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In the first part of the paper, which outlines the laboratory and field investigations on corrosion by flue gases from solid fuel combustion carried out by the British Coal Utilisation Research Association, the effects of different flue gas constituents on corrosion phenomena are discussed. Laboratory studies of the effects of fuel type and method of combustion on the sulphuric acid content of combustion gases are described. The second part presents the results of measurements of the condensation characteristics of flue gases from water‐tube boilers in power stations and from various industrial boilers and furnaces; investigations into the use of additives are described briefly. The final section is concerned with some theoretical considerations of effects of fuel type, burning rate, etc., on the amounts of sulphuric acid likely to be present in the combustion products from domestic appliances.

Combustion modifications to minimize NO_X emissions have magnified the importance of waterwall corrosion in coal‐fired boilers. The physics and chemistry controlling corrosion processes can be highly non‐linear and are challenging to describe in terms of their likely overall combustion behavior. This paper describes the application of a multi‐point, real time corrosion surveillance system to a large boiler firing high sulfur coal. This technology, incorporating electrochemical sensing and wireless signal transmission, enables combustion engineers and plant operating personnel to make informed decisions regarding the quantitative relationships between operating conditions, NO_X emissions, and any resultant extent/magnitude of waterwall corrosion.

The corrosive attack of boiler surfaces exposed to flue gases has occurred from time to time in plant operated by the Central Electricity Generating Board. The availability lost in this way is little more than 1% of the total boiler capacity installed, but when the load demand is met mainly by unit generators with a steaming capacity over 100 MW, the enforced shut‐down of one of these units would be a more serious loss. This article describes the ways in which various parts of boilers are attacked, the factors affecting corrosion in these cases and the chemical basis of the processes. Investigations are being carried out, and several preventive methods are mentioned.

Many power producers are looking for ways to develop smarter energy capabilities to tackle challenges in the sophisticated, non-linear dynamic processes due to the complicated operating conditions. One prominent strategy is to deploy advanced intelligence systems and analytics to monitor key performance indicators, capture insights about the behavior of the electricity generation processes, and identify factors affecting combustion efficiency. Thus, the purpose of this paper is to outline a way to incorporate a business intelligence framework into existing coal-fired power plant data to transform the data into insights and deliver analytical solutions to power producers.

The proposed ten-step business intelligence framework combines the architectures of database management, business analytics, business performance management, and data visualization to manage existing enterprise data in a coal-fired power plant.

The results of this study provide plant-wide signals of any unusual operational and coal-quality factors that impact the level of NO_x and consequently explain and predict the leading causes of variation in the emission of NO_x in the combustion process.

Once the framework is integrated into the power generation process, it is important to ensure that the top management and the data analysts at the plants have the same perceptions of the benefits of big data and analytics in the long run and continue to provide support and awareness of the use of business intelligence technology and infrastructure in operational decision making.

The key finding of this study helps the power plant prioritize the important factors associated with the emission of NO_x; closer attention to those factors can be promptly initiated in order to improve the performance of the plant.

The use of big data is not just about implementing new technologies to store and manage bigger databases but rather about extracting value and creating insights from large volumes of data. The challenge is to strategically and operationally reconsider the entire process not only to prepare, integrate, and manage big data but also to make proper decisions as to which data to select for the analysis and how to apply analytical techniques to create value from the data that is in line with the strategic direction of the enterprise. This study seeks to fill this gap by outlining how to implement the proposed business intelligence framework to provide plant-wide signals of any unusual operational and coal-quality factors that impact the level of NO_x and to explain and predict the leading causes of variation in the emission of NO_x in the combustion process.

The design and retrofit of the heat exchangers in a boiler should take into account the processes occurring on the side of combustion and steam. For this reason, this study aims to couple a one-dimensional hydrodynamic model of steam with computational fluid dynamics (CFD) simulation of flue gas.

Radiant/semi-radiant platen heat exchangers are simplified as plane surfaces for CFD, while convective heat exchangers are introduced into the CFD simulation as energy/momentum absorption sources.

Numerical simulation is performed for a 1,000 MWe coal-fired ultra-supercritical boiler. The calculation results are validated by the thermodynamic design data. Tube outside surface temperature, as well as ash deposit temperature distributions, are obtained.

Complex tube arrangements can be completed with the aid of AutoCAD, and therefore, the simulation could offer detailed information of heat exchangers. In a word, a more reliable modeling of the whole steam generation process is achieved.

The purpose of this study is to investigate the application of Ni₃Al coating for boilers and other power plant equipment, which suffer severe erosion-corrosion problems resulting in substantial losses. Currently, superalloys are being used to increase the service life of the boilers. Although the superalloys have adequate mechanical strength at elevated temperature, they often lack resistance to erosion-corrosion environments.

In this paper, the erosion-corrosion performance of plasma-sprayed nickel aluminide (Ni₃Al) coating on nickel- and iron-based superalloys have been evaluated by exposing them to the low temperature primary superheater zone of the coal-fired thermal power plant at the temperature zone of 540^°C for ten cycles of 100 h duration. The exposed products were analysed along the surface and cross-section using scanning electron microscopy (SEM), X-ray diffraction (XRD) and electron micro probe analysis (EPMA).

The XRD, SEM and EPMA analyses have shown the formation of mainly NiO, NiAl₂O₄ and indicated the presence of Ni₃Al, Ni and Al₂O₃. In the boiler environment, Ni₃Al coating partially oxidizes and acts as a perfect barrier against erosion-corrosion of superalloys. The partially oxidised Ni₃Al coating remains intact even after 1,000 h cycle exposure.

The probable mechanism of attack for the plasma-sprayed Ni₃Al coating in the given boiler environment is presented.

A fully three‐dimensional model has been applied to an oil‐fired water tube boiler in order to predict the flow, temperature, mixture fraction, species concentrations and the heat flux distributions to the furnace walls. The partial differential equations governing conservation of mass, momentum and energy as well as those describing the combustion phenomena are discretized by a finite volume method and solved numerically. Radiative heat transfer is handled by the discrete transfer method. Predicted results are presented and compared with experimental data for the heat fluxes. The results have suggested that 3‐D models of the present kind can be used with some confidence for design calculations.

The aim of this paper is to study the effect of the parametric sensitivity of all critical parameters of feed water and other operating variables on the corrosion rate and oxide scale deposition on economizer tubes of a typical coal-fired 250-MW boiler.

In this paper, a multilayer perceptron-based artificial neural network (ANN) model has been developed to envisage the corrosion rate and oxide scale deposition rate in economizer tubes of a coal-fired boiler. The neural network architecture has been optimized using an efficient gradient-based network optimization algorithm to minimize the training and testing errors rapidly during simulation runs.

The parametric sensitivity of all critical parameters of feed water and other operating variables on the corrosion rate and oxide scale deposition activities has been investigated. It has been observed that dissolved oxygen, dissolved copper content, residual hydrazine content and pH of the feed water have a relatively predominant influence on the corrosion rate, whereas dissolved iron content, silica content, pH and temperature of the feed water have a moderately major influence on oxide scale deposition phenomenon. There has been very good agreement between ANN model predictions and the measured values of corrosion rate and oxide scale deposition rate substantiated by the regression fit between these values.

This paper details the development of an alternative model to accurately predict corrosion rate and deposition rate on the inner surface of economizer tubes of a boiler over first principle-based kinetic model.

Dutch power companies recently have become aware of a hazard due to the presence of radioactive ²¹⁰Pb in their boilers. In an investigation conducted by KEMA in The Netherlands it was discovered that ²¹⁰Pb isotope accumulates within fire‐side deposit layers that form on membrane waterwall tubes. Because the measured levels of total radioactivity exceeded the governmental regulation limit of 100 Bq/g, refurbishment activities in the boiler are subject to governmental authorization and must be conducted under the supervision of authorised radiation protection officers. The ²¹⁰Pb material originates from decay of ²³⁸U, which is present in small amounts in coal. During combustion of the coal, heavy metals such as Pb evaporate and are present in the flue gas in the form of Pb, PbCl, PbCl₂, PbS, PbS₂ or PbSO₄, dependent on the gas environment and temperature. These volatile products subsequently will condense at lower temperatures on the waterwall tubes in the boiler.

This paper aims to propose an efficient artificial neural network (ANN) model using multi-layer perceptron philosophy to predict the fireside corrosion rate of superheater tubes in coal fire boiler assembly using operational data of an Indian typical thermal power plant.

An efficient gradient-based network training algorithm has been used to minimize the network training errors. The input parameters comprise of coal chemistry, namely, coal ash and sulfur contents, flue gas temperature, SOX concentrations in flue gas, fly ash chemistry (Wt.% Na₂O and K₂O).

Effects of coal ash and sulfur contents, Wt.% of Na₂O and K₂O in fly ash and operating variables such as flue gas temperature and percentage excess air intake for coal combustion on the fireside corrosion behavior of superheater boiler tubes have been computationally investigated and parametric sensitivity analysis has been undertaken.

Quite good agreement between ANN model predictions and the measured values of fireside corrosion rate has been observed which is corroborated by the regression fit between these values.