Coal-fired utility boiler modelling for advanced economical low-NOx combustion controller design

This paper focuses on developing a control-oriented coal-fired utility boiler model for advanced economical Low-NO_x combustion (ELNC) controller design. Two boiler combustion models are proposed in this paper: one is a mathematical model describing the key dynamics of the real-time boiler thermal efficiency and the furnace one-dimensional NO_x concentration distribution under conventional fuel and overfire air operations; the other recast from the first model is a control-oriented grey-box model with a data-driven furnace combustion submodel. Simulation studies on static and dynamic properties of the first mathematical model indicate that the model can function as a real-time simulator for both advanced boiler combustion control laws testing and generating training and validation data for the control-oriented grey-box model. At the end of this paper, the control-oriented grey-box modelling procedure as well as an optional discrete time linear state-space model are summarised to facilitate model-based advanced combustion controllers design.     

RBF neural network inferential sensor for process emission monitoring

Inferential sensing, or soft sensing, gained popularity in recent years as an alternative to continuous emission monitoring systems because of its simplicity, reliability, and cost effectiveness as compared to analogous hardware sensors. In this paper we address the problem of NO_x emission using a model of furnace of an industrial boiler, and propose a neural network structure for high performance prediction of NO_x as well as O₂. The studied boiler is 160 MW, gas fired with natural gas, water-tube boiler, having two vertically aligned burners. The boiler model is a 3D problem that involves turbulence, combustion, radiation in addition to NO_x modeling. The 3D computational fluid dynamic model is developed using Fluent simulation package. The model provides calculations of the 3D temperature distribution as well as the rate of formation of the NO_x pollutant, enabling a better understanding on how and where NO_x are produced. The boiler was simulated under various operating conditions. The generated data is then used for initial development and assessment of neural network soft sensors for emission prediction based on the conventional process variable measurements. The performance of the proposed soft sensor is then evaluated using actual data from an industrial boiler. The developed soft sensor achieves comparable accuracy to the continuous emission monitor analyzer, however, with substantial reduction in the cost of equipment and maintenance.     

Diesel engine dynamometer testing of impedancemetric NOx sensors

Prototype solid-state electrochemical sensors using a dense gold sensing electrode, porous yttria-stabilized zirconia (YSZ) electrolyte, and a platinum counter electrode (Au/YSZ/Pt) were evaluated for measuring NO_x (NO and NO₂) in diesel exhaust. Both electrodes were exposed to the test gas (i.e., there was no reference gas for the counter electrode). An impedancemetric method was used for NO_x measurements, where the phase angle was used as the response signal. A portion of the tailpipe exhaust from the dynamometer test stand was extracted and fed into a furnace containing the experimental sensor. The prototype sensor was tested along with a commercially available NO_x sensor. Simultaneous measurements for NO_x, O₂, CO₂, H₂O, CO, and CH₄ in a separate feed stream were made using Fourier transform infrared (FTIR) spectroscopy and an oxygen paramagnetic analyzer. The experimental sensor showed very good measurement capability for NO in the range of 25-250 ppm, with a response paralleling that of the FTIR and commercial sensor. The prototype sensor showed better sensitivity to NO_x at the lower concentration ranges. O₂ is an interferent for the experimental sensor, resulting in decreased sensitivity for measurement of NO_x. Methods to overcome this interference are discussed.     

Semi-physical mean-value NOx model for diesel engine control

A semi-physical model has been developed to predict nitrogen oxide (NO_x) emissions produced by diesel engines. This model is suitable for online NO_x estimation and for model-based engine control. It is derived from a zero-dimensional thermodynamic model which was simplified by only retaining main phenomena contributing to NO_x formation. The crank angle evolution of the burned gas temperature, which has a strong impact on NO_x formation rate, is described by a semi-empirical model whose key variable is the maximum burned gas temperature. This variable presents a good correlation with the molar fraction of NO_x at the end of combustion and can be expressed as a function of the intake burned gas ratio and the start of combustion. The maximum burned gas temperature sub-model is then coupled to an averaged NO_x formation kinetic model (based on the Zeldovich mechanism) to form a mean-value model for NO_x computation. This latter model was validated using data sets recorded in two diesel engines for steady-state operating conditions as well as for several driving cycles including parametric variations of the engine calibration.     

Absolute potential analysis of the mixed potential occurring at the oxide/YSZ electrode at high temperature in NOx-containing air

Mixed potential phenomena occurring at the oxide/YSZ electrode in the presence of NO_x-containing air is explained in terms of the absolute potential model, which demonstrates how maintaining a non-equilibrium state with gas phase, through catalytic inactiveness of the electrode, is important to obtain a noticeable EMF response to NO₂ and NO. The directions of the EMF response of oxide electrodes to NO and NO₂ gas are coincident with the prediction from the absolute potential analysis. Simulated results of anodic and cathodic currents for Pt electrode clearly show the insensitivity to NO₂ and NO as suggested in the analysis.     

In2O3 + xBaO (x = 0.5-5 at.%) - A novel material for trace level detection of NOx in the ambient

Indium oxide (In₂O₃) doped with 0.5-5 at.% of Ba was examined for their response towards trace levels of NO_x in the ambient. Crystallographic phase studies, electrical conductivity and sensor studies for NO_x with cross interference for hydrogen, petroleum gas (PG) and ammonia were carried out. Bulk compositions with x ≤ 1 at.% of Ba exhibited high response towards NO_x with extremely low cross interference for hydrogen, PG and ammonia, offering high selectivity. Thin films of 0.5 at.% Ba doped In₂O₃ were deposited using pulsed laser deposition technique using an excimer laser (KrF) operating at a wavelength of (λ) 248 nm with a fluence of ∼3 J/cm² and pulsed at 10 Hz. Thin film sensors exhibited better response towards 3 ppm NO_x quite reliably and reproducibly and offer the potential to develop NO_x sensors (Threshold limit value of NO₂ and NO is 3 and 25 ppm, respectively).     

Design and experimental validation of an extended Kalman filter-based NOx concentration estimator in selective catalytic reduction system applications

This paper presents an extended Kalman filter (EKF) based approach of integrating NO_x and NH₃ sensors to estimate the NO_x concentrations in Diesel engine selective catalytic reduction (SCR) aftertreatment systems. NO_x sensors have been commonly used by vehicles for aftertreatment system control and onboard diagnostics (OBD) purposes. However, most currently available NO_x sensors are cross-sensitive to ammonia. Based on the experimental observations and physical inferences, the cross-sensitivity characteristics may change with temperature and is hard to be predicted by a model. This feature limits the applications of NO_x sensors on urea-SCR systems where ammonia is the reductant for NO_x conversions. Grounded in the insight into SCR dynamics and NO_x sensor properties, a novel approach of using an extended Kalman filter to estimate the actual exhaust gas NO_x concentration was proposed. The estimator was examined by NO_x measurements from a Horiba gas analyzer under different engine operating conditions. The experimental results show that the EKF-based approach can significantly improve the accuracy of NO_x concentration measurements from the original NO_x sensor readings.     

Prediction and reduction of diesel engine emissions using a combined ANN–ACO method

In this study, the combination of artificial neural network (ANN) and ant colony optimization (ACO) algorithm has been utilized for modeling and reducing NO_x and soot emissions from a direct injection diesel engine. A feed-forward multi-layer perceptron (MLP) network is used to represent the relationship between the input parameters (i.e., engine speed, intake air temperature, rate of fuel mass injected, and power) on the one hand and the output parameters (i.e., NO_x and soot emissions) on the other hand. The ACO algorithm is employed to find the optimum air intake temperatures and the rates of fuel mass injected for different engine speeds and powers with the purpose of simultaneous reduction of NO_x and soot. The obtained results reveal that the ANN can appropriately model the exhaust NO_x and soot emissions with the correlation factors of 0.98, 0.96, respectively. Further, the employed ACO algorithm gives rise to 32% and 7% reduction in the NO_x and soot, respectively. The response time of the optimization process was obtained to be less than 4 min for the particular PC system used in the present work. The high accuracy and speed of the model show its potential for application in intelligent controlling systems of the diesel engines.     

Modeling NOx emissions from coal-fired utility boilers using support vector regression with ant colony optimization

Modeling NO_x emissions from coal fired utility boiler is critical to develop a predictive emissions monitoring system (PEMS) and to implement combustion optimization software package for low NO_x combustion. This paper presents an efficient NO_x emissions model based on support vector regression (SVR), and compares its performance with traditional modeling techniques, i.e., back propagation (BPNN) and generalized regression (GRNN) neural networks. A large number of NO_x emissions data from an actual power plant, was employed to train and validate the SVR model as well as two neural networks models. Moreover, an ant colony optimization (ACO) based technique was proposed to select the generalization parameter C and Gaussian kernel parameter γ. The focus is on the predictive accuracy and time response characteristics of the SVR model. Results show that ACO optimization algorithm can automatically obtain the optimal parameters, C and γ, of the SVR model with very high predictive accuracy. The predicted NO_x emissions from the SVR model, by comparing with the BPNN model, were in good agreement with those measured, and were comparable to those estimated from the GRNN model. Time response of establishing the optimum SVR model was in scale of minutes, which is suitable for on-line and real-time modeling NO_x emissions from coal-fired utility boilers.     

NOx adsorption behavior of LaFeO3 and LaMnO3+δ and its influence on potentiometric sensor response

NO_x adsorption behavior on LaFeO₃ (LFO) and LaMnO_3+δ (LMO) was characterized using temperature controlled methods and mass spectrometry. Temperature program desorption revealed decomposition of complex surface species formation when NO or NO₂ was adsorbed on LFO and LMO. LFO exhibited higher adsorption capacity for NO_x species than LMO and was shown to be more active for NO_x surface conversion. Both effects were attributed to the different B-site cations, with iron in LFO in the 3+ valence state, and manganese in LMO in the 3+ and 4+ valence states. Results from diffuse reflectance infrared spectroscopy were used to identify specific nitrite and nitrate species that are formed on the surfaces of LFO and LMO at room temperature. Temperature programmed reaction revealed a complex NO₂ decomposition mechanism to NO and O₂ for LFO and LMO in which the formation of nitrite and nitrate species serve as intermediates below ∼600 °C. NO_x sensing mechanisms were considered and predicted based on the types and quantities of surface species formed.     

MEMS-based combustor with hairpin-shape design of gas recirculation channel

This paper describes the design, fabrication and test of a silicon-based micro combustor, which is a part of a micro power generation system under development. Based on the three-dimensional computational fluid dynamics (CFD) simulation and analysis of different micro combustor design, a hairpin-shape design for air/fuel recirculation channel is adopted. The combustor is fabricated from seven single crystal silicon wafers using deep reactive ion etching (DRIE) process. It has been assembled successfully with gas tubing and thermal couplers for monitoring the exit gas temperature. The effect of mass flow rate on the combustion characteristics is studied experimentally and numerically under several operating conditions. The exhaust gas temperature can reach the range from 870 to 1,100 K. The results indicate that with the increases of the mass flow rate, the combustor exhaust gas temperature increase as well in both experimental and the simulated results. This is due to the heat released in the combustor increases with the fuel/air mass flow rate.

     

An organic field effect transistor as a selective NOx sensor operated at room temperature

A solution processable decyl alkyl chain functionalised 9,10-ter-anthryleneethynylene (D_3A) amorphous organic semiconductor, endowed with good stability in air, has been investigated as NO_x OTFT sensor. The D_3A OTFT operated at room temperature exhibited differential sensitivity to NO and NO₂. NO₂ detection down to 250 ppb could be achieved with very low cross sensitivity to interfering species such as carbon monoxide and hydrogen sulphide.     

Nanowire structured SnOx–SWNT composites: High performance sensor for NOx detection

A nanowire structured nanocomposite of tin oxide (SnO_x) and a single-walled carbon nanotube (SWNT) are fabricated using rheotaxial growth and thermal oxidation method for gas sensor application. The morphology, gas sensing properties, as well as the chemical and electrical properties are investigated. The oxidation temperature for Sn mainly determines the stoichiometry of the SnO_x nano-beads, and consequently the electrical and gas sensing properties of the nanocomposite sensors. The gas sensing to nitrogen oxide, hydrogen, oxygen, xylene, acetone, carbon monoxide, and ammonia are also examined to determine the gas selectivity of the sensor. The high sensitivity and selectivity towards NO_x of the nanocomposite sensor is realized via the porous structure of the SWNT template. The gas sensing mechanism of the nanocomposite structure is also discussed.     

Sensing characteristics of dc reactive sputtered WO3 thin films as an NOx gas sensor

In order to apply WO₃ thin films to the NO_x gas sensor, WO₃ thin films (3000 Å) were fabricated by using dc reactive sputtering method on alumina substrate and assembled as a unit of an NO_x gas sensor by adopting a patterned heater on the back side of substrate. The deposition temperatures of WO₃ thin film were changed from 200°C to 500°C, and then post-annealed for the crystallization for 4 h at 600°C. There were no WO₃ phases at the substrate temperature of 200°C, but the crystalline phases of WO₃ thin film were appeared with the increase of substrate temperature from 200°C to 500°C. The post-annealing of as-deposited WO₃ thin films at 600°C resulted in the enhancements of crystallinity, but it was observed that the quality of the final phases severely depends on the initial formation of phase during deposition. From the SEM images, crack free morphologies were found, which was different from the room temperature growth films. The sensitivity (R_gas/R_air) of as-deposited thin films was ranged from 4 to 10 for the 5 ppm NO test gas at the measuring temperature of 200°C. However, after post-annealing process at the temperature of 600°C, the sensitivities were increased around the values of 70–180 at the same test condition. These results show the WO₃ thin films need to be processed at least at the temperature of 600°C for the well-improved sensitivity against NO_x gas. It was also observed that the recovery rate of a sensing signal after measuring sensitivity was faster in the in-situ sputtered films than in the evaporated films or room temperature sputtered films.     

Improvement of the NOx selectivity for a planar YSZ sensor

In recent years planar yttria-stabilized zirconia (YSZ) based electrochemical gas sensors for automotive exhaust applications have become a major source of interest. The present work aims to develop a sensor for industrialisation. For this reason planar YSZ-based electrochemical sensors using two metallic electrodes (platinum and gold) were fabricated using screen-printing technology and tested in a laboratory test bench for different concentrations of pollutant gas such as CO, NO, NO₂ and hydrocarbons in oxygen rich atmosphere. It was furthermore shown that the selectivity towards NO_x could be highly reinforced by deposing a catalytic filter consisting of 1.7-4.5 wt.% Pt dispersed on alumina directly on the sensing elements. This filter was characterized by the use of SEM, TPD and XRD.     

Effect of La2CuO4 electrode area on potentiometric NOx sensor response and its implications on sensing mechanism

The intent of this work is to look at the effects of varying the La₂CuO₄ electrode area and the asymmetry between the sensing and counter electrode in a solid state potentiometric sensor with respect to NO_x sensitivity. NO₂ sensitivity was observed at 500-600 °C with a maximum sensitivity of ∼22 mV/decade [NO₂] observed at 500 °C for the sensor with a La₂CuO₄ electrode area of ∼30 mm². The relationship between NO₂ sensitivity and area is nearly parabolic at 500 °C, decreases linearly with increasing electrode area at 600 °C, and was a mixture of parabolic and linear behavior 550 °C. NO sensitivity varied non-linearly with electrode area with a minima (maximum sensitivity) of ∼−22 mV/decade [NO] at 450 °C for the sensor with a La₂CuO₄ electrode area of 16 mm². The behavior at 400 °C was similar to that of 450 °C, but with smaller sensitivities due to a saturation effect. At 500 °C, NO sensitivity decreases linearly with area.We also used electrochemical impedance spectroscopy (EIS) to investigate the electrochemical processes that are affected when the sensing electrode area is changed. Changes in impedance with exposure to NO_x were attributed to either changes in La₂CuO₄ conductivity due to gas adsorption (high frequency impedance) or electrocatalysis occurring at the electrode/electrolyte interface (total electrode impedance). NO₂ caused a decrease in high frequency impedance while NO caused an increase. In contrast, NO₂ and NO both caused a decrease in the total electrode impedance. The effect of area on both the potentiometric and impedance responses show relationships that can be explained through the mechanistic contributions included in differential electrode equilibria.     

New method for selectivity enhancement of SiC field effect gas sensors for quantification of NO x

A silicon carbide based enhancement type metal insulator field effect transistor with porous gate metallization has been investigated as a total NO _x sensor operated in a temperature cycling mode. This operating mode is quite new for gas sensors based on the field effect but promising results have been reported earlier. Based on static investigations we have developed a suitable T-cycle optimized for NO _x detection and quantification in a mixture of typical exhaust gases (CO, C₂H₄, and NH₃). Significant features describing the shape of the sensor response have been extracted and evaluated with multivariate statistics (e.g. linear discriminant analysis) allowing quantification of NO _x . Additional cleaning-cycles every 30?min improve the stability of the sensor further. With this kind of advanced signal processing the influence of sensor drift and cross sensitivity to ambient gases can be reduced effectively. Measurements have proven that different concentrations of NO _x can be detected even in a changing mixture of other typical exhaust gases under dry and humid conditions. In addition to that, unknown concentrations of NO _x can be detected based on a small set of training data. It can be concluded that the performance of GasFETs for NO _x determination can be enhanced considerably with temperature cycling and appropriate signal processing.     

MEMS-based combustor with hairpin-shape design of gas recirculation channel

This paper describes the design, fabrication and test of a silicon-based micro combustor, which is a part of a micro power generation system under development. Based on the three-dimensional computational fluid dynamics (CFD) simulation and analysis of different micro combustor design, a hairpin-shape design for air/fuel recirculation channel is adopted. The combustor is fabricated from seven single crystal silicon wafers using deep reactive ion etching (DRIE) process. It has been assembled successfully with gas tubing and thermal couplers for monitoring the exit gas temperature. The effect of mass flow rate on the combustion characteristics is studied experimentally and numerically under several operating conditions. The exhaust gas temperature can reach the range from 870 to 1,100 K. The results indicate that with the increases of the mass flow rate, the combustor exhaust gas temperature increase as well in both experimental and the simulated results. This is due to the heat released in the combustor increases with the fuel/air mass flow rate.     

柴油机可调涡流比燃烧过程三维仿真

在柴油机进气歧管前安装蝶形涡流调节阀,通过调整直气道侧的有效流通面积改变缸内涡流强度。在稳流吹风试验平台,研究涡流调节阀角度对进气道流量因数和涡流比的影响,并结合粒子图像测速（particle image velocimetry,PIV）分析缸内涡流的形成过程。采用计算流体力学（computational fluid dynamics,CFD）评估涡流调节阀角度对缸内混合气体形成过程的影响,计算结果可复现三维 PIV测量的缸内流场结构和相似的涡心位置。随着进气门关闭,涡流比从0.57提高到2.05,油气在周向的相互作用增强,从而加速预混燃烧阶段的放热速度,促使燃烧重心提前、燃烧持续期缩短。在相同进气流量条件下,强涡流运动也促使累积放热量增加。     

A nitric oxide gas sensor based on Rayleigh surface acoustic wave resonator for room temperature operation

A Rayleigh surface acoustic wave (RSAW) resonator with polyaniline/tungsten oxide nanocomposite thin film is investigated as a gas sensor for detecting the presence of nitric oxide (NO) in air. The sensor developed in this work was sensitive to NO gas at room temperature. It is shown that the sensor had a frequency shift of 1.2 ppm when it was exposed to 138 ppb NO. The negative frequency response increased with NO concentration increasing. The response and recovery times of the NO sensor in this work were about 20-80 s. In addition, this RSAW sensor also exhibited reversibility and repeatability to the presence of NO gas. Especially, the presented sensor showed high selectivity with NO gas to separate from NO₂ and CO₂ gases.