Flame Characterisation in a Multi-burner Heat Recovery Boiler through Digital Imaging and Spectrometry

Fossil fuel fired utility boilers fire a range of fuels under variable operation conditions. This variability in fuel diet and load conditions is linked to various problems in boiler performances, particularly the flame quality which is closely associated with furnace safety, combustion efficiency and pollutant emissions. Reliable flame monitoring is thus critical as the flame can fluctuate significantly in terms of size, shape, location, colour and temperature distribution. For instance, heat recovery water tube boilers are commonly used in industry to recover the energy in the exhaust gas from gas turbines. The boiler is fitted with multiple burners which allow flexibility with tuning of the boiler firing rates depending on process steam demand. It was reported that flame properties in such boilers had a direct impact on the flame stability and pollutant emissions (i.e., NOx and CO). There is, however, no technique available for online monitoring and quantifying the flame properties of individual burners. This has resulted in a lack of understanding in how each burner operates with regard to the overall performance of the boiler, particularly the emissions. Under the support of the BF2RA and EPSRC, an imaging and spectrometry based instrumentation system is being developed for flame monitoring and emission. Fig 1 shows the block diagram of the system. An optical probe, protected by the air-cooled jacket, transmits the light of flame to the camera house. The light of flame is then split into two beams. The first beam is captured by a camera to provide images for determining the physical parameters of the flame. The second beam is received by a miniature spectrometer for flame spectral analysis. Intelligent computing algorithms are developed for flame monitoring and emission prediction. The system, once fully developed, will be assessed under a range of operation conditions on a heat recovery water tube boiler at a British Sugar’s factory. More test results will be presented at the conference

Advanced Flame Monitoring and Emission Prediction through Digital Imaging and Spectrometry

This thesis describes the design, implementation and experimental evaluation of a prototype instrumentation system for burner condition monitoring and NOx emissions prediction on fossil-fuel-fired furnaces. A review of methodologies and technologies for burner condition monitoring and NOx emissions prediction is given, together with the discussions of existing problems and technical requirements in their applications. A technical strategy, incorporating digital imaging, UV-visible spectrum analysis and soft computing techniques, is proposed. Based on these techniques, a prototype flame imaging system is developed. The system consists mainly of an optical and fibre probe protected by water-air cooling jacket, a digital camera, a miniature spectrometer and a mini-motherboard with associated application software. Detailed system design, implementation, calibration and evaluation are reported. A number of flame characteristic parameters are extracted from flame images and spectral signals. Luminous and geometric parameters, temperature and oscillation frequency are collected through imaging, while flame radical information is collected by the spectrometer. These parameters are then used to construct a neural network model for the burner condition monitoring and NOx emission prediction. Extensive experimental work was conducted on a 120 MWth gas-fired heat recovery boiler to evaluate the performance of the prototype system and developed algorithms. Further tests were carried out on a 40 MWth coal-fired combustion test facility to investigate the production of NOx emissions and the burner performance. The results obtained demonstrate that an Artificial Neural Network using the above inputs has produced relative errors of around 3%, and maximum relative errors of 8% under real industrial conditions, even when predicting flame data from test conditions not disclosed to the network during the training procedure. This demonstrates that this off the shelf hardware with machine learning can be used as an online prediction method for NOx

Planning, operation, and design of market-based virtual power plant considering uncertainty

The power systems of today seem inseparable from clean energy sources such as wind turbines (WTs) and photovoltaics (PVs). However, due to their uncertain nature, operational challenges are expected when WT and PV energy is added to the electricity network. It is necessary to introduce new technologies to compensate for the intermittent nature of renewable energy sources (RESs). Therefore, rationally implementing a demand response (DR) program with energy storage systems (ESSs) in a virtual power plant (VPP) environment is recommended as a way forward to minimize the volatile nature of RESs and improve power system reliability. Our proposed approach aims to maximize social welfare (SW) (i.e., maximization of consumer benefits while minimizing energy costs). Our method assesses the impact of the DR program on SW maximization. Two scenarios are examined, one with and one without a DR program. Stochastic programming theory is used to address the optimization problem. The uncertain behavior of WTs, PVs, and load demand is modeled using a scenario-based approach. The correctness of the proposed approach is demonstrated on a 16-bus UK generic distribution system. Our results show that SW and active power dispatch capacity of WT, PV, and ESS are fairly increased using the proposed approach

Flame monitoring and characterisation through digital imaging and spectrometry

Fossil fuel fired boilers are often required to work under variable operation conditions. The variability in fuel diet and load conditions result in various problems in boiler performances. A methodology based on digital imaging and spectrometric techniques is proposed for flame monitoring and characterisation on utility boilers. The system developed consists of an optical probe/water jacket, a digital camera, a spectrometer covering a spectral range from 200nm to 900nm and an embedded computer with associated application software. Computer algorithms are established to determine flame characteristic parameters, including size, shape, temperature and spectral distributions. The spontaneous emissions of flame radicals (e.g., CH*and C2*) and alkali elements such as Sodium (Na) and Potassium (K) are characterised and their relationships with the combustion inputs (e.g., fuel, air-to-fuel ratio) and pollutant emissions (e.g., NOx) are studied. The methodology proposed are examined on a gas-fired heat recovery boiler under different operation conditions. The results obtained suggest there exist close correlations between flame parameters computed and boiler operation conditions. In particular, flame radicals (CH* and C2*) and their ratio show a close relationship with the air-to-fuel ratio. The spectral intensities of Na (589nm) and K (767nm) also illustrate a strong link to the type of fuel. Current work focuses on quantifying the relationship between the flame parameters and the boiler operation conditions and establishing a computational model for online prediction of emissions from flame characteristic parameters

A 2-D imaging-assisted geometrical transformation method for non-destructive evaluation of the volume and surface area of avian eggs

Egg volume and surface area are reliable predictors of quality traits for both table and hatching chicken eggs. A new non-destructive technique for the fast and accurate evaluation of these two egg variables is addressed in the present study. The proposed method is based on the geometrical transformation of actual egg contour into a well-known geometrical figure which shape most of all resembles the examined egg. The volume and surface area of an examined egg were recomputed using the formulae appropriate for three figures including sphere, ellipsoid, and egg-shape ovoid. The method of the geometrical transformation includes the measurements of the egg length and the area of the examined eggs. These variables were determined using two-dimensional (2-D) digital imaging and image processing techniques. The geometrical transformation approach is proven to be reliable to turn the studied chicken eggs into the three chosen ovoid models, with the best prediction being shown for the ellipsoid and egg-shape ovoid, whilst the former was slightly more preferable. Depending on the avian species studied, we hypothesise that it would be more suitable to use the sphere model for more round shaped eggs and the egg-shaped ovoid model if the examined eggs are more conical. The choice of the proposed transformation technique would be applicable not only for the needs of poultry industry but also in ornithological, basically zoological studies when handling the varieties of eggs of different shapes. The experimental results show that the method proposed is accurate, reliable, robust and fast when coupled and assisted with the digital imaging and image processing techniques, and can serve as a basis for developing an appropriate instrumental technology and bringing it into the practice of poultry enterprises and hatcheries

How oviform is the chicken egg? New mathematical insight into the old oomorphological problem

The chicken egg, a major food product, has been, as a model oviform object, the focus of advanced description in mathematical terms, and such a description has practical applications in engineering, industrial and biological disciplines. A precise mathematical description circumscribing major oomorphological characteristics, coupled with their non-destructive measurement, remains, to a certain extent, problematic and has not yet been achieved, hampering their effective control and use. A contour of any chicken egg can be accurately defined with Hügelschäffer's model by means of three main measures: length, L, maximum breadth, B, and a parameter w that corresponds to a distance of shifting the ellipse center to form an egg ovoid. The goal of the study was a comprehensive theoretical evaluation and development of basic geometrical formulae to define egg external traits by using Hügelschäffer's model and employing simulation modelling, digital imaging and image processing. As a result, we deduced novel geometrical formulae for the egg long circumference, C, volume, V, area of a plane curve obtained by the normal/orthogonal projection, A, surface area, S, distance parameter, w, and radius of curvature, R, at any point on the x-axis. For practical use in the poultry industry and food engineering, the proposed formulae can be instrumental in the non-destructive and accurate definition of the external parameters of any chicken egg

Investigations into the Impact of Coal Moisture on Burner Performance through Flame Imaging and Spectroscopic Analysis

Despite increasing use of renewable energy worldwide, coal remains to be the primary energy resource to meet the increasing demand for electric power in many countries. However, coal-fired power plants have to cope with coals with different properties, including those with high moisture content. It is known that moisture content in coal does not only affect coal handling but also burner performance, and thus combustion efficiency and emission formation process. A study is recently carried out to investigate the impact of moisture content in coal on the burner performance through flame imaging and spectroscopic analysis. Experimental tests were conducted on a 40MWth coal-fired combustion test facility (CTF). A typical pulverised coal was fired in the study. The variation in evaporated coal moisture was replicated by injecting steam into the primary coal flow in the range of 7%-55% (PFM, primary flow moisture) under different operation conditions including variations in furnace load and fuel-to-air ratio. A flame imaging system and a miniature spectrometer were employed to acquire concurrently flame images and spetroscopic data (Fig. 1). The characteristic parameters of the flame such as spreading angle, temperature, oscillation frequency and spectral intensity are computed and their relationship with the operation conditions including PFM and emissions (NOx, CO) are quantified. Fig. 2 illustrates typical flame images under different steam injections. Detailed experimental results and analysis will be presented at the conference

Burner Condition Monitoring based on Flame Imaging and Data Fusion Techniques

Rapid growth in electricity generation from intermittent renewables has resulted in increasing demand in conventional fossil-fuel power stations for plant flexibility, load balancing and fuel flexibility. This has led to new challenges in plant monitoring and control, particularly securing combustion stability for optimizing combustion process in terms of furnace safety, fuel efficiency and pollutant emissions. An unstable combustion process can cause many problems including furnace vibration, non-uniform thermal distribution in the furnace, high pollutant emissions and unburnt carbon in the flue gas. The stability of burners should therefore be continuously monitored and maintained for the improved overall performance of the furnace. A study is carried out to investigate the burner stability based on flame imaging and data fusion techniques. Experiments were carried out on a 915 MWth coal-fired power station. A bespoke flame imaging system (Fig. 1) was employed to acquire flame images from 16 individual burners (4 mills each with 4 burners) with a frame rate up to 200 frames per second. The characteristic parameters of the flame, including temperature, non-uniformity, entropy, oscillation frequency and colour characteristics (hue, saturation and intensity), are computed. The relationship between the flame characteristics and burner inputs and flue gas emissions (e.g., NOx) is quantified. Stability index is then introduced as an indicator of the stability of individual burner. Fig. 2 illustrates typical flame images for different burners. Detailed test results and analysis will be presented at the conference

How oviform is the chicken egg? New mathematical insight into the old oomorphological problem

The chicken egg, a major food product, has been, as a model oviform object, the focus of advanced description in mathematical terms, and such a description has practical applications in engineering, industrial and biological disciplines. A precise mathematical description circumscribing major oomorphological characteristics, coupled with their non-destructive measurement, remains, to a certain extent, problematic and has not yet been achieved, hampering their effective control and use. A contour of any chicken egg can be accurately defined with Hügelschaeffer’s model by means of three main measures: length, L, maximum breadth, B, and a parameter w that corresponds to a distance of shifting the ellipse center to form an egg ovoid. The goal of the study was a comprehensive theoretical evaluation and development of basic geometrical formulae to define egg external traits by using Hügelschaeffer’s model and employing simulation modelling, digital imaging and image processing. As a result, we deduced novel geometrical formulae for the egg long circumference, C, volume, V, area of a plane curve obtained by the normal/orthogonal projection, A, surface area, S, distance parameter, w, and radius of curvature, R, at any point on the x-axis. For practical use in the poultry industry and food engineering, the proposed formulae can be instrumental in the non-destructive and accurate definition of the external parameters of any chicken egg