima a vazão mássica de vapor com base em registros de um ano de operação da Usina. A configuração das RNAs é obtida após uma série de testes com o objetivo de reduzir o erro de predição através do erro absoluto médio (EAM) em diferentes patamares de operação, obtendo-se um MAE de 1,28% para o conjunto total de dados de operação, 8,11% para a faixa de operação de 240 MW e 10,82% para a faixa de operação de 360 MW. O desempenho das redes é comparado ao de modelos de regressão linear múltipla aplicados ao mesmo conjunto de dados, para os quais se têm valores de MAE de 2,05%, 9,47% e 15,76%. Esses resultados mostram a capacidade da RNA de estimar a produção de vapor com erro abaixo daqueles de modelos de regressão. O modelo de RNA é desenvolvido para um dos conjuntos de torres úmidas de resfriamento ligado ao sistema de condensação de uma das plantas do sitio de geração. Essa planta é referenciada como de melhor desempenho e o modelo RNA gerado é aplicado aos dados de operação do segundo conjunto de torres, ajudando na identificação de possíveis desvios ou problemas de desempenho. Ferramentas estatísticas são usadas para avaliar os dois conjuntos de dados referentes as torres de cada usina e identificar correlações de parâmetros. Os modelos de RNA com melhor desempenho são obtidos com um coeficiente máximo de correlação R² de 0,9956 para a taxa de calor rejeitada e 0,8699 para a taxa de vazão mássica de água de reposição para o conjunto de dados de referência. O coeficiente R² encontrado para o segundo conjunto de torres é de 0,748 para a taxa de calor rejeitada e 0,905 para a vazão mássica de água de reposição. Esse resultado ajuda a identificar alguns comportamentos não padronizados da torre. Uma nova simulação sem os pontos de fora da curva (outlier) exibiu valores de R² de 0,98 e 0,99, respectivamente.The modeling of equipment operation is an important methodological option for improving the efficiency of power plants. One of these methodologies is the artificial neural network (ANN), which is gaining space due to its ability to model complex problems based on acquired data from real systems. The objective of the present study is to develop ANN models capable of reproducing the operation of the steam generator and the wet cooling tower of the PECÉM coal-fired power plant in the state of Ceara, Brazil. The ANN model for the coal superheated steam generator estimates the steam mass flow rate based on year-long records of operation. ANN configuration is obtained after a series of tests with the objective of reducing the ANN mean absolute error (MAE) in different levels of operation, obtaining an MAE of 1,28% for the total set of data of operation, 8.11% for the 240 MW operating range and 10.82% for the 360 MW operating range. The network performance is compared to that of multiple linear regression models applied to the same data set, with MAE values of 2.05%, 9.47% and 15.76%. These results show the ability of ANN to estimate the production of vapor with errors below those of regression models. The ANN model is developed for one set of wet cooling towers connected to the condensation system. This plant is referred to present the best performance and the generated ANN model is applied to the operation data of the second plant, helping to identify possible deviations or performance problems. Statistical tools are used to evaluate the two cooling towers and to identify parameter correlations. The best performing ANN models are obtained with a R² correlation coefficient of 0.9956 for the rejected heat rate and 0.8699 for the makeup water mass flow rate for the reference data set. The coefficient R² found for the second set of towers is 0.748 for the rejected heat rate and 0.905 for the makeup water mass flow rate. The coefficient R² found for the second set of towers is 0.748 for the rejected heat rate and 0.905 for the makeup water mass flow rate. This result helps to identify some non-standard behavior of the tower. A new simulation without the outlier points exhibited R² values of 0.98 and 0.99, respectively