Machine learning solutions for maintenance of power plants

The primary goal of this work is to present analysis of current market for predictive maintenance software solutions applicable to a generic coal/gas-fired thermal power plant, as well as to present a brief discussion on the related developments of the near future. This type of solutions is in essence an advanced condition monitoring technique, that is used to continuously monitor entire plants and detect sensor reading deviations via correlative calculations. This approach allows for malfunction forecasting well in advance to a malfunction itself and any possible unforeseen consequences. Predictive maintenance software solutions employ primitive artificial intelligence in the form of machine learning (ML) algorithms to provide early detection of signal deviation. Before analyzing existing ML based solutions, structure and theory behind the processes of coal/gas driven power plants is going to be discussed to emphasize the necessity of predictive maintenance for optimal and reliable operation. Subjects to be discussed are: basic theory (thermodynamics and electrodynamics), primary machinery types, automation systems and data transmission, typical faults and condition monitoring techniques that are also often used in tandem with ML. Additionally, the basic theory on the main machine learning techniques related to malfunction prediction is going to be briefly presented

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

A Survey on Intelligent Optimization Approaches to Boiler Combustion Optimization

This paper reviews the researches on boiler combustion optimization, which is an important direction in the field of energy saving and emission reduction. Many methods have been used to deal with boiler combustion optimization, among which evolutionary computing (EC) techniques have recently gained much attention. However, the existing researches are not sufficiently focused and have not been summarized systematically. This has led to slow progress of research on boiler combustion optimization and has obstacles in the application. This paper introduces a comprehensive survey of the works of intelligent optimization algorithms in boiler combustion optimization and summarizes the contributions of different optimization algorithms. Finally, this paper discusses new research challenges and outlines future research directions, which can guide boiler combustion optimization to improve energy efficiency and reduce pollutant emission concentrations

Study on the state of play of energy efficiency of heat and electricity production technologies

This report provides an overview of the current state of the art of the technologies used in EU for power and heat generation as well as combined heat and power generation (cogeneration or CHP). The technologies are categorised per fuel but also in terms of technology selection. The fuels considered are the ones reported in the Strategic European Energy Review report on Energy Sources, Production Costs and Performance of Technologies for Power Generation, Heating and Transport (SEC(2008) 2872).JRC.F.6-Energy systems evaluatio

Improving quality and combustion control in pyrometallurgical processes using multivariate image analysis of flames

La combustion est utilisé dans l’industrie chimique et du traitement des minéraux dans le but de produire de la vapeur dans les chaudières, de sécher les concentrés dans les fours rotatifs, et d’appliquer des traitements thermiques dans les fours pyrométallurgiques. Le contrôle serré de la combustion dans ces fours est très important parce que les conditions de combustion affectent directement la qualité du produit fini. Arriver à un contrôle serré de la combustion n’est pas facile à cause du fait que les flammes qu’on retrouve dans ces industries sont obtenues avec des combustibles non pré-mélangés et aussi parce que la combustion est affectée par des perturbations non-mesurées comme l’utilisation fréquente de plusieurs combustibles, certains étant des sous-produits de l’usine, et de débit et composition variables. Une nouvelle méthode est proposée dans cette étude afin d’améliorer le contrôle de la qualité des produits de ces fours tout en réduisant la consommation de carburants. Cette méthode s’appuie sur l’extraction d’information provenant d’images de flammes. La méthode d’analyse et de régression sur les images multivariées est utilisée pour l’extraction des caractéristiques de couleur de la flamme qui sont ensuite utilisées pour prédire la température de décharge des solides d’un four rotatif (qualité). Cette étude démontre que cette méthode est capable de très bien prédire la température de décharge du solide 20, 40, et jusqu’à 80 minutes dans le futur. Ceci devrait permettre une réduction substantielle de la variabilité de la qualité du produit et de la consommation de combustible.Combustion is used throughout the mineral processing industry to produce steam in boilers, to dry concentrates in rotary dryers, and to apply heat treatments in pyrometallurgical furnaces. Tight combustion control is very important in the latter type of furnace since the combustion conditions directly affect final ore quality. However, achieving tight combustion control is not straightforward since most of the flames encountered in industry are turbulent non-premixed flames, they are affected by several unmeasured disturbances, various flow rates, continuous variation in the mix between fuels since they are often produced by simultaneously burning several types of fuel, some of them coming from other parts of the plant. A novel method is proposed in this study to improve process and product quality control as well as to optimize the combustion conditions based on digital flame color images. Multivariate Image Analysis and Regression is used to extract the flame color characteristics from images to predict the solids discharge temperature of an industrial rotary kiln related to product quality. It is shown that this method yield extremely good 20 minutes, 40 minutes as well as 80 minutes ahead forecasts of the discharge temperature of mineral ore. This should lead to a substantial reduction in product quality variability as well as in fuel consumption

Development of a Dynamic Model and Control System for Load-Following Studies of Supercritical Pulverized Coal Power Plants

Traditional energy production plants are increasingly forced to cycle their load and operate under low-load conditions in response to growth in intermittent renewable generation. A plant-wide dynamic model of a supercritical pulverized coal (SCPC) power plant has been developed in the Aspen Plus Dynamics® (APD) software environment and the impact of advanced control strategies on the transient responses of the key variables to load-following operation and disturbances can be studied. Models of various key unit operations, such as the steam turbine, are developed in Aspen Custom Modeler® (ACM) and integrated in the APD environment. A coordinated control system (CCS) is developed above the regulatory control layer. Three control configurations are evaluated for the control of the main steam; the reheat steam temperature is also controlled. For studying servo control performance of the CCS, the load is decreased from 100% to 40% at a ramp rate of 3% load per min. The impact of a disturbance due to a change in the coal feed composition is also studied. The CCS is found to yield satisfactory performance for both servo control and disturbance rejection