Ultra-low Nitrogen Oxides Research Articles

Synergistic activation of reburned char for ultra-low NOx emissions using flue gas recirculation and natural gas in a 10kW furnace.

The technology of powdered coal injection with recirculating flue gas and natural gas conditioning for reburning represents an advanced and innovative approach to enhancing the efficiency of coal powder reburning. By consuming excess oxygen in the recirculated flue gas, natural gas fosters an environment enriched with reducing agents, which stimulates the reactivity of reburning coal powder and augments its effectiveness in reducing nitrogen oxides (NO). This technology has been comprehensively investigated through experiments conducted in a segmented multi-reactor flow system, simulating conditions akin to those in industrial boilers. To achieve a high level of NO abatement, complete combustion of coal powder, and operational cost-effectiveness, a series of optimal operating parameters has been identified: the temperature in the reburning zone (T1) should be controlled at approximately 1573K; the reburning fuel ratio (Rf) should be maintained around 20%; the excess air coefficient (λfuel) in the reburning zone should be approximately 0.228; the residence time in the reburning zone (t2) should be 0.6s, and the burnout zone residence time (t3) should also be 0.6s; finally, the oxygen concentration in the recirculating flue gas should be regulated to around 10%. This configuration ensures that reactive intermediates such as CO∗, OH∗, H, and CHi generated through natural gas modification enhance the physicochemical structure of coal char, thus amplifying the coal char's capacity for the chemical reduction of NO. Precise control of these parameters is expected to facilitate ultra-low NO emissions, while minimizing the consumption of costly active gases and ensuring the economic efficiency of the system.

Adoptability assessment of HCDI and RCCI modes in plug-in parallel hybrid electric vehicles using sustainable fuels and model-based torque structure calibration strategies

In this research, a comparative analysis of plug-in hybrid electric vehicle (PHEV) engine was conducted with two advanced low temperature combustion (LTC) techniques viz. Homogeneous charge with direct injection (HCDI) and reactivity-controlled compression ignition (RCCI), utilizing alternative fuels diethyl ether (DEE) and ethanol. This was attempted to exhibit the promising potential of LTC based PHEV to achieve the highest efficiency and low emissions. The experimental results disclosed that DEE powered HCDI can achieve superior thermal efficiency with an impressive peak torque of 14 Nm. On the other hand, ethanol powered RCCI produced a peak torque of 17.5 Nm with remarkable emission reduction characteristics, especially CO2. It was vindicated that HCDI-PHEV outperformed the RCCI-PHEV in terms of fuel economy despite both HCDI and RCCI being lower than the conventional diesel PHEV. The NOx emissions from HCDI were almost negligible. About 20 % lower nitrogen oxide (NOx) emissions were recorded for RCCI respectively as compared to diesel (36 g/s). HCDI and RCCI showcased about 2 and 1.3 times higher soot emissions than diesel (7.12 g/s). Fuel economy was improved by 15 % and 43 % respectively under HCDI and RCCI modes over the conventional diesel mode of PHEV operation. When the PHEV was operated continuously for about 3.5 h, the battery's SOC dropped from 80 % to 45 % under diesel operation, whereas it only reached 50 % and 60 % under RCCI and HCDI mode operation. Low fuel consumption characteristics, acceptable torque production capabilities and ultralow NOx reduction potential established HCDI as the preferable choice for PHEV over RCCI.

Performance of a Methanol-Fueled Direct-Injection Compression-Ignition Heavy-Duty Engine under Low-Temperature Combustion Conditions

Low-temperature combustion (LTC) concepts, such as homogeneous charge compression ignition (HCCI) and partially premixed combustion (PPC), aim to reduce in-cylinder temperatures in internal combustion engines, thereby lowering emissions of nitrogen oxides (NOx) and soot. These LTC concepts are particularly attractive for decarbonizing conventional diesel engines using renewable fuels such as methanol. This paper uses numerical simulations and a finite-rate chemistry model to investigate the combustion and emission processes in LTC engines operating with pure methanol. The aim is to gain a deeper understanding of the physical and chemical processes in the engine and to identify optimal engine operation in terms of efficiency and emissions. The simulations replicated the experimentally observed trends for CO, unburned hydrocarbons (UHCs), and NOx emissions, the required intake temperature to achieve consistent combustion phasing at different injection timings, and the distinctively different combustion heat release processes at various injection timings. It was found that the HCCI mode of engine operation required a higher intake temperature than PPC operation due to methanol’s low ignition temperature in fuel-richer mixtures. In the HCCI mode, the engine exhibited ultra-low NOx emissions but higher emissions of UHC and CO, along with lower combustion efficiency compared to the PPC mode. This was attributed to poor combustion efficiency in the near-wall regions and engine crevices. Low emissions and high combustion efficiency are achievable in PPC modes with a start of injection around a crank angle of 30° before the top dead center. The fundamental mechanism behind the engine performance is analyzed.

Multiphase flow simulation and industrial application of an in-line gasifier for NOx emissions control from cement kiln

Following the power and steel industries, nitrogen oxides (NOx) in the cement industry have become an important part of the next stage of air pollutant control. Consequently, the multiphase flow simulation of an in-line coal gasification denitration system was carried out using the computational fluid dynamics (CFD) approach. Among them, the solid phase is simulated by the Multiphase Particle-In-Cell (MP-PIC) method, and the gas phase turbulence is accurately captured by the large eddy simulation (LES) method. The flow field characteristics distribution and the reaction kinetic of NO under different parameters (coal, raw meal, tertiary air) in the gasifier were investigated, and the operating condition values for guiding industrial applications are obtained. The simulation results show that the gasifier can completely remove NOx from the rotary kiln at 2.8 kg/s for coal, 30 kg/s for raw meal and 2.5 kg/s for tertiary air. The application results show that under 1.6 kg/t.cl of ammonia water combined with SNCR, NOx emission and ammonia slip can be controlled below 50 mg/Nm3 and 5 mg/Nm3, respectively. Compared to similar denitrification technologies, this technology shows the potential to achieve ultra-low NOx emission levels in the cement industry.

Development of the two-stage SCR control strategy to satisfy ultra-low NOX emission regulation for heavy-duty diesel engine

The emission regulations for heavy-duty diesel engines regarding nitrogen oxide (NOX) are becoming increasingly stringent, particularly in relation to cold start cycles. While the two-stage selective catalytic reduction (SCR) has the potential to achieve ultra-low NOX emissions, several challenges remain, including the accurate prediction of ammonia (NH3) storage mass and the co-control of the two-stage SCR. The first step in this study involved the establishment of a rapid control prototype platform to facilitate the development and validation of a two-stage SCR control strategy. Secondly, an initial method for predicting the NH3 storage based on the mass conservation law was proposed, which was subsequently improved by filling and emptying experiments. The third step involved the development of a two-stage SCR co-control strategy, including obtaining the steady-state NH3 storage target value, dynamic correction for NH3 storage target value, regulation of NH3 storage, and control of the close-coupled SCR urea injector state. Finally, the two-stage SCR urea injection control strategy was certified under the world harmonized transient cycle (WHTC). The results demonstrate that the composite value of engine outlet NOX emissions under cold and hot start WHTC cycles is 13 g/(kW·h). Meanwhile, the composite value of tailpipe NOX emissions under cold and hot start WHTC cycles is 0.065 g/(kW·h), representing only 14% of the EU VI limit value of 0.46 g/(kW·h). Thus, the findings demonstrate that integrating an accurate NH3 storage prediction method with the two-stage SCR co-control function is crucial for heavy-duty diesel engines to achieve ultra-low NOX emissions.

Low Temperature Combustion Modeling and Predictive Control of Marine Engines

The increase of popularity of reactivity-controlled compression ignition (RCCI) is attributed to its capability of achieving ultra-low nitrogen oxides (NOx) and soot emissions with high brake thermal efficiency (BTE). The complex and nonlinear nature of the RCCI combustion makes it challenging for model-based control design. In this work, a model-based control system is developed to control the combustion phasing and the indicated mean effective pressure (IMEP) of RCCI combustion through the adjustments of total fuel energy and blend ratio (BR) in fuel injection. A physics-based nonlinear control-oriented model (COM) is developed to predict the main combustion performance indicators of an RCCI marine engine. The model is validated against a detailed thermo-kinetic multizone model. A novel linear parameter-varying (LPV) model coupled with a model predictive controller (MPC) is utilized to control the aforementioned parameters of the developed COM. The developed system is able to control combustion phasing and IMEP with a tracking error that is within a 5% error margin for nominal and transient engine operating conditions. The developed control system promotes the adoption of RCCI combustion in commercial marine engines.

Thermal Management of Diesel Engine Aftertreatment System Based on Ultra-Low Nitrogen Oxides Emission

To achieve diesel engine ultra-low nitrogen oxide emission, light-off selective catalyst reduction (LO-SCR) has been suggested for better performance with lower exhaust temperature. An electric heater upstream of the exhaust aftertreatment system was applied to significantly decrease the NOx emission at a low exhaust temperature. With a 7.2 kW electric heater coupled with LO-SCR, the NOx emission during 200~500 s of the world harmonized transient cycle (WHTC) decreased from 282.6 ppm to 61.5 ppm, which is a decrease of 45%. Application of an upstream diesel oxidation catalyst (DOC) decreased the NOx emission by 63% at the same interval at the cost of worse cold-start performance. The urea input was also adjusted to avoid NOx emission during the latter part of the WHTC.

Eulerian investigation of the mechanism of Ultra-low NOx emissions from CFB boilers with finer bed material

Industrial practice has confirmed that decreasing the bed material size substantially diminishes nitrogen oxide (NOx) emissions in coal-fired circulating fluidized bed (CFB) boilers. This reduction allows for compliance with the stringent ultra-low emissions standard (NOx < 50 mg/Nm3@6%O₂, dry) proposed by the Chinese government, all without the need for supplementary denitrification devices. Consequently, it becomes essential to delve into the mechanisms through which particle size, as a gas–solid flow characteristic, directly affects the chemical reaction dynamics within the coal combustion process. Due to the difficulty of field tests, this paper employs the Eulerian-Eulerian numerical method suitable for wide particle size distributions to investigate the impact of bed material particle size on gas–solid flow and NOx concentration. Furthermore, the generalized QC-EMMS drag model is utilized to predict the heterogeneous flow across varying particle size conditions. Industrial test data validated the simulation accuracy, with prediction errors of bed pressure drop, temperature, and NOx emission concentration ranging from 5 % to 8.1 %. The analysis revealed that the particle concentration distribution heterogeneity decreases as the particle size diminishes. The variation trends of NOx with particle size exhibit distinct differences between the lower dense phase region and the upper dilute phase region within the bed. For finer bed material particles, the high NOx concentration generated in the dense phase region experiences a more thorough reduction in the dilute phase region. Through the analysis of material concentration and reaction rate, it is believed that this phenomenon is related to the increased catalytic effect of fine ash particles in the dilute phase region, that is, the reduction in particle size leads to an increase in catalytic specific surface area, enhancing NOx reduction, thus achieving ultra-low emissions at the outlet. These research results provide theoretical support for further development of ultra-low emission coal-fired CFB boilers.

On-road NOx and NH3 emissions measurements from in-use heavy-duty diesel and natural gas trucks in the South Coast air Basin of California

This study characterized nitrogen oxides (NOx) and ammonia (NH3) emissions from five in-use goods movement vehicles, including one diesel vehicle without selective catalytic reduction (SCR), two diesel vehicles with SCR, and two ultra-low NOx natural gas vehicles equipped with three-way catalysts (TWCs). Emissions testing was performed under real-world driving conditions in typical goods movement routes in the South Coast Air Basin (SCAB) of California. NOx emissions varied depending on the vehicle and the route. The no-SCR diesel vehicle showed the highest NOx emissions over all the routes. One of the SCR-equipped vehicles showed NOx emission rates that were two times higher than the other SCR-equipped vehicle with similar engine model year and mileage, which may be attributed to catalyst deterioration. The SCR-equipped diesel vehicles were within two to three times 0.2 g/bhp-hr certification standard over the different routes. The natural gas vehicles showed average NOx emissions around or below the optional Low NOx emissions standard of 0.02 g/bhp-hr. The three-bin moving average window (MAW) method was utilized to show the NOx emissions across various modes of operation, including idle, low load, and medium/high load. The highest fraction of vehicle operation was found in either the low load or medium/high load bins, depending on the route. NOx emissions for each of the bins varied between different vehicles, and are further analyzed in this paper. NH3 emissions formation was favored for the TWC-equipped natural gas vehicles, which produced about 20 times more NH3 emissions than the SCR-equipped diesel vehicles.

In-use NOx and black carbon emissions from heavy-duty freight diesel vehicles and near-zero emissions natural gas vehicles in California's San Joaquin Air Basin

This study assessed the real-world nitrogen oxide (NOx) and black carbon emissions from six goods movement heavy-duty diesel and compressed natural gas (CNG) vehicles operating in California's San Joaquin Valley and Sacramento regions. The diesel vehicles were all equipped with diesel oxidation catalysts (DOCs) and diesel particulate filters (DPFs), while two diesel vehicles were also equipped with selective catalytic reduction (SCR). All CNG vehicles were equipped with three-way catalysts and fitted with stoichiometric engines meeting the optional ultra-low NOx standard of 0.02 g/bhp-hr. Emissions measurements were conducted with a portable emissions measurement systems (PEMS) during typical goods movement vehicle operation. Black carbon emissions were about 3–7 times higher for the CNG vehicles than those of the DPF-equipped diesel vehicles. NOx emissions for the CNG vehicles were found at or below the optional NOx standard and on average 35 times lower NOx than those of the diesel vehicles. Diesel vehicle NOx hotspots were identified in urban areas and intersections with frequent stop-and-go driving events, whereas the CNG vehicles showed uniform NOx emissions rates along the route. The dispersion modeling results showed elevated NOx and PM emissions exposures to receptors in close proximity to the highway. Our findings suggest that real-time emissions measurements at the tailpipe provide more accurate population exposure assessments near freight corridors compared to utilizing trip-averaged emissions rates values in dispersion models. Under the present test conditions, >70 % of black carbon and NOx were emitted within disadvantaged communities, characterized by low-income minority populations.

Investigative research of diesel/ethanol advanced combustion strategies: A comparison of Premixed Charge Compression Ignition (PCCI) and Direct Dual Fuel Stratification (DDFS)

Direct Dual Fuel Stratification (DDFS) is a new low-temperature combustion (LTC) approach that employs dual fuel direct injection in the combustion chamber. In this method, a relatively small proportion of diesel fuel was pre-injected and utilized to activate the burning of the premixed charge followed by direct injection of a diesel/ethanol mixture (75% diesel, 25% ethanol by volume) into the combustion chamber near TDC. The DDFS approach generates a lower reactivity charge in the central sector of the combustion chamber and a stronger reactivity charge along the wall. For the examination of combustion vibrations and acoustic, a new technique dependent on the fast Fourier transform (FFT) of the cylinder vibration statistics was used. Whereas the PCCI combustion technique is more likely to cause knocking than the typical diesel CI, the results show that charge stratification in DDFS combustion does have a good effect in reducing vibration intensity. Owing to the improved controllability of the start of combustion and burning period, DDFS combustion attained a brake thermal efficiency (BTE) of 51%, which was larger than CI and PCCI. DDFS shows ultra-low nitrogen oxide (NOx) (below 1 g/kW-h), mild carbon monoxide (CO) (below 6 g/kW-h), and unburned hydrocarbons (UHC) emissions. Because of the higher thermal efficiency and a decreased carbon source due to presence of ethanol, DFFS combustion could produce lower CO levels by up to 34% when compared to PCCI.

Cylinder Pressure Feedback Control for Ideal Thermodynamic Cycle Tracking: Towards Self-learning Engines

To meet increasingly strict future greenhouse gas and pollutant emission targets, development time and costs of heavy-duty internal combustion engines will reach unacceptable levels. This is mainly due to increased system complexity and need to guarantee robust performance under a wide range of real-world conditions. Cylinder Pressure Based Control is a major contributor to achieve these goals in advanced, highly-efficient engine concepts. Current Cylinder Pressure Based Control approaches use combustion and air-path parameters as feedback signals. These signals are not directly linked to engine efficiency; therefore, compensation for changing ambient conditions, engine ageing or differences in fuel qualities is a non-trivial problem. Contrary to other methods, the method presented in this paper aims to realise an idealised thermodynamic cycle by directly control of the entire cylinder pressure curve. From measured in-cylinder pressure, a new set of feedback signals is derived using principle component decomposition. With these signals, optimal fuel path settings are determined. The potential of this method is demonstrated for a dual fuel Reactivity Controlled Compression Ignition (RCCI) engine, which combines very high efficiency and ultra low nitrogen oxides and particle matter emission. For the studied RCCI engine, it is shown that the newly proposed optimisation method gave the same optimal fuel path settings as existing methods. This is an important step towards self-learning engines.

Numerical and experimental investigation of surface-stabilized combustion in a gas-fired condensing boiler

Gas-fired condensing boilers are widely used in domestic heating. However, emission of nitrogen oxides (NOx) remains a concern due to their adverse environmental effects. Employing lean premixed surface-stabilized combustion, ultra-low NOx generation is achievable.We studied NOx formation in realistic, axi-symmetric condensing boiler setups featuring lean combustion of methane stabilized by a porous cylindrical burner using a dedicated numerical model. To accurately represent the physical conditions, our model comprises most notably laminar combustion using a GRI-3.0 kinetic mechanism, a volumetric heat transfer coefficient for heat exchange inside the porous solid, conjugate heat transfer at the gas-solid interface and radiative heat exchange between burner surface and combustion chamber wall. A comprehensive assessment included validation of individual model aspects against literature data, a mesh study and validation with experimental measurements from a full-scale condensing boiler setup. Our findings suggest that the effective diffusion due to dispersive flow in porous media is critical for accurately capturing the reaction zone, thus preventing overprediction of flame temperature and NO concentration.The dependencies of NOx generation and heat release parameters on varying parametric and geometric configurations of the setup were assessed in a comprehensive numerical parameter study. We found that NO emission increases in a near-linear way with firing rate, while radiation efficiency decreases. At constant input power, NO emission rises with increasing equivalence ratio alongside radiation efficiency and peak flame temperature. By optimizing inflow uniformity, peak temperatures can be reduced by 9%, leading to significantly lower NOx generation without affecting the heat flow rate.

Coupling effect of Fe-based catalyst on Nitrogen oxides control in the process of nitrogen reduction and combustion denitration of long flame coal pyrolysis

ABSTRACT Civil dispersed coal combustion has become an important source of Nitrogen oxides (NOx), which has caused serious pollution to the ecological environment. In order to control NOx emission in the combustion process of civil coal, coal-based solid clean fuel should be used instead of dispersed combustion of coal, so as to achieve the purpose of reducing NOx emission. The pyrolysis and combustion tests of different iron additives loaded coals were performed. The raw and pyrolytic chars were prepared and characterized by X-ray photoelectron spectroscopy (XPS), nitrogen adsorption, scanning electron microscope (SEM) and X-ray diffraction (XRD). The results indicate that iron additives introduced into raw coal in one step have coupling effect on NOx emission control during pyrolysis and combustion. The addition of iron can lead to more Fe2O3, Fe3O4 and α-Fe in coke and promote coke cracking. Supporting iron catalysts could make coke structure more porous, which increases the contact area of gas. These all make the conversion of N2 increase from 16.71% to 29.64% during pyrolysis. The existence of iron catalyst can also promote the proportion of pyridinic N (N-6) and quaternary nitrogen (N-Q) relatively increase. At the same time, the existence of iron catalyst is beneficial to catalytic reduction of NOx by C and CO. The emissions of NOx during combustion can be reduced by 34.01%. The iron catalyst introduced in one step has a coupling effect on NOx control in coal pyrolysis-combustion process, which realizes a two-step nitrogen reduction effect in combustion. It provides theoretical basis for ultra-low NOx emission in clean fuel combustion.

Experimental Study on the Emission Performance of Lean Premixed Bunsen Flame with Piloted Rich Premixed Flame

Lean premixed flame has lower nitrogen oxide emission for lower flame temperature. Whereas its poor flame stability, an annual rich pilot premixed flame was used to enhance flame stability and reduce emissions with lower ratio of pilot flame and main lean flame load. With the increasing of pilot flame load, air coefficient of main flame increases gradually. Increasing the main flame load, the air coefficient of stable main flame decreases, while the nitrogen oxide and carbon monoxide changes little. Under all main flame load, nitrogen oxide and carbon monoxide decrease sharply at first and then gets slowly. The ultralow Nitrogen Oxide of 18 mg/m³and carbon monoxide of 50 mg/m³can be reached, under the condition of main flame air coefficient from 1.3 to 1.5.

Catalyst-Heating Operation in a Medium-Duty Diesel Engine: Operating Strategy Calibration, Fuel Reactivity, and Fuel Oxygen Effects

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Compliance with future ultra-low nitrogen oxide regulations with diesel engines requires the fastest possible heating of the exhaust aftertreatment system to its proper operating temperature upon cold starting. Late post injections are commonly integrated into catalyst-heating operating strategies. This experimental study provides insight into the complex interactions between the injection-strategy calibration and the tradeoffs between exhaust heat and pollutant emissions. Experiments are performed with certification diesel fuel and blends of diesel fuel with butylal and hexyl hexanoate. Further analyses of experimental data provide insight into fuel reactivity and oxygen content as potential enablers for improved catalyst-heating operation.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;A statistical design-of-experiments approach is developed to investigate a wide range of injection strategy calibrations at three different intake dilution levels. Thermodynamic and exhaust emissions measurements are taken using a new medium-duty, single-cylinder research engine. Analysis of the results provides insight into the effects of exhaust gas recirculation, oxygenated fuel blends, and fuel reactivity on exhaust heat and pollutant emissions. Late-cycle heat release is an important factor in determining exhaust temperatures. Intake dilution and fuel properties certainly affect late-cycle heat release, but the methods applied in this work are not sufficient to reproduce or explain the mechanisms by which improved fuel cetane rating promotes operation with hotter exhaust and lower pollutant emissions.&lt;/div&gt;&lt;/div&gt;

Multiple-objective optimization of heavy-duty compression ignition engine fueled by gasoline/hydrogenated catalytic biodiesel blends at low loads

Multiple-objective optimization of a heavy-duty compression ignition engine fueled by gasoline/hydrogenated catalytic biodiesel (HCB) blends at low loads was performed by employing the KIVA-3V code and genetic algorithm. In addition, the mechanism of multiple-injection and sensitivity of operating parameters on engine performance of the optimal cases were also explored. The results indicated that efficient combustions for G70H30 (70% gasoline and 30% HCB) and G100 (pure gasoline) with ultra-low nitrogen oxides (NOx) and soot emissions could be obtained after optimization. As HCB fraction increases, the ranges of operating parameters become more extensive, and the required initial temperature for optimal cases can be effectively reduced. When the main injection occurs after the ignition caused by pilot injection, main injection moderates the heat release rate (HRR) by creating concentration and temperature stratifications in the spray area simultaneously, and the exhaust gas recirculation (EGR) rate, pilot, and main start of injections and pilot fraction play dominant roles on engine performance. Moreover, when main injection is much more advanced than the ignition timing, main injection controls the HRR only through the concentration stratification in the reaction zone, and the EGR rate, initial temperature, and pilot faction have dominated effects on engine performance.

Prediction of emission and performance characteristics of reactivity-controlled compression ignition engine with the intelligent software based on adaptive neural-fuzzy and neural-network

A fuel reactivity-controlled compression ignition concept promises to overcome increasingly stringent emission standards with ultra-low nitrogen oxide and soot emissions and very high fuel efficiency. In the present work, it was developed an intelligent software-based on adaptive neural-fuzzy inference systems in order to predict the emission and performance values of reactivity-controlled compression ignition engine fueled with natural gas and diesel under different operating conditions through an experimentally validated computational fluid dynamics model with the four cases generated in the study for intelligent software-based on neural-fuzzy systems simulation considering different input and output parameters. The engine model was run at input variables as initial pressure (1.67–2.79 bar), total fuel mass (60–130 mg), exhaust gas recirculation ratio (0–20%), the start of injection (32–56° before top dead center) and total 180 different conditions were generated while output responses such as indicated power, maximum in-cylinder pressure, soot, and nitrogen oxide emissions. The main operating parameters of the reactivity-controlled compression ignition engine were determined for different load conditions, enabling the engine to operate in a wide range with high efficiency and low emission. For the neural-fuzzy inference model presented study, the different number Gaussian curve membership functions (5, 6, 7, 8) (gaussmf) have been used considering different adaptive neural-fuzzy cases, and generally, about 13500 training cycles is found to be optimum neural-fuzzy inference parameters and minimum error value. Mean square error, mean error percentage, and the absolute fraction of variance (R2) were used to assess and compare the performance of the adaptive neural-fuzzy and artificial neural network. Results of the adaptive neural-fuzzy confirm that the model successfully predicts the performance and emission of the engine with the R2 value % 99.8, %99.9, and %96.5 for the output parameters indicated power, maximum in-cylinder pressure, and nitrogen oxide, respectively. Moreover, the performance of the neural-fuzzy inference algorithm compared with artificial neural network and consequently, it was observed that neural-fuzzy inference gives more accurate predict value according to the artificial neural network. Moreover, generating a neural-fuzzy inference architecture using the proposed mathematical foundation given the study shows that neural-fuzzy inference is a very effective and helpful technique to predict the reactivity-controlled compression ignition engine performance and emission parameters.

Pressure Analysis of the Initial Process of Diffusion Combustion Surge in a 350 kW Gas Boiler

In order to meet the increasingly stringent requirements for nitrogen oxides (NOx) emissions from gas boilers, flue gas recirculation (FGR) technology is commonly used to achieve ultra-low NOx emissions. However, under some ultra-low NOx combustion conditions with FGR, a surge phenomenon occurs in the boiler, which causes a flameout in severe cases, and seriously affects the safe and stable operation of the boiler. In this study, the diffusion combustion surge of gas boiler with a rated power of 350 kW and equipped with FGR device was investigated. Pressure characteristic analysis results of the initial process of combustion surge showed that the high-frequency component of pressure is closely related to combustion stability and its change can provide reference for the occurrence of surge. Besides, the initial process of surge was analyzed by wavelet packet entropy method. Results indicated that the wavelet packet entropy of pressure signals could effectively reflect the stability of combustion in the furnace, and it could also be used to study the occurrence of surge.

Technical Route to Achieve Ultra-Low Emission of Nitrogen Oxides with Predictive Model of Nitrogen Oxide Background Concentration

As the most mature denitration technology in the cement clinker burning process, selective non-catalytic reduction (SNCR) has been unable to meet the requirements of ultra-low nitrogen oxide (NOX) emissions under low ammonia escape, thus a hybrid denitration process based on SNCR was established. The process had three steps: reducing the NOX background concentration (NBC), implementing staged combustion, and optimizing the effect of the SNCR. One of the keys to this process was the real-time acquisition of the NBC. In this paper, a multivariate linear regression model for the prediction of NBC was constructed and applied to one 12,000 t/d production line and one 5000 t/d production line. For the 12,000 t/d production line, NBC had a positive correlation with the temperature of the calciner outlet, the pressure, and the temperature of the kiln hood, and it had a negative correlation with the quantity of the kiln coal, the temperature of the smoke chamber, and the main motor current of the kiln. The influence degree of each parameter on the NBC is gradually weakened according to the above order. The determination coefficient (R2) of the model was 0.771, and the mean absolute error and maximum relative error between the predicted and measured NBC were 6.300 mg/m3 and 18.670 mg/m3 respectively.