Search results for: wastewater modelling

Authors: Esmaeil Kouhgardi, Elaheh Shakerdargah

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The mangrove ecosystem is a complex of various inter-related elements in the land-sea interface zone which is linked with other natural systems of the coastal region such as corals, sea-grass, coastal fisheries and beach vegetation. The mangrove ecosystem consists of water, muddy soil, trees, shrubs, and their associated flora, fauna and microbes. It is a very productive ecosystem sustaining various forms of life. Its waters are nursery grounds for fish, crustacean, and mollusk and also provide habitat for a wide range of aquatic life, while the land supports a rich and diverse flora and fauna, but pollutions may affect these characteristics. Iran has the lowest share of Persian Gulf pollution among the eight littoral states; environmental experts are still deeply concerned about the serious consequences of the pollution in the oil-rich gulf. Prolongation of critical conditions in the Persian Gulf has endangered its aquatic ecosystem. Water purification equipment, refineries, wastewater emitted by onshore installations, especially petrochemical plans, urban sewage, population density and extensive oil operations of Arab states are factors contaminating the Persian Gulf waters. Population density has been the major cause of pollution and environmental degradation in the Persian Gulf. Persian Gulf is a closed marine environment which is connected to open waterways only from one way. It usually takes between three and four years for the gulf's water to be completely replaced. Therefore, any pollution entering the water will remain there for a relatively long time. Presently, the high temperature and excessive salt level in the water have exposed the marine creatures to extra threats, which mean they have to survive very tough conditions. The natural environment of the Persian Gulf is very rich with good fish grounds, extensive coral reefs and pearl oysters in abundance, but has become increasingly under pressure due to the heavy industrialization and in particular the repeated major oil spillages associated with the various recent wars fought in the region. Pollution may cause the mortality of mangrove forests by effect on root, leaf and soil of the area. Study was showed the high correlation between industrial pollution and mangrove forests health in south of Iran and increase of population, coupled with economic growth, inevitably caused the use of mangrove lands for various purposes such as construction of roads, ports and harbors, industries and urbanization.

Keywords: Mangrove forest, pollution, Persian Gulf, population, environment

Procedia PDF Downloads 375

Authors: Randa Alharbi, Vladislav Vyshemirsky

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Systems biology is an important field in science which focuses on studying behaviour of biological systems. Modelling is required to produce detailed description of the elements of a biological system, their function, and their interactions. A well-designed model requires selecting a suitable mechanism which can capture the main features of the system, define the essential components of the system and represent an appropriate law that can define the interactions between its components. Complex biological systems exhibit stochastic behaviour. Thus, using probabilistic models are suitable to describe and analyse biological systems. Continuous-Time Markov Chain (CTMC) is one of the probabilistic models that describe the system as a set of discrete states with continuous time transitions between them. The system is then characterised by a set of probability distributions that describe the transition from one state to another at a given time. The evolution of these probabilities through time can be obtained by chemical master equation which is analytically intractable but it can be simulated. Uncertain parameters of such a model can be inferred using methods of Bayesian inference. Yet, inference in such a complex system is challenging as it requires the evaluation of the likelihood which is intractable in most cases. There are different statistical methods that allow simulating from the model despite intractability of the likelihood. Approximate Bayesian computation is a common approach for tackling inference which relies on simulation of the model to approximate the intractable likelihood. Particle Markov chain Monte Carlo (PMCMC) is another approach which is based on using sequential Monte Carlo to estimate intractable likelihood. However, both methods are computationally expensive. In this paper we discuss the efficiency and possible practical issues for each method, taking into account the computational time for these methods. We demonstrate likelihood-free inference by performing analysing a model of the Repressilator using both methods. Detailed investigation is performed to quantify the difference between these methods in terms of efficiency and computational cost.

Keywords: Approximate Bayesian computation(ABC), Continuous-Time Markov Chains, Sequential Monte Carlo, Particle Markov chain Monte Carlo (PMCMC)

Procedia PDF Downloads 182

Authors: Marine Reymond, Michael Descostes, Marie Muguet, Clemence Besancon, Martine Leermakers, Catherine Beaucaire, Sophie Billon, Patricia Patrier

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²²⁶Ra is one of the radionuclides resulting from the disintegration of ²³⁸U. Due to its half-life (1600 y) and its high specific activity (3.7 x 1010 Bq/g), ²²⁶Ra is found at the ultra-trace level in the natural environment (usually below 1 Bq/L, i.e. 10-13 mol/L). Because of its decay in ²²²Rn, a radioactive gas with a shorter half-life (3.8 days) which is difficult to control and dangerous for humans when inhaled, ²²⁶Ra is subject to a dedicated monitoring in surface waters especially in the context of uranium mining. In natural waters, radionuclides occur in dissolved, colloidal or particular forms. Due to the size of colloids, generally ranging between 1 nm and 1 µm and their high specific surface areas, the colloidal fraction could be involved in the transport of trace elements, including radionuclides in the environment. The colloidal fraction is not always easy to determine and few existing studies focus on ²²⁶Ra. In the present study, a complete multidisciplinary approach is proposed to assess the colloidal transport of ²²⁶Ra. It includes water sampling by conventional filtration (0.2µm) and the innovative Diffusive Gradient in Thin Films technique to measure the dissolved fraction (<10nm), from which the colloidal fraction could be estimated. Suspended matter in these waters were also sampled and characterized mineralogically by X-Ray Diffraction, infrared spectroscopy and scanning electron microscopy. All of these data, which were acquired on a rehabilitated former uranium mine, allowed to build a geochemical model using the geochemical calculation code PhreeqC to describe, as accurately as possible, the colloidal transport of ²²⁶Ra. Colloidal transport of ²²⁶Ra was found, for some of the sampling points, to account for up to 95% of the total ²²⁶Ra measured in water. Mineralogical characterization and associated geochemical modelling highlight the role of barite, a barium sulfate mineral well known to trap ²²⁶Ra into its structure. Barite was shown to be responsible for the colloidal ²²⁶Ra fraction despite the presence of kaolinite and ferrihydrite, which are also known to retain ²²⁶Ra by sorption.

Keywords: colloids, mining context, radium, transport

Procedia PDF Downloads 132

Authors: Ankit Kumar Yadav, Nagendra R. Velaga

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Driving under the influence of alcohol impairs the driving performance and increases the crash risks worldwide. The present study investigated the effect of different Blood Alcohol Concentrations (BAC) on the accelerating and braking behaviour of drivers with the help of driving simulator experiments. Eighty-two licensed Indian drivers drove on the rural road environment designed in the driving simulator at BAC levels of 0.00%, 0.03%, 0.05%, and 0.08% respectively. Driving performance was analysed with the help of vehicle control performance indicators such as mean acceleration and mean brake pedal force of the participants. Preliminary analysis reported an increase in mean acceleration and mean brake pedal force with increasing BAC levels. Generalized linear mixed models were developed to quantify the effect of different alcohol levels and explanatory variables such as driver’s age, gender and other driver characteristic variables on the driving performance indicators. Alcohol use was reported as a significant factor affecting the accelerating and braking performance of the drivers. The acceleration model results indicated that mean acceleration of the drivers increased by 0.013 m/s², 0.026 m/s² and 0.027 m/s² for the BAC levels of 0.03%, 0.05% and 0.08% respectively. Results of the brake pedal force model reported that mean brake pedal force of the drivers increased by 1.09 N, 1.32 N and 1.44 N for the BAC levels of 0.03%, 0.05% and 0.08% respectively. Age was a significant factor in both the models where one year increase in drivers’ age resulted in 0.2% reduction in mean acceleration and 19% reduction in mean brake pedal force of the drivers. It shows that driving experience could compensate for the negative effects of alcohol to some extent while driving. Female drivers were found to accelerate slower and brake harder as compared to the male drivers which confirmed that female drivers are more conscious about their safety while driving. It was observed that drivers who were regular exercisers had better control on their accelerator pedal as compared to the non-regular exercisers during drunken driving. The findings of the present study revealed that drivers tend to be more aggressive and impulsive under the influence of alcohol which deteriorates their driving performance. Drunk driving state can be differentiated from sober driving state by observing the accelerating and braking behaviour of the drivers. The conclusions may provide reference in making countermeasures against drinking and driving and contribute to traffic safety.

Keywords: alcohol, acceleration, braking behaviour, driving simulator

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Authors: Kayode Oshinubi, Brice Kammegne, Jacques Demongeot

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The use of mathematical tools like Partial Differential Equations and Ordinary Differential Equations have become very important to predict the evolution of a viral disease in a population in order to take preventive and curative measures. In December 2019, a novel variety of Coronavirus (SARS-CoV-2) was identified in Wuhan, Hubei Province, China causing a severe and potentially fatal respiratory syndrome, i.e., COVID-19. Since then, it has become a pandemic declared by World Health Organization (WHO) on March 11, 2020 which has spread around the globe. A reaction-diffusion system is a mathematical model that describes the evolution of a phenomenon subjected to two processes: a reaction process in which different substances are transformed, and a diffusion process that causes a distribution in space. This article provides a mathematical study of the Susceptible, Exposed, Infected, Recovered, and Vaccinated population model of the COVID-19 pandemic by the bias of reaction-diffusion equations. Both local and global asymptotic stability conditions for disease-free and endemic equilibria are determined using the Lyapunov function are considered and the endemic equilibrium point exists and is stable if it satisfies Routh–Hurwitz criteria. Also, adequate conditions for the existence and uniqueness of the solution of the model have been proved. We showed the spatial distribution of the model compartments when the basic reproduction rate $\mathcal{R}_0 < 1$ and $\mathcal{R}_0 > 1$ and sensitivity analysis is performed in order to determine the most sensitive parameters in the proposed model. We demonstrate the model's effectiveness by performing numerical simulations. We investigate the impact of vaccination and the significance of spatial distribution parameters in the spread of COVID-19. The findings indicate that reducing contact with an infected person and increasing the proportion of susceptible people who receive high-efficacy vaccination will lessen the burden of COVID-19 in the population. To the public health policymakers, we offered a better understanding of the COVID-19 management.

Keywords: COVID-19, SEIRV epidemic model, reaction-diffusion equation, basic reproduction number, vaccination, spatial distribution

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Authors: Zuhal Mohammed, Khalid Siddig, Harald Grethe

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Vocational training (VT) has been identified as a potential engine for achieving economic and social development, particularly in developing countries, while during the last two decades it is deemed as an essential determinant of human capital accumulation. Furthermore, it has a crucial role in reducing inequality, wage gaps and unemployment and in promoting skill decomposition. Government plays an important role in the human capital formulation by providing finance for education. In some countries, a large portion of the public educational investment is devoted to academic education (primary, secondary and tertiary). This is reflected in disproportionately increasing investment in various education sectors other than vocational education and VT. Nevertheless, the finance of VT system is not likely to increase or even remain at its existing level. This paper conducts an in-depth analysis to quantify the impacts of various options for expanding the public expenditure on education as well as vocational training in the Sudan. The study uses a recursive dynamic CGE modelling framework that accommodates VT and allows depicting the impact of various policies targeting the vocational training system with special focus on the agricultural sector. This allows for depicting the potential effects of various resource allocation policies not only among education versus non-education sectors, but also between the various types of education and training. Moreover, the study assesses the role of VT system in the economy through its influence on workers’ skill improvement and their movement across sectors. The results show that an increase in the public educational investment will lead to decrease the supply of low and high educated workers as results of increasing the school participation of the students in the short run. While in the medium to long run, this measure guides to increase the productivity of the labour and thus the growth rate of the gross domestic product (GDP). Therefore, the findings of the study provide Sudanese policymakers with needed information to help to adopt measures to reduce unemployment, enhance workers’ skill and ultimately improve livelihoods.

Keywords: vocational training, recursive dynamic CGE, skill level, labour market, economic growth, Sudan

Procedia PDF Downloads 171

Authors: Shreyas Srinivas Rangan, Jurgis Porins

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The internet traffic has increased exponentially due to the high demand for data rates by the users, and the constantly increasing metro networks and access networks are focused on improving the maximum transmit distance of the long-reach optical networks. One of the common methods to improve the maximum transmit distance of the long-reach optical networks at the component level is to use broadband optical amplifiers. The Erbium Doped Fiber Amplifier (EDFA) provides high amplification with low noise figure but due to the characteristics of EDFA, its operation is limited to C-band and L-band. In contrast, the Raman amplifier exhibits a wide amplification spectrum, and negative noise figure values can be achieved. To obtain such results, high powered pumping sources are required. Operating Raman amplifiers with such high-powered optical sources may cause fire hazards and it may damage the optical system. In this paper, we implement a hybrid optical amplifier configuration. EDFA and Raman amplifiers are used in this hybrid setup to combine the advantages of both EDFA and Raman amplifiers to improve the reach of the system. Using this setup, we analyze the maximum transmit distance of the network by obtaining a correlation diagram between the length of the single-mode fiber (SMF) and the Bit Error Rate (BER). This hybrid amplifier configuration is implemented in a Wavelength Division Multiplexing (WDM) system with a BER of 10⁻⁹ by using NRZ modulation format, and the gain uniformity noise ratio (signal-to-noise ratio (SNR)), the efficiency of the pumping source, and the optical signal gain efficiency of the amplifier are studied experimentally in a mathematical modelling environment. Numerical simulations were implemented in RSoft OptSim simulation software based on the nonlinear Schrödinger equation using the Split-Step method, the Fourier transform, and the Monte Carlo method for estimating BER.

Keywords: Raman amplifier, erbium doped fibre amplifier, bit error rate, hybrid optical amplifiers

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Authors: Abduladim Maitieg

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The oil manufacture is one of the main productive activities in Libya and has a massive infrastructure, including offshore drilling and exploration and wide oil export platform sites that located in coastal area. There is a threat to marine and coastal area of oil spills is greatest in those sites with a high spills comes from urban and industry, parallel to that, monitoring oil spills and risk emergency strategy is weakness, An approach for estimating a coastal resources vulnerability to oil spills is presented based on abundance, environmental and Scio-economic importance, distance to oil spill resources and oil risk likelihood. As many as 10 coastal resources were selected for oil spill assessment at the coast. This study aims to evaluate, determine and establish vulnerable coastal resource maps and estimating the rate of oil spill comes for different oil spill resources in Misratah marine environment. In the study area there are two type of oil spill resources, major oil resources come from offshore oil industries which are 96 km from the Coast and Loading/Uploading oil platform. However, the miner oil resources come from urban sewage pipes and fish ports. In order to analyse the collected database, the Geographic information system software has been used to identify oil spill location, to map oil tracks in front of study area, and developing seasonal vulnerable costal resources maps. This work shows that there is a differential distribution of the degree of vulnerability to oil spills along the coastline, with values ranging from high vulnerability and low vulnerability, and highlights the link between oil spill movement and coastal resources vulnerability. The results of assessment found most of costal freshwater spring sites are highly vulnerable to oil spill due to their location on the intertidal zone and their close to proximity to oil spills recourses such as Zreag coast. Furthermore, the Saltmarsh coastline is highly vulnerable to oil spill risk due to characterisation as it contains a nesting area of sea turtles and feeding places for migratory birds and the . Oil will reach the coast in winter season according to oil spill movement. Coastal tourist beaches in the north coast are considered as highly vulnerable to oil spill due to location and closeness to oil spill resources.

Keywords: coastal recourses vulnerability, oil spill trajectory, gnome software, Misratah coast- Libya, GIS

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Authors: Mohd Sabri Minhat, Nurul Adilla Mohd Subha

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The PUSPATI TRIGA Reactor (RTP), Malaysia reached its first criticality on June 28, 1982, with power capacity 1MW thermal. The Feedback Control Algorithm (FCA) which is conventional Proportional-Integral (PI) controller, was used for present power control method to control fission process in RTP. It is important to ensure the core power always stable and follows load tracking within acceptable steady-state error and minimum settling time to reach steady-state power. At this time, the system could be considered not well-posed with power tracking performance. However, there is still potential to improve current performance by developing next generation of a novel design nuclear core power control. In this paper, the dual-mode predictions which are proposed in modelling Optimal Model Predictive Control (OMPC), is presented in a state-space model to control the core power. The model for core power control was based on mathematical models of the reactor core, OMPC, and control rods selection algorithm. The mathematical models of the reactor core were based on neutronic models, thermal hydraulic models, and reactivity models. The dual-mode prediction in OMPC for transient and terminal modes was based on the implementation of a Linear Quadratic Regulator (LQR) in designing the core power control. The combination of dual-mode prediction and Lyapunov which deal with summations in cost function over an infinite horizon is intended to eliminate some of the fundamental weaknesses related to MPC. This paper shows the behaviour of OMPC to deal with tracking, regulation problem, disturbance rejection and caters for parameter uncertainty. The comparison of both tracking and regulating performance is analysed between the conventional controller and OMPC by numerical simulations. In conclusion, the proposed OMPC has shown significant performance in load tracking and regulating core power for nuclear reactor with guarantee stabilising in the closed-loop.

Keywords: core power control, dual-mode prediction, load tracking, optimal model predictive control

Procedia PDF Downloads 139

Authors: Kanti Sapkota, Do Hyun Lee, Sung Soo Han

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Nanocomposites have been widely used in various fields such as electronics, catalysis, and in chemical, biological, biomedical and optical fields. They display broad biomedical properties like antidiabetic, anticancer, antioxidant, antimicrobial and antibacterial activities. Moreover, nanomaterials have been used for wastewater treatment. Particularly, bimetallic hybrid nanocomposites exhibit unique features as compared to their monometallic components. Hybrid nanomaterials not only afford the multifunctionality endowed by their constituents but can also show synergistic properties. In addition, these hybrid nanomaterials have noteworthy catalytic and optical properties. Notably, Au−Ag based nanoparticles can be employed in sensor and catalysis due to their characteristic composition-tunable plasmonic properties. Due to their importance and usefulness, various efforts were developed for their preparation. Generally, chemical methods have been described to synthesize such bimetallic nanocomposites. In such chemical synthesis, harmful and hazardous chemicals cause environmental contamination and increase toxicity levels. Therefore, ecologically benevolent processes for the synthesis of nanomaterials are highly desirable to diminish such environmental and safety concerns. In this regard, here we disclose a simple, cost-effective, external additive free and eco-friendly method for the synthesis of Au-Ag@AgCl nanocomposites using Nephrolepis cordifolia root extract. Au-Ag@AgCl NCs were obtained by the simultaneous reduction of cationic Ag and Au into AgCl in the presence of plant extract. The particle size of 10 to 50 nm was observed with the average diameter of 30 nm. The synthesized nanocomposite was characterized by various modern characterization techniques. For example, UV−visible spectroscopy was used to determine the optical activity of the synthesized NCs, and Fourier transform infrared (FT-IR) spectroscopy was employed to investigate the functional groups present in the biomolecules that were responsible for both reducing and capping agents during the formation of nanocomposites. Similarly, powder X-ray diffraction (XRD), transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), thermogravimetric analysis (TGA) and energy-dispersive X-ray (EDX) spectroscopy were used to determine crystallinity, size, oxidation states, thermal stability and weight loss of the synthesized nanocomposites. As a synthetic application, the synthesized nanocomposite exhibited excellent catalytic activity for the multicomponent synthesis of biologically interesting quinoline molecules via domino annulation-aromatization reaction of aniline, arylaldehyde, and phenyl acetylene derivatives. Interestingly, the nanocatalyst was efficiently recycled for five times without substantial loss of catalytic properties.

Keywords: nanoparticles, catalysis, multicomponent, quinoline

Procedia PDF Downloads 102

Authors: Alok Agrawal, Alok Satapathy

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In this paper, an attempt to determine the effective thermal conductivity (keff) of particulate filled polymer composites using finite element method (FEM) a powerful computational technique is made. A commercially available finite element package ANSYS is used for this numerical analysis. Three-dimensional spheres-in-cube lattice array models are constructed to simulate the microstructures of micro-sized particulate filled polymer composites with filler content ranging from 2.35 to 26.8 vol %. Based on the temperature profiles across the composite body, the keff of each composition is estimated theoretically by FEM. Composites with similar filler contents are than fabricated using compression molding technique by reinforcing micro-sized aluminium oxide (Al2O3) in polypropylene (PP) resin. Thermal conductivities of these composite samples are measured according to the ASTM standard E-1530 by using the Unitherm™ Model 2022 tester, which operates on the double guarded heat flow principle. The experimentally measured conductivity values are compared with the numerical values and also with those obtained from existing empirical models. This comparison reveals that the FEM simulated values are found to be in reasonable good agreement with the experimental data. Values obtained from the theoretical model proposed by the authors are also found to be in even closer approximation with the measured values within percolation limit. Further, this study shows that there is gradual enhancement in the conductivity of PP resin with increase in filler percentage and thereby its heat conduction capability is improved. It is noticed that with addition of 26.8 vol % of filler, the keff of composite increases to around 6.3 times that of neat PP. This study validates the proposed model for PP-Al2O3 composite system and proves that finite element analysis can be an excellent methodology for such investigations. With such improved heat conduction ability, these composites can find potential applications in micro-electronics, printed circuit boards, encapsulations etc.

Keywords: analytical modelling, effective thermal conductivity, finite element method, polymer matrix composite

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Authors: Eranga N. Somaratna, Pradeep Dharmadasa

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In the current competitive environment, the factors leading to organization success are not limited to the investment of capital, labor, and raw material, but in the ability of knowledge innovation from all the members of an organization. However, knowledge on its own cannot provide organizations with its promised benefits unless it is shared, as organizations are increasingly experiencing unsuccessful knowledge sharing efforts. In such a backdrop and due to the dearth of research in this area in the South Asian context, the study set forth to develop an understanding of the factors that influence knowledge-sharing behavior within an organizational framework, using widely accepted social psychology theories. The purpose of the article is to discover the determinants of knowledge-sharing intention and actual knowledge sharing behaviors of bank employees in Sri Lanka using an aggregate model. Knowledge sharing intentions are widely discussed in literature through the application of Ajzen’s Theory of planned behavior (TPB) and Theory of Social Capital (SCT) separately. Both the theories are rich to explain knowledge sharing intention of workers with limitations. The study, therefore, combines the TPB with SCT in developing its conceptual model. Data were collected through a self-administrated paper-based questionnaire of 199 bank managers from 6 public and private banks of Sri Lanka and analyzed the suggested research model using Structural Equation Modelling (SEM). The study supported six of the nine hypotheses, where Attitudes toward Knowledge Sharing Behavior, Perceived Behavioral Control, Trust, Anticipated Reciprocal Relationships and Actual Knowledge Sharing Behavior were supported while Organizational Climate, Sense of Self-Worth and Anticipated Extrinsic Rewards were not, in determining knowledge sharing intentions. Furthermore, the study investigated the effect of demographic factors of bankers (age, gender, position, education, and experiences) to the actual knowledge sharing behavior. However, findings should be confirmed using a larger sample, as well as through cross-sectional studies. The results highlight the need for theoreticians to combined TPB and SCT in understanding knowledge workers’ intentions and actual behavior; and for practitioners to focus on the perceptions and needs of the individual knowledge worker and the need to cultivate a culture of sharing knowledge in the organization for their mutual benefit.

Keywords: banks, employees behavior, knowledge management, knowledge sharing

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Authors: Bernard Kumi-Boateng, Issaka Yakubu

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Forest fire, which is, an uncontrolled fire occurring in nature has become a major concern for the Forestry Commission of Ghana (FCG). The forest fires in Ghana usually result in massive destruction and take a long time for the firefighting crews to gain control over the situation. In order to assess the effect of forest fire at local scale, it is important to consider the role fire plays in vegetation composition, biodiversity, soil erosion, and the hydrological cycle. The occurrence, frequency and behaviour of forest fires vary over time and space, primarily as a result of the complicated influences of changes in land use, vegetation composition, fire suppression efforts, and other indigenous factors. One of the forest zones in Ghana with a high level of vegetation stress is the Goaso forest area. The area has experienced changes in its traditional land use such as hunting, charcoal production, inefficient logging practices and rural abandonment patterns. These factors which were identified as major causes of forest fire, have recently modified the incidence of fire in the Goaso area. In spite of the incidence of forest fires in the Goaso forest area, most of the forest services do not provide a cartographic representation of the burned areas. This has resulted in significant amount of information being required by the firefighting unit of the FCG to understand fire risk factors and its spatial effects. This study uses Remote Sensing and Geographic Information System techniques to develop a fire risk hazard model using the Goaso Forest Area (GFA) as a case study. From the results of the study, natural forest, agricultural lands and plantation cover types were identified as the major fuel contributing loads. However, water bodies, roads and settlements were identified as minor fuel contributing loads. Based on the major and minor fuel contributing loads, a forest fire risk hazard model with a reasonable accuracy has been developed for the GFA to assist decision making.

Keywords: forest, GIS, remote sensing, Goaso

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Authors: Esmaeil Abbaszadeh Molaei, Zongyan Zhou, Aibing Yu

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The applications of liquid fluidizations have been increased in many parts of industries such as particle classification, backwashing of granular filters, crystal growth, leaching and washing, and bioreactors due to high-efficient liquid–solid contact, favorable mass and heat transfer, high operation flexibilities, and reduced back mixing of phases. In most of these multiphase operations the particles properties, i.e. size, density, and shape, may change during the process because of attrition, coalescence or chemical reactions. Previous studies, either experimentally or numerically, mainly have focused on studies of liquid-solid fluidized beds containing spherical particles; however, the role of particle shape on the hydrodynamics of liquid fluidized beds is still not well-known. A three-dimensional Discrete Element Model (DEM) and Computational Fluid Dynamics (CFD) are coupled to study the influence of particles shape on particles and liquid flow patterns in liquid-solid fluidized beds. In the simulations, ellipsoid particles are used to study the shape factor since they can represent a wide range of particles shape from oblate and sphere to prolate shape particles. Different particle shapes from oblate (disk shape) to elongated particles (rod shape) are selected to investigate the effect of aspect ratio on different flow characteristics such as general particles and liquid flow pattern, pressure drop, and particles orientation. First, the model is verified based on experimental observations, then further detail analyses are made. It was found that spherical particles showed a uniform particle distribution in the bed, which resulted in uniform pressure drop along the bed height. However for particles with aspect ratios less than one (disk-shape), some particles were carried into the freeboard region, and the interface between the bed and freeboard was not easy to be determined. A few particle also intended to leave the bed. On the other hand, prolate particles showed different behaviour in the bed. They caused unstable interface and some flow channeling was observed for low liquid velocities. Because of the non-uniform particles flow pattern for particles with aspect ratios lower (oblate) and more (prolate) than one, the pressure drop distribution in the bed was not observed as uniform as what was found for spherical particles.

Keywords: CFD, DEM, ellipsoid, fluidization, multiphase flow, non-spherical, simulation

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Authors: Loonibha Manandhar, Rajendra Bhandari, Kumud Raj Kafle

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Nepal’s Terai region is a part of the Ganges river basin which is one of the most disaster-prone areas of the world, with recurrent monsoon flooding causing millions in damage and the death and displacement of hundreds of people and households every year. The vulnerability of human settlements to natural disasters such as floods is increasing, and mapping changes in land use practices and hydro-geological parameters is essential in developing resilient communities and strong disaster management policies. The objective of this study was to develop a flood hazard zonation map of Orai Khola watershed and map the decadal land use/land cover dynamics of the watershed. The watershed area was delineated using SRTM DEM, and LANDSAT images were classified into five land use classes (forest, grassland, sediment and bare land, settlement area and cropland, and water body) using pixel-based semi-automated supervised maximum likelihood classification. Decadal changes in each class were then quantified using spatial modelling. Flood hazard mapping was performed by assigning weights to factors slope, rainfall distribution, distance from the river and land use/land cover on the basis of their estimated influence in causing flood hazard and performing weighed overlay analysis to identify areas that are highly vulnerable. The forest and grassland coverage increased by 11.53 km² (3.8%) and 1.43 km² (0.47%) from 1996 to 2016. The sediment and bare land areas decreased by 12.45 km² (4.12%) from 1996 to 2016 whereas settlement and cropland areas showed a consistent increase to 14.22 km² (4.7%). Waterbody coverage also increased to 0.3 km² (0.09%) from 1996-2016. 1.27% (3.65 km²) of total watershed area was categorized into very low hazard zone, 20.94% (60.31 km²) area into low hazard zone, 37.59% (108.3 km²) area into moderate hazard zone, 29.25% (84.27 km²) area into high hazard zone and 31 villages which comprised 10.95% (31.55 km²) were categorized into high hazard zone area.

Keywords: flood hazard, land use/land cover, Orai river, supervised maximum likelihood classification, weighed overlay analysis

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Authors: Periklis Brakatsoulas

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Recent empirical research shows a growing interest in investment decision-making under market anomalies that contradict the rational paradigm. Momentum is undoubtedly one of the most robust anomalies in the empirical asset pricing research and remains surprisingly lucrative ever since first documented. Although predominantly phenomena identified across equities, momentum premia are now evident across various asset classes. Yet few many attempts are made so far to provide traders a diversified portfolio of strategies across different assets and markets. Moreover, literature focuses on patterns from past returns rather than mechanisms to signal future price directions prior to momentum runs. The aim of this paper is to develop a diversified portfolio approach to price distortion signals using daily position data on stocks, credit derivatives, and bonds. An algorithm allocates assets periodically, and new investment tactics take over upon price momentum signals and across different ranking groups. We focus on momentum life cycles, trend reversals, and price acceleration signals. The main effort here concentrates on the density, time span and maturity of momentum phenomena to identify consistent patterns over time and measure the predictive power of buy-sell signals generated by these anomalies. To tackle this, we propose a two-stage modelling process. First, we generate forecasts on core macroeconomic drivers. Secondly, satellite models generate market risk forecasts using the core driver projections generated at the first stage as input. Moreover, using a combination of the ARFIMA and FIGARCH models, we examine the dependence of consecutive observations across time and portfolio assets since long memory behavior in volatilities of one market appears to trigger persistent volatility patterns across other markets. We believe that this is the first work that employs evidence of volatility transmissions among derivatives, equities, and bonds to identify momentum life cycle patterns.

Keywords: forecasting, long memory, momentum, returns

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Authors: Prasad Pokkunuri

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Visco-elastic materials combine the stress response properties of both solids and fluids and have found use in a variety of damping applications – both vibrational and acoustic. Defense and automotive applications, in particular, are subject to high impact and shock loading – for example: aircraft landing gear, firearms, and shock absorbers. Field responsive fluids – a class of smart materials – are the preferred choice of energy absorbents because of their controllability. These fluids’ stress response can be controlled by the application of a magnetic or electric field, in a closed loop. Their rheological properties – elasticity, plasticity, and viscosity – can be varied all the way from that of a liquid such as water to a hard solid. This work presents a simplified model to study the impulse response behavior of such fluids for use in recoil damping systems. The well-known Burger’s equation, in conjunction with various visco-elastic constitutive models, is used to represent fluid behavior. The Kelvin-Voigt, Upper Convected Maxwell (UCM), and Oldroyd-B constitutive models are implemented in this study. Using these models in a one-dimensional framework eliminates additional complexities due to geometry, pressure, body forces, and other source terms. Using a finite difference formulation to numerically solve the governing equation(s), the response to an initial impulse is studied. The disturbance is confined within the problem domain with no-inflow, no-outflow boundary conditions, and its decay characteristics studied. Visco-elastic fluids typically involve a time-dependent stress relaxation which gives rise to interesting behavior when subjected to an impulsive load. For particular values of viscous damping and elastic modulus, the fluid settles into a stable oscillatory state, absorbing and releasing energy without much decay. The simplified formulation enables a comprehensive study of different modes of system response, by varying relevant parameters. Using the insights gained from this study, extension to a more detailed multi-dimensional model is considered.

Keywords: Burgers Equation, Impulse Response, Recoil Damping Systems, Visco-elastic Fluids

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Authors: Dileep Maarisetty, Janaki Komandur, Saroj S. Baral

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In the current work, photocatalytic degradation of phenol was carried both in UV and visible light to find the slowest step that is limiting the rate of photo-degradation process. Characterization such as XRD, SEM, FT-IR, TEM, XPS, UV-DRS, PL, BET, UPS, ESR and zeta potential experiments were conducted to assess the credibility of catalysts in boosting the photocatalytic activity. To explore the synergy, TiO₂ was doped with graphene and alumina. The orbital hybridization with alumina doping (mediated by graphene) resulted in higher electron transfer from the conduction band of TiO₂ to alumina surface where oxygen reduction reactions (ORR) occur. Besides, the doping of alumina and graphene introduced defects into Ti lattice and helped in improving the adsorptive properties of modified photo-catalyst. Results showed that these defects promoted the oxygen reduction reactions (ORR) on the catalyst’s surface. ORR activity aims at producing reactive oxygen species (ROS). These ROS species oxidizes the phenol molecules which is adsorbed on the surface of photo-catalysts, thereby driving the photocatalytic reactions. Since mass transfer is considered as rate limiting step, various mathematical models were applied to the experimental data to probe the best fit. By varying the parameters, it was found that intra-particle diffusion was the slowest step in the degradation process. Lagergren model gave the best R² values indicating the nature of rate kinetics. Similarly, different adsorption isotherms were employed and realized that Langmuir isotherm suits the best with tremendous increase in uptake capacity (mg/g) of TiO₂-rGO-Al₂O₃ as compared undoped TiO₂. This further assisted in higher adsorption of phenol molecules. The results obtained from experimental, kinetic modelling and adsorption isotherms; it is concluded that apart from changes in surface, optoelectronic and morphological properties that enhanced the photocatalytic activity, the intra-particle diffusion within the catalyst’s pores serve as rate-limiting step in deciding the fate of photo-catalytic degradation of phenol.

Keywords: ORR, phenol degradation, photo-catalyst, rate kinetics

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Authors: Eman M. Sarhan, Doaa A. Ghareeb, Gabriella Ortore, Amr A. Amara, Mohamed M. El-Sayed

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Drug salting and nanoparticle-based drug delivery formulations are considered to be an effective means for rendering the hydrophobic drugs’ nano-scale dispersion in aqueous media, and thus circumventing the pitfalls of their poor solubility as well as enhancing their membrane permeability. The current study aims to increase the bioavailability of quaternary ammonium berberine through acid salting and biodegradable bovine serum albumin (BSA)-based nanoparticulate drug formulation. Berberine hydroxide (BBR-OH) that was chemically synthesized by alkalization of the commercially available berberine hydrochloride (BBR-HCl) was then acidified to get Di-berberine sulfate (BBR)₂SO₄. The purified crystals were spectrally characterized. The desolvation technique was optimized for the preparation of size-controlled BSA-BBR-HCl, BSA-BBR-OH, and BSA-(BBR)₂SO₄ nanoparticles. Particle size, zeta potential, drug release, encapsulation efficiency, Fourier transform infrared spectroscopy (FTIR), tandem MS-MS spectroscopy, energy-dispersive X-ray spectroscopy (EDX), scanning and transmitting electron microscopic examination (SEM, TEM), in vitro bioactivity, and in silico drug-polymer interaction were determined. BSA (PDB ID; 4OR0) protonation state at different pH values was predicted using Amber12 molecular dynamic simulation. Then blind docking was performed using Lamarkian genetic algorithm (LGA) through AutoDock4.2 software. Results proved the purity and the size-controlled synthesis of berberine-BSA-nanoparticles. The possible binding poses, hydrophobic and hydrophilic interactions of berberine on BSA at different pH values were predicted. Antioxidant, anti-hemolytic, and cell differentiated ability of tested drugs and their nano-formulations were evaluated. Thus, drug salting and the potentially effective albumin berberine nanoparticle formulations can be successfully developed using a well-optimized desolvation technique and exhibiting better in vitro cellular bioavailability.

Keywords: berberine, BSA, BBR-OH, BBR-HCl, BSA-BBR-HCl, BSA-BBR-OH, (BBR)₂SO₄, BSA-(BBR)₂SO₄, FTIR, AutoDock4.2 Software, Lamarkian genetic algorithm, SEM, TEM, EDX

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Authors: M. S. Maglasang, S. O. Palacio, L. P. Ogdoc

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Business Process Outsourcing has been one of the fastest growing and emerging industry in the Philippines today. Unlike most of the contact service centers, more popularly known as "call centers", The BPO Industry’s primary outsourced service is performing audits of the global clients' logistics. As a service industry, manpower is considered as the most important yet the most expensive resource in the company. Because of this, there is a need to maximize the human resources so people are effectively and efficiently utilized. The main purpose of the study is to optimize the current manpower resources through effective distribution and assignment of different types of bills to the different skill-level of data entry operators. The assignment model parameters include the average observed time matrix gathered from through time study, which incorporates the learning curve concept. Subsequently, a simulation model was made to duplicate the arrival rate of demand which includes the different batches and types of bill per day. Next, a mathematical linear programming model was formulated. Its objective is to minimize direct labor cost per bill by allocating the different types of bills to the different skill-levels of operators. Finally, a hypothesis test was done to validate the model, comparing the actual and simulated results. The analysis of results revealed that the there’s low utilization of effective capacity because of its failure to determine the product-mix, skill-mix, and simulated demand as model parameters. Moreover, failure to consider the effects of learning curve leads to overestimation of labor needs. From 107 current number of operators, the proposed model gives a result of 79 operators. This results to an increase of utilization of effective capacity to 14.94%. It is recommended that the excess 28 operators would be reallocated to the other areas of the department. Finally, a manpower capacity planning model is also recommended in support to management’s decisions on what to do when the current capacity would reach its limit with the expected increasing demand.

Keywords: optimization modelling, linear programming, simulation, time and motion study, capacity planning

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Authors: A. Kesraoui, M. Seffen

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Applications of adsorption onto activated carbon for water treatment are well known. The process has been demonstrated to be widely effective for removing dissolved organic substances from wastewaters, but this treatment has a major drawback is the high operating cost. The main goal of our research work is to improve the retention capacity of Tunisian biomass for the depollution of industrial wastewater and retention of pollutants considered toxic. The biosorption process is based on the retention of molecules and ions onto a solid surface composed of biological materials. The evaluation of the potential use of these materials is important to propose as an alternative to the adsorption process generally expensive, used to remove organic compounds. Indeed, these materials are very abundant in nature and are low cost. Certainly, the biosorption process is effective to remove the pollutants, but it presents a slow kinetics. The improvement of the biosorption rates is a challenge to make this process competitive with respect to oxidation and adsorption onto lignocellulosic fibers. In this context, the alternating current appears as a new alternative, original and a very interesting phenomenon in the acceleration of chemical reactions. Our main goal is to increase the retention acceleration of dyes (indigo carmine, methylene blue) and phenol by using a new alternative: alternating current. The adsorption experiments have been performed in a batch reactor by adding some of the adsorbents in 150 mL of pollutants solution with the desired concentration and pH. The electrical part of the mounting comprises a current source which delivers an alternating current voltage of 2 to 15 V. It is connected to a voltmeter that allows us to read the voltage. In a 150 mL capacity cell, we plunged two zinc electrodes and the distance between two Zinc electrodes has been 4 cm. Thanks to alternating current, we have succeeded to improve the performance of activated carbon by increasing the speed of the indigo carmine adsorption process and reducing the treatment time. On the other hand, we have studied the influence of the alternating current on the biosorption rate of methylene blue onto Luffa cylindrica fibers and the hybrid material (Luffa cylindrica-ZnO). The results showed that the alternating current accelerated the biosorption rate of methylene blue onto the Luffa cylindrica and the Luffa cylindrica-ZnO hybrid material and increased the adsorbed amount of methylene blue on both adsorbents. In order to improve the removal of phenol, we performed the coupling between the alternating current and the biosorption onto two adsorbents: Luffa cylindrica and the hybrid material (Luffa cylindrica-ZnO). In fact, the alternating current has succeeded to improve the performance of adsorbents by increasing the speed of the adsorption process and the adsorption capacity and reduce the processing time.

Keywords: adsorption, alternating current, dyes, modeling

Procedia PDF Downloads 132

Authors: Mia Françoise

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This report aims to develop a strategy for assisting short-term investors to benefit from the current economic climate in South Africa by utilizing technical analysis techniques and predictive analytics. As part of this research, value investing and technical analysis principles will be combined to maximize returns for South African investors while optimizing volatility. As an emerging market, South Africa offers many opportunities for high growth in sectors where other developed countries cannot grow at the same rate. Investing in South African companies with significant growth potential can be extremely rewarding. Although the risk involved is more significant in countries with less developed markets and infrastructure, there is more room for growth in these countries. According to recent research, the offshore market is expected to outperform the local market over the long term; however, short-term investments in the local market will likely be more profitable, as the Johannesburg Stock Exchange is predicted to outperform the S&P500 over the short term. The instabilities in the economy contribute to increased market volatility, which can benefit investors if appropriately utilized. Price prediction and portfolio optimization comprise the two primary components of this methodology. As part of this process, statistics and other predictive modeling techniques will be used to predict the future performance of stocks listed on the Johannesburg Stock Exchange. Following predictive data analysis, Modern Portfolio Theory, based on Markowitz's Mean-Variance Theorem, will be applied to optimize the allocation of assets within an investment portfolio. By combining different assets within an investment portfolio, this optimization method produces a portfolio with an optimal ratio of expected risk to expected return. This methodology aims to provide a short-term investment with a stock portfolio that offers the best risk-to-return profile for stocks listed on the JSE by combining price prediction and portfolio optimization.

Keywords: financial stocks, optimized asset allocation, prediction modelling, South Africa

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Authors: Farid Khosravikia, Patricia Clayton

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The main objective of this study is to evaluate the advantages and disadvantages of various machine learning techniques in forecasting ground-motion intensity measures given source characteristics, source-to-site distance, and local site condition. Intensity measures such as peak ground acceleration and velocity (PGA and PGV, respectively) as well as 5% damped elastic pseudospectral accelerations at different periods (PSA), are indicators of the strength of shaking at the ground surface. Estimating these variables for future earthquake events is a key step in seismic hazard assessment and potentially subsequent risk assessment of different types of structures. Typically, linear regression-based models, with pre-defined equations and coefficients, are used in ground motion prediction. However, due to the restrictions of the linear regression methods, such models may not capture more complex nonlinear behaviors that exist in the data. Thus, this study comparatively investigates potential benefits from employing other machine learning techniques as a statistical method in ground motion prediction such as Artificial Neural Network, Random Forest, and Support Vector Machine. The algorithms are adjusted to quantify event-to-event and site-to-site variability of the ground motions by implementing them as random effects in the proposed models to reduce the aleatory uncertainty. All the algorithms are trained using a selected database of 4,528 ground-motions, including 376 seismic events with magnitude 3 to 5.8, recorded over the hypocentral distance range of 4 to 500 km in Oklahoma, Kansas, and Texas since 2005. The main reason of the considered database stems from the recent increase in the seismicity rate of these states attributed to petroleum production and wastewater disposal activities, which necessities further investigation in the ground motion models developed for these states. Accuracy of the models in predicting intensity measures, generalization capability of the models for future data, as well as usability of the models are discussed in the evaluation process. The results indicate the algorithms satisfy some physically sound characteristics such as magnitude scaling distance dependency without requiring pre-defined equations or coefficients. Moreover, it is shown that, when sufficient data is available, all the alternative algorithms tend to provide more accurate estimates compared to the conventional linear regression-based method, and particularly, Random Forest outperforms the other algorithms. However, the conventional method is a better tool when limited data is available.

Keywords: artificial neural network, ground-motion models, machine learning, random forest, support vector machine

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Authors: Mohamed Sadok Bensalem, Soulef Amamria, Khaled Lazzez, Mohamed Ghanmi

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The southern-central Tunisian Atlas presents a typical example of external zone. It occupies a particular position in the North African chains: firstly, it is the eastern limit of atlassicstructures; secondly, it is the edges between the belts structures to the north and the stable Saharan platform in the south. The evolution of deformation studyis based on several methods such as classical or numerical methods. The principals parameters controlling the genesis of folds in the southern central Tunisian Atlas are; the reactivation of pre-existing faults during later compressive phase, the evolution of decollement level, and the relation between thin and thick-skinned. One of the more principal characters of the southern-central Tunisian Atlas is the variation of belts structures directions determined by: NE-SW direction named the attlassic direction in Tunisia, the NW-SE direction carried along the Gafsa fault (the oriental limit of southern atlassic accident), and the E-W direction defined in the southern Tunisian Atlas. This variation of direction is the result of an important variation of deformation during different tectonics phases. A classical modeling of the Jebel ElKebar anticline, based on faults throw of the pre-existing faults and its reactivation during compressive phases, shows the importance of extensional deformation, particular during Aptian-Albian period, comparing with that of later compression (Alpine phases). A numerical modeling, based on the software Rampe E.M. 1.5.0, applied on the anticline of Jebel Orbata confirms the interpretation of “fault related fold” with decollement level within the Triassic successions. The other important parameter of evolution of deformation is the vertical migration of decollement level; indeed, more than the decollement level is in the recent series, most that the deformation is accentuated. The evolution of deformation is marked the development of duplex structure in Jebel AtTaghli (eastern limit of Jebel Orbata). Consequently, the evolution of deformation is proportional to the depth of the decollement level, the most important deformation is in the higher successions; thus is associated to the thin-skinned deformation; the decollement level permit the passive transfer of deformation in the cover.

Keywords: evolution of deformation, pre-existing faults, decollement level, thin-skinned

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Authors: Jiazhen Chen, Yue Sun, Joanne Yip, Kit-Lun Yick

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The design of sports bras poses a considerable challenge due to the difficulty in accurately predicting the wearing result after computer-aided design (CAD). It needs repeated physical try-on or virtual try-on to obtain a comfortable pressure range during motion. Specifically, in the context of running, the exact support area and force exerted on the breasts remain unclear. Consequently, obtaining an effective method to design the sports bra pads shape becomes particularly challenging. This predicament hinders the successful creation and production of sports bras that cater to women's health needs. The purpose of this study is to propose an effective method to obtain the 3D shape of sports bra pads and to understand the relationship between the supporting force and the 3D shape parameters of the pads. Firstly, the static 3D shape of the sports bra pad and human motion data (Running) are obtained by using the 3D scanner and advanced 4D scanning technology. The 3D shape of the sports bra pad is parameterised and simplified by Free-form Deformation (FFD). Then the sub-models of sports bra and human body are constructed by segmenting and meshing them with MSC Apex software. The material coefficient of sports bras is obtained by material testing. The Marc software is then utilised to establish a dynamic contact model between the human breast and the sports bra pad. To realise the reverse design of the sports bra pad, this contact model serves as a forward model for calculating the inverse problem. Based on the forward contact model, the inverse problem of the 3D shape parameters of the sports bra pad with the target bra-wearing pressure range as the boundary condition is solved. Finally, the credibility and accuracy of the simulation are validated by comparing the experimental results with the simulations by the FE model on the pressure distribution. On the one hand, this research allows for a more accurate understanding of the support area and force distribution on the breasts during running. On the other hand, this study can contribute to the customization of sports bra pads for different individuals. It can help to obtain sports bra pads with comfortable dynamic pressure.

Keywords: sports bra design, breast motion, running, inverse problem, finite element dynamic model

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Authors: Ahmad Kayvani Fard, Gordon Mckay, Muataz Hussien

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Adsorption/sorption is believed to be one of the optimal processes for the removal of heavy metals from water due to its low operational and capital cost as well as its high removal efficiency. Different materials have been reported in literature as adsorbent for heavy metal removal in waste water such as natural sorbents, organic polymers (synthetic) and mineral materials (inorganic). The selection of adsorbents and development of new functional materials that can achieve good removal of heavy metals from water is an important practice and depends on many factors, such as the availability of the material, cost of material, and material safety and etc. In this study we reported the synthesis of doped Activated carbon and Carbon nanotube (CNT) with different loading of metal oxide nanoparticles such as Fe2O3, Fe3O4, Al2O3, TiO2, SiO2 and Ag nanoparticles and their application in removal of heavy metals, hydrocarbon, and organics from waste water. Commercial AC and CNT with different loadings of mentioned nanoparticle were prepared and effect of pH, adsorbent dosage, sorption kinetic, and concentration effects are studied and optimum condition for removal of heavy metals from water is reported. The prepared composite sorbent is characterized using field emission scanning electron microscopy (FE-SEM), high transmission electron microscopy (HR-TEM), thermogravimetric analysis (TGA), X-ray diffractometer (XRD), the Brunauer, Emmett and Teller (BET) nitrogen adsorption technique, and Zeta potential. The composite materials showed higher removal efficiency and superior adsorption capacity compared to commercially available carbon based adsorbent. The specific surface area of AC increased by 50% reaching up to 2000 m2/g while the CNT specific surface area of CNT increased by more than 8 times reaching value of 890 m2/g. The increased surface area is one of the key parameters along with surface charge of the material determining the removal efficiency and removal efficiency. Moreover, the surface charge density of the impregnated CNT and AC have enhanced significantly where can benefit the adsorption process. The nanoparticles also enhance the catalytic activity of material and reduce the agglomeration and aggregation of material which provides more active site for adsorbing the contaminant from water. Some of the results for treating wastewater includes 100% removal of BTEX, arsenic, strontium, barium, phenolic compounds, and oil from water. The results obtained are promising for the use of AC and CNT loaded with metal oxide nanoparticle in treatment and pretreatment of waste water and produced water before desalination process. Adsorption can be very efficient with low energy consumption and economic feasibility.

Keywords: carbon nanotube, activated carbon, adsorption, heavy metal, water treatment

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Authors: Simran Kaur, Paul J. Van Geel

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A coupled Thermal-Mechanical-Biological (TMB) model was developed for the analysis of impacts of temperatures on waste stabilization at a Municipal Solid Waste (MSW) landfill in Quebec, Canada using COMSOL Multiphysics, a finite element-based software. For waste placed in landfills in Northern climates during winter months, it can take months or even years before the waste approaches ideal temperatures for biodegradation to occur. Therefore, the proposed model links biodegradation induced strain in MSW to waste temperatures and corresponding heat generation rates as a result of anaerobic degradation. This provides a link between the thermal-biological and mechanical behavior of MSW. The thermal properties of MSW are further linked to density which is tracked and updated in the mechanical component of the model, providing a mechanical-thermal link. The settlement of MSW is modelled based on the concept of viscoelasticity. The specific viscoelastic model used is a single Kelvin – Voight viscoelastic body in which the finite element response is controlled by the elastic material parameters – Young’s Modulus and Poisson’s ratio. The numerical model was validated with 10 years of temperature and settlement data collected from a landfill in Ste. Sophie, Quebec. The coupled TMB modelling framework, which simulates placement of waste lifts as they are placed progressively in the landfill, allows for optimization of several thermal and mechanical parameters throughout the depth of the waste profile and helps in better understanding of temperature dependence of MSW stabilization. The model is able to illustrate how waste placed in the winter months can delay biodegradation-induced settlement and generation of landfill gas. A delay in waste stabilization will impact the utilization of the approved airspace prior to the placement of a final cover and impact post-closure maintenance. The model provides a valuable tool to assess different waste placement strategies in order to increase airspace utilization within landfills operating under different climates, in addition to understanding conditions for increased gas generation for recovery as a green and renewable energy source.

Keywords: coupled model, finite element modeling, landfill, municipal solid waste, waste stabilization

Procedia PDF Downloads 105

Authors: Mirjana Petronijević, Sanja Panić, Aleksandra Cvetanović, Branko Kordić, Nenad Grba

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Enzyme peroxidases are biological catalysts and play a major role in phenolic wastewater treatments and other environmental applications. The most studied species from the peroxidases family is horseradish peroxidase (HRP). In environmental processes, HRP could be used in its free or immobilized form. Enzyme immobilization onto solid support is performed to improve the enzyme properties, prolong its lifespan and operational stability and allow its reuse in industrial applications. One of the enzyme supports of a newer generation is magnetic particles (MPs). Fe₃O₄ MPs are the most widely pursued immobilization of enzymes owing to their remarkable advantages of biocompatibility and non-toxicity. Also, MPs can be easily separated and recovered from the water by applying an external magnetic field. On the other hand, metals and metal oxides are not suitable for the covalent binding of enzymes, so it is necessary to perform their surface modification. Fe₃O₄ MPs functionalization could be performed during the process of their synthesis if it takes place in the presence of plant extracts. Extracts of plant material, such as wild plants, herbs, even waste materials of the food and agricultural industry (bark, shell, leaves, peel), are rich in various bioactive components such as polyphenols, flavonoids, sugars, etc. When the synthesis of magnetite is performed in the presence of plant extracts, bioactive components are incorporated into the surface of the magnetite, thereby affecting its functionalization. In this paper, the suitability of bio-magnetite as solid support for covalent immobilization of HRP across glutaraldehyde was examined. The activity of immobilized HRP at different pH values (4-9) and temperatures (20-80°C) and reusability were examined. Bio-MP was synthesized by co-precipitation method from Fe(II) and Fe(III) sulfate salts in the presence of water extract of the Allium cepa peel. The water extract showed 81% of antiradical potential (according to DPPH assay), which is connected with the high content of polyphenols. According to the FTIR analysis, the bio-magnetite contains oxygen functional groups (-OH, -COOH, C=O) suitable for binding to glutaraldehyde, after which the enzyme is covalently immobilized. The immobilized enzyme showed high activity at ambient temperature and pH 7 (30 U/g) and retained ≥ 80% of its activity at a wide range of pH (5-8) and temperature (20-50°C). The HRP immobilized onto bio-MPs showed remarkable stability towards temperature and pH variations compared to the free enzyme form. On the other hand, immobilized HRP showed low reusability after the first washing cycle enzyme retains 50% of its activity, while after the third washing cycle retains only 22%.

Keywords: bio-magnetite, enzyme immobilization, water extracts, environmental protection

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Authors: Vincenzo Piemonte

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The liver is a complex, large-scale biochemical reactor which plays a unique role in the human physiology. When liver ceases to perform its physiological activity, a functional replacement is required. Actually, liver transplantation is the only clinically effective method of treating severe liver disease. Anyway, the aforementioned therapeutic approach is hampered by the disparity between organ availability and the number of patients on the waiting list. In order to overcome this critical issue, research activities focused on liver support device systems (LSDs) designed to bridging patients to transplantation or to keep them alive until the recovery of native liver function. In recirculating albumin dialysis devices, such as MARS (Molecular Adsorbed Recirculating System), adsorption is one of the fundamental steps in albumin-dialysate regeneration. Among the albumin-bound toxins that must be removed from blood during liver-failure therapy, bilirubin and tryptophan can be considered as representative of two different toxin classes. The first one, not water soluble at physiological blood pH and strongly bounded to albumin, the second one, loosely albumin bound and partially water soluble at pH 7.4. Fixed bed units are normally used for this task, and the design of such units requires information both on toxin adsorption equilibrium and kinetics. The most common adsorptive media used in LSDs are activated carbon, non-ionic polymeric resins and anionic resins. In this paper, bilirubin adsorption isotherms on different adsorptive media, such as polymeric resin, albumin-coated resin, anionic resin, activated carbon and alginate beads with entrapped albumin are presented. By comparing all the results, it can be stated that the adsorption capacity for bilirubin of the five different media increases in the following order: Alginate beads < Polymeric resin < Albumin-coated resin < Activated carbon < Anionic resin. The main focus of this paper is to provide useful guidelines for the optimization of liver support devices which implement adsorption columns to remove albumin-bound toxins from albumin dialysate solutions.

Keywords: adsorptive media, adsorption equilibrium, artificial liver devices, bilirubin, mathematical modelling

Procedia PDF Downloads 238

Authors: Mayowa Saheed Sanusi A, Musiliu Olushola Sunmonua, Abdulquadri Alakab Owolabi Raheema, Adeyemi Ikimot Adejokea

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The development of healthy cookies for health-conscious consumers cannot be overemphasized in the present global health crisis. This study was aimed to evaluate and model the influence of ripeness levels of papaya puree (unripe, ripe and overripe), oven temperature (130°C, 150°C and 170°C) and oven rack speed (stationary, 10 and 20 rpm) on physicochemical properties of papaya-based cookies using Response Surface Methodology (RSM). The physicochemical properties (baking time, cookies mass, cookies thickness, spread ratio, proximate composition, Calcium, Vitamin C and Total Phenolic Content) were determined using standard procedures. The data obtained were statistically analysed at p≤0.05 using ANOVA. The polynomial regression model of response surface methodology was used to model the physicochemical properties. The adequacy of the models was determined using the coefficient of determination (R²) and the response optimizer of RSM was used to determine the optimum physicochemical properties for the papaya-based cookies. Cookies produced from overripe papaya puree were observed to have the shortest baking time; ripe papaya puree favors cookies spread ratio, while the unripe papaya puree gives cookies with the highest mass and thickness. The highest crude protein content, fiber content, calcium content, Vitamin C and Total Phenolic Content (TPC) were observed in papaya based-cookies produced from overripe puree. The models for baking time, cookies mass, cookies thickness, spread ratio, moisture content, crude protein and TPC were significant, with R2 ranging from 0.73 – 0.95. The optimum condition for producing papaya based-cookies with desirable physicochemical properties was obtained at 149°C oven temperature, 17 rpm oven rack speed and with the use of overripe papaya puree. The Information on the use of puree from unripe, ripe and overripe papaya can help to increase the use of underutilized unripe or overripe papaya and also serve as a strategic means of obtaining a fat substitute to produce new products with lower production cost and health benefit.

Keywords: papaya based-cookies, modeling, response surface methodology, physicochemical properties

Procedia PDF Downloads 137