Search results for: adsorption isotherm models

Authors: Ahmed M. Al-Mutarreb, Shiferaw R. Jufar

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Global shale gas resource assessment is still in its preliminary stage in most of the countries including the development of shale gas reservoirs in Malaysia. This project presents the main geochemical and mineral characteristics of few Malaysian’s shale samples which contribute on evaluating shale gas reserve world resource evaluations. Three shale samples from the western part of Peninsular Malaysia (Batu-Caja, Kuala Lumpur, and Johor Baru shale formations) were collected for this study. Total organic carbon wt.%, thermal maturity, kerogen type, mineralogy and adsorption/desorption characteristics are measured at Universiti Teknologi PETRONAS laboratories. Two samples show good potential in TOC results exhibited > 2wt.% exceeding the minimum values of Shale gas potential, while the third revealed < 1.5wt. Mineralogical compositions for the three samples are within the acceptable range percentage% of quartz and clays compared to shale plays in USA. This research’s results are promising and recommend to continue exploring and assessing unconventional shale gas reserves values in these areas.

Keywords: shale gas characterizations, geochemical properties, Malaysia, shale gas reserve

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Authors: Takeshi Takemoto, Naoya Suzuki, Naohisa Takagaki, Satoru Komori, Masako Terui, George Truscott

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Drag coefficient is an important parameter in order to correctly estimate the air-sea momentum flux. However, The parameterization of the drag coefficient hasn’t been established due to the variation in the field data. Instead, a number of drag coefficient model formulae have been proposed, even though almost all these models haven’t discussed the extreme wind speed range. With regards to such models, it is unclear how the drag coefficient changes in the extreme wind speed range as the wind speed increased. In this study, we investigated the effect of the drag coefficient models concerning the air-sea momentum flux in the extreme wind range on a global scale, comparing two different drag coefficient models. Interestingly, one model didn’t discuss the extreme wind speed range while the other model considered it. We found that the difference of the models in the annual global air-sea momentum flux was small because the occurrence frequency of strong wind was approximately 1% with a wind speed of 20m/s or more. However, we also discovered that the difference of the models was shown in the middle latitude where the annual mean air-sea momentum flux was large and the occurrence frequency of strong wind was high. In addition, the estimated data showed that the difference of the models in the drag coefficient was large in the extreme wind speed range and that the largest difference became 23% with a wind speed of 35m/s or more. These results clearly show that the difference of the two models concerning the drag coefficient has a significant impact on the estimation of a regional air-sea momentum flux in an extreme wind speed range such as that seen in a tropical cyclone environment. Furthermore, we estimated each air-sea momentum flux using several kinds of drag coefficient models. We will also provide data from an observation tower and result from CFD (Computational Fluid Dynamics) concerning the influence of wind flow at and around the place.

Keywords: air-sea interaction, drag coefficient, air-sea momentum flux, CFD (Computational Fluid Dynamics)

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Authors: Josip Visković, Josip Arnerić, Ante Rozga

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Based on the fact that volatility is time varying in high frequency data and that periods of high volatility tend to cluster, the most successful and popular models in modelling time varying volatility are GARCH type models. When financial returns exhibit sudden jumps that are due to structural breaks, standard GARCH models show high volatility persistence, i.e. integrated behaviour of the conditional variance. In such situations models in which the parameters are allowed to change over time are more appropriate. This paper compares different GARCH models in terms of their ability to describe structural changes in returns caused by financial crisis at stock markets of six selected central and east European countries. The empirical analysis demonstrates that Markov regime switching GARCH model resolves the problem of excessive persistence and outperforms uni-regime GARCH models in forecasting volatility when sudden switching occurs in response to financial crisis.

Keywords: central and east European countries, financial crisis, Markov switching GARCH model, transition probabilities

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Authors: Akmez Nabeerasool, Michaelis Massaros, Nigel Brown, David Sanderson, David Parocki, Charlotte Thompson, Mike Lodge, Mikael Khan

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The provision of clean and safe drinking water is of paramount importance and is a basic human need. Water scarcity coupled with tightening of regulations and the inability of current treatment technologies to deal with emerging contaminants and Pharmaceuticals and personal care products means that alternative treatment technologies that are viable and cost effective are required in order to meet demand and regulations for clean water supplies. Logistically, the application of water treatment in rural areas presents unique challenges due to the decentralisation of abstraction points arising from low population density and the resultant lack of infrastructure as well as the need to treat water at the site of use. This makes it costly to centralise treatment facilities and hence provide potable water direct to the consumer. Furthermore, across the UK there are segments of the population that rely on a private water supply which means that the owner or user(s) of these supplies, which can serve one household to hundreds, are responsible for the maintenance. The treatment of these private water supply falls on the private owners, and it is imperative that a chemical free technological solution that can operate unattended and does not produce any waste is employed. Arvia’s patented advanced oxidation technology combines the advantages of adsorption and electrochemical regeneration within a single unit; the Organics Destruction Cell (ODC). The ODC uniquely uses a combination of adsorption and electrochemical regeneration to destroy organics. Key to this innovative process is an alternative approach to adsorption. The conventional approach is to use high capacity adsorbents (e.g. activated carbons with high porosities and surface areas) that are excellent adsorbents, but require complex and costly regeneration. Arvia’s technology uses a patent protected adsorbent, Nyex™, which is a non-porous, highly conductive, graphite based adsorbent material that enables it to act as both the adsorbent and as a 3D electrode. Adsorbed organics are oxidised and the surface of the Nyex™ is regenerated in-situ for further adsorption without interruption or replacement. Treated water flows from the bottom of the cell where it can either be re-used or safely discharged. Arvia™ Technology Ltd. has trialled the application of its tertiary water treatment technology in treating reservoir water abstracted near Glasgow, Scotland, with promising results. Several other pilot plants have also been successfully deployed at various locations in the UK showing the suitability and effectiveness of the technology in removing recalcitrant organics (including pharmaceuticals, steroids and hormones), COD and colour.

Keywords: Arvia™ process, adsorption, water treatment, electrochemical oxidation

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Authors: Nagini Sabbineni, Rajini T. V. Kanth, B. V. Kiranmayee

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India’s economy primarily depends on agriculture yield growth and their allied agro industry products. The agriculture yield prediction is the toughest task for agricultural departments across the globe. The agriculture yield depends on various factors. Particularly countries like India, majority of agriculture growth depends on rain water, which is highly unpredictable. Agriculture growth depends on different parameters, namely Water, Nitrogen, Weather, Soil characteristics, Crop rotation, Soil moisture, Surface temperature and Rain water etc. In our paper, lot of Explorative Data Analysis is done and various predictive models were designed. Further various regression models like Linear, Multiple Linear, Non-linear models are tested for the effective prediction or the forecast of the agriculture yield for various crops in Andhra Pradesh and Telangana states.

Keywords: agriculture yield growth, agriculture yield prediction, explorative data analysis, predictive models, regression models

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Authors: M. Abd elfattah, M. Ossman, Nahla A. Taha

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The present work explored the use of Egyptian rice straw, an agricultural waste that leads to global warming problem through brown cloud, as a potential feedstock for the preparation of activated carbon by physical and chemical activation. The results of this study showed that it is feasible to prepare activated carbons with relatively high surface areas and pore volumes from the Egyptian rice straw by direct chemical and physical activation. The produced activated carbon from the two methods (AC1 and AC2) could be used as potential adsorbent for the removal of Fe(III) from aqueous solution contains heavy metals and polluted water. The adsorption of Fe(III) was depended on the pH of the solution. The optimal Fe(III) removal efficiency occurs at pH 5. Based on the results, the optimum contact time is 60 minutes and adsorbent dosage is 3 g/L. The adsorption breakthrough curves obtained at different bed depths indicated increase of breakthrough time with increase in bed depths. A rise in inlet Fe(III) concentration reduces the throughput volume before the packed bed gets saturated. AC1 showed higher affinity for Fe(III) as compared to Raw rice husk.

Keywords: rice straw, activated carbon, Fe(III), fixed bed column, pyrolysis

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Authors: Ali S. Gargoum

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Graphical modeling plays an important role in providing efficient probability calculations in high dimensional problems (computational efficiency). In this paper, we address one of such problems where we discuss fragmenting puff models and some distributional assumptions concerning models for the instantaneous, emission readings and for the fragmenting process. A graphical representation in terms of a junction tree of the conditional probability breakdown of puffs and puff fragments is proposed.

Keywords: graphical models, influence diagrams, junction trees, Bayesian nets

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Authors: Valdir Moura, FranciléIa De Oliveira E. Silva, Erivelto Mercante, Ranieli Dos Anjos De Souza, Jerry Adriani Johann

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Several colonization projects were implemented in the Brazilian Legal Amazon in the 1970s and 1980s. Among all of these colonization projects, the most prominent were those with the Fishbone and Topographic models. Within this scope, the projects of settlements known as Anari and Machadinho were created, which stood out because they are contiguous areas with different models and structure of occupation and colonization. The main objective of this work was to evaluate the dynamics of Land-Use and Land-Cover (LULC) in two different colonization models, implanted in the State of Rondonia in the 1980s. The Fishbone and Topographic models were implanted in the Anari and Machadinho settlements respectively. The understanding of these two forms of occupation will help in future colonization programs of the Brazilian Legal Amazon. These settlements are contiguous areas with different occupancy structures. A 32-year Landsat time series (1984-2016) was used to evaluate the rates and trends in the LULC process in the different colonization models. In the different occupation models analyzed, the results showed a rapid loss of primary and secondary forests (deforestation), mainly due to the dynamics of use, established by the Agriculture/Pasture (A/P) relation and, with heavy dependence due to road construction.

Keywords: land-cover, deforestation, rate fragments, remote sensing, secondary succession

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Authors: Burcu Ileri, Deniz Sanliyuksel Yucel, Onder Ayyildiz

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The oxidation of pyrite in water results in the formation of acid mine drainage, which typically forms extremely acid mine lake (AML) in the depression areas of abandoned Etili open-pit coal mine site, Northwest Turkey. Nine acid mine lakes of various sizes have been located in the Etili coal mine site. Hayirtepe AML is one of the oldest lake having a mean pH value of 2.9 and conductivity of 4550 μS/cm, and containing elevated concentrations of Al, B, Ba, Ca, Cd, Co, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, Pb, and Zn. The water quality of the lake has been deteriorated due to its high chemical composition, in particular, increasing heavy metal pollution. In this study, fly ash (FA), a coal combustion by-product from fluidized bed thermal power plant in the northwestern part of Turkey, was used as an adsorbent for the treatment of Hayirtepe AML. The FA is a relatively abundant and cost effective material, but its use in adsorption processes usually require excessive adsorbent doses. To increase adsorption efficiency and lower the adsorbent dose, we modified the FA by means of ultrasonic treatment (20 kHz and 40 kHz). The images of scanning electron microscopy (SEM) have demonstrated that ultrasonic treatment not only decreased the size of ash particles but also created pits and cracks on their surfaces which in turn led to a significant increase in the BET surface area. Both FA and modified fly ash were later tested for the removal of heavy metals from the AML. The effect of various operating parameters such as ultrasonic power, pH, ash dose, and adsorption contact time were examined to obtain the optimum conditions for the treatment process. The results have demonstrated that removal of heavy metals by ultrasound-modified fly ash requires much shorter treatment times and lower adsorbent doses than those attained by the unmodified fly ash. This research was financially supported by the Scientific and Technological Research Council of Turkey (TUBITAK), (Project no: 116Y510).

Keywords: acid mine lake, heavy metal, modified fly ash, ultrasonic treatment

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Authors: Tarun Jain, Lovnish Gupta, Soumen Basu

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Organic pollutants like phenols and organic dyes present in industrial waste water are posing a hazardous threat to aquatic ecosystem. Several measures have been adopted for the neutralization and photodecomposition of these harmful organic moieties, among these semiconductor photocatalysis has been provided a major thrust after the discovery of Honda-Fujishema effect. Present study demonstrates the adsorption of TiO2- P25 in nano size (~36 nm) on cement balls for effective photodegradation of Alizarin and penta chlorophenol (PCP) under UV light illumination. Triton-X was used as a stabilizer for effective adsorption of TiO2 on cement balls (TCB) followed by calcination at ~300oC for 4 h. The TCB’s were dispersed randomly in a self designed reactor for phototcatalytic performance as shown in scheme 1. The change in concentration of alizarin and PCP was observed under UV-Vis spectroscopy, PCP was detoxified within 40 min while alizarin photodecomposed within 15 min of UV light irradiation. Taking into consideration the go green slogan and future prospective this technique can be also utilized under visible light and on mass scale because this is an effective tool for environmental remediation and waste water treatment.

Keywords: organic pollutants, TiO2 cement balls, photodegradation, UV light irradiation

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Authors: Raquel Zydeck, Karina Azzolin, Luis Kosteski, Alisson Milani

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This work presents numerical models in plane deformations (2D), using the Discrete Element Method formedbybars (LDEM) andtheFiniteElementMethod (FEM), in structuralmasonrywallswith horizontal chasesof 20%, 30%, and 50% deep, located in the central part and 1/3 oftheupperpartofthewall, withcenteredandeccentricloading. Differentcombinationsofboundaryconditionsandinteractionsbetweenthemethodswerestudied.

Keywords: chases in structural masonry walls, discrete element method formed by bars, finite element method, numerical models, boundary condition

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Authors: Nurudeen O. Lasisi, Sirajo Abdulrahman, Abdulkareem A. Ibrahim

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Newcastle disease is an infection of domestic poultry and other bird species with the virulent Newcastle disease virus (NDV). In this paper, we study the dynamics of the modeling of the Newcastle disease virus (NDV) using a novel quarantine-adjusted incidence. The comparison of Vaccination, linear incident rate and novel quarantine-adjusted incident rate in the models are discussed. The dynamics of the models yield disease-free and endemic equilibrium states.The effective reproduction numbers of the models are computed in order to measure the relative impact of an individual bird or combined intervention for effective disease control. We showed the local and global stability of endemic equilibrium states of the models and we found that the stability of endemic equilibrium states of models are globally asymptotically stable if the effective reproduction numbers of the models equations are greater than a unit.

Keywords: effective reproduction number, Endemic state, Mathematical model, Newcastle disease virus, novel quarantine-adjusted incidence, stability analysis

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Authors: Alvarez Barrantes, Robert Dorrell, Christopher Hackney, Anne Baar, Roberto Fernandez, Daniel Parsons

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Microplastic particles entering fluvial environments are deposited with natural sediments. Their settling properties can change by the absorption or adsorption of contaminants, organic matter, and organisms. These deposits include positively, neutrally, and negatively buoyant particles. This study aims to understand how plastic particles of different densities interact with natural sediments as they settle and how they are stored within the sediment deposit. The results of this study contribute to a better understanding of the deposition of microplastic particles and associated pollution in rivers. A set of 48 experiments was designed to investigate the settling process of microplastic particles in freshwater. The experimental work describes the vertical variation of cohesive and/or non-cohesive sediment versus microplastic densities in deposited sediment. The experiment consisted of adding microplastic particles, sediment, and water in a waterproof carton tube of a height of 24 cm and a diameter of 5 cm. The plastic selected is positively, neutrally, and negatively buoyant. The sediments consist of sand and clay with four different concentrations. The mixture of materials was shaken until is thoroughly mixed and left to settle for 24 hours. After the settlement, the tubes were frozen at -20 °C to be able to cut them and measure the thickness of the deposits and analyze the sediment and plastic distribution. The most representative experiments were repeated in a glass tube of the same size; to analyse the influences of current flows and depositional process. Finally, the glass tube experiments were used to study organic materials adsorption in plastic, settling the sample for four months. Defined microplastic layers were identified as the density of the plastic change. Preliminary results show that most of the positive buoyancy particles floated, neutral buoyancy particles form a layer above the sediment and negative buoyancy particles mixed with the sediment. The vertical grain size distribution of the deposits was analysed to determine deposition variation with and without plastic. It is expected that the positively buoyant particles are trapped in the sediment by the currents flows and sink due to organic material adsorption. Finally, the experiments will explain how microplastic particles, including positively buoyant ones, are stored in natural sediment deposits.

Keywords: microplastic adsorption process, microplastic deposition in natural sediment, microplastic pollution in rivers, storages of positive buoyancy microplastic particles

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Authors: Hatem I. Mokhtar, Randa A. Abd-El-Salam, Ghada M. Hadad

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An improved nano-composite of self-doped polyaniline nanofibers and silica-coated magnetite nanoparticles were prepared and evaluated for suitability to magnetic dispersive micro solid-phase extraction. The work focused on optimization of the composite capacity to extract four fluoroquinolones (FQs) antibiotics, ciprofloxacin, enrofloxacin, danofloxacin, and difloxacin from water and improvement of composite stability towards acid and atmospheric degradation. Self-doped polyaniline nanofibers were prepared by oxidative co-polymerization of aniline with anthranilic acid. Magnetite nanopariticles were prepared by alkaline co-precipitation and coated with silica by silicate hydrolysis on magnetite nanoparticles surface at pH 6.5. The composite was formed by self-assembly by mixing self-doped polyaniline nanofibers with silica-coated magnetite nanoparticles dispersions in ethanol. The composite structure was confirmed by transmission electron microscopy (TEM). Self-doped polyaniline nanofibers and magnetite chemical structures were confirmed by FT-IR while silica coating of the magnetite was confirmed by Energy Dispersion X-ray Spectroscopy (EDS). Improved stability of the composite magnetic component was evidenced by resistance to degrade in 2N HCl solution. The adsorption capacity of self-doped polyaniline nanofibers based composite was higher than previously reported corresponding composite prepared from polyaniline nanofibers instead of self-doped polyaniline nanofibers. Adsorption-pH profile for the studied FQs on the prepared composite revealed that the best pH for adsorption was in range of 6.5 to 7. Best extraction recovery values were obtained at pH 7 using phosphate buffer. The best solvent for FQs desorption was found to be 0.1N HCl in methanol:water (8:2; v/v) mixture. 20 mL of Spiked water sample with studied FQs were preconcentrated using 4.8 mg of composite and resulting extracts were analysed by HPLC-UV method. The prepared composite represented a suitable adsorbent phase for magnetic dispersive micro-solid phase application.

Keywords: fluoroquinolones, magnetic dispersive micro extraction, nano-composite, self-doped polyaniline nanofibers

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Authors: Saad M. Algharib

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The major concern of planners when placing fire stations is finding their optimal locations such that the fire companies can reach fire locations within reasonable response time or distance. Planners are also concerned with the numbers of fire stations that are needed to cover all service areas and the fires, as demands, with standard response time or distance. One of the tools for such analysis is location-allocation models. Location-allocation models enable planners to determine the optimal locations of facilities in an area in order to serve regional demands in the most efficient way. The purpose of this study is to examine the geographic distribution of the existing fire stations in Kuwait City. This study utilized location-allocation models within the Geographic Information System (GIS) environment and a number of statistical functions to assess the current locations of fire stations in Kuwait City. Further, this study investigated how well all service areas are covered and how many and where additional fire stations are needed. Four different location-allocation models were compared to find which models cover more demands than the others, given the same number of fire stations. This study tests many ways to combine variables instead of using one variable at a time when applying these models in order to create a new measurement that influences the optimal locations for locating fire stations. This study also tests how location-allocation models are sensitive to different levels of spatial dependency. The results indicate that there are some districts in Kuwait City that are not covered by the existing fire stations. These uncovered districts are clustered together. This study also identifies where to locate the new fire stations. This study provides users of these models a new variable that can assist them to select the best locations for fire stations. The results include information about how the location-allocation models behave in response to different levels of spatial dependency of demands. The results show that these models perform better with clustered demands. From the additional analysis carried out in this study, it can be concluded that these models applied differently at different spatial patterns.

Keywords: geographic information science, GIS, location-allocation models, geography

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Authors: Fazıl Gökgöz, Fahrettin Filiz

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Electricity prices have sophisticated features such as high volatility, nonlinearity and high frequency that make forecasting quite difficult. Electricity price has a volatile and non-random character so that, it is possible to identify the patterns based on the historical data. Intelligent decision-making requires accurate price forecasting for market traders, retailers, and generation companies. So far, many shallow-ANN (artificial neural networks) models have been published in the literature and showed adequate forecasting results. During the last years, neural networks with many hidden layers, which are referred to as DNN (deep neural networks) have been using in the machine learning community. The goal of this study is to investigate electricity price forecasting performance of the shallow-ANN and DNN models for the Turkish day-ahead electricity market. The forecasting accuracy of the models has been evaluated with publicly available data from the Turkish day-ahead electricity market. Both shallow-ANN and DNN approach would give successful result in forecasting problems. Historical load, price and weather temperature data are used as the input variables for the models. The data set includes power consumption measurements gathered between January 2016 and December 2017 with one-hour resolution. In this regard, forecasting studies have been carried out comparatively with shallow-ANN and DNN models for Turkish electricity markets in the related time period. The main contribution of this study is the investigation of different shallow-ANN and DNN models in the field of electricity price forecast. All models are compared regarding their MAE (Mean Absolute Error) and MSE (Mean Square) results. DNN models give better forecasting performance compare to shallow-ANN. Best five MAE results for DNN models are 0.346, 0.372, 0.392, 0,402 and 0.409.

Keywords: deep learning, artificial neural networks, energy price forecasting, turkey

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Authors: A. Feliczak Guzik, I. Nowak

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Zeolites, classified as microporous materials, are a large group of crystalline aluminosilicate materials commonly used in the chemical industry. These materials are characterized by large specific surface area, high adsorption capacity, hydrothermal and thermal stability. However, the micropores present in them impose strong mass transfer limitations, resulting in low catalytic performance. Consequently, mesoporous (hierarchical) zeolites have attracted considerable attention from researchers. These materials possess additional porosity in the mesopore size region (2-50 nm according to IUPAC). Mesoporous zeolites, based on commercial MFI-type zeolites modified with silver, were synthesized as follows: 0.5 g of zeolite was dispersed in a mixture containing CTABr (template), water, ethanol, and ammonia under ultrasound for 30 min at 65°C. The silicon source, which was tetraethyl orthosilicate, was then added and stirred for 4 h. After this time, silver(I) nitrate was added. In a further step, the whole mixture was filtered and washed with water: ethanol mixture. The template was removed by calcination at 550°C for 5h. All the materials obtained were characterized by the following techniques: X-ray diffraction (XRD), transmission electron microscopy (TEM), scanning electron microscopy (SEM), nitrogen adsorption/desorption isotherms, FTIR spectroscopy. X-ray diffraction and low-temperature nitrogen adsorption/desorption isotherms revealed additional secondary porosity. Moreover, the structure of the commercial zeolite was preserved during most of the material syntheses. The aforementioned materials were used in the epoxidation reaction of cyclohexene using conventional heating and microwave radiation heating. The composition of the reaction mixture was analyzed every 1 h by gas chromatography. As a result, about 60% conversion of cyclohexene and high selectivity to the desired reaction products i.e., 1,2-epoxy cyclohexane and 1,2-cyclohexane diol, were obtained.

Keywords: catalytic application, characterization, epoxidation, hierarchical zeolites, synthesis

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Authors: Pablo Martinez, Emad Mohamed, Osama Mohsen, Yasser Mohamed

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Highway and bridge agencies, such as the Ministry of Transportation in Ontario, use the Bridge Condition Index (BCI) which is defined as the weighted condition of all bridge elements to determine the rehabilitation priorities for its bridges. Therefore, accurate forecasting of BCI is essential for bridge rehabilitation budgeting planning. The large amount of data available in regard to bridge conditions for several years dictate utilizing traditional mathematical models as infeasible analysis methods. This research study focuses on investigating different classification models that are developed to predict the bridge condition index in the province of Ontario, Canada based on the publicly available data for 2800 bridges over a period of more than 10 years. The data preparation is a key factor to develop acceptable classification models even with the simplest one, the k-NN model. All the models were tested, compared and statistically validated via cross validation and t-test. A simple k-NN model showed reasonable results (within 0.5% relative error) when predicting the bridge condition in an incoming year.

Keywords: asset management, bridge condition index, data mining, forecasting, infrastructure, knowledge discovery in databases, maintenance, predictive models

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Authors: Mohd Adib Abd Muin, Shuhairimi Abdullah, Azizan Bahari

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Problem: The research problem is lacking of model on social entrepreneurship that focus on Islamic perspective. Objective: The objective of this paper is to analyse the existing model on social entrepreneurship and to identify the research gap on Islamic perspective from existing models. Research Methodology: The research method used in this study is literature review and comparative analysis from 6 existing models of social entrepreneurship. Finding: The research finding shows that 6 existing models on social entrepreneurship has been analysed and it shows that the existing models on social entrepreneurship do not emphasize on Islamic perspective.

Keywords: social entrepreneurship, Islamic perspective, research gap, business management

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Authors: Sumaira Ashraf, Elisabete G.S. Félix, Zélia Serrasqueiro

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Traditional financial distress prediction models performed well to predict bankrupt and insolvent firms of the developed markets. Previous studies particularly focused on the predictability of financial distress, financial failure, and bankruptcy of firms. This paper contributes to the literature by extending the definition of financial distress with the inclusion of early warning signs related to quotation of face value, dividend/bonus declaration, annual general meeting, and listing fee. The study used five well-known distress prediction models to see if they have the ability to predict early warning signs of financial distress. Results showed that the predictive ability of the models varies over time and decreases specifically for the sample with early warning signs of financial distress. Furthermore, the study checked the differences in the predictive ability of the models with respect to the financial crisis. The results conclude that the predictive ability of the traditional financial distress prediction models decreases for the firms with early warning signs of financial distress and during the time of financial crisis. The study developed a new model comprising significant variables from the five models and one new variable earning response. This new model outperforms the old distress prediction models before, during and after the financial crisis. Thus, it can be used by researchers, organizations and all other concerned parties to indicate early warning signs for the emerging markets.

Keywords: financial distress, emerging market, prediction models, Z-Score, logit analysis, probit model

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Authors: H. Jung, N. Kim, B. Kang, J. Choe

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History matching is a crucial procedure for predicting reservoir performances and making future decisions. However, it is difficult due to uncertainties of initial reservoir models. Therefore, it is important to have reliable initial models for successful history matching of highly heterogeneous reservoirs such as channel reservoirs. In this paper, we proposed a novel scheme for regenerating geological models using support vector machine (SVM) and principal component analysis (PCA). First, we perform PCA for figuring out main geological characteristics of models. Through the procedure, permeability values of each model are transformed to new parameters by principal components, which have eigenvalues of large magnitude. Secondly, the parameters are projected into two-dimensional plane by multi-dimensional scaling (MDS) based on Euclidean distances. Finally, we train an SVM classifier using 20% models which show the most similar or dissimilar well oil production rates (WOPR) with the true values (10% for each). Then, the other 80% models are classified by trained SVM. We select models on side of low WOPR errors. One hundred channel reservoir models are initially generated by single normal equation simulation. By repeating the classification process, we can select models which have similar geological trend with the true reservoir model. The average field of the selected models is utilized as a probability map for regeneration. Newly generated models can preserve correct channel features and exclude wrong geological properties maintaining suitable uncertainty ranges. History matching with the initial models cannot provide trustworthy results. It fails to find out correct geological features of the true model. However, history matching with the regenerated ensemble offers reliable characterization results by figuring out proper channel trend. Furthermore, it gives dependable prediction of future performances with reduced uncertainties. We propose a novel classification scheme which integrates PCA, MDS, and SVM for regenerating reservoir models. The scheme can easily sort out reliable models which have similar channel trend with the reference in lowered dimension space.

Keywords: history matching, principal component analysis, reservoir modelling, support vector machine

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Authors: S. Benessam, T. H. Debieche, S. Amiour, A. Chine, S. Khelili

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The metallic trace elements (MTE) are present in water and sediments of the rivers with weak concentrations. Several physicochemical parameters (Eh, pH and oxygen dissolved) and chemical processes (adsorption, absorption, complexation and precipitation) as well as nature of the sediments control their mobility. In order to determine the effect of these factors on the mobility of some MTE (Cd, Cr, Cu, Fe, Pb and Zn) in water of the rivers, a two-monthly monitoring of the physicochemical parameters and chemistry of water and sediments of the Nil wadi (Algeria) was carried out during the period from November 2013 to January 2015. The results show that each MTE has its own conditions of mobility and generally are very influence by the variations of the pH and Eh. Under the natural conditions, neutral pH with basic and medium oxidizing, only the lead presented in water with raised values, indicating its solubility in water and its salting out of the sediments. The other MTE present raised concentrations in the sediments, indicating their trapping by adsorption and/or chemical precipitation. The chemical form of each ETM was given by Eh-pH diagrams. The spatio-temporal monitoring of these ETM shows the effect of the rains, the dry periods and the rejects in the variation of their concentrations.

Keywords: chemistry, metallic trace elements, sediment, water

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Authors: Eric York, James Tadio, Silas Owusu Antwi

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Laundry wastewater contains a diverse range of chemical pollutants that can have detrimental effects on human health and the environment. In this study, simulation studies by Spyder Python software v 3.2 to assess the efficacy of biochar in removing PO₄³⁻ from wastewater were conducted. Through modeling and simulation, the mechanisms involved in the adsorption process of phosphate by biochar were studied by altering variables which is specific to the phosphate from common laundry phosphate detergents, such as the aqueous solubility, initial concentration, and temperature using the Dudinin Approach (DA). Results showed that the concentration equilibrate at near the highest concentrations for Sugar beet-120 mgL⁻¹, Tailing-85 mgL⁻¹, CaO- rich-50 mgL⁻¹, Eggshell and rice straw-48 mgL⁻¹, Undaria Pinnatifida Roots-190 mgL⁻¹, Ca-Alginate Granular Beads -240 mgL⁻¹, Laminaria Japonica Powder -900 mgL⁻¹, Pinesaw dust-57 mgL⁻¹, Ricehull-190 mgL⁻¹, sesame straw- 470 mgL⁻¹, Sugar Bagasse-380 mgL⁻¹, Miscanthus Giganteus-240 mgL⁻¹, Wood Bc-130 mgL⁻¹, Pine-25 mgL⁻¹, Sawdust-6.8 mgL⁻¹, Sewage Sludge-, Rice husk-12 mgL⁻¹, Corncob-117 mgL⁻¹, Maize straw- 1800 mgL⁻¹ while Peanut -Eucalyptus polybractea-, Crawfish equilibrated at near concentration. CO₂ activated Thalia, sewage sludge biochar, Broussonetia Papyrifera Leaves equilibrated just at the lower concentration. Only Soyer bean Stover exhibited a sharp rise and fall peak in mid-concentration at 2 mgL⁻¹ volume. The modelling results were consistent with experimental findings from the literature, ensuring the accuracy, repeatability, and reliability of the simulation study. The simulation study provided insights into adsorption for PO₄³⁻ from wastewater by biochar using concentration per volume that can be adsorbed ideally under the given conditions. Studies showed that applying the principle experimentally in real wastewater with all its complexity is warranted and not far-fetched.

Keywords: simulation studies, phosphate removal, biochar, adsorption, wastewater treatment

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Authors: N. Allouache, O. Rahli

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Solar radiation is by far the largest and the most world’s abundant, clean, and permanent energy source. In recent years, many promising technologies have been developed to harness the sun's energy. These technologies help in environmental protection, economizing energy, and sustainable development, which are the major issues of the world. One of these important technologies is the solar refrigerating machines that make use of either absorption or adsorption technologies. In this present work, the adsorbent bed is modelized and optimized using different working pairs, such as zeolite-water, silica gel-water, activated carbon-ammonia, calcium chlorid-ammonia, activated carbon fiber- methanol and activated carbon AC35-methanol. The results show that the enhancement of the heat and mass transfer depends on the properties of the working pair; the performances of the adsorption cycle are essentially influenced by the choice of the adsorbent-adsorbate pair. The system can operate successfully for optimal parameters such as the evaporator, condenser, and generating temperatures. The activated carbon is the best adsorbent due to its high surface area and micropore volume.

Keywords: adsorbent bed, heat and mass transfer, numerical analysis, working pairs

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Authors: J. Suwanprateeb, F. Thammarakcharoen

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Calcium phosphate coating (CaP) has been employed for protein delivery, but the typical direct protein adsorption on the coating led to low incorporation content and fast release of the protein from the coating. By using bovine serum albumin (BSA) as a model protein, rapid biomimetic co-precipitation between calcium phosphate and BSA was employed to control the distribution of BSA within calcium phosphate coating during biomimetic formation on titanium surface for only 6 h at 50 oC in an accelerated calcium phosphate solution. As a result, the amount of BSA incorporation and release duration could be increased by using a rapid biomimetic co-precipitation technique. Up to 43 fold increases in the BSA incorporation content and the increase from 6 h to more than 360 h in release duration compared to typical direct adsorption technique were observed depending on the initial BSA concentration used during co-precipitation (1, 10, and 100 microgram/ml). From X-ray diffraction and Fourier transform infrared spectroscopy studies, the coating composition was not altered with the incorporation of BSA by this rapid biomimetic co-precipitation and mainly comprised octacalcium phosphate and hydroxyapatite. However, the microstructure of calcium phosphate crystals changed from straight, plate-like units to curved, plate-like units with increasing BSA content.

Keywords: biomimetic, Calcium Phosphate Coating, protein, titanium

Procedia PDF Downloads 364

Authors: Nurudeen Oluwasola Lasisi, Abdulkareem Afolabi Ibrahim

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Newcastle disease is an infection of domestic poultry and other bird species with virulent Newcastle disease virus (NDV). In this paper, we study the dynamics of modeling the Newcastle disease virus (NDV) using a novel quarantine-adjusted incidence. We do a comparison of Vaccination, linear incident rate, and novel quarantine adjusted incident rate in the models. The dynamics of the models yield disease free and endemic equilibrium states. The effective reproduction numbers of the models are computed in order to measure the relative impact for the individual bird or combined intervention for effective disease control. We showed the local and global stability of endemic equilibrium states of the models, and we found that stability of endemic equilibrium states of models are globally asymptotically stable if the effective reproduction numbers of the models equations are greater than a unit.

Keywords: effective reproduction number, endemic state, mathematical model, Newcastle disease virus, novel quarantine-adjusted incidence, stability analysis

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Authors: Ahmed El-Banbi

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Several PVT models exist to represent how PVT properties are handled in sub-surface and surface engineering calculations for oil and gas production. The most commonly used models include black oil, modified black oil (MBO), and compositional models. These models are used in calculations that allow engineers to optimize and forecast well and reservoir performance (e.g., reservoir simulation calculations, material balance, nodal analysis, surface facilities, etc.). The choice of which model is dependent on fluid type and the production process (e.g., depletion, water injection, gas injection, etc.). Based on close to 2,000 reservoir fluid samples collected from different basins and locations, this paper presents some conclusions on the occurrence of reservoir fluids. It also reviews the common methods used to classify reservoir fluid types. Based on new criteria related to the production behavior of different fluids and economic considerations, an updated classification of reservoir fluid types is presented in the paper. Recommendations on the use of different PVT models to simulate the behavior of different reservoir fluid types are discussed. Each PVT model requirement is highlighted. Available methods for the calculation of PVT properties from each model are also discussed. Practical recommendations and tips on how to control the calculations to achieve the most accurate results are given.

Keywords: PVT models, fluid types, PVT properties, fluids classification

Procedia PDF Downloads 54

Authors: Meng-Fei Cheng, Wei-Lun Chen, Han-Chang Ma, Chi-Che Tsai

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Recent K–12 Taiwan Science Education Curriculum Guideline emphasize the essential role of modeling curriculum in science learning; however, few modeling curricula have been designed and adopted in current science teaching. Therefore, this study aims to develop modeling curriculum on electric circuits to investigate any learning difficulties students have with modeling curriculum and further enhance modeling teaching. This study was conducted with 44 10th-grade students in Central Taiwan. Data collection included a students’ understanding of models in science (SUMS) survey that explored the students' epistemology of scientific models and modeling and a complex circuit problem to investigate the students’ modeling abilities. Data analysis included the following: (1) Paired sample t-tests were used to examine the improvement of students’ modeling abilities and conceptual understanding before and after the curriculum was taught. (2) Paired sample t-tests were also utilized to determine the students’ modeling abilities before and after the modeling activities, and a Pearson correlation was used to understand the relationship between students’ modeling abilities during the activities and on the posttest. (3) ANOVA analysis was used during different stages of the modeling curriculum to investigate the differences between the students’ who developed microscopic models and macroscopic models after the modeling curriculum was taught. (4) Independent sample t-tests were employed to determine whether the students who changed their models had significantly different understandings of scientific models than the students who did not change their models. The results revealed the following: (1) After the modeling curriculum was taught, the students had made significant progress in both their understanding of the science concept and their modeling abilities. In terms of science concepts, this modeling curriculum helped the students overcome the misconception that electric currents reduce after flowing through light bulbs. In terms of modeling abilities, this modeling curriculum helped students employ macroscopic or microscopic models to explain their observed phenomena. (2) Encouraging the students to explain scientific phenomena in different context prompts during the modeling process allowed them to convert their models to microscopic models, but it did not help them continuously employ microscopic models throughout the whole curriculum. The students finally consistently employed microscopic models when they had help visualizing the microscopic models. (3) During the modeling process, the students who revised their own models better understood that models can be changed than the students who did not revise their own models. Also, the students who revised their models to explain different scientific phenomena tended to regard models as explanatory tools. In short, this study explored different strategies to facilitate students’ modeling processes as well as their difficulties with the modeling process. The findings can be used to design and teach modeling curricula and help students enhance their modeling abilities.

Keywords: electric circuits, modeling curriculum, science learning, scientific model

Procedia PDF Downloads 437

Authors: R. Hellmuth

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The method of factory planning has changed a lot, especially when it is about planning the factory building itself. Factory planning has the task of designing products, plants, processes, organization, areas, and the building of a factory. Regular restructuring is becoming more important in order to maintain the competitiveness of a factory. Restrictions in new areas, shorter life cycles of product and production technology as well as a VUCA world (Volatility, Uncertainty, Complexity and Ambiguity) lead to more frequent restructuring measures within a factory. A digital factory model is the planning basis for rebuilding measures and becomes an indispensable tool. Furthermore, digital building models are increasingly being used in factories to support facility management and manufacturing processes. The main research question of this paper is, therefore: What kind of digital factory model is suitable for the different areas of application during the operation of a factory? First, different types of digital factory models are investigated, and their properties and usabilities for use cases are analysed. Within the scope of investigation are point cloud models, building information models, photogrammetry models, and these enriched with sensor data are examined. It is investigated which digital models allow a simple integration of sensor data and where the differences are. Subsequently, possible application areas of digital factory models are determined by means of a survey and the respective digital factory models are assigned to the application areas. Finally, an application case from maintenance is selected and implemented with the help of the appropriate digital factory model. It is shown how a completely digitalized maintenance process can be supported by a digital factory model by providing information. Among other purposes, the digital factory model is used for indoor navigation, information provision, and display of sensor data. In summary, the paper shows a structuring of digital factory models that concentrates on the geometric representation of a factory building and its technical facilities. A practical application case is shown and implemented. Thus, the systematic selection of digital factory models with the corresponding application cases is evaluated.

Keywords: building information modeling, digital factory model, factory planning, maintenance

Procedia PDF Downloads 94

Authors: Yoko Yamada, Chizumi Yamada

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Narrative psychology is based on the dialogical relationship between self and other. The dialogue can consist of divided, competitive, or opposite communication between self and other. We constructed models of coexistent dialogue in which self and other were positioned side by side and communicated sympathetically. We propose new mediation models for narrative relationships. The mediation models are based on triadic relationships that incorporate a medium or a mediator along with self and other. We constructed three types of mediation model. In the first type, called the “Joint Attention Model”, self and other are positioned side by side and share attention with the medium. In the second type, the “Triangle Model”, an agent mediates between self and other. In the third type, the “Caring Model”, a caregiver stands beside the communication between self and other. We apply the three models to the illness narratives of medical professionals and patients. As these groups have different views and experiences of disease or illness, triadic mediation facilitates the ability to see things from the other person’s perspective and to bridge differences in people’s experiences and feelings. These models would be useful for medical education in various situations, such as in considering the relationships between senior and junior doctors and between old and young patients.

Keywords: illness narrative, mediation, psychology, model, medical education

Procedia PDF Downloads 390