Direct determination of Al, As, Cu, Fe, Mn, and Ni in fuel ethanol by simultaneous GFAAS using integrated platforms pretreated with W-Rh permanent modifier together with Pd plus Mg modifier

A method is proposed for the simultaneous determination of Al, As, Cu, Fe, Mn, and Ni in fuel ethanol by electrothermal atomic absorption spectrometry (ETAAS) using W-Rh permanent modifier together with Pd(NO3)(2) + Mg(NO3)(2) conventional modifier. The integrated platform of a transversely heated graphite atomizer (THGA) was treated with tungsten, followed by rhodium, forming a deposit containing 250 mug W + 200 mug Rh. A 500-muL, volume of fuel ethanol was diluted with 500 muL, of 0.14 mol L-1 HNO3 in an autosampler cup of the spectrometer. Then, 20 muL, of the diluted ethanol was introduced into the pretreated graphite platform followed by the introduction of 5 mug Pd(NO3)(2) + 3 mug Mg(NO3)(2). The injection of this modifier was required to improve arsenic and iron recoveries in fuel ethanol. Calibrations were carried out using multi-element reference solutions prepared in diluted ethanol (1 + 1, v/v) acidified to 0. 14 mol L-1 HNO3. The pyrolysis and atomization temperatures of the heating program were 1200degreesC and 2200degreesC, respectively, which were obtained with multielement reference solutions in acidic diluted ethanol (1 + 1, v/v; 0. 14 mol L-1 HNO3). The characteristic masses for the simultaneous determination in ethanol fuel were 78 pg Al, 33 pg As, 10 pg Cu, 14 pg Fe, 7 pg Mn, and 24 pg Ni. The lifetime of the pretreated tube was about 700 firings. The detection limits (D.L.) were 1.9 mug L-1 Al, 2.9 mug L-1 As, 0.57 mug L-1.Cu, 1.3 mug L-1 Fe, 0.40 mug L-1 Mn, and 1.3 mug L-1 Ni. The relative standard deviations (n = 12) were 4%, 4%, 3%, 1.5%, 1.2%, and 2.2% for Al, As, Cu, Fe, Mn, and Ni, respectively. The recoveries of Al, As, Cu, Fe, Mn, and Ni added to the fuel ethanol samples varied from 81% to 95%, 80% to 98%, 97% to 109%, 85% to 107%, 98% to 106% and 97% to 103%, respectively. Accuracy was checked for the Al, As, Cu, Fe, Mn, and Ni determination in 10 samples purchased at a local gas station in Araraquara-SP City, Brazil. A paired t-test showed that at the 95% confidence level the results were in agreement with those obtained by single-element ETAAS.

Previsão multi-passos a frente do preço de energia elétrica de curto prazo no mercado brasileiro

A predição do preço da energia elétrica é uma questão importante para todos os participantes do mercado, para que decidam as estratégias mais adequadas e estabeleçam os contratos bilaterais que maximizem seus lucros e minimizem os seus riscos. O preço da energia tipicamente exibe sazonalidade, alta volatilidade e picos. Além disso, o preço da energia é influenciado por muitos fatores, tais como: demanda de energia, clima e preço de combustíveis. Este trabalho propõe uma nova abordagem híbrida para a predição de preços de energia no mercado de curto prazo. Tal abordagem combina os filtros autorregressivos integrados de médias móveis (ARIMA) e modelos de Redes Neurais (RNA) numa estrutura em cascata e utiliza variáveis explanatórias. Um processo em dois passos é aplicado. Na primeira etapa, as variáveis explanatórias são preditas. Na segunda etapa, os preços de energia são preditos usando os valores futuros das variáveis exploratórias. O modelo proposto considera uma predição de 12 passos (semanas) a frente e é aplicada ao mercado brasileiro, que possui características únicas de comportamento e adota o despacho centralizado baseado em custo. Os resultados mostram uma boa capacidade de predição de picos de preço e uma exatidão satisfatória de acordo com as medidas de erro e testes de perda de cauda quando comparado com técnicas tradicionais. Em caráter complementar, é proposto um modelo classificador composto de árvores de decisão e RNA, com objetivo de explicitar as regras de formação de preços e, em conjunto com o modelo preditor, atuar como uma ferramenta atrativa para mitigar os riscos da comercialização de energia.

Multi-scale structural and chemical analysis of sugarcane bagasse in the process of sequential acid-base pretreatment and ethanol production by Scheffersomyces shehatae and Saccharomyces cerevisiae

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

A numerical methodology for the multi-objective optimization of the DI Diesel engine combustion

DI Diesel engine are widely used both for industrial and automotive applications due to their durability and fuel economy. Nonetheless, increasing environmental concerns force that type of engine to comply with increasingly demanding emission limits, so that, it has become mandatory to develop a robust design methodology of the DI Diesel combustion system focused on reduction of soot and NOx simultaneously while maintaining a reasonable fuel economy. In recent years, genetic algorithms and CFD three-dimensional combustion simulations have been successfully applied to that kind of problem. However, combining GAs optimization with actual CFD three-dimensional combustion simulations can be too onerous since a large number of calculations is usually needed for the genetic algorithm to converge, resulting in a high computational cost and, thus, limiting the suitability of this method for industrial processes. In order to make the optimization process less time-consuming, CFD simulations can be more conveniently used to generate a training set for the learning process of an artificial neural network which, once correctly trained, can be used to forecast the engine outputs as a function of the design parameters during a GA optimization performing a so-called virtual optimization. In the current work, a numerical methodology for the multi-objective virtual optimization of the combustion of an automotive DI Diesel engine, which relies on artificial neural networks and genetic algorithms, was developed.

Characterization of thermal and mechanical properties of polypropylene-based composites for fuel cell bipolar plates and development of educational tools in hydrogen and fuel cell technologies

In this project we developed conductive thermoplastic resins by adding varying amounts of three different carbon fillers: carbon black (CB), synthetic graphite (SG) and multi-walled carbon nanotubes (CNT) to a polypropylene matrix for application as fuel cell bipolar plates. This component of fuel cells provides mechanical support to the stack, circulates the gases that participate in the electrochemical reaction within the fuel cell and allows for removal of the excess heat from the system. The materials fabricated in this work were tested to determine their mechanical and thermal properties. These materials were produced by adding varying amounts of single carbon fillers to a polypropylene matrix (2.5 to 15 wt.% Ketjenblack EC-600 JD carbon black, 10 to 80 wt.% Asbury Carbon's Thermocarb TC-300 synthetic graphite, and 2.5 to 15 wt.% of Hyperion Catalysis International's FIBRILTM multi-walled carbon nanotubes) In addition, composite materials containing combinations of these three fillers were produced. The thermal conductivity results showed an increase in both through-plane and in-plane thermal conductivities, with the largest increase observed for synthetic graphite. The Department of Energy (DOE) had previously set a thermal conductivity goal of 20 W/m·K, which was surpassed by formulations containing 75 wt.% and 80 wt.% SG, yielding in-plane thermal conductivity values of 24.4 W/m·K and 33.6 W/m·K, respectively. In addition, composites containing 2.5 wt.% CB, 65 wt.% SG, and 6 wt.% CNT in PP had an in–plane thermal conductivity of 37 W/m·K. Flexural and tensile tests were conducted. All composite formulations exceeded the flexural strength target of 25 MPa set by DOE. The tensile and flexural modulus of the composites increased with higher concentration of carbon fillers. Carbon black and synthetic graphite caused a decrease in the tensile and flexural strengths of the composites. However, carbon nanotubes increased the composite tensile and flexural strengths. Mathematical models were applied to estimate through-plane and in-plane thermal conductivities of single and multiple filler formulations, and tensile modulus of single-filler formulations. For thermal conductivity, Nielsen's model yielded accurate thermal conductivity values when compared to experimental results obtained through the Flash method. For prediction of tensile modulus Nielsen's model yielded the smallest error between the predicted and experimental values. The second part of this project consisted of the development of a curriculum in Fuel Cell and Hydrogen Technologies to address different educational barriers identified by the Department of Energy. By the creation of new courses and enterprise programs in the areas of fuel cells and the use of hydrogen as an energy carrier, we introduced engineering students to the new technologies, policies and challenges present with this alternative energy. Feedback provided by students participating in these courses and enterprise programs indicate positive acceptance of the different educational tools. Results obtained from a survey applied to students after participating in these courses showed an increase in the knowledge and awareness of energy fundamentals, which indicates the modules developed in this project are effective in introducing students to alternative energy sources.

Spray characterization under flash boiling conditions of a DI multi-hole injector at high injection pressures in a combustion vessel

Spray characterization under flash boiling conditions was investigated utilizing a symmetric multi-hole injector applicable to the gasoline direct injection (GDI) engine. Tests were performed in a constant volume combustion vessel using a high-speed schlieren and Mie scattering imaging systems. Four fuels including n-heptane, 100% ethanol, pure ethanol blended with 15% iso-octane by volume, and test grade E85 were considered in the study. Experimental conditions included various ambient pressure, fuel temperature, and fuel injection pressure. Visualization of the vaporizing spray development was acquired by utilizing schlieren and laser-based Mie scattering techniques. Time evolved spray tip penetration, spray angle, and the ratio of the vapor to liquid region were analyzed by utilizing digital image processing techniques in MATLAB. This research outlines spray characteristics at flash boiling and non-flash boiling conditions. At flash boiling conditions it was observed that individual plumes merge together, leading to significant contraction in spray angle as compared to non-flash boiling conditions. The results indicate that at flash boiling conditions, spray formation and expansion of vapor region is dependent on momentum exchange offered by the ambient gas. A relation between momentum exchange and liquid spray angle formed was also observed.

BIOMASS FUEL-BRIQUETTES AND IMPROVED STOVES IN DINSHO, ETHIOPIA

This report is a study of the development and implementation of a biomass fuel briquette and improved stove project in the highlands of Ethiopia. The primary goal of the project was to determine if the introduction of an improved stove would affect the acceptability of fuel briquettes. The secondary goal was to establish briquette and improved stove manufacturing associations in Dinsho and Rira towns. Two problems encountered during the project were cultural differences in material valuation, and difficulty working with local administrative frameworks and multi-organization communication difficulties. Both briquettes and improved stoves received positive feedback from respondents. Survey data indicated that a price of 0.75 Ethiopian birr per briquette would make them a competitive fuel source against fuelwood. Recommendations for feedstock sourcing and supply, capital investment, labor reduction, estimating cost effectiveness, appropriate technology design, development work setbacks, and valuation paradigms for fuel briquette, improved stove, and development work projects.

A multi-level stakeholder approach to sustainable land management

Soils provide us with over 90% of all human food, livestock feed, fibre and fuel on Earth. Soils, however, have more than just productive functions. The key challenge in coming years will be to address the diverse and potentially conflicting demands now being made by human societies and other forms of life, while ensuring that future generations have the same potential to use soils and land of comparable quality. In a multi-level stakeholder approach, down-to-earth action will have to be supplemented with measures at various levels, from households to communities, and from national policies to international conventions. Knowledge systems, both indigenous and scientific, and related research and learning processes must play a central role. Ongoing action can be enhanced through a critical assessment of the impact of past achievements, and through better cooperation between people and institutions.

Multi-scale study of hydrogen isotopes structural properties in solid phase in the context of inertial fusion target manufacturing

Designing the ignition and high-gain targets for inertial confinement fusion (ICF) requires a condensed uniform layer of the hydrogen fuel on the inner surface of a spherical polymer shell. The fuel layers have to be highly uniform in thickness and roughness.

Performance, emission and combustion characteristics of an indirect injection (IDI) multi-cylinder compression ignition (CI) engine operating on neat jatropha and karanj oils preheated by jacket water

Renewable non-edible plant oils such as jatropha and karanj have potential to substitute fossil diesel fuels in CI engines. A multi-cylinder water cooled IDI type CI engine has been tested with jatropha and karanj oils and comparisons made against fossil diesel. The physical and chemical properties of the three fuels were measured to investigate the suitability of jatropha and karanj oils as fuels for CI engines. The engine cooling water circuit and fuel supply systems were modified such that hot jacket water preheated the neat plant oil prior to injection. Between jatropha and karanj there was little difference in the performance, emission and combustion results. Compared to fossil diesel, the brake specific fuel consumption on volume basis was around 3% higher for the plant oils and the brake thermal efficiency was almost similar. Jatropha and karanj operation resulted in higher CO 2 and NO x emissions by 7% and 8% respectively, as compared to diesel. The cylinder gas pressure diagram showed stable engine operation with both plant oils. At full load, the plant oils gave around 3% higher peak cylinder pressure than fossil diesel. With the plant oils, cumulative heat release was smaller at low load and almost similar at full load, compared to diesel. At full load, the plant oils exhibited 5% shorter combustion duration. The study concludes that the IDI type CI engine can be efficiently operated with neat jatropha (or karanj) oil preheated by jacket water, after small modifications of the engine cooling and fuel supply circuits. © 2012 Elsevier Ltd.

Experimental investigation of performance, emission and combustion characteristics of an indirect injection multi-cylinder CI engine fuelled by blends of de-inking sludge pyrolysis oil with biodiesel

De-inking sludge can be converted into useful forms of energy to provide economic and environmental benefits. In this study, pyrolysis oil produced from de-inking sludge through an intermediate pyrolysis technique was blended with biodiesel derived from waste cooking oil, and tested in a multi-cylinder indirect injection type CI engine. The physical and chemical properties of pyrolysis oil and its blends (20 and 30 vol.%) were measured and compared with those of fossil diesel and pure biodiesel (B100). Full engine power was achieved with both blends, and very little difference in engine performance and emission results were observed between 20% and 30% blends. At full engine load, the brake specific fuel consumption on a volume basis was around 6% higher for the blends when compared to fossil diesel. The brake thermal efficiencies were about 3-6% lower than biodiesel and were similar to fossil diesel. Exhaust gas emissions of the blends contained 4% higher CO2 and 6-12% lower NOx, as compared to fossil diesel. At full load, CO emissions of the blends were decreased by 5-10 times. The cylinder gas pressure diagram showed stable engine operation with the 20% blend, but indicated minor knocking with 30% blend. Peak cylinder pressure of the 30% blend was about 5-6% higher compared to fossil diesel. At full load, the peak burn rate of combustion from the 30% blend was about 26% and 12% higher than fossil diesel and biodiesel respectively. In comparison to fossil diesel the combustion duration was decreased for both blends; for 30% blend at full load, the duration was almost 12% lower. The study concludes that up to 20% blend of de-inking sludge pyrolysis oil with biodiesel can be used in an indirect injection CI engine without adding any ignition additives or surfactants.

Effects of engine cooling water temperature on performance and exhaust emission characteristics of a multi-cylinder CI engine operated with Jatropha-Butanol blend

Renewable alternatives such as biofuels and optimisation of the engine operating parameters can enhance engine performance and reduce emissions. The temperature of the engine coolant is known to have significant influence on engine performance and emissions. Whereas much existing literature describes the effects of coolant temperature in engines using fossil derived fuels, very few studies have investigated these effects when biofuel is used as an alternative fuel. Jatropha oil is a non-edible biofuel which can substitute fossil diesel for compression ignition (CI) engine use. However, due to the high viscosity of Jatropha oil, technique such as transesterification, preheating the oil, mixing with other fuel is recommended for improved combustion and reduced emissions. In this study, Jatropha oil was blended separately with ethanol and butanol, at ratios of 80:20 and 70:30. The fuel properties of all four blends were measured and compared with diesel and jatropha oil. It was found that the 80% jatropha oil + 20% butanol blend was the most suitable alternative, as its properties were closest to that of diesel. A 2 cylinder Yanmar engine was used; the cooling water temperature was varied between 50°C and 95°C. In general, it was found that when the temperature of the cooling water was increased, the combustion process enhanced for both diesel and Jatropha-Butanol blend. The CO2 emissions for both diesel and biofuel blend were observed to increase with temperature. As a result CO, O2 and lambda values were observed to decrease when cooling water temperature increased. When the engine was operated using diesel, NOX emissions correlated in an opposite manner to smoke opacity; however, when the biofuel blend was used, NOX emissions and smoke opacity correlated in an identical manner. The brake thermal efficiencies were found to increase slightly as the temperature was increased. In contrast, for all fuels, the volumetric efficiency was observed to decrease as the coolant temperature was increased. Brake specific fuel consumption was observed to decrease as the temperature was increased and was higher on average when the biofuel was used, in comparison to diesel. The study concludes that the effects of engine coolant temperature on engine performance and emission characteristics differ between biofuel blend and fossil diesel operation. The coolant temperature needs to be optimised depending on the type of biofuel for optimum engine performance and reduced emissions.

Uses of frontier efficiency methodologies and multi-criteria decision making for performance measurement in the energy sector

Guest editorial Ali Emrouznejad is a Senior Lecturer at the Aston Business School in Birmingham, UK. His areas of research interest include performance measurement and management, efficiency and productivity analysis as well as data mining. He has published widely in various international journals. He is an Associate Editor of IMA Journal of Management Mathematics and Guest Editor to several special issues of journals including Journal of Operational Research Society, Annals of Operations Research, Journal of Medical Systems, and International Journal of Energy Management Sector. He is in the editorial board of several international journals and co-founder of Performance Improvement Management Software. William Ho is a Senior Lecturer at the Aston University Business School. Before joining Aston in 2005, he had worked as a Research Associate in the Department of Industrial and Systems Engineering at the Hong Kong Polytechnic University. His research interests include supply chain management, production and operations management, and operations research. He has published extensively in various international journals like Computers & Operations Research, Engineering Applications of Artificial Intelligence, European Journal of Operational Research, Expert Systems with Applications, International Journal of Production Economics, International Journal of Production Research, Supply Chain Management: An International Journal, and so on. His first authored book was published in 2006. He is an Editorial Board member of the International Journal of Advanced Manufacturing Technology and an Associate Editor of the OR Insight Journal. Currently, he is a Scholar of the Advanced Institute of Management Research. Uses of frontier efficiency methodologies and multi-criteria decision making for performance measurement in the energy sector This special issue aims to focus on holistic, applied research on performance measurement in energy sector management and for publication of relevant applied research to bridge the gap between industry and academia. After a rigorous refereeing process, seven papers were included in this special issue. The volume opens with five data envelopment analysis (DEA)-based papers. Wu et al. apply the DEA-based Malmquist index to evaluate the changes in relative efficiency and the total factor productivity of coal-fired electricity generation of 30 Chinese administrative regions from 1999 to 2007. Factors considered in the model include fuel consumption, labor, capital, sulphur dioxide emissions, and electricity generated. The authors reveal that the east provinces were relatively and technically more efficient, whereas the west provinces had the highest growth rate in the period studied. Ioannis E. Tsolas applies the DEA approach to assess the performance of Greek fossil fuel-fired power stations taking undesirable outputs into consideration, such as carbon dioxide and sulphur dioxide emissions. In addition, the bootstrapping approach is deployed to address the uncertainty surrounding DEA point estimates, and provide bias-corrected estimations and confidence intervals for the point estimates. The author revealed from the sample that the non-lignite-fired stations are on an average more efficient than the lignite-fired stations. Maethee Mekaroonreung and Andrew L. Johnson compare the relative performance of three DEA-based measures, which estimate production frontiers and evaluate the relative efficiency of 113 US petroleum refineries while considering undesirable outputs. Three inputs (capital, energy consumption, and crude oil consumption), two desirable outputs (gasoline and distillate generation), and an undesirable output (toxic release) are considered in the DEA models. The authors discover that refineries in the Rocky Mountain region performed the best, and about 60 percent of oil refineries in the sample could improve their efficiencies further. H. Omrani, A. Azadeh, S. F. Ghaderi, and S. Abdollahzadeh presented an integrated approach, combining DEA, corrected ordinary least squares (COLS), and principal component analysis (PCA) methods, to calculate the relative efficiency scores of 26 Iranian electricity distribution units from 2003 to 2006. Specifically, both DEA and COLS are used to check three internal consistency conditions, whereas PCA is used to verify and validate the final ranking results of either DEA (consistency) or DEA-COLS (non-consistency). Three inputs (network length, transformer capacity, and number of employees) and two outputs (number of customers and total electricity sales) are considered in the model. Virendra Ajodhia applied three DEA-based models to evaluate the relative performance of 20 electricity distribution firms from the UK and the Netherlands. The first model is a traditional DEA model for analyzing cost-only efficiency. The second model includes (inverse) quality by modelling total customer minutes lost as an input data. The third model is based on the idea of using total social costs, including the firm’s private costs and the interruption costs incurred by consumers, as an input. Both energy-delivered and number of consumers are treated as the outputs in the models. After five DEA papers, Stelios Grafakos, Alexandros Flamos, Vlasis Oikonomou, and D. Zevgolis presented a multiple criteria analysis weighting approach to evaluate the energy and climate policy. The proposed approach is akin to the analytic hierarchy process, which consists of pairwise comparisons, consistency verification, and criteria prioritization. In the approach, stakeholders and experts in the energy policy field are incorporated in the evaluation process by providing an interactive mean with verbal, numerical, and visual representation of their preferences. A total of 14 evaluation criteria were considered and classified into four objectives, such as climate change mitigation, energy effectiveness, socioeconomic, and competitiveness and technology. Finally, Borge Hess applied the stochastic frontier analysis approach to analyze the impact of various business strategies, including acquisition, holding structures, and joint ventures, on a firm’s efficiency within a sample of 47 natural gas transmission pipelines in the USA from 1996 to 2005. The author finds that there were no significant changes in the firm’s efficiency by an acquisition, and there is a weak evidence for efficiency improvements caused by the new shareholder. Besides, the author discovers that parent companies appear not to influence a subsidiary’s efficiency positively. In addition, the analysis shows a negative impact of a joint venture on technical efficiency of the pipeline company. To conclude, we are grateful to all the authors for their contribution, and all the reviewers for their constructive comments, which made this special issue possible. We hope that this issue would contribute significantly to performance improvement of the energy sector.

Performance Comparison of MATLAB and Neuro Solution Software on Estimation of Fuel Economy by Using Artificial Neural Network

In the world, scientific studies increase day by day and computer programs facilitate the human’s life. Scientists examine the human’s brain’s neural structure and they try to be model in the computer and they give the name of artificial neural network. For this reason, they think to develop more complex problem’s solution. The purpose of this study is to estimate fuel economy of an automobile engine by using artificial neural network (ANN) algorithm. Engine characteristics were simulated by using “Neuro Solution” software. The same data is used in MATLAB to compare the performance of MATLAB is such a problem and show its validity. The cylinder, displacement, power, weight, acceleration and vehicle production year are used as input data and miles per gallon (MPG) are used as target data. An Artificial Neural Network model was developed and 70% of data were used as training data, 15% of data were used as testing data and 15% of data is used as validation data. In creating our model, proper neuron number is carefully selected to increase the speed of the network. Since the problem has a nonlinear structure, multi layer are used in our model.