Carlos E. Romero

A new approach for function approximation in boiler combustion optimization based on modified structural AOSVR

In the scheme of boiler combustion optimization, a group of optimal controller settings is found to provide recommendations to balance desired thermal efficiency and lowest emissions limit. Characteristic functions between particular objectives and controlling variables can be approximated based on data sets obtained from field tests. These relationships can change with variations in coal quality, slag/soot deposits and the condition of plant equipment, which can not be sampled on-line. Thus, approximation relationships based on test conditions could have little applicability for on-line optimization of the combustion process. In this paper, a new approach is proposed to adaptively perform function approximation based on a modified accurate on-line support vector regression method. Two modified criteria are proposed for selection of the unwanted trained sample to be removed. A structural matrix is used to process and save the model parameters and training data sets, which can be adaptively regulated by the on-line learning method. The proposed method is illustrated with an example and is also applied to real boiler data successfully. The results reveal their validity in the prediction of NOx emissions and function approximation, which can correctly be adapted to actual variable operating conditions in the boiler.

A globally enhanced general regression neural network for on-line multiple emissions prediction of utility boiler

Prediction of multiple emissions of boiler such as NOX, SO2 and mercury accurately and quickly can help operating engineers understanding the power unit deeply and controlling harmful emissions efficiently. However, most of existing works did not consider multiple emissions prediction, on-line prediction and accurately prediction in a simple and uniform framework. To this end, a new neural network system, named Globally Enhanced General Regression Neural Network (GE-GRNN), is proposed to solve multiple emissions prediction problem for utility boiler under on-line environment. The proposed GE-GRNN is based on General Regression Neural Network (GRNN), and employs Gaussian Adapted Resonance Theory (GART) as an incremental learning method to reduce memory cost of GRNN emission model, which is very suitable for large-scale real-time input samples. As contributions, three methods are introduced in GE-GRNN: (1) a modified wiggle-method is proposed to adjust smooth factors of GRNN dynamically to enhance both global and local estimations; (2) a fast polynomial extrapolation structure is designed in hidden layers of GRNN to improve the quality of extreme value estimation; and (3) a hybrid estimation mechanism is established to integrate wiggle-method and extrapolation into an uniform estimation framework. The simulation experiments are conducted on mathematical functions and real-world emissions predication of NOX and loss on ignition (LOI) of fly ash in a 600MW boiler. Results show that the proposed system performs attractive on-line performance while keeping agreement with testing samples well. The predicted emission performance of the tested boiler is reasonable, and can provide valuable reference to emission optimization. The proposed framework is domain independent and can be used to other fields for on-line multiple performances prediction.

COMPARISON AND VALIDATION OF OHM AND SCEM MEASUREMENTS FOR A FULL-SCALE COAL-FIRED POWER PLANT

Mercury emission measurements were performed at a 250 MW coal-fired power plant using the Ontario Hydro method (OHM) and semi-continuous emission monitors (SCEM). Flue gas sampling was performed at the inlet of the air preheater and at the outlet of the electrostatic precipitator. The results indicated that there is some agreement between the OHM and SCEM measurements on the total mercury species. However, the SCEM results were not always in good agreement with the OHM measurements on the elemental mercury species. These discrepancies in elemental mercury concentrations are probably the result of the differences in the location of the SCEM and OHM probes, the temperature difference between the SCEM sampling probe and the flue gas, and the nonuniformities in mercury concentration over the flue gas duct cross section. The other factor that contributed to the deviation between the SCEM and OHM measurement results is the sampling method: the SCEM measurements were performed at a single point while the OHM probe was traversed over multiple points over the duct cross section and the results were averaged. The effect of the SCEM sampling probe temperature was investigated by designing a sampling probe that could be heated to the sampled flue gas temperatures. This resulted in improvements in the accuracy of the elemental mercury measurements by the SCEM system.

Integrated real-time optimization of boiler and post-combustion system in coal-based power plants via extremum seeking

AES Cayuga Unit 1 is a 160MW unit, equipped with a low nitrogen oxide (NOx) firing system and an anhydrous ammonia (NH3), TiO2=V2O5=WO3 selective catalytic reduction (SCR) system for NOx emission control. An ammonium bisulfate (ABS) probe was retrofit to the SCR to monitor ABS formation in real-time with the ultimate goal of minimizing air preheater (APH) plugging (ABS concentration) by regulating the APH bypass damper. Recent work on static optimization of coal-based power plants has played a crucial role in improving overall efficiency. However, static optimization falls short in dealing with real-time scenario changes (i.e., cycling unit load, coal quality, firing system maintenance conditions, subsystem failures, plant aging, etc.). Extremum seeking (ES) is proposed in this work to optimally tune boiler operation in order to minimize NOx production in real-time. The effectiveness of the ES adaptive controller in keeping the system at an optimal operation point in presence of input disturbances and system changes is demonstrated through simulations based on identified models of the boiler, SCR and APH systems.

Reduced kinetic mechanism for NOx formation in laminar premixed CH4/air flames

Abstract A reduced mechanism was developed for the chemical kinetics of hydrocarbon oxidation and NO x formation in freely propagating, laminar premixed methane/air flames. The reduction is based on sensitivity and rate of reaction analysis. The reduced scheme was evaluated for a range of equivalence ratios from 0.85 to 1.15, and it was found to provide good predictions of both thermal and prompt NO.

LIBS Analysis for Coal

Coal is a non-uniform material with large inherent variability in composition, and other important properties, such as calorific value and ash fusion temperature. This quality variability is very important when coal is used as fuel in steam generators, since it affects boiler operation and control, maintenance and availability, and the extent and treatment of environmental pollution associated with coal combustion. On-line/in situ monitoring of coal before is fed into a boiler is a necessity. A very few analytical techniques like X-ray fluorescence and prompt gamma neutron activation analysis are available commercially with enough speed and sophistication of data collection for continuous coal monitoring. However, there is still a need for a better on-line/in situ technique that has higher selectivity, sensitivity, accuracy and precision, and that is safer and has a lower installation and operating costs than the other options. Laser induced breakdown spectroscopy (LIBS) is ideal for coal monitoring in boiler applications as it need no sample preparation, it is accurate and precise it is fast, and it can detect all of the elements of concern to the coal-fired boiler industry. LIBS data can also be adapted with advanced data processing techniques to provide real-time information required by boiler operators nowadays. This chapter summarizes development of LIBS for on-line/in situ coal applications in utility boilers.

Measuring Thermal Properties of Coal with a Commercial Bench Top LIBS System

Measurements with a bench top commercial LIBS system demonstrate that thermal properties of coal can be measured using LIBS. Knowledge of these properties is crucial for efficient operation of coal fired boilers in power plants and other industries.

Deploying LIBS in Industry: Four Examples of Applied LIBS Technologies

Energy Research Company and its partners have developed LIBS for aluminum manufacturing, coal-fired power production, and other industrial applications. In this paper we review essential design aspects of the equipment and their resulting performance advantages.

Simulación Numérica del Proceso de Requemado de Gases para la Reducción de Óxidos de Nitrógeno (NOx)

El presente trabajo tiene como objetivo desarrollar capacidades de modelado que simulen el proceso de reduccion de NOx por la tecnologia del requemado de gases. En el modelo cinetico construido solo se consideraron algunos de los factores que determinan la produccion de oxidos de nitrogeno. La emision de NOx procedente de la combustion del carbon mineral en generadores de vapor, es un problema ambiental importante, ya que se ha demostrado que contribuye a la formacion de la lluvia acida y del esmog troposferico. El requemado de gases proporciona una buena opcion para reducir las emisiones de NOx en instalaciones donde usan calderas a carbon. Los gases de NOx emitidos por la caldera, entran a la zona de requemado y se combinan con gas natural que es inyectado para que actue como un agente reductor. El analisis se hace para un tiempo de residencia de 0.1 y 0.2 seg., y temperaturas de gases 1,000 a 2,000 K, con concentracion de gas natural de 7.5 a 35 %. Los resultados muestran una buena equivalencia de reduccion de NOx comparados con el modelo de Braun aunque se puede mejorar el modelo presentado integrandolo con un modelo detallado de la mecanica de fluidos turbulenta.

A reduced thermokinetic model for the autoignition of low-heating-value gases with variable cetane ratings

The autoignition of low-heating-value gaseous fuels for direct injection into diesel engines was studied in the context of providing a direct correlation and a methodology for rating their ignition quality. A computer model which includes a detailed chemistry and incorporates the effects of fluid mixing through the Damkohler number is presented. The computer model demonstrates the relative importance of the chemical kinetic characteristic time as compared to the characteristic flow time. It also demonstrates high sensitivity to the cylinder temperature and methane and propane concentrations in these types of low-quality fuels. Pressure was identified as a second-order variable. The deficiencies of the current cetane and methane number rating scales are discussed in terms of their inability to rate these fuels because of the range of the scales and their delay time fixed nature for variable compression ratios. These results show that the ignition characteristics of these fuels cannot be accurately modeled by the cetane scale, by the reference fuels, and for the whole range of cold start to warmed-up, ignition-assisted probable engines. An ignition delay correlation is presented, for a limited range of the engine parameters, in terms of the classical Arrhenius type of expressions as a function of the Damkohler number, the compression ratio, engine speed, and mixture composition. A rating scale corresponding to the range of valid parameters for this correlation is introduced as a linear scale for the delay time and a newly defined low-Btu number.