NOx Model Research Articles

Prediction of SO2, NOx and PM in the sintering process based on deep learning

The accurate prediction of SO2, NOx and PM emissions in the iron ore sintering process could adjust the desulfurization and denitrification operation in time. The study presented an integrated prediction model for SO2, NOx and PM in sintering flue gas. Gradient boosting decision tree, recurrent neural network, gated recurrent unit were chosen as sub-models to predict SO2, NOx and PM by comparing different regression prediction models, which were then combined to form an integrated prediction model (MMEP). The box plots, empirical mode decomposition algorithm, Pearson correlation coefficient and maximum information coefficient to select independent variables for the predictive model. The MMEP model had an overall accuracy greater than 0.82, as verified by production data, which could provide guidance for on-site sintering production.

Intelligent optimal control of furnace temperature for the municipal solid waste incineration process using multi-loop controller and particle swarm optimization

Furnace temperature (FT) is a key variable in municipal solid waste incineration (MSWI) processes, influenced by many manipulated variables and directly impacting pollutant concentrations in exhaust gas. Domain experts cannot achieve the optimal FT setpoint value under manual control, leading to abnormal pollutant emission concentrations. To address this, we propose an intelligent optimal control framework for FT aiming to minimize pollutant emission concentration. First, the FT controlled object model is established using the Tikhonov regularization-least regression decision tree (TR-LRDT) algorithm. Then, based on the experience of domain experts, a multi-loop controller is developed using an improved single neuron adaptive PID (ISNA-PID) algorithm to stabilize FT. Next, after establishing NOx and CO2 indicator models, the particle swarm optimization (PSO) algorithm is employed to determine the FT setpoint value in terms of minimum pollutant emission concentration. Finally, the FT intelligent optimal control framework is verified. Experimental results indicate that the optimal FT setpoint value can reduce NOx and CO2 emission concentrations by 19.93 % and 6.99 %, respectively.

Retrofit and application of pulverized coal burners with LNG and oxygen ignition in utility boiler under ultra-low load operation

An oxygen-rich and low NOx burner integrated with liquefied natural gas (LNG) was proposed to address unstable combustion and high NOx emissions from a 330 MW subcritical boiler under ultra-low load operation in China. To assess the effectiveness of the retrofit, Chemkin and Fluent softwares were utilized to construct a new NOx model and calculate NOx generation, based on the combustion of pulverized coal gas and LNG. Further, an eddy dissipation concept (EDC) model, which can reflect detailed chemical reactions, was applied to calculate gas-phase reactions in the furnace. The results showed that when performing the deep peak shaving after the retrofit, the combustion in the furnace was stable under 50% or more load, and NOx emission level at the furnace outlet was lower than 350 mg/m3 (6% O2 content, dry basis). Under 25% load, the oxygen-rich burner integrated with LNG was applied, and the pulverized coal flow entered the furnace in a state of high-intensity combustion, which effectively promoted the stability of combustion in the furnace. The reductive combustion state with reductive free radicals generated by LNG decomposition inhibited NOx formation. Consequently, NOx emissions from the furnace outlet decreased from 380 mg/m3 to 316 mg/m3.

Numerical simulation on coking process of coke oven with external flue gas recirculation

The coking behaviour and NOx emission characteristics of the coke oven are of great significance for coke production. Based on the realisation k-epsilon turbulence model, the P-1 radiation model, the species transport model with eddy dissipation, and the thermal NOx model. A comprehensive model for traditional regenerative coke oven was established with reversing mode and external flue gas recirculation model considering temperature-dependent thermophysical properties. The convective term of model equations was the discretised using the second-order upwind scheme, while the others were applied by the central difference scheme. The coking behaviour of the coke oven with external flue gas recirculation was numerically solved through the SIMPLE algorithm, and the effects of reversing period and external flue gas recirculation ratio on the coking behaviour were explored. When increasing the reversing period, the mean temperature of the vertical flue increases, and the coking time decreases, but the temperature uniformity of the carbonisation chamber becomes worse. As the external flue gas recirculation ratio increases, the average temperature of the vertical flue decreases, and the NOx concentration at the outlet decreases, yet the coking time increases. These results would provide useful information for guiding the improvement of coke oven operation and reducing NOx emissions.

Numerical simulation of altering the raw meal inlet position in a novel swirl precalciner

Conducted a numerical simulation to model a novel swirl precalciner, investigating how altering in the position of the raw meal inlet affects the internal gas flow, temperature field, and component concentration field within the precalciner. Applied the Realizable k-? two-equation turbulent model to the continuous phase. For the particle phase (pulverized coal), employed the Discrete Particle Model and the discrete random walk model. Simulated the combustion of pulverized coal and the decomposition of calcium carbonate by using the Species Transport model combined with the Finite-Rate/Eddy-Dissipation model. Modeled the generation of NOx using a NOx model. The results show that, in comparison to the condition with four raw meal inlets, the six raw meal inlets condition has a better coupling of pulverized coal combustion and raw meal decomposition. The decomposition rate of raw meal has seen a slight improvement, and there is a significant improvement in the occurrence of localized high temperatures within the precalciner, resulting in a reduction of the outlet NOx concentration from 1251 ppm to 225 ppm.

Numerical investigation on NOx formation of staged oxy-fuel combustion in a 35 MW large pilot boiler

In this study, high-fidelity simulations are conducted on a 35 MW pulverized coal oxy-fuel boiler to investigate NOx formation characteristics under staged oxy-fuel combustion, including both fuel-staged and oxygen-fuel two-way staged modes. Detailed volatile components are considered by using the chemical percolation devolatilization model. A skeletal reaction mechanisms consisting of 35 species developed for fuel-NOx formation under oxy-fuel conditions are employed for the gas phase combustion and NOx modeling. The radiative property models are also optimized for oxy-fuel combustion. The results show that in the fuel-staged mode, a reduction atmosphere is established in the reburning zone to reduce the NOx concentration in the flue gas. A reduction of 5.6% in the average NO concentration at the furnace outlet is observed relative to the baseline oxy-fuel case. Moreover, the application of oxygen-fuel two-way staged combustion leads to a further reduction in NOx formation. The average NO concentration at the furnace outlet is decreased by 17% compared to the baseline oxy-fuel case. Pathway analysis indicates a reduction in the NO formation pathway involving HCN → NCO → NO, while the NO reduction pathway of NO → HCN is enhanced.

Investigation of the NH3 consumption and H2O/NO formation during the oxidation of n-heptane and iso-octane blended with ammonia

Adding highly active fuels with ammonia is a compromising way to increase the mixture's reactivity. However, there are very few studies blending ammonia with large hydrocarbons, such as n-heptane and isooctane. In this study, the NH3, NO, and H2O time-histories were simultaneously measured during the oxidation of ammonia/n-heptane and ammonia/isooctane mixtures (equivalence ratio ϕ ∼ 1.2) in a shock tube. For the ammonia/n-heptane mixture, a reduced kinetics model, the Wang 2023 model, shows the best predictions in terms of the mixture reactivity and the H2O production but underpredicts the NO plateau. For the ammonia/isooctane mixture, the CRECK_TPRF_Alcohols_NOx (CRECK) model is the only available kinetics model containing ammonia and isooctane mechanisms. The CRECK model captures the H2O plateau but underpredicts the ammonia/isooctane mixture reactivity and the NO plateau. Detailed analyses are conducted based on the Wang 2023 and CRECK models. Rate-of-production (ROP) analyses reveal that: the consumption or formation pathways of the three species in the two models are basically the same but the magnitudes of the ROPs are quite different, especially for the NO formation; the reaction of ethylene or propylene with NH2 radical in the Wang 2023 model also contributes significantly to the NH3 consumption but they are not considered in the CRECK model. Sensitivity analyses show that: H + O2 = O + OH is the most promoting reaction for both mixtures and models; however, several reactions show strong effects in one mixture while having little effect in another mixture, or even playing an opposite effect. After modifying or adding some key reactions, the Wang 2023 and CRECK models get significantly improved in predicting the species' time-history during the oxidation of the ammonia/n-heptane or ammonia/isooctane mixture.

LES Investigation of Kerosene Spray Flame Emission Characteristics in a Staged Combustor

ABSTRACT Staged combustion technology is widely applied in modern low-emission energy and power systems. This study employs large eddy simulations (LES) to investigate the flame dynamics and emission characteristics in a staged two-phase partially premixed model combustor. To improve the suitability of the thickened flame model (TFM) for turbulent flames in the thin-reaction-zone regime, a new model for efficiency function is formulated such that the effects of small-scale turbulence on flame propagation are taken into account. The enhanced LES-TFM approach is then integrated with an efficient NOx model, in which an additional NOx transport equation is solved with pre-tabulated NOx formation source terms. Five LES runs are performed to investigate the evolution of flame dynamics and emission characteristics with staging ratios at the scaled cruise operating conditions, in which the optimal staging ratio is critical for NOx reduction. Results show that the trend of flame morphology and NOx emission predictions with staging ratios are consistent with the experimental results. The flame zones in the staged model combustor shift from a pilot-stage-only flame to a pilot-primary-stage flame, then to a primary-stage-only flame with decreasing fuel staging ratio. Due to the redistribution of the equivalence ratio and the NOx emission characteristics trade-off between the two-stage flows, there is an optimal staging ratio for NOx reduction in the staged model combustor. The proposed LES-TFM-NOx approach, which has both the benefits of efficient flame dynamics modeling with the global reaction mechanism and the advantages of prediction accuracy with the detailed mechanism for NOx emission, demonstrates its advantages in simulating flame and emission characteristics in low-emission combustion devices.

A kinetics mechanism of NOX formation and reduction based on density functional theory

NOX are serious pollutants emitted during combustion, which are greatly harmful to human health and the environment. However, previous studies have not accurately elucidated the NOX conversion mechanism in complicated combustion reactions. To reveal the micro-chemical mechanism of NOX conversion and obtain accurate kinetics data, advanced quantum chemistry methods are employed in this study to systematically explore the pathways of NOX formation and reduction, and determine the new rate coefficients. An energy barrier analysis revealed that during NOX formation (N2 → N2O → NO→NO2), NO is primarily produced by a sequence of reactions (N2 + O → N2O → NO) rather than the traditional reaction (O + N2 → NO+N). Meanwhile, NO2 formation (NO→NO2) largely depends on the O and HO2 radicals, while the active O atom can promote both the formation and destruction of NO2. During NOX reduction (NO2 → NO→N2O → N2), NO2 reduction (NO2 → NO) is closely related to H, CO, and O, whereas CO plays a critical role in NO2 destruction. However, NO reduction (NO→N2O) is unfavourable because of a high energy barrier, while N2O reduction (N2O → N2) is strongly affected by the O atom instead of CO. HONO is mainly formed when NO2 reacts with the HO2 and H radicals, and when NO reacts with OH radicals; thus, HONO consumption largely depends on OH and H radicals. Based on the transition state theory, we obtained new kinetic parameters for NOX conversion, which supplement and correct critical kinetics data obtained from the current NOX model. Performance assessment of the proposed NOX kinetic mechanism reveals that it can improve the existing NOX kinetic mode, which is in good agreement with experimental data.

Uticaj parametara mreže i sagorevanja na CFD simulaciju motora sa varničkim paljenjem

In the current paper, a CFD simulation study on a pent-roof, spark ignition engine was performed. The scope was to analyze the basic parameters that influence the performances of the simulation: mesh configuration, mesh refinement, turbulence model, and flame wrinkling model parameters. It was used as the OpenFOAM solver for spark ignition engines. The solver was modified by adding the layer addition/removal mechanism for the mesh. Further, a Zeldovich extended NOx model was implemented. Four different mesh configurations were tested with different refinement levels. A set of different turbulence models and initial parameters and different flame wrinkling model parameters were tested. It was observed how all these parameters influence the rate of heat release, cylinder pressure, temperature, the level of turbulent kinetic energy, the flame wrinkling factor, and finally the resulting concentration of nitride monoxide.

NOx Emission Prediction Based on SSA-DELM via CFD and DCS Data Fusion

The objective of this study was to obtain NOx emission prediction model at the inlet selective catalytic reduction (SCR) reactors, which was the basis of combustion optimization and denitrification treatment. A deep extreme learning machine (DELM) optimized by the sparrow optimization algorithm (SSA) was adopted to establish the NOx model based on data fusion of Computational Fluid Dynamics (CFD) simulation and Distributed Control System (DCD). The mechanism analysis and XGBoost algorithm was used to select input variables. The results show that the XGBoost-SSA-DELM-based prediction model has high prediction accuracy with mean absolute error of 2.54 mg/m3. The results of this study have important implications for research on improving combustion efficiency and reducing pollutant emissions.

Prediction of Real Driving Emission of Light Vehicles in China VI Based on GA-BP Algorithm

In the China VI regulations for light-duty vehicles, the (RDE) test is introduced as a supplementary test procedure. In the actual test process, the RDE results are more significantly influenced by driving behavior and vehicle type. To reduce the test cost, the NOx and PN prediction models are established based on the GA-BP method. The results showed that the coefficients of determination of the GA-BP model for NOx and PN predictions are all greater than 0.9 and are linearly highly correlated at the instantaneous emission level. At the overall emission level, the overall error of the GA-BP model is less than 7% for NOx prediction and less than 6% for PN prediction. The model has high accuracy for both instantaneous and overall emissions of light-duty vehicles. This provides practical engineering value for guiding the RDE test.

UK hyperlocal air pollution model – Use of inverse modelling to increase the spatial detail of air pollution predictions

Background: Air pollution models are increasingly able to provide forecasts of NOx, NO₂, O₃, PM₂.₅ and PM₁₀ at country scales (every 1-2km) and close to roads (every 10-20m). However, a significant limitation of these models is the lack of road traffic flow, speed and emissions. Aim: To develop a hyperlocal model which predicts air pollution every 20m across all of UK’s roads. Method: Here we address the lack of detailed traffic counts, speed and emissions by utilising ~12,000 diffusion tube and local sensor measurements in the UK, with a road traffic emissions optimisation method (EOM). The EOM uses model sensitivity coefficients in Taylor series expansions of the models’ response to varying emissions, allowing Monte Carlo emissions calibration to be performed at every monitoring location. This has allowed the creation of previously unavailable road emissions estimates, as well as the calibration of emissions on those roads that were already in the model. For other sources we used a combination of European, UK National Atmospheric Emissions Inventories. Air pollution was predicted using the WRF met. model and the CMAQ-urban coupled model. Results: The EOM has enabled an emissions calibration process of unprecedented scale – improving traffic emissions estimates and enabling air pollution predictions at 20m resolution for every road in the UK. The NOx, NO₂ and O₃ model performance compares favourably with out of sample measurements and the EOM addresses the uncertainty NOx, NO₂ emissions from road transport, which has hitherto been under predicted. Conclusions: We have demonstrated what is possible with coupled CTM and local scale air pollution models, when combined with sufficiently detailed emissions inventories. This new approach is ideally suited for use with low cost sensor networks globally, providing data, including for PM₂.₅ and PM₁₀, for local populations, policy development and health research, across entire cities and even countries.

Analysis and comparison of flow velocities and in-cylinder temperature distributions to quantify oxides of nitrogen in a compression ignition engine using diesel and biodiesel fuels

PurposeCombustion of fuel with oxidizer inside a combustion chamber of an internal combustion engine forms inevitable oxides of nitrogen (NOx) due to high temperature at different locations of the combustion chamber. This study aims to quantify NOx formed inside the combustion chamber using two fuels, a conventional diesel (n-heptane) and a biodiesel (methyl oleate).Design/methodology/approachThis research uses a computational fluid dynamics simulation of chemically reacting fluid flow to quantify and compare oxides of nitrogen (NOx) in a compression ignition (CI) engine. The study expends species transport model of ANSYS FLUENT. The simulation model has provided the temperature profile inside the combustion chamber, which is subsequently used to calculate NOx using the NOx model. The simulation uses a single component hydrocarbon and oxygenated hydrocarbon to represent fuels; for instance, it uses n-heptane (C7H16) for diesel and methyl-oleate (C19H36O2) for biodiesel. A stoichiometric air–fuel mixture is used for both fuels. The simulation runs a single cylinder CI engine of 650 cm3 swept volume with inlet and exhaust valves closed.FindingsThe pattern for variation of velocity, an important flow parameter, which affects combustion and subsequently oxides of nitrogen (NOx) formation at different piston locations, is similar for the two fuels. The variations of in-cylinder temperature and NOx formation with crank angles have similar patterns for the fuels, diesel and biodiesel. However, the numerical values of in-cylinder temperature and mass fraction of NOx are different. The volume averaged static peak temperatures are 1,013 K in case of diesel and 1,121 K in case of biodiesel, while the mass averaged mass fractions of NOx are 15 ppm for diesel and 141 ppm for biodiesel. The temperature rise after combustion is more in case of biodiesel, which augments the oxides of nitrogen formation. A new parameter, relative mass fraction of NOx, yields 28% lower value for biodiesel than for diesel.Originality/valueThis work uses a new concept of simulating simple chemical reacting system model to quantify oxides of NOx using single component fuels. Simplification has captured required fluid flow data to analyse NOx emission from CI engine while reducing computational time and expensive experimental tests.

Physical-oriented and machine learning-based emission modeling in a diesel compression ignition engine: Dimensionality reduction and regression

The development of internal combustion engines is affected by the exhaust gas emissions legislation and the striving to increase performance. This demands for engine-out emission models that can be used for engine optimization for real driving emission controls. The prediction capability of physically and data-driven engine-out emission models is influenced by the system inputs, which are specified by the user and can lead to an improved accuracy with increasing number of inputs. Thereby the occurrence of irrelevant inputs becomes more probable, which have a low functional relation to the emissions and can lead to overfitting. Alternatively, data-driven methods can be used to detect irrelevant and redundant inputs. In this work, thermodynamic states are modeled based on 772 stationary measured test bench data from a commercial vehicle diesel engine. Afterward, 37 measured and modeled variables are led into a data-driven dimensionality reduction. For this purpose, approaches of supervised learning, such as lasso regression and linear support vector machine, and unsupervised learning methods like principal component analysis and factor analysis are applied to select and extract the relevant features. The selected and extracted features are used for regression by the support vector machine and the feedforward neural network to model the NOx, CO, HC, and soot emissions. This enables an evaluation of the modeling accuracy as a result of the dimensionality reduction. Using the methods in this work, the 37 variables are reduced to 25, 22, 11, and 16 inputs for NOx, CO, HC, and soot emission modeling while maintaining the accuracy. The features selected using the lasso algorithm provide more accurate learning of the regression models than the extracted features through principal component analysis and factor analysis. This results in test errors RMSETe for modeling NOx, CO, HC, and soot emissions 19.22 ppm, 6.46 ppm, 1.29 ppm, and 0.06 FSN, respectively.

Calculation of Intake Oxygen Concentration through Intake CO2 Measurement and Evaluation of Its Effect on Nitrogen Oxide Prediction Accuracy in a Heavy-Duty Diesel Engine

A new procedure, based on measurement of intake CO2 concentration and ambient humidity was developed and assessed in this study for different diesel engines in order to evaluate the oxygen concentration in the intake manifold. Steady-state and transient datasets were used for this purpose. The method is very fast to implement since it does not require any tuning procedure and it involves just one engine-related input quantity. Moreover, its accuracy is very high since it was found that the absolute error between the measured and predicted intake O2 levels is in the ±0.15% range. The method was applied to verify the performance of a previously developed NOx model under transient operating conditions. This model had previously been adopted by the authors during the IMPERIUM H2020 EU project to set up a model-based controller for a heavy-duty diesel engine. The performance of the NOx model was evaluated considering two cases in which the intake O2 concentration is either derived from engine-control unit sub-models or from the newly developed method. It was found that a significant improvement in NOx model accuracy is obtained in the latter case, and this allowed the previously developed NOx model to be further validated under transient operating conditions.

Support vector machine based emissions modeling using particle swarm optimization for homogeneous charge compression ignition engine.

The internal combustion engine faces increasing societal and governmental pressure to improve both efficiency and engine out emissions. Currently, research has moved from traditional combustion methods to new highly efficient combustion strategies such as Homogeneous Charge Compression Ignition (HCCI). However, predicting the exact value of engine out emissions using conventional physics-based or data-driven models is still a challenge for engine researchers due to the complexity the of combustion and emission formation. Research has focused on using Artificial Neural Networks (ANN) for this problem but ANN's require large training datasets for acceptable accuracy. This work addresses this problem by presenting the development of a simple model for predicting the steady-state emissions of a single cylinder HCCI engine which is created using an metaheuristic optimization based Support Vector Machine (SVM). The selection of input variables to the SVM model is explored using five different feature sets, considering up to seven engine inputs. The best results are achieved with a model combining linear and squared inputs as well as cross correlations and their squares totaling 26 features. In this case the model fit represented by R 2 values were between 0.72 and 0.95. The best model fits were achieved for CO and CO2, while HC and NOx models have reduced model performance. Linear and non-linear SVM models were then compared to an ANN model. This comparison showed that SVM based models were more robust to changes in feature selection and better able to avoid local minimums compared to the ANN models leading to a more consistent model prediction when limited training data is available. The proposed machine learning based HCCI emission models and the feature selection approach provide insight into optimizing the model accuracy while minimizing the computational costs.

Effect of nitric oxide and exhaust gases on gasoline surrogate autoignition: iso-octane experiments and modeling

Exhaust gas recirculation (EGR) is widely used in advanced internal combustion engines to reduce engine emissions as well as control combustion phasing. Among various species present in EGR gases, CO2 and H2O are two major components that can thermally and chemically affect fuel autoignition. It is of fundamental interest to isolate the thermal and chemical effects of CO2 and H2O on fuel autoignition, especially as such an effort has not been reported in the literature. Moreover, nitric oxide (NO) is known to exhibit strong chemical effects on fuel autoignition, which in turn affects engine combustion phasing. The effects of ultra-low NO addition (< 100 ppm) on fuel autoignition at low temperatures are also not well understood. Recognizing these problems, autoignition experiments of iso-octane (a major gasoline surrogate component) in air are performed in this study using a rapid compression machine at varying compressed pressures, equivalence ratios, dilution levels with an EGR gas analogue (consisting of CO2, H2O, O2, and N2) and N2 only, and varying amounts of NO addition. The thermal and chemical effects of the EGR gas analogue are isolated and evaluated by comparing the ignition delay time datasets of EGR and N2-only diluted cases. It is found that the dilution/thermal effect on autoignition dominates at all test conditions, and the combined chemical effect of CO2 and H2O is insignificant. The NO doping experiments show that the promoting and inhibiting effects of NO addition on iso-octane ignition delay time are temperature dependent. Furthermore, a chemical kinetic model for gasoline surrogate components and NOx is developed by validating it against the experimental data obtained in this study and those available in the literature for various gasoline surrogate components. The validation of the model across different experimental datasets and for several fuels demonstrates its reliability and wide applicability in several operating conditions. Comparisons with previously developed models shed light on the gaps still present in understanding the effect of nitrogen species on the reactivity of gasoline surrogate molecules. Chemical kinetic analyses have also been conducted to identify the important reaction pathways controlling iso-octane autoignition at various conditions, and to elucidate the underlying mechanism leading to different reactivity trends at different NO doping levels and temperatures.

Flamelet Versus Detailed Chemistry Large Eddy Simulation for a Liquid-Fueled Gas Turbine Combustor: A Comparison of Accuracy and Computational Cost

Abstract A Honeywell liquid-fueled gas turbine test combustor at idle conditions is numerically investigated in simcenterstar-ccm+ version 2020.3. This work presents large eddy simulation (LES) results using both the flamelet generated manifold (FGM) and detailed chemistry combustion models. Both take advantage of a hybrid chemical (HyChem) mechanism which has previously demonstrated very good accuracy for real fuels such as Jet-A with only 47 species. The objective of this work is to investigate the ability of FGM and detailed chemistry modeling to capture pollutant formation in an aero-engine combustor. Comparisons for NOx, CO, unburned hydrocarbons (UHC), and soot are made, along with the radial temperature profile. To fully capture potential emissions, a soot moment model and Zeldovich NOx model are employed along with radiation. A comparison of results with and without chemistry acceleration techniques for detailed chemistry is included. Then, computational costs are assessed by comparing the performance and scalability of the simulations with each of the combustion models. It is found that the detailed chemistry case with clustering can reproduce nearly identical results to detailed chemistry without any acceleration if CO is added as a clustering variable. With the Lagrangian model settings chosen for this study, the detailed chemistry results compared more favorably with the experimental data than FGM; however, there is uncertainty in the secondary breakup parameters. Sensitivity of the results to a key parameter in the spray breakup model is provided for both FGM and complex chemistry (CC). By varying this breakup rate, the FGM case can predict CO, NOx, and UHC equally well. The smoke number, however, is predicted most accurately by CC. The cost for running detailed chemistry with clustering is found to be about four times that of FGM for this combustor and chemical mechanism.

Performance assessment of multiple biodiesel blended diesel engine and NOx modeling using ANN

This paper discusses the effect of dual blends of multiple biodiesels on characteristics of DICI engine in conjunction with NOx modeling using ANN. Six sets of dual biodiesel/diesel blends are prepared on a volume basis (10% and 90%) by using four different biodiesels such as Jatropha, Karanja, Mahua, and Neem. The experimentations are performed on a single-cylinder VCR engine with full throttle condition and constant compression ratio (CR) of 17.5:1. The physicochemical and thermal properties are analysed to assess the suitability and compatibility of biodiesel as an alternative fuel. The physicochemical properties are found within the permissible limits as prescribed by ASTM. Mass loss study and functional groups are analysed through TGA and FTIR analysis and maximum weight loss is observed in pyrolytic zone (Zone-II). Reduction in performance characteristics of BTE, AFRηMech., ηVol. varies from −0.77% to −5.60%. Variations are observed in BSEC (8.91%) and EGT (11.95%). Combustion characteristics (CP, MGT, MFB, NHR, and RPR) of biodiesel blends are similar to neat diesel. Overall reduction in CO (34.05%) and increment in CO2 (42.54%) are observed for all the blends at the penalty of HC and NOx. An attempt has been made to predict NOx emission through physicochemical properties.