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This study aims to improve the performance and to regulate the harmful emission from the diesel engine. For this purpose, palm oil biodiesel (POBD), waste cooking biodiesel (WCBD) and animal fat biodiesel (AFBD) are used for examination.

The transesterification process was followed to convert the three raw oils into biodiesels and the experiments are conducted at various loads with fixed 25 rps. Diesel as a reference fuel and three neat biodiesels are tested for emissions and performance. By training the experimental results in an artificial neural network (ANN), the best biodiesel was predicted.

The biodiesels are tested for significant fuel properties with the American Society for testing and materials standards and observed that kinematic viscosity, density and cetane number are recorded higher than diesel fuel. The fatty acid composition (FAC) from chromatography reveals the presence of unsaturated FAC is more in POBD (70.89%) followed by WCBD (57.67%) and AFBD (43.13%). The combustion pressures measured at every degree of crank angle reveal that WCBD and AFBD exhibited on far with diesel fuel. Compared to diesel fuel WCBD and AFBD achieved maximum brake thermal efficiency of 31.99% and 30.93% at 75% load. However, there is a penalty in fuel consumption and NOx emissions from biodiesels. On the other hand, low carbon monoxide, unburnt hydrocarbon emissions and exhaust smoke are reported for biodiesels. Finally, WCBD was chosen as the best choice based on ANN modeling prediction results.

There is no evident literature on these three neat biodiesel applications with the mapping of ANN modeling.

The purpose of this study is to describe the combustion of a magnesium particle falling into a hot oxidizer medium.

The governing equations, including mass, momentum and energy conservation equations, are numerically solved. Afterward, the influences of effective parameters on the temperature distribution and burning time are investigated. Artificial neural network (ANN) is applied to approximate the particle temperature as a function of time, diameter and porosity factor. To obtain the best arrangement of the ANN structure, an optimization process is conducted.

The results show that by considering variations of the particle size, the maximum temperature increases compared to the case in which the particle diameter is constant. Also, the ignition and burning times and the maximum temperature of the moving particle are lower than those of the motionless particle. Optimum network has the best values of regression coefficient and mean relative error whose values are found to be 0.99991 and 1.58 per cent, respectively.

In this study, particle size varies over the combustion process that leads to calculation of particle burning time. In addition, the effects of the motion and porosity of the particle are examined.

The purpose of this paper is to estimate different air–fuel ratio motor shaft speed and fuel flow rates under the performance parameters depending on the indices of combustion efficiency and exhaust emission of the engine, a turboprop multilayer feed forward artificial neural network model. For this purpose, emissions data obtained experimentally from a T56-A-15 turboprop engine under various loads were used.

The designed multilayer feed forward neural network models consist of two hidden layers. 75% of the experimental data used was allocated as training, 25% as test data and cross-referenced by the k-fold four value. Fuel flow, rotate per minute and air–fuel ratio data were used for the training of emission index input values on the designed models and EI_CO, EI_CO2, EI_NO2 and EI_UHC data were used on the output. In the system trained for combustion efficiency, EI_CO and EI_UHC data were used at the input and fuel combustion efficiency data at the output.

Mean square error, normalized mean square error, absolute mean error functions were used to evaluate the error obtained from the system as a result of the test. As a result of modeling the system, absolute mean error values were 0.1473 for CO, 0.0442 for CO₂, 0.0369 for UHC, 0.0028 for NO₂, success for all exhaust emission data was 0.0266 and 7.6165e-10 for combustion efficiency, respectively.

This study has been added to the literature T56-A-15 turboprop engine for the current machine learning methods to multilayer feed forward neural network methods, exhaust emission and combustion efficiency index value calculation.

The study aims to determine the the optimal value of output parameters of a variable compression ratio (CR) diesel engine are investigated at different loads, CR and fuel modes of operation experimentally. The output parameters of a variable compression ratio (CR) diesel engine are investigated at different loads, CR and fuel modes of operation experimentally. The performance parameters like brake thermal efficiency (BTE) and brake specific energy consumption (BSEC), whereas CO emission, HC emission, CO₂ emission, NO_x emission, exhaust gas temperature (EGT) and opacity are the emission parameters measured during the test. Tests are conducted for 2, 6 and 10 kg of load, 16.5 and 17.5 of CR.

In this investigation, the first engine was fueled with 100% diesel and 100% Calophyllum inophyllum oil in single-fuel mode. Then Calophyllum inophyllum oil with producer gas was fed to the engine. Calophyllum inophyllum oil offers lower BTE, CO and HC emissions, opacity and higher EGT, BSEC, CO₂ emission and NO_x emissions compared to diesel fuel in both fuel modes of operation observed. The performance optimization using the Taguchi approach is carried out to determine the optimal input parameters for maximum performance and minimum emissions for the test engine. The optimized value of the input parameters is then fed into the prediction techniques, such as the artificial neural network (ANN).

From multiple response optimization, the minimum emissions of 0.58% of CO, 42% of HC, 191 ppm NO_x and maximum BTE of 21.56% for 16.5 CR, 10 kg load and dual fuel mode of operation are determined. Based on generated errors, the ANN is also ranked for precision. The proposed ANN model provides better prediction with minimum experimental data sets. The values of the R² correlation coefficient are 1, 0.95552, 0.94367 and 0.97789 for training, validation, testing and all, respectively. The said biodiesel may be used as a substitute for conventional diesel fuel.

The blend of Calophyllum inophyllum oil-producer gas is used to run the diesel engine. Performance and emission analysis has been carried out, compared, optimized and validated.

This paper aims to propose an efficient artificial neural network (ANN) model using multi-layer perceptron philosophy to predict the fireside corrosion rate of superheater tubes in coal fire boiler assembly using operational data of an Indian typical thermal power plant.

An efficient gradient-based network training algorithm has been used to minimize the network training errors. The input parameters comprise of coal chemistry, namely, coal ash and sulfur contents, flue gas temperature, SOX concentrations in flue gas, fly ash chemistry (Wt.% Na₂O and K₂O).

Effects of coal ash and sulfur contents, Wt.% of Na₂O and K₂O in fly ash and operating variables such as flue gas temperature and percentage excess air intake for coal combustion on the fireside corrosion behavior of superheater boiler tubes have been computationally investigated and parametric sensitivity analysis has been undertaken.

Quite good agreement between ANN model predictions and the measured values of fireside corrosion rate has been observed which is corroborated by the regression fit between these values.

A significant number of studies have been conducted to analyze and understand the relationship between gas emissions and global temperature using conventional statistical approaches. However, these techniques follow assumptions of probabilistic modeling, where results can be associated with large errors. Furthermore, such traditional techniques cannot be applied to imprecise data. The purpose of this paper is to avoid strict assumptions when studying the complex relationships between variables by using the three innovative, up-to-date, statistical modeling tools: adaptive neuro-fuzzy inference systems (ANFIS), artificial neural networks (ANNs) and fuzzy time series models.

These three approaches enabled us to effectively represent the relationship between global carbon dioxide (CO₂) emissions from the energy sector (oil, gas and coal) and the average global temperature increase. Temperature was used in this study (1900-2012). Investigations were conducted into the predictive power and performance of different fuzzy techniques against conventional methods and among the fuzzy techniques themselves.

A performance comparison of the ANFIS model against conventional techniques showed that the root means square error (RMSE) of ANFIS and conventional techniques were found to be 0.1157 and 0.1915, respectively. On the other hand, the correlation coefficients of ANN and the conventional technique were computed to be 0.93 and 0.69, respectively. Furthermore, the fuzzy-based time series analysis of CO₂ emissions and average global temperature using three fuzzy time series modeling techniques (Singh, Abbasov–Mamedova and NFTS) showed that the RMSE of fuzzy and conventional time series models were 110.51 and 1237.10, respectively.

The paper provides more awareness about fuzzy techniques application in CO₂ emissions studies.

These techniques can be extended to other models to assess the impact of CO₂ emission from other sectors.

Determining the performance parameters of the propulsion systems of the aircraft, which is the key product of the aviation industry, plays a critical role in reducing adverse environmental impacts. Therefore, the purpose of this paper is to present a temperature performance template for turbojet engines at the design stage using a neural network model that defines the relationship between the performance parameters obtained from ground tests of a turbojet engine used in unmanned aerial vehicles (UAV).

The main parameters of the flow passing through the engine of the UAV propulsion system, where ground tests were performed, were obtained through the data acquisition system and injected into a neural network model created. Fifteen sensors were mounted on the engine – six temperature sensors, six pressure sensors, two flow meters and one load cell were connected to the data acquisition system to make sense of this physical environment. Subsequently, the artificial neural network (ANN) model as a complement to the approach was used. Thus, the predicted model relationship with the experimental data was created.

Fuel flow and thrust parameters were estimated using these components as inputs in the feed-forward neural network. In the network experiments to estimate fuel flow parameter, r-square and mean absolute error were calculated as 0.994 and 0.02, respectively. Similarly, for thrust parameter, these metrics were calculated as 0.994 and 1.42, respectively. In addition, the correlation between fuel flow, thrust parameters and each input parameters was examined. According to this, air compressor inlet (AC_inlet,temp) and outlet (AC_outlet,temp) temperatures and combustion chamber (CC_inlet,temp, CC_outlet,temp) temperature parameters were determined to affect the output the most. The proposed ANN model is applicable to any turbojet engines to model its behavior.

Today, deep neural networks are the driving force of artificial intelligence studies. In this study, the behavior of a UAV is modeled with neural networks. Neural networks are used here as a regressor. A neural network model has been developed that predicts fuel flow and thrust parameters using the real parameters of a UAV turbojet engine. As a result, satisfactory findings were obtained. In this regard, fuel flow and thrust values of any turbojet engine can be estimated using the neural network hyperparameters proposed in this study. Python codes of the study can be accessed from https://github.com/tekinonlayn/turbojet.

The originality of the study is that it reports the relationships between turbojet engine performance parameters obtained from ground tests using the neural network application with open source Python code. Thus, small-scale unmanned aerial propulsion system provides designers with a template showing the relationship between engine performance parameters.

The purpose of this paper is to present a three‐dimensional CFD model that simulates the pyrolysis, combustion and heat transfer phenomena in a refuse‐derived fuel (RDF) gasifier. Correlations between different operation conditions and the waste stack morphology are also investigated. Parametric studies are conducted to optimize operating conditions to achieve an even stack surface minimal the local oxidation in the waste stack.

This paper proposes a Lagrangian pyrolysis submodel which can be applied to determine the local pyrolysis rate and porosity field by introducing the local characteristic diameter of the waste solid sphere. The flow field is described by a single‐phase porous flow model using the SIMPLE algorithm with momentum extrapolation. A one‐step global reaction was adapted for the chemical reactions inside the gasifier.

Computational results produced three‐dimensional distribution of the flow field, temperature, species concentration, porosity and the morphology of the waste stack under different operation conditions. Some parametric studies were conducted to assess the effects of the inlet temperature and the feeding rate on the waste stack shape. The results demonstrated that the model can properly capture the essential physical and chemical processes in the gasifier and thus can be used as a predictive simulation tool.

Due to the lack of accurate reaction rate information, the computational results have not been directly compared against experimental data. Additional refinement and subsequent validation against prototype gasifier experiment will be reported in future work.

A full three‐dimensional computational model is developed for the complex two‐phase flow based on porous medium representation of the solid stack. A Lagrangian pyrolysis model based on the characteristic diameter of the solid waste material was proposed to describe the pyrolysis rate history. The developed model reproduces correct physical and chemical behavior inside gasifier with adequate computational efficiency and accuracy.

The present work aims at improving the performance of the engine using optimized fuel injection strategies and operating parameters for plastic oil ethanol blends. To optimize and predict the engine injection and operational parameters, response surface methodology (RSM) and artificial neural networks (ANN) are used respectively.

The engine operating parameters such as load, compression ratio, injection timing and the injection pressure are taken as inputs whereas brake thermal efficiency (BTHE), brake-specific fuel consumption (BSFC), carbon monoxide (CO), hydrocarbons (HC), oxides of nitrogen (NO_x) and smoke emissions are treated as outputs. The experiments are designed according to the design of experiments, and optimization is carried out to find the optimum operational and injection parameters for plastic oil ethanol blends in the engine.

Optimum operational parameters of the engine when fuelled with plastic oil and ethanol blends are obtained at 8 kg of load, injection pressure of 257 bar, injection timing of 17° before top dead center and blend of 15%. The engine performance parameters obtained at optimum engine running conditions are BTHE 32.5%, BSFC 0.24 kg/kW.h, CO 0.057%, HC 10 ppm, NO_x 324.13 ppm and smoke 79.1%. The values predicted from ANN are found to be more close to experimental values when compared with the values of RSM.

In the present work, a comparative analysis is carried out on the prediction capabilities of ANN and RSM for variable compression ratio engine fuelled with ethanol blends of plastic oil. The error of prediction for ANN is less than 5% for all the responses such as BTHE, BSFC, CO and NO_x except for HC emission which is 12.8%.

This paper gives a bibliographical review of the finite element methods (FEMs) applied to the analysis of ceramics and glass materials. The bibliography at the end of the paper contains references to papers, conference proceedings and theses/dissertations on the subject that were published between 1977‐1998. The following topics are included: ceramics – material and mechanical properties in general, ceramic coatings and joining problems, ceramic composites, ferrites, piezoceramics, ceramic tools and machining, material processing simulations, fracture mechanics and damage, applications of ceramic/composites in engineering; glass – material and mechanical properties in general, glass fiber composites, material processing simulations, fracture mechanics and damage, and applications of glasses in engineering.