Search results for: flame regression

Authors: Sambit Supriya Dash, Rahul Ravi R, Vikram Ramanan, Vinayak Malhotra

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Combustion in itself is a complex phenomenon that involves the interaction and interplay of multiple phenomena, the combined effect of which gives rise to the common flame that we see and use in our daily life applications from cooking to propelling our vehicles to space. The most important thing that goes unnoticed about these flames is the effect of the various phenomena from its surrounding environment that affects its behavior and properties. These phenomena cause a variety of energy interactions that lead to various types of energy transformations which in turn affect the flame behavior. This paper focuses on experimentally investigating the effect of one such phenomenon, which is the acoustics or sound energy on diffusion flames. The subject in itself is extensively studied upon as thermo-acoustics globally, whereas the current work focuses on studying its effect on soot formation on diffusion flames. The said effect is studied in this research work by the use of a butane as fuel, fitted with a nozzle that houses 3 arrays consisting of 4 holes each that are placed equidistant to each other and the resulting flame impinged with sound from two independent and similar sound sources that are placed equidistant from the centre of the flame. The entire process is systematically video graphed using a 60 fps regular CCD and analysed for variation in flame heights and flickering frequencies where the fuel mass flow rate is maintained constant and the configuration of entrainment holes and frequency of sound are varied, whilst maintaining constant ambient atmospheric conditions. The current work establishes significant outcomes on the effect of acoustics on soot formation; it is noteworthy that soot formation is the main cause of pollution and a major cause of inefficiency of current propulsion systems. This work is one of its kinds, and its outcomes are widely applicable to commercial and domestic appliances that utilize combustion for energy generation or propulsion and help us understand them better, so that we can increase their efficiency and decrease pollution.

Keywords: thermoacoustics, entrainment, propulsion system, efficiency, pollution

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Authors: Abhijit Pattnayak, Abhijith N.V, Deepak Kumar, Jayant Jain, Vijay Chaudhry

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Hard and dense ceramic coatings deposited on the surface provide the ideal solution to the poor tribological properties exhibited by some popular stainless steels like EN-36, 17-4PH, etc. These steels are widely used in nuclear, fertilizer, food processing, and marine industries under extreme environmental conditions. The present study focuses on the development of Al₂O₃-CeO₂-rGO-based coatings on the surface of 17-4PH steel using High-Velocity Oxygen Flame (HVOF) thermal spray process. The coating is developed using an oxyacetylene flame. Further, we report the physical (Density, Surface roughness, Surface energetics), Metallurgical (Scanning electron microscopy, X-ray diffraction, Raman), Mechanical (Hardness(Vickers and Nano Hard-ness)), Tribological (Wear, Scratch hardness) and Chemical (corrosion) characterization of both As-sprayed coating and the Substrate (17-4 PH steel). The comparison of the properties will help us to understand the microstructure-property relationship of the coating and reveal the necessity and challenges of such coatings.

Keywords: thermal spray process, HVOF, ceramic coating, hardness, wear, corrosion

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Authors: Masahiro Ohmura, Koh Kakusho, Takeshi Okadome

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This paper presents a regression model with autocorrelated errors in which the inputs are social moods obtained by analyzing the adjectives in Twitter posts using a document topic model. The regression model predicts Dow Jones Industrial Average (DJIA) more precisely than autoregressive moving-average models.

Keywords: stock market prediction, social moods, regression model, DJIA

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Authors: Norhaslina Mat Zian, Hasril Hasini, Nur Irmawati Om

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This study focuses on the flow and combustion behavior inside gas turbine combustor used in thermal power plant. The combustion process takes place using synthetic gas and the baseline solution was made on gas turbine combustor firing natural gas (100% Methane) as the main source of fuel. Attention is given to the effect of the H2/CO ratio on the variation of the flame profile, temperature distribution, and emissions. The H2/CO ratio varies in the range of 10-80 % and the CH4 values are fixed 10% for each case. While keeping constant the mass flow rate and operating pressure, the preliminary result shows that the flow inside the can-combustor is highly swirling which indicates good mixing of fuel and air prior to the entrance of the mixture to the main combustion zone.

Keywords: cfd, combustion, flame, syngas

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Authors: Shiwei Deng, Yang Bao

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In this paper, we present a model-based regression test suite reducing approach that uses EFSM model dependence analysis and probability-driven greedy algorithm to reduce software regression test suites. The approach automatically identifies the difference between the original model and the modified model as a set of elementary model modifications. The EFSM dependence analysis is performed for each elementary modification to reduce the regression test suite, and then the probability-driven greedy algorithm is adopted to select the minimum set of test cases from the reduced regression test suite that cover all interaction patterns. Our initial experience shows that the approach may significantly reduce the size of regression test suites.

Keywords: dependence analysis, EFSM model, greedy algorithm, regression test

Procedia PDF Downloads 398

Authors: Suparman

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Piecewise polynomial regression model is very flexible model for modeling the data. If the piecewise polynomial regression model is matched against the data, its parameters are not generally known. This paper studies the parameter estimation problem of piecewise polynomial regression model. The method which is used to estimate the parameters of the piecewise polynomial regression model is Bayesian method. Unfortunately, the Bayes estimator cannot be found analytically. Reversible jump MCMC algorithm is proposed to solve this problem. Reversible jump MCMC algorithm generates the Markov chain that converges to the limit distribution of the posterior distribution of piecewise polynomial regression model parameter. The resulting Markov chain is used to calculate the Bayes estimator for the parameters of piecewise polynomial regression model.

Keywords: piecewise regression, bayesian, reversible jump MCMC, segmentation

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Authors: A. M. Tahsini

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Computational study of two dimensional supersonic reacting hydrogen-air flows is performed to investigate the nitrogen effects on ignition delay time for premixed and diffusion flames. Chemical reaction is treated using detail kinetics and the advection upstream splitting method is used to calculate the numerical inviscid fluxes. The results show that only in the stoichiometric condition for both premixed and diffusion flames, there is monotone dependency of the ignition delay time to the nitrogen addition. In other situations, the optimal condition from ignition viewpoint should be found using numerical investigations.

Keywords: diffusion flame, ignition delay time, mixing layer, numerical simulation, premixed flame, supersonic flow

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Authors: M. Zake, I. Barmina, R. Valdmanis, A. Kolmickovs

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Experimental investigations of the DC electric field effect on thermal decomposition of biomass, formation of the axial flow of volatiles (CO, H2, CxHy), mixing of volatiles with swirling airflow at low swirl intensity (S ≈ 0.2-0.35), their ignition and on formation of combustion dynamics are carried out with the aim to understand the mechanism of electric field influence on biomass gasification, combustion of volatiles and heat energy production. The DC electric field effect on combustion dynamics was studied by varying the positive bias voltage of the central electrode from 0.6 kV to 3 kV, whereas the ion current was limited to 2 mA. The results of experimental investigations confirm the field-enhanced biomass gasification with enhanced release of volatiles and the development of endothermic processes at the primary stage of thermochemical conversion of biomass determining the field-enhanced heat energy consumption with the correlating decrease of the flame temperature and heat energy production at this stage of flame formation. Further, the field-enhanced radial expansion of the flame reaction zone correlates with a more complete combustion of volatiles increasing the combustion efficiency by 3 % and decreasing the mass fraction of CO, H2 and CxHy in the products, whereas by 10 % increases the average volume fraction of CO2 and the heat energy production downstream the combustor increases by 5-10 %

Keywords: biomass, combustion, electrodynamic control, gasification

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Authors: Shih-Pin Chen, Shih-Syuan You

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Fuzzy regression models are useful for investigating the relationship between explanatory variables and responses in fuzzy environments. To overcome the deficiencies of previous models and increase the explanatory power of fuzzy data, the graded mean integration (GMI) representation is applied to determine representative crisp regression coefficients. A fuzzy regression model is constructed based on the modified dissemblance index (MDI), which can precisely measure the actual total error. Compared with previous studies based on the proposed MDI and distance criterion, the results from commonly used test examples show that the proposed fuzzy linear regression model has higher explanatory power and forecasting accuracy.

Keywords: dissemblance index, fuzzy linear regression, graded mean integration, mathematical programming

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Authors: Jan Henrik Wosnitza

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The logistic regression has developed into a standard approach for estimating conditional probabilities in a wide range of applications including credit risk prediction. The article at hand contributes to the current literature on logistic regression fourfold: First, it is demonstrated that the binary logistic regression automatically meets its model assumptions under very general conditions. This result explains, at least in part, the logistic regression's popularity. Second, the requirement of homoscedasticity in the context of binary logistic regression is theoretically substantiated. The variances among the groups of defaulted and non-defaulted obligors have to be the same across the level of the aggregated default indicators in order to achieve linear logits. Third, this article sheds some light on the question why nonlinear logits might be superior to linear logits in case of a small amount of data. Fourth, an innovative methodology for estimating correlations between obligor-specific log-odds is proposed. In order to crystallize the key ideas, this paper focuses on the example of credit risk prediction. However, the results presented in this paper can easily be transferred to any other field of application.

Keywords: correlation, credit risk estimation, default correlation, homoscedasticity, logistic regression, nonlinear logistic regression

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Authors: Geetal Mahajan, R. V. Adivarekar

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In this research, an attempt has been made to develop integrated finish on textile fabrics. The demand for mosquito repellent, flame retardant, and water repellent finished fabric has increased. Integrated finishing was done using commercially available products. These finishing agents were first assessed individually for their functional properties and then used in combination with other agents. Dip-air dry and pad-dry-cure (PDC) were two different methods used for fabric finishing. The finished fabric was assessed using spray test, limiting oxygen index and mosquito repellence test. Integrated finished fabric is in great demand by the customers as it increases the aesthetic as well as the functional properties of the fabric with added benefit of water and energy conservation.

Keywords: flame retardant, integrated finishing, mosquito repellent, textiles, water repellent

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Authors: Kaoutar Chbihi, Aziza Menouni, Emilie Hardy, Matteo Creta, Nathalie Grova, An Van Nieuwenhuyse, Lode Godderis, Samir El Jaafari, Radu-Corneliu Duca

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Industrial products and materials are often treated with additional compounds like brominated flame retardants (BFRs) and heavy metals in order to prevent their ignition, increase their functionality and improve their performance like electrical conductivity. Consequently, this could potentially expose children to harmful chemicals through indoor dust and through hand-to-mouth or toy-chewing behaviors. The aim of this study was to assess the exposure of Moroccan children aged 5-11 years to BFRs and heavy metal elements and investigate their impacts on the epigenetic profile, namely through global DNA methylation modifications. First, parents were asked to answer a questionnaire on children’s lifestyle, then blood and urine samples were collected from (n= 93) children, following the ethical guidelines, for biomonitoring and DNA methylation analysis, using a set of solid phase extraction (SPE), LC-MS/MS, GC-MS/MS and ICP/MS techniques. BFRs were detected in 54.84% of samples with a median concentration of 0.01 nmol/mL (range: 0.004-0.051 nmol/mL), while metal elements were detected in more than 90% of samples. No association was found between BFRs and global DNA methylation, unlike metal element levels that showed significant variations with global DNA methylation biomarkers, namely 5-mdC, 5-OH-mdC and N⁶-mA levels. To conclude, Moroccan children could be significantly exposed to flame retardant compounds and heavy metal elements through several routes, such as dust or equipment usage and are therefore susceptible to the adverse health effects that could be linked with such chemicals. Further research is required to assess the exposure to environmental pollutants among the Moroccan population in order to protect Moroccan health and prevent the incidence of diseases.

Keywords: biomonitoring, children, DNA methylation, epigenetics, flame retardants, heavy metals, Morocco

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Authors: Zhou Jianhong

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Model averaging is a desirable approach to deal with model uncertainty, which, however, has rarely been explored for Poisson regression. In this paper, we propose a model averaging procedure based on an unbiased estimator of the expected Kullback-Leibler distance for the Poisson regression. Simulation study shows that the proposed model average estimator outperforms some other commonly used model selection and model average estimators in some situations. Our proposed methods are further applied to a real data example and the advantage of this method is demonstrated again.

Keywords: model averaging, poission regression, Kullback-Leibler distance, statistics

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Authors: L. Q. Yuan, J. Yang, A. Siddiqui

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A new regression uncertainty analysis methodology was applied to determine the uncertainties of the critical heat flux (CHF) power correlation for an advanced 43-element bundle design, which was developed by Canadian Nuclear Laboratories (CNL) to achieve improved economics, resource utilization and energy sustainability. The new methodology is considered more appropriate than the traditional methodology in the assessment of the experimental uncertainty associated with regressions. The methodology was first assessed using both the Monte Carlo Method (MCM) and the Taylor Series Method (TSM) for a simple linear regression model, and then extended successfully to a non-linear CHF power regression model (CHF power as a function of inlet temperature, outlet pressure and mass flow rate). The regression uncertainty assessed by MCM agrees well with that by TSM. An equation to evaluate the CHF power regression uncertainty was developed and expressed as a function of independent variables that determine the CHF power.

Keywords: CHF experiment, CHF correlation, regression uncertainty, Monte Carlo Method, Taylor Series Method

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Authors: A. El-S. Makled, M. K. Al-Tamimi

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A mathematical model to predict the performance parameters (thrusts, chamber pressures, fuel mass flow rates, mixture ratios, and regression rates during firing time) of hybrid rocket motor (HRM) is evaluated. The internal ballistic (IB) hybrid combustion model assumes that the solid fuel surface regression rate is controlled only by heat transfer (convective and radiative) from flame zone to solid fuel burning surface. A laboratory HRM is designed, manufactured, and tested for low thrust profile space missions (10-15 N) and for validating the mathematical model (computer program). The polymer material and gaseous oxidizer which are selected for this experimental work are polymethyle-methacrylate (PMMA) and polyethylene (PE) as solid fuel grain and gaseous oxygen (GO₂) as oxidizer. The variation of various operational parameters with time is determined systematically and experimentally in firing of up to 20 seconds, and an average combustion efficiency of 95% of theory is achieved, which was the goal of these experiments. The comparison between recording fire data and predicting analytical parameters shows good agreement with the error that does not exceed 4.5% during all firing time. The current mathematical (computer) code can be used as a powerful tool for HRM analytical design parameters.

Keywords: hybrid combustion, internal ballistics, hybrid rocket motor, performance parameters

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Authors: Jude Opara, Esemokumo Perewarebo Akpos

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This paper is on non-parametric linear regression over its parametric counterparts with large sample size. Data set on anthropometric measurement of primary school pupils was taken for the analysis. The study used 50 randomly selected pupils for the study. The set of data was subjected to normality test, and it was discovered that the residuals are not normally distributed (i.e. they do not follow a Gaussian distribution) for the commonly used least squares regression method for fitting an equation into a set of (x,y)-data points using the Anderson-Darling technique. The algorithms for the nonparametric Theil’s regression are stated in this paper as well as its parametric OLS counterpart. The use of a programming language software known as “R Development” was used in this paper. From the analysis, the result showed that there exists a significant relationship between the response and the explanatory variable for both the parametric and non-parametric regression. To know the efficiency of one method over the other, the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) are used, and it is discovered that the nonparametric regression performs better than its parametric regression counterparts due to their lower values in both the AIC and BIC. The study however recommends that future researchers should study a similar work by examining the presence of outliers in the data set, and probably expunge it if detected and re-analyze to compare results.

Keywords: Theil’s regression, Bayesian information criterion, Akaike information criterion, OLS

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Authors: Svetlana Petlitckaia, Toussaint Barboni, Paul-Antoine Santoni

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Plastic is a revolutionary material. However, the pollution caused by plastics damages the environment, human health and the economy of different countries. It is important to find new ways to recycle and reuse plastic material. The use of waste materials as filler and as a matrix for composite materials is receiving increasing attention as an approach to increasing the economic value of streams. In this study, a new composite material based on high-density polyethylene (HDPE) and polypropylene (PP) wastes from bottle caps and cork powder from unused cork (virgin cork), which has a high capacity for thermal insulation, was developed. The composites were prepared with virgin and modified cork. The composite materials were obtained through twin-screw extrusion and injection molding. The composites were produced with proportions of 0 %, 5 %, 10 %, 15 %, and 20 % of cork powder in a polymer matrix with and without coupling agent and flame retardant. These composites were investigated in terms of mechanical, structural and thermal properties. The effect of cork fraction, particle size and the use of flame retardant on the properties of composites were investigated. The properties of samples elaborated with the polymer and the cork were compared to them with the coupling agent and commercial flame retardant. It was observed that the morphology of HDPE/cork and PP/cork composites revealed good distribution and dispersion of cork particles without agglomeration. The results showed that the addition of cork powder in the polymer matrix reduced the density of the composites. However, the incorporation of natural additives doesn’t have a significant effect on water adsorption. Regarding the mechanical properties, the value of tensile strength decreases with the addition of cork powder, ranging from 30 MPa to 19 MPa for PP composites and from 19 MPa to 17 MPa for HDPE composites. The value of thermal conductivity of composites HDPE/cork and PP/ cork is about 0.230 W/mK and 0.170 W/mK, respectively. Evaluation of the flammability of the composites was performed using a cone calorimeter. The results of thermal analysis and fire tests show that it is important to add flame retardants to improve fire resistance. The samples elaborated with the coupling agent and flame retardant have better mechanical properties and fire resistance. The feasibility of the composites based on cork and PP and HDPE wastes opens new ways of valorizing plastic waste and virgin cork. The formulation of composite materials must be optimized.

Keywords: composite materials, cork and polymer wastes, flammability, modificated cork

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Authors: E. Fereydouni, Laleh Maleknia , M. E. Olya

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The present research, nylon fabric dyed by pressure method with nano-Si, nano-Ti particles and SiO2-TiO2 nancomposite. The influence of the amount of Si, Ti and SiO2-TiO2 on the performance of nylon fabric was investigated by the use of Fourier transform infrared spectrophotometer (FTIR), horizontal flammability apparatus (HFA), scanning electron microscope (SEM), electron dispersive X-ray spectroscope (EDX), water contact angle tester (WCA) and CIE LAB colorimetric system. The possible interactions between particles and nylon fiber were elucidated by the FTIR spectroscopy. Results indicated that the stabilized nanoparticles and nanocomposite enhances flame retardancy of nylon fabrics. Also, the prominet features of nanoparticles and nanocomposite treatment can note increase of adsorption and fixation of dye.

Keywords: nano-Si, nano- Ti, SiO2-TiO2 nancomposite, nylon fabric, flame retardant nylon

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Authors: Denys Osipenko, Jonathan Crook

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Because of the variety of the card holders’ behaviour types and income sources each consumer account can be transferred to a variety of states. Each consumer account can be inactive, transactor, revolver, delinquent, defaulted and requires an individual model for the income prediction. The estimation of transition probabilities between statuses at the account level helps to avoid the memorylessness of the Markov Chains approach. This paper investigates the transition probabilities estimation approaches to credit cards income prediction at the account level. The key question of empirical research is which approach gives more accurate results: multinomial logistic regression or multistage conditional logistic regression with binary target. Both models have shown moderate predictive power. Prediction accuracy for conditional logistic regression depends on the order of stages for the conditional binary logistic regression. On the other hand, multinomial logistic regression is easier for usage and gives integrate estimations for all states without priorities. Thus further investigations can be concentrated on alternative modeling approaches such as discrete choice models.

Keywords: multinomial regression, conditional logistic regression, credit account state, transition probability

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Authors: Ksenija Dumičić, Anita Čeh Časni, Irena Palić

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This paper examines economic and Information and Communication Technology (ICT) development influence on recently increasing Internet purchases by individuals for European Union member states. After a growing trend for Internet purchases in EU27 was noticed, all possible regression analysis was applied using nine independent variables in 2011. Finally, two linear regression models were studied in detail. Conducted simple linear regression analysis confirmed the research hypothesis that the Internet purchases in analysed EU countries is positively correlated with statistically significant variable Gross Domestic Product per capita (GDPpc). Also, analysed multiple linear regression model with four regressors, showing ICT development level, indicates that ICT development is crucial for explaining the Internet purchases by individuals, confirming the research hypothesis.

Keywords: European union, Internet purchases, multiple linear regression model, outlier

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Authors: Hyeuk Ju Ko, Eui Ju Lee

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Many electronic devices are powered by various rechargeable batteries such as lithium-ion today, but occasionally the batteries undergo thermal runaway and cause fire, explosion, and other hazards. If a battery fire should occur in an electronic device of vehicle and aircraft cabin, it is important to quickly extinguish the fire and cool the batteries to minimize safety risks. Attempts to minimize these risks have been carried out by many researchers but the number of study on the successful extinguishment is limited. Because most rechargeable batteries are operated on the ion state with electron during charge and discharge of electricity, and the reaction of this electrolyte has a big difference with normal combustion. Here, we focused on the effect of ions on reaction stability and pollutant emissions during combustion process. The other importance for understanding ionized fuel combustion could be found in high efficient and environment-friendly combustion technologies, which are used to be operated an extreme condition and hence results in unintended flame instability such as extinction and oscillation. The use of electromagnetic energy and non-equilibrium plasma is one of the way to solve the problems, but the application has been still limited because of lack of excited ion effects in the combustion process. Therefore, the understanding of ion role during combustion might be promised to the energy safety society including the battery safety. In this study, the effects of an ionized fuel on the flame stability and pollutant emissions were experimentally investigated in the hydrocarbon jet diffusion flames. The burner used in this experiment consisted of 7.5 mm diameter tube for fuel and the gaseous fuels were ionized with the ionizer (SUNJE, SPN-11). Methane (99.9% purity) and propane (commercial grade) were used as a fuel and open ambient air was used as an oxidizer. As the performance of ionizer used in the experiment was evaluated at first, ion densities of both propane and methane increased linearly with volume flow rate but the ion density of propane is slightly higher than that of methane. The results show that the overall flame stability and shape such as flame length has no significant difference even in the higher ion concentration. However, the fuel ionization affects to the pollutant emissions such as NOx and soot. NOx and CO emissions measured in post flame region decreased with increasing fuel ionization, especially at high fuel velocity, i.e. high ion density. TGA analysis and morphology of soot by TEM indicates that the fuel ionization makes soot to be matured.

Keywords: battery fires, ionization, jet flames, stability, NOx and soot

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Authors: Galal Elkobrosy, Amr M. Abdelrazek, Bassuny M. Elsouhily, Mohamed E. Khidr

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Crank shaft length, connecting rod length, crank angle, engine rpm, cylinder bore, mass of piston and compression ratio are the inputs that can control the performance of the slider crank mechanism and then its efficiency. Several combinations of these seven inputs are used and compared. The throughput engine torque predicted by the simulation is analyzed through two different regression models, with and without interaction terms, developed according to multi-linear regression using LU decomposition to solve system of algebraic equations. These models are validated. A regression model in seven inputs including their interaction terms lowered the polynomial degree from 3^rd degree to 1^st degree and suggested valid predictions and stable explanations.

Keywords: design of experiments, regression analysis, SI engine, statistical modeling

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Authors: Arindam Chaudhuri

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The research presents epsilon- hierarchical fuzzy twin support vector regression (epsilon-HFTSVR) based on epsilon-fuzzy twin support vector regression (epsilon-FTSVR) and epsilon-twin support vector regression (epsilon-TSVR). Epsilon-FTSVR is achieved by incorporating trapezoidal fuzzy numbers to epsilon-TSVR which takes care of uncertainty existing in forecasting problems. Epsilon-FTSVR determines a pair of epsilon-insensitive proximal functions by solving two related quadratic programming problems. The structural risk minimization principle is implemented by introducing regularization term in primal problems of epsilon-FTSVR. This yields dual stable positive definite problems which improves regression performance. Epsilon-FTSVR is then reformulated as epsilon-HFTSVR consisting of a set of hierarchical layers each containing epsilon-FTSVR. Experimental results on both synthetic and real datasets reveal that epsilon-HFTSVR has remarkable generalization performance with minimum training time.

Keywords: regression, epsilon-TSVR, epsilon-FTSVR, epsilon-HFTSVR

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Authors: Dharminder Singh, Sanjeev Yadav, Pravakar Mohanty

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In this work, study of temperature profile in a pilot scale air bubbling fluidized bed (ABFB) gasifier for rice husk gasification was carried out. Effects of different factors such as multiple cyclones, gas cooling system, ventilate gas pipe length, and catalyst on temperature profile was examined. ABFB gasifier used in this study had two sections, one is bed section and the other is freeboard section. River sand was used as bed material with air as gasification agent, and conventional charcoal as start-up heating medium in this gasifier. Temperature of different point in both sections of ABFB gasifier was recorded at different ER value and ER value was changed by changing the feed rate of biomass (rice husk) and by keeping the air flow rate constant for long durational of gasifier operation. ABFB with double cyclone with gas coolant system and with short length ventilate gas pipe was found out to be optimal gasifier design to give temperature profile required for high gasification performance in long duration operation. This optimal design was tested with different ER values and it was found that ER of 0.33 was most favourable for long duration operation (8 hr continuous operation), giving highest carbon conversion efficiency. At optimal ER of 0.33, bed temperature was found to be stable at 700 °C, above bed temperature was found to be at 628.63 °C, bottom of freeboard temperature was found to be at 600 °C, top of freeboard temperature was found to be at 517.5 °C, gas temperature was found to be at 195 °C, and flame temperature was found to be 676 °C. Temperature at all the points showed fluctuations of 10 – 20 °C. Effect of catalyst i.e. dolomite (20% with sand bed) was also examined on temperature profile, and it was found that at optimal ER of 0.33, the bed temperature got increased to 795 °C, above bed temperature got decreased to 523 °C, bottom of freeboard temperature got decreased to 548 °C, top of freeboard got decreased to 475 °C, gas temperature got decreased to 220 °C, and flame temperature got increased to 703 °C. Increase in bed temperature leads to higher flame temperature due to presence of more hydrocarbons generated from more tar cracking at higher temperature. It was also found that the use of dolomite with sand bed eliminated the agglomeration in the reactor at such high bed temperature (795 °C).

Keywords: air bubbling fluidized bed gasifier, bed temperature, charcoal heating, dolomite, flame temperature, rice husk

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Authors: Anupriya, Bikramjit Sinfh, Radhay Shyam

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In order to improve mixing phenomena and combustion processes in supersonic flow, the current work has concentrated on identifying the ideal cavity parameters using CFD ANSYS Fluent. Offset ratios (OR) and aft ramp angles () have been manipulated in simulations of several models, but the length-to-depth ratio has remained the same. The length-to-depth ratio of all cavity flows is less than 10, making them all open. Hydrogen fuel was injected into a supersonic air flow with a Mach number of 3.75 using a chamber with a 1 mm diameter and a transverse slot nozzle. The free stream had conditions of a pressure of 1.2 MPa, a temperature of 299K, and a Reynolds number of 2.07x107. This method has the ability to retain a flame since the cavity facilitates rapid mixing of fuel and oxidizer and decreases total pressure losses. The impact of the cavity on combustion efficiency and total pressure loss is discussed, and the results are compared to those of a model without a cavity. Both the mixing qualities and the combustion processes were enhanced in the model with the cavity. The overall pressure loss as well as the effectiveness of the combustion process both increase with the increase in the ramp angle to the rear. When OR is increased, however, resistance to the supersonic flow field is reduced, which has a detrimental effect on both parameters. For a given ramp height, larger pressure losses were observed at steeper ramp angles due to increased eddy-viscous turbulent flow and increased wall drag.

Keywords: total pressure loss, flame holder, supersonic combustion, combustion efficiency, cavity, nozzle

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Authors: Kornelius Ronald Demu, Dewi Retno Sari Saputro, Purnami Widyaningsih

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Human Development Index (HDI) is a standard measurement for a country's human development. Several factors may have influenced it, such as life expectancy, gross domestic product (GDP) based on the province's annual expenditure, the number of poor people, and the percentage of an illiterate people. The scatter plot between HDI and the influenced factors show that the plot does not follow a specific pattern or form. Therefore, the HDI's data in Indonesia can be applied with a nonparametric regression model. The estimation of the regression curve in the nonparametric regression model is flexible because it follows the shape of the data pattern. One of the nonparametric regression's method is a truncated spline. Truncated spline regression is one of the nonparametric approach, which is a modification of the segmented polynomial functions. The estimator of a truncated spline regression model was affected by the selection of the optimal knots point. Knot points is a focus point of spline truncated functions. The optimal knots point was determined by the minimum value of generalized cross validation (GCV). In this article were applied the data of Human Development Index with a truncated spline nonparametric regression model. The results of this research were obtained the best-truncated spline regression model to the HDI's data in Indonesia with the combination of optimal knots point 5-5-5-4. Life expectancy and the percentage of an illiterate people were the significant factors depend to the HDI in Indonesia. The coefficient of determination is 94.54%. This means the regression model is good enough to applied on the data of HDI in Indonesia.

Keywords: generalized cross validation (GCV), Human Development Index (HDI), knots point, nonparametric regression, truncated spline

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Authors: James Purnama, Riri Fitri Sari

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We investigate the machine learning algorithm selection problem in the term of a depth image based eye gaze estimation, with respect to its essential difficulty in reducing the number of required training samples and duration time of training. Statistics based prediction accuracy are increasingly used to assess and evaluate prediction or estimation in gaze estimation. This article evaluates Root Mean Squared Error (RMSE) and R-Squared statistical analysis to assess machine learning methods on depth camera data for gaze estimation. There are 4 machines learning methods have been evaluated: Random Forest Regression, Regression Tree, Support Vector Machine (SVM), and Linear Regression. The experiment results show that the Random Forest Regression has the lowest RMSE and the highest R-Squared, which means that it is the best among other methods.

Keywords: gaze estimation, gaze tracking, eye tracking, kinect, regression model, orange python

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Authors: Fujio Akagi, Hiroaki Ito, Shin-Ichi Inage

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The aim of this study is to develop a combustion model that can be applied uniformly to laminar and turbulent premixed flames while considering the effect of the Lewis number (Le). The model considers the effect of Le on the transport equations of the reaction progress, which varies with the chemical species and temperature. The distribution of the reaction progress variable is approximated by a hyperbolic tangent function, while the other distribution of the reaction progress variable is estimated using the approximated distribution and transport equation of the reaction progress variable considering the Le. The validity of the model was evaluated under the conditions of propane with Le > 1 and methane with Le = 1 (equivalence ratios of 0.5 and 1). The estimated results were found to be in good agreement with those of previous studies under all conditions. A method of introducing a turbulence model into this model is also described. It was confirmed that conventional turbulence models can be expressed as an approximate theory of this model in a unified manner.

Keywords: combustion model, laminar flame, Lewis number, turbulent flame

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Authors: Retius Chifurira

Abstract:

Logistic regression model is the most used regression model to predict meteorological drought probabilities. When the dependent variable is extreme, the logistic model fails to adequately capture drought probabilities. In order to adequately predict drought probabilities, we use the generalized linear model (GLM) with the quantile function of the generalized extreme value distribution (GEVD) as the link function. The method maximum likelihood estimation is used to estimate the parameters of the generalized extreme value (GEV) regression model. We compare the performance of the logistic and the GEV regression models in predicting drought probabilities for Zimbabwe. The performance of the regression models are assessed using the goodness-of-fit tests, namely; relative root mean square error (RRMSE) and relative mean absolute error (RMAE). Results show that the GEV regression model performs better than the logistic model, thereby providing a good alternative candidate for predicting drought probabilities. This paper provides the first application of GLM derived from extreme value theory to predict drought probabilities for a drought-prone country such as Zimbabwe.

Keywords: generalized extreme value distribution, general linear model, mean annual rainfall, meteorological drought probabilities

Procedia PDF Downloads 159

Authors: D. S. Vardar, A. S. Kipcak, F. T. Senberber, E. M. Derun, S. Piskin, N. Tugrul

Abstract:

Zinc borate is an important inorganic hydrate borate material, which can be use as a flame retardant agent and corrosion resistance material. This compound can loss its structural water content at higher than 290°C. Due to thermal stability; Zinc Borate can be used as flame reterdant at high temperature process of plastic and gum. In this study, the ultrasonic reaction of zinc borates were studied using hydrozincite (Zn5(CO3)2•(OH)6) and boric acid (H3BO3) raw materials. Before the synthesis raw materials were characterized by X-Ray Diffraction (XRD) and Fourier Transform Infrared Spectroscopy (FT-IR). Ultrasonic method is a new application on the zinc borate synthesis. The synthesis parameters were set to 90°C reaction temperature and 55 minutes of reaction time, with 1:1, 1:2, 1:3, 1:4 and 1:5 molar ratio of starting materials (Zn5(CO3)2•(OH)6 : H3BO3). After the zinc borate synthesis, the products analyzed by XRD and FT-IR. As a result, optimum molar ratio of 1:5 (Zn5(CO3)2•(OH)6:H3BO3) is determined for the synthesis of zinc borates with ultrasonic method.

Keywords: borate, ultrasonic method, zinc borate, zinc borate synthesis

Procedia PDF Downloads 364