Search results for: morphology optimization

Authors: A. Hamza, H. Kathyayini, N. Nagaraju

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Aluminophosphates, both amorphous and crystalline materials find applications as adsorbents, ceramics, and pigments and as catalysts/catalyst supports in organic fine chemical synthesis. Most of the applications are varied depending on the type of metal incorporated, particle size, surface area, porosity and morphology of aluminophosphate. The porous and surface properties of these materials are normally fine-tuned by adopting various preparation methodologies. Numerous crystalline microporous and mesoporous aluminophosphates and metal-aluminophosphates have been reported in literature, in which the synthesis has been carried out by using structure directing organic molecules/surfactants. In present work, amorphous aluminophosphate (AlP) and metal-aluminophosphates MAlP (M = Cu, Zn, Cr, Fe, Ce and Zr) and their mixed forms M-1M2AlP are prepared under a typical precipitation condition, i.e. at low temperature in order to keep the Von-Weirmann relative super saturation of the precipitating medium and obtain small size precipitate particles. These materials are prepared without using any surfactants. All materials are thoroughly characterised for surface and bulk properties by N2 adsorption-desorption technique, XRD, FT-IR, TG and SEM. The materials are also analysed for the amount and the strength of their surface acid sites, by NH3-TPD and CO2-TPD techniques respectively. All the materials prepared in the work are investigated for their catalytic activity in following applications in the synthesis of industrially important Jasminaldehyde via, aldol condensation of n-heptanal and benzaldehyde, in the synthesis of biologically important chalcones by Claisen-shmidth condensation of benzaldehyde and substituted chalcones. The effect of the amount of the catalysts, duration of the reaction, temperature of the reaction, molar ratio of the reactants has been studied. The porosity of pure aluminophosphate is found to be changed significantly by the incorporation of transition metals during preparation of aluminophosphate. The pore size increased from microporous to mesoporous and finally to macroporous by following order of metals Cu = Zn < Cr < Ce < Fe = Zr. The change in surface area and porosity of double metal-aluminophosphates depended on the concentration of both the metals. The acidity of aluminophosphate is either increased or decreased which depended on the type and valence of metals loaded. A good number of basic sites are created in metal-aluminophosphates irrespective of the metals used. A maximum catalytic activity for synthesis of both jasminaldehyde and chalcone is obtained by FeAlP as catalysts; these materials are characterized by decreased strength and concentration of acidic sites with optimum level basic sites.

Keywords: amorphous metal-aluminophosphates, surface properties, acidic-basic properties, Aldol, Claisen-Shmidth condensation, jasminaldehyde, chalcone

Procedia PDF Downloads 307

Authors: Chayanoot Sangwichien, Taweesak Reungpeerakul, Kyaw Thu

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Oil palm mills in Southern Thailand produced a large amount of biomass solid wastes. Lignocellulose biomass is the main source for production of biofuel which can be combined or used as an alternative to fossil fuels. Biomass composed of three main constituents of cellulose, hemicellulose, and lignin. Thermochemical conversion process applied to produce biofuel from biomass. Pyrolysis of biomass is the best way to thermochemical conversion of biomass into pyrolytic products (bio-oil, gas, and char). Operating parameters play an important role to optimize the product yields from fast pyrolysis of biomass. This present work concerns with the modeling of reaction kinetics parameters for fast pyrolysis of empty fruit bunch in the fluidized bed reactor. A global kinetic model used to predict the product yields from fast pyrolysis of empty fruit bunch. The reaction temperature and vapor residence time parameters are mainly affected by product yields of EFB pyrolysis. The reaction temperature and vapor residence time parameters effects on empty fruit bunch pyrolysis are considered at the reaction temperature in the range of 450-500˚C and at a vapor residence time of 2 s, respectively. The optimum simulated bio-oil yield of 53 wt.% obtained at the reaction temperature and vapor residence time of 450˚C and 2 s, 500˚C and 1 s, respectively. The simulated data are in good agreement with the reported experimental data. These simulated data can be applied to the performance of experiment work for the fast pyrolysis of biomass.

Keywords: kinetics, empty fruit bunch, fast pyrolysis, modeling

Procedia PDF Downloads 218

Authors: Muhammad Fanseem E. M., Rama Satya Satish Kumar, Indrajeet Bhausaheb Bhavar, Deepak M.

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Keeping the right balance of electricity between the supply and demand sides of the grid is one of the most important objectives of electrical grid operation. Power generation and demand forecasting are the core of power management and generation scheduling. Large, centralized producing units were used in the construction of conventional power systems in the past. A certain level of balance was possible since the generation kept up with the power demand. However, integrating renewable energy sources into power networks has proven to be a difficult challenge due to its intermittent nature. The power imbalance caused by rising demands and peak loads is negatively affecting power quality and dependability. Demand side management and demand response were one of the solutions, keeping generation the same but altering or rescheduling or shedding completely the load or demand. However, shedding the load or rescheduling is not an efficient way. There comes the significance of virtual power plants. The virtual power plant integrates distributed generation, dispatchable load, and distributed energy storage organically by using complementing control approaches and communication technologies. This would eventually increase the utilization rate and financial advantages of distributed energy resources. Most of the writing on virtual power plant models ignored technical limitations, and modeling was done in favor of a financial or commercial viewpoint. Therefore, this paper aims to address the modeling intricacies of VPPs and their technical limitations, shedding light on a holistic understanding of this innovative power management approach.

Keywords: cost optimization, distributed energy resources, dynamic modeling, model quality tests, power system modeling

Procedia PDF Downloads 65

Authors: Wajid Rehman, Sirajul Haq, Bakhtiar Muhammad, Syed Fahad Hassan, Amin Badshah, Muhammad Waseem, Fazal Rahim, Obaid-Ur-Rahman Abid, Farzana Latif Ansari, Umer Rashid

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Five novel triorganotin (IV) compounds have been synthesized and characterized. The tin atom is penta-coordinated to assume trigonal-bipyramidal geometry. Using in silico derived parameters; the objective of our study is to design and synthesize promiscuous antibacterials potent enough to combat resistance. Among various synthesized organotin (IV) complexes, compound 5 was found as potent antibacterial agent against various bacterial strains. Further lead optimization of drug-like properties was evaluated through in silico predictions. Data mining and computational analysis were utilized to derive compound promiscuity phenomenon to avoid drug attrition rate in designing antibacterials. Xanthine oxidase and human glucose- 6-phosphatase were found as only true positive off-target hits by ChEMBL database and others utilizing similarity ensemble approach. Propensity towards a-3 receptor, human macrophage migration factor and thiazolidinedione were found as false positive off targets with E-value 1/4> 10^-4 for compound 1, 3, and 4. Further, displaying positive drug-drug interaction of compound 1 as uricosuric was validated by all databases and docked protein targets with sequence similarity and compositional matrix alignment via BLAST software. Promiscuity of the compound 5 was further confirmed by in silico binding to different antibacterial targets.

Keywords: antibacterial activity, drug promiscuity, ADMET prediction, metallo-pharmaceutical, antimicrobial resistance

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Authors: Wei Zhang

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With the rapid development of deep learning, neural network and deep learning algorithms play a significant role in various practical applications. Due to the high accuracy and good performance, Convolutional Neural Networks (CNNs) especially have become a research hot spot in the past few years. However, the size of the networks becomes increasingly large scale due to the demands of the practical applications, which poses a significant challenge to construct a high-performance implementation of deep learning neural networks. Meanwhile, many of these application scenarios also have strict requirements on the performance and low-power consumption of hardware devices. Therefore, it is particularly critical to choose a moderate computing platform for hardware acceleration of CNNs. This article aimed to survey the recent advance in Field Programmable Gate Array (FPGA)-based acceleration of CNNs. Various designs and implementations of the accelerator based on FPGA under different devices and network models are overviewed, and the versions of Graphic Processing Units (GPUs), Application Specific Integrated Circuits (ASICs) and Digital Signal Processors (DSPs) are compared to present our own critical analysis and comments. Finally, we give a discussion on different perspectives of these acceleration and optimization methods on FPGA platforms to further explore the opportunities and challenges for future research. More helpfully, we give a prospect for future development of the FPGA-based accelerator.

Keywords: deep learning, field programmable gate array, FPGA, hardware accelerator, convolutional neural networks, CNN

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Authors: J. Martin, A. Dannenberg, G. Detrell, R. Ewald, S. Fasoulas

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Microalgae-based photobioreactors (PBR) for Life Support Systems (LSS) are currently being investigated for future space missions such as a crewed base on planets or moons. Biological components may help reducing resupply masses by closing material mass flows with the help of regenerative components. Via photosynthesis, the microalgae use CO₂, water, light and nutrients to provide oxygen and biomass for the astronauts. These capabilities could have synergies with Earth applications that tackle current problems and the developed technologies can be transferred. For example, a current worldwide discussed issue is the increased nitrate and phosphate pollution of ground water from agricultural waste waters. To investigate the potential use of a biological system based on the ability of the microalgae to extract and use nitrate and phosphate for the treatment of polluted ground water from agricultural applications, a scalable test stand is being developed. This test stand investigates the maximization of intake rates of nitrate and quantifies the produced biomass and oxygen. To minimize the required energy, for the uptake of nitrate from artificial waste water (AWW) the Flashing Light Effect (FLE) and the adaption of the illumination spectrum were realized. This paper describes the composition of the AWW, the development of the illumination unit and the possibility of non-invasive process optimization and control via the adaption of the illumination spectrum and illumination cycles. The findings were a doubling of the energy related growth rate by adapting the illumination setting.

Keywords: microalgae, illumination, nitrate uptake, flashing light effect

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Authors: Dyah Purwaningsih, Roto Roto, Hari Sutrisno

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Manganese dioxide (MnO₂) and its derivatives are among the most widely used materials for the positive electrode in both primary and rechargeable lithium batteries. The MnO₂ derivative compound of LiMₓMn₂₋ₓO₄ (M: Co, Ni, Cr) is one of the leading candidates for positive electrode materials in lithium batteries as it is abundant, low cost and environmentally friendly. Over the years, synthesis of LiMₓMn₂₋ₓO₄ (M: Co, Ni, Cr) has been carried out using various methods including sol-gel, gas condensation, spray pyrolysis, and ceramics. Problems with these various methods persist including high cost (so commercially inapplicable) and must be done at high temperature (environmentally unfriendly). This research aims to: (1) synthesize LiMₓMn₂₋ₓO₄ (M: Co, Ni, Cr) by reflux technique; (2) develop microstructure analysis method from XRD Powder LiMₓMn₂₋ₓO₄ data with the two-stage method; (3) study the electrical conductivity of LiMₓMn₂₋ₓO₄. This research developed the synthesis of LiMₓMn₂₋ₓO₄ (M: Co, Ni, Cr) with reflux. The materials consisting of Mn(CH₃COOH)₂. 4H₂O and Na₂S₂O₈ were refluxed for 10 hours at 120°C to form β-MnO₂. The doping of Co, Ni and Cr were carried out using solid-state method with LiOH to form LiMₓMn₂₋ₓO₄. The instruments used included XRD, SEM-EDX, XPS, TEM, SAA, TG/DTA, FTIR, LCR meter and eight-channel battery analyzer. Microstructure analysis of LiMₓMn₂₋ₓO₄ was carried out on XRD powder data by two-stage method using FullProf program integrated into WinPlotR and Oscail Program as well as on binding energy data from XPS. The morphology of LiMₓMn₂₋ₓO₄ was studied with SEM-EDX, TEM, and SAA. The thermal stability test was performed with TG/DTA, the electrical conductivity was studied from the LCR meter data. The specific capacity of LiMₓMn₂₋ₓO₄ as lithium battery cathode was tested using an eight-channel battery analyzer. The results showed that the synthesis of LiMₓMn₂₋ₓO₄ (M: Co, Ni, Cr) was successfully carried out by reflux. The optimal temperature of calcination is 750°C. XRD characterization shows that LiMn₂O₄ has a cubic crystal structure with Fd3m space group. By using the CheckCell in the WinPlotr, the increase of Li/Mn mole ratio does not result in changes in the LiMn₂O₄ crystal structure. The doping of Co, Ni and Cr on LiMₓMn₂₋ₓO₄ (x = 0.02; 0.04; 0; 0.6; 0.08; 0.10) does not change the cubic crystal structure of Fd3m. All the formed crystals are polycrystals with the size of 100-450 nm. Characterization of LiMₓMn₂₋ₓO₄ (M: Co, Ni, Cr) microstructure by two-stage method shows the shrinkage of lattice parameter and cell volume. Based on its range of capacitance, the conductivity obtained at LiMₓMn₂₋ₓO₄ (M: Co, Ni, Cr) is an ionic conductivity with varying capacitance. The specific battery capacity at a voltage of 4799.7 mV for LiMn₂O₄; Li₁.₀₈Mn₁.₉₂O₄; LiCo₀.₁Mn₁.₉O₄; LiNi₀.₁Mn₁.₉O₄ and LiCr₀.₁Mn₁.₉O₄ are 88.62 mAh/g; 2.73 mAh/g; 89.39 mAh/g; 85.15 mAh/g; and 1.48 mAh/g respectively.

Keywords: LiMₓMn₂₋ₓO₄, solid-state, reflux, two-stage method, ionic conductivity, specific capacity

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Authors: M. Magrini, D. Moroni, G. Palazzese, G. Pieri, D. Azzarelli, A. Spada, L. Fanucci, O. Salvetti

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Railway crossings are complex entities whose optimal management cannot be addressed unless with the help of an intelligent transportation system integrating information both on train and vehicular flows. In this paper, we propose an integrated system named SIMPLE (Railway Safety and Infrastructure for Mobility applied at level crossings) that, while providing unparalleled safety in railway level crossings, collects data on rail and road traffic and provides value-added services to citizens and commuters. Such services include for example alerts, via variable message signs to drivers and suggestions for alternative routes, towards a more sustainable, eco-friendly and efficient urban mobility. To achieve these goals, SIMPLE is organized as a System of Systems (SoS), with a modular architecture whose components range from specially-designed radar sensors for obstacle detection to smart ETSI M2M-compliant camera networks for urban traffic monitoring. Computational unit for performing forecast according to adaptive models of train and vehicular traffic are also included. The proposed system has been tested and validated during an extensive trial held in the mid-sized Italian town of Montecatini, a paradigmatic case where the rail network is inextricably linked with the fabric of the city. Results of the tests are reported and discussed.

Keywords: Intelligent Transportation Systems (ITS), railway, railroad crossing, smart camera networks, radar obstacle detection, real-time traffic optimization, IoT, ETSI M2M, transport safety

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Authors: Zohreh Mohammadi, Jan Kaspar, Peter Lohmander, Robert Marusak, Harald Vacik, Ljusk Ola Eriksson

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Storms are important damaging agents in European forest ecosystems. In the latest decades, significant economic losses in European forestry occurred due to storms. This study investigates the problem of optimal harvest planning when forest stands risk to be felled by storms. One of the most applicable mathematical methods which are being used to optimize forest management is stochastic dynamic programming (SDP). This method belongs to the adaptive optimization class. Sequential decisions, such as harvest decisions, can be optimized based on sequential information about events that cannot be perfectly predicted, such as the future storms and the future states of wind protection from other forest stands. In this paper, stochastic dynamic programming is used to maximize the expected present value of the profits from an area consisting of several forest stands. The region of analysis is the Czech Republic. The harvest decisions, in a particular time period, should be simultaneously taken in all neighbor stands. The reason is that different stands protect each other from possible winds. The optimal harvest age of a particular stand is a function of wind speed and different wind protection effects. The optimal harvest age often decreases with wind speed, but it cannot be determined for one stand at a time. When we consider a particular stand, this stand also protects other stands. Furthermore, the particular stand is protected by neighbor stands. In some forest stands, it may even be rational to increase the harvest age under the influence of stronger winds, in order to protect more valuable stands in the neighborhood. It is important to integrate wind risk in forestry decision-making.

Keywords: Czech republic, forest stands, stochastic dynamic programming, wind risk

Procedia PDF Downloads 148

Authors: Hana Al-Baghaoi, Kumar Shiva Gubbiyappa, Mayuren Candasamy, Kiruthiga Perumal Vijayaraman

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Acne is a chronic inflammatory disease of the sebaceous glands characterized by areas of skin with seborrhea, comedones, papules, pustules, nodules, and possibly scarring. Propionibacterium acnes (P. acnes), plays a key role in the pathogenesis of acne. Their colonization and proliferation trigger the host’s inflammatory response leading to the production of pro-inflammatory cytokines such as interleukin-8 (IL-8) and tumour necrosis factor-α (TNF-α). The usage of honey and natural compounds to treat skin ailments has strong support in the current trend of drug discovery. The present study was carried out evaluate antimicrobial and anti-inflammatory potential of Doani Sidr honey and its fractions against P. acnes and to screen madecassoside alone and in combination with fractions of honey. The broth dilution method was used to assess the antibacterial activity. Also, ultra structural changes in cell morphology were studied before and after exposure to Sidr honey using transmission electron microscopy (TEM). The three non-toxic concentrations of the samples were investigated for suppression of cytokines IL 8 and TNF α by testing the cell supernatants in the co-culture of the human peripheral blood mononuclear cells (hPBMCs) heat killed P. acnes using enzyme immunoassay kits (ELISA). Results obtained was evaluated by statistical analysis using Graph Pad Prism 5 software. The Doani Sidr honey and polysaccharide fractions were able to inhibit the growth of P. acnes with a noteworthy minimum inhibitory concentration (MIC) value of 18% (w/v) and 29% (w/v), respectively. The proximity of MIC and MBC values indicates that Doani Sidr honey had bactericidal effect against P. acnes which is confirmed by TEM analysis. TEM images of P. acnes after treatment with Doani Sidr honey showed completely physical membrane damage and lysis of cells; whereas non honey treated cells (control) did not show any damage. In addition, Doani Sidr honey and its fractions significantly inhibited (> 90%) of secretion of pro-inflammatory cytokines like TNF α and IL 8 by hPBMCs pretreated with heat-killed P. acnes. However, no significant inhibition was detected for madecassoside at its highest concentration tested. Our results suggested that Doani Sidr honey possesses both antimicrobial and anti-inflammatory effects against P. acnes and can possibly be used as therapeutic agents for acne. Furthermore, polysaccharide fraction derived from Doani Sidr honey showed potent inhibitory effect toward P. acnes. Hence, we hypothesize that fraction prepared from Sidr honey might be contributing to the antimicrobial and anti-inflammatory activity. Therefore, this polysaccharide fraction of Doani Sidr honey needs to be further explored and characterized for various phytochemicals which are contributing to antimicrobial and anti-inflammatory properties.

Keywords: Doani sidr honey, Propionibacterium acnes, IL-8, TNF alpha

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Authors: Suleiman Hassan Otuoze, Dexter Vernon Lloyd Hunt, Ian Jefferson

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Urbanization has continued to widen the gap between demand and resources available to provide resilient and sustainable transport services in many fast-growing developing countries' cities. Transport demand management is a decision-based optimization concept for both benchmarking and ensuring efficient use of transport resources. This study assesses the service quality of infrastructure and mobility services in the Nigerian cities of Kano and Lagos through five dimensions of quality (i.e., Tangibility, Reliability, Responsibility, Safety Assurance and Empathy). The methodology adopts a hybrid AHP-SERVQUAL model applied on questionnaire surveys to gauge the quality of satisfaction and the views of experts in the field. The AHP results prioritize tangibility, which defines the state of transportation infrastructure and services in terms of satisfaction qualities and intervention decision weights in the two cities. The results recorded ‘unsatisfactory’ indices of quality of performance and satisfaction rating values of 48% and 49% for Kano and Lagos, respectively. The satisfaction indices are identified as indicators of low performances of transportation demand management (TDM) measures and the necessity to re-order priorities and take proactive steps towards infrastructure. The findings pilot a framework for comparative assessment of recognizable standards in transport services, best ethics of management and a necessity of quality infrastructure to guarantee both resilient and sustainable urban mobility.

Keywords: transportation demand management, multi-criteria decision support, transport infrastructure, service quality, sustainable transport

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Authors: Ahmed Banda, Alaa Maghrabi, Aiman Fakieh

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Terahertz (THz) range lies in the area between 0.1 to 10 THz. The process of generating and detecting THz can be done through different techniques. One of the most familiar techniques is done through a photoconductive antenna (PCA). The process of generating THz radiation at PCA includes applying a laser pump in femtosecond and DC voltage difference. However, photocurrent is generated at PCA, which its value is affected by different parameters (e.g., dielectric properties, DC voltage difference and incident power of laser pump). THz radiation is used for biomedical applications. However, different biomedical fields need new technologies to meet patients’ needs (e.g. blood-related conditions). In this work, a novel method to check the red blood cells (RBCs) concentration of a blood sample using PCA is presented. RBCs constitute 44% of total blood volume. RBCs contain Hemoglobin that transfers oxygen from lungs to body organs. Then it returns to the lungs carrying carbon dioxide, which the body then gets rid of in the process of exhalation. The configuration has been simulated and optimized using COMSOL Multiphysics. The differentiation of RBCs concentration affects its dielectric properties (e.g., the relative permittivity of RBCs in the blood sample). However, the effects of four blood samples (with different concentrations of RBCs) on photocurrent value have been tested. Photocurrent peak value and RBCs concentration are inversely proportional to each other due to the change of dielectric properties of RBCs. It was noticed that photocurrent peak value has dropped from 162.99 nA to 108.66 nA when RBCs concentration has risen from 0% to 100% of a blood sample. The optimization of this method helps to launch new products for diagnosing blood-related conditions (e.g., anemia and leukemia). The resultant electric field from DC components can not be used to count the RBCs of the blood sample.

Keywords: biomedical applications, photoconductive antenna, photocurrent, red blood cells, THz radiation

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Authors: R. Sharma, S. Kumar, C. Sharma

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A number of toxic chlorophenolic compounds are formed during pulp bleaching. The nature and concentration of these chlorophenolic compounds largely depends upon the amount and nature of bleaching chemicals used. These compounds are highly recalcitrant and difficult to remove but are partially removed by the biochemical treatment processes adopted by the paper industry. Identification and estimation of these chlorophenolic compounds has been carried out in the primary and secondary clarified effluents from the paper mill by GCMS. Twenty-six chorophenolic compounds have been identified and estimated in paper mill waste waters. Electrochemical treatment is an efficient method for oxidation of pollutants and has successfully been used to treat textile and oil waste water. Electrochemical treatment using less expensive anode material, stainless steel electrodes has been tried to study their removal. The electrochemical assembly comprised a DC power supply, a magnetic stirrer and stainless steel (316 L) electrode. The optimization of operating conditions has been carried out and treatment has been performed under optimized treatment conditions. Results indicate that 68.7% and 83.8% of cholorphenolic compounds are removed during 2 h of electrochemical treatment from primary and secondary clarified effluent respectively. Further, there is a reduction of 65.1, 60 and 92.6% of COD, AOX and color, respectively for primary clarified and 83.8%, 75.9% and 96.8% of COD, AOX and color, respectively for secondary clarified effluent. EC treatment has also been found to increase significantly the biodegradability index of wastewater because of conversion of non- biodegradable fraction into biodegradable fraction. Thus, electrochemical treatment is an efficient method for the degradation of cholorophenolic compounds, removal of color, AOX and other recalcitrant organic matter present in paper mill waste water.

Keywords: chlorophenolics, effluent, electrochemical treatment, wastewater

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Authors: Daud Mustafa Minhas, Raja Rehan Khalid, Georg Frey

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The next generation power systems are equipped with abundantly available free renewable energy resources (RES). During their low-cost operations, the price of electricity significantly reduces to a lower value, and sometimes it becomes negative. Therefore, it is recommended not to operate the traditional power plants (e.g. coal power plants) and to reduce the losses. In fact, it is not a cost-effective solution, because these power plants exhibit some shutdown and startup costs. Moreover, they require certain time for shutdown and also need enough pause before starting up again, increasing inefficiency in the whole power network. Hence, there is always a trade-off between avoiding negative electricity prices, and the startup costs of power plants. To exploit this trade-off and to increase the profit of a power plant, two main contributions are made: 1) introducing retrofit technology for state of art coal power plant; 2) proposing optimal control strategy for a power plant by exploiting different flexibility features. These flexibility features include: improving ramp rate of power plant, reducing startup time and lowering minimum load. While, the control strategy is solved as mixed integer linear programming (MILP), ensuring optimal solution for the profit maximization problem. Extensive comparisons are made considering pre and post-retrofit coal power plant having the same efficiencies under different electricity price scenarios. It concludes that if the power plant must remain in the market (providing services), more flexibility reflects direct economic advantage to the plant operator.

Keywords: discrete optimization, power plant flexibility, profit maximization, unit commitment model

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Authors: J. B. Frisch, D. Evandiloff, P. Martin, N. Ouizille, F. Pires

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Smart Beta strategies intend to be an asset management revolution with reference to classical cap-weighted indices. Indeed, these strategies allow a better control on portfolios risk factors and an optimized asset allocation by taking into account specific risks or wishes to generate alpha by outperforming indices called 'Beta'. Among many strategies independently used, this paper focuses on four of them: Minimum Variance Portfolio, Equal Risk Contribution Portfolio, Maximum Diversification Portfolio, and Equal-Weighted Portfolio. Their efficiency has been proven under constraints like momentum or market phenomenon, suggesting a reconsideration of cap-weighting.
 To further increase strategy return efficiency, it is proposed here to compare their strengths and weaknesses inside time intervals corresponding to specific identifiable market phases, in order to define adapted strategies depending on pre-specified situations. 
Results are presented as performance curves from different combinations compared to a benchmark. If a combination outperforms the applicable benchmark in well-defined actual market conditions, it will be preferred. It is mainly shown that such investment 'rules', based on both historical data and evolution of Smart Beta strategies, and implemented according to available specific market data, are providing very interesting optimal results with higher return performance and lower risk.
 Such combinations have not been fully exploited yet and justify present approach aimed at identifying relevant elements characterizing them.

Keywords: smart beta, minimum variance portfolio, equal risk contribution portfolio, maximum diversification portfolio, equal weighted portfolio, combinations

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Authors: Yanping Liao, Congcong He, Ruigang Zhao

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The application of magnetocardiography signals to detect cardiac electrical function is a new technology developed in recent years. The magnetocardiography signal is detected with Superconducting Quantum Interference Devices (SQUID) and has considerable advantages over electrocardiography (ECG). It is difficult to extract Magnetocardiography (MCG) signal which is buried in the noise, which is a critical issue to be resolved in cardiac monitoring system and MCG applications. In order to remove the severe background noise, the Total Variation (TV) regularization method is proposed to denoise MCG signal. The approach transforms the denoising problem into a minimization optimization problem and the Majorization-minimization algorithm is applied to iteratively solve the minimization problem. However, traditional TV regularization method tends to cause step effect and lacks constraint adaptability. In this paper, an improved TV regularization method for denoising MCG signal is proposed to improve the denoising precision. The improvement of this method is mainly divided into three parts. First, high-order TV is applied to reduce the step effect, and the corresponding second derivative matrix is used to substitute the first order. Then, the positions of the non-zero elements in the second order derivative matrix are determined based on the peak positions that are detected by the detection window. Finally, adaptive constraint parameters are defined to eliminate noises and preserve signal peak characteristics. Theoretical analysis and experimental results show that this algorithm can effectively improve the output signal-to-noise ratio and has superior performance.

Keywords: constraint parameters, derivative matrix, magnetocardiography, regular term, total variation

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Authors: Youssef Mazloum, Haytham Sayah, Maroun Nemer

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The penetration of renewable energy sources into the electric grid is significantly increasing. However, the intermittence of these sources breaks the balance between supply and demand for electricity. Hence, the importance of the energy storage technologies, they permit restoring the balance and reducing the drawbacks of intermittence of the renewable energies. This paper discusses the modeling and the cost-effectiveness of an isobaric adiabatic compressed air energy storage (IA-CAES) system. The proposed system is a combination among a compressed air energy storage (CAES) system with pumped hydro storage system and thermal energy storage system. The aim of this combination is to overcome the disadvantages of the conventional CAES system such as the losses due to the storage pressure variation, the loss of the compression heat and the use of fossil fuel sources. A steady state model is developed to perform an energy and exergy analyses of the IA-CAES system and calculate the distribution of the exergy losses in the latter system. A sensitivity analysis is also carried out to estimate the effects of some key parameters on the system’s efficiency, such as the pinch of the heat exchangers, the isentropic efficiency of the rotating machinery and the pressure losses. The conducted sensitivity analysis is a local analysis since the sensibility of each parameter changes with the variation of the other parameters. Therefore, an exergoeconomic study is achieved as well as a cost optimization in order to reduce the electricity cost produced during the production phase. The optimizer used is OmOptim which is a genetic algorithms based optimizer.

Keywords: cost-effectiveness, Exergoeconomic analysis, isobaric adiabatic compressed air energy storage (IA-CAES) system, thermodynamic modeling

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Authors: Sabeeh Lafta Farhan, Ihsan Abbass Jasim, Sohaib Kareem Al-Mamoori

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This study addresses the subject of transformation of urban structures and how does this transformation affect the character of traditional cities, which represents the research issue. Hence, the research has aimed at studying and learning about the urban structure characteristics and morphological transformation features in the traditional cities centers, and to look for means and methods to preserve the character of those cities. Cities are not merely locations inhabited by a large number of people, they are political and legal entities, in addition to economic activities that distinguish these cities, thus, they are a complex set of institutions, and the transformation in urban environment cannot be recognized without understanding these relationships. The research presumes an existing impact of urbanization on the properties of traditional structure of the Holy City of Najaf. The research has defined urbanization as restructuring and re-planning of urban areas that have lost their functions and bringing them into social and cultural life in the city, to be able to serve economy in order to better respond to the needs of users. Sacred Cities provide the organic connection between acts of worship and dealings and reveal the mechanisms and reasons behind the regulatory nature of the sacred shrine and their role in achieving organizational assimilation of urban morphology. The research has reached a theoretical framework of the particulars of urbanization. This framework has been applied to the historic center of the old city of Najaf, where the most important findings of the research were that the visual and structural dominant presence of holy shrine of Imam Ali (peace be upon him) remains to emphasize the visual particularity, and the main role of the city, which hosts one of the most important Muslim shrines in the world, in addition to the visible golden dome rising above the skyline, and the Imam Ali Mosque the hub and the center for religious activities. Thus, in view of being a place of main importance and a symbol of religious and Islamic culture, it is very important to have the shrine of Imam Ali (AS) prevailing on all zones of re-development in the old city. Consequently, the research underlined that the distinctive and unique character of the city of Najaf did not proceed from nothing, but was achieved through the unrivaled characteristics and features possessed by the city of Najaf alone, which allowed it and enabled it to occupy this status among the Arab and Muslim cities. That is why the activities arising from the development have to enhance the historical role of the city in order to have this development as clear support, strength and further addition to the city assets and its cultural heritage, and not seeing the developmental activities crushing the city urban traditional fabric, cultural heritage and its historical specificity.

Keywords: Iraq, the city of Najaf, heritage, traditional cities, morphological transformation

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Authors: Zoran Herceg, Višnja Stulić, Anet Režek Jambrak, Tomislava Vukušić

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Electrical discharge plasma is a new non-thermal processing technique which is used for the inactivation of contaminating and hazardous microbes in liquids. Plasma is a source of different antimicrobial species including UV photons, charged particles, and reactive species such as superoxide, hydroxyl radicals, nitric oxide and ozone. Escherichia coli was studied as foodborne pathogen. The aim of this work was to examine inactivation effects of electrical discharge plasma treatment on the Escherichia coli MG 1655 in pure culture. Two types of plasma configuration and polarity were used. First configuration was with titanium wire as high voltage needle and another with medical stainless steel needle used to form bubbles in treated volume and titanium wire as high voltage needle. Model solution samples were inoculated with Escerichia coli MG 1655 and treated by electrical discharge plasma at treatment time of 5 and 10 min, and frequency of 60, 90 and 120 Hz. With the first configuration after 5 minutes of treatment at frequency of 120 Hz the inactivation rate was 1.3 log₁₀ reduction and after 10 minutes of treatment the inactivation rate was 3.0 log₁₀ reduction. At the frequency of 90 Hz after 10 minutes inactivation rate was 1.3 log₁₀ reduction. With the second configuration after 5 minutes of treatment at frequency of 120 Hz the inactivation rate was 1.2 log₁₀ reduction and after 10 minutes of treatment the inactivation rate was also 3.0 log₁₀ reduction. In this work it was also examined the formation of biofilm, nucleotide and protein leakage at 260/280 nm, before and after treatment and recuperation of treated samples. Further optimization of method is needed to understand mechanism of inactivation.

Keywords: electrical discharge plasma, escherichia coli MG 1655, inactivation, point-to-plate electrode configuration

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Authors: Kulvinder Bajwa, Narsi R. Bishnoi

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Finding a sustainable alternative for today’s petrochemical industry is a major challenge facing by researchers, scientists, chemical engineers, and society at the global level. Microorganisms are considered to be sustainable feedstock for 3rd generation biofuel production. In this study, we have investigated the potential of a native bacterial strain isolated from a petrol contaminated site for the production of biodiesel. The bacterium was identified to be Rhodococcus opacus by biochemical test and 16S rRNA. Compositional analysis of bacterial biomass has been carried out by Fourier transform infrared spectroscopy (FTIR) in order to confirm lipid profile. Lipid and biomass were optimized by combination with Box Behnken design (BBD) of response surface methodology. The factors selected for the optimization of growth condition were glucose, yeast extract, and ammonium nitrate concentration. The experimental model developed through RSM in terms of effective operational factors (BBD) was found to be suitable to describe the lipid and biomass production, which indicated higher lipid and biomass with a minimum concentration of ammonium nitrate, yeast extract, and quite higher dose of glucose supplementation. Optimum results of the experiments were found to be 2.88 gL⁻¹ biomass and lipid content 38.75% at glucose 20 gL⁻¹, ammonium nitrate 0.5 gL⁻¹ and yeast extract 1.25 gL⁻¹. Furthermore, GCMS study revealed that Rhodococcus opacus has favorable fatty acid profile for biodiesel production.

Keywords: biofuel, Oleaginious bacteria, Rhodococcus opacus, FTIR, BBD, free fatty acids

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Authors: R. Khenata, T. Djied, R. Ahmed, H. Baltache, S. Bin-Omran, A. Bouhemadou

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We predict structural, phase transition as well as opto-electronic properties of the filled-tetrahedral (Nowotny-Juza) NaZnAs compound in this study. Calculations are carried out by employing the full potential (FP) linearized augmented plane wave (LAPW) plus local orbitals (lo) scheme developed within the structure of density functional theory (DFT). Exchange-correlation energy/potential (EXC/VXC) functional is treated using Perdew-Burke and Ernzerhof (PBE) parameterization for generalized gradient approximation (GGA). In addition to Trans-Blaha (TB) modified Becke-Johnson (mBJ) potential is incorporated to get better precision for optoelectronic properties. Geometry optimization is carried out to obtain the reliable results of the total energy as well as other structural parameters for each phase of NaZnAs compound. Order of the structural transitions as a function of pressure is found as: Cu2Sb type → β → α phase in our study. Our calculated electronic energy band structures for all structural phases at the level of PBE-GGA as well as mBJ potential point out; NaZnAs compound is a direct (Γ–Γ) band gap semiconductor material. However, as compared to PBE-GGA, mBJ potential approximation reproduces higher values of fundamental band gap. Regarding the optical properties, calculations of real and imaginary parts of the dielectric function, refractive index, reflectivity coefficient, absorption coefficient and energy loss-function spectra are performed over a photon energy ranging from 0.0 to 30.0 eV by polarizing incident radiation in parallel to both [100] and [001] crystalline directions.

Keywords: NaZnAs, FP-LAPW+lo, structural properties, phase transition, electronic band-structure, optical properties

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Authors: Julian Wise

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Newcrest Mining is one of the world’s top five gold and rare earth mining organizations by production, reserves and market capitalization in the world. This paper elaborates on the data acquisition processes employed by Newcrest in collaboration with Fortune 500 listed organization, Insight Enterprises, to standardize machine learning solutions which process data from over a hundred thousand distributed Internet of Things (IoT) devices located at mine sites globally. Through the utilization of software architecture cloud technologies and edge computing, the technological developments enable for standardized processes of machine learning applications to influence the strategic optimization of mineral processing. Target objectives of the machine learning optimizations include time savings on mineral processing, production efficiencies, risk identification, and increased production throughput. The data acquired and utilized for predictive modelling is processed through edge computing by resources collectively stored within a data lake. Being involved in the digital transformation has necessitated the standardization software architecture to manage the machine learning models submitted by vendors, to ensure effective automation and continuous improvements to the mineral process models. Operating at scale, the system processes hundreds of gigabytes of data per day from distributed mine sites across the globe, for the purposes of increased improved worker safety, and production efficiency through big data applications.

Keywords: mineral technology, big data, machine learning operations, data lake

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Authors: Bokkasam Sasidhar, Ibrahim Aljasser

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The problem of finding optimal schedules for each equipment in a production process is considered, which consists of a single stage of manufacturing and which can handle different types of products, where changeover for handling one type of product to the other type incurs certain costs. The machine capacity is determined by the upper limit for the quantity that can be processed for each of the products in a set up. The changeover costs increase with the number of set ups and hence to minimize the costs associated with the product changeover, the planning should be such that similar types of products should be processed successively so that the total number of changeovers and in turn the associated set up costs are minimized. The problem of cost minimization is equivalent to the problem of minimizing the number of set ups or equivalently maximizing the capacity utilization in between every set up or maximizing the total capacity utilization. Further, the production is usually planned against customers’ orders, and generally different customers’ orders are assigned one of the two priorities – “normal” or “priority” order. The problem of production planning in such a situation can be formulated into a Multiple Arc Network (MAN) model and can be solved sequentially using the algorithm for maximizing flow along a MAN and the algorithm for maximizing flow along a MAN with priority arcs. The model aims to provide optimal production schedule with an objective of maximizing capacity utilization, so that the customer-wise delivery schedules are fulfilled, keeping in view the customer priorities. Algorithms have been presented for solving the MAN formulation of the production planning with customer priorities. The application of the model is demonstrated through numerical examples.

Keywords: scheduling, maximal flow problem, multiple arc network model, optimization

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Authors: Xiaocong Liu, Huazhen Wang, Ting He, Xiaozheng Li, Weihan Zhang, Jian Chen

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The utilization of electronic medical record (EMR) data to establish the disease diagnosis model has become an important research content of biomedical informatics. Deep learning can automatically extract features from the massive data, which brings about breakthroughs in the study of EMR data. The challenge is that deep learning lacks semantic knowledge, which leads to impracticability in medical science. This research proposes a method of incorporating lexical-semantic knowledge from abundant entities into a convolutional neural network (CNN) framework for pediatric disease diagnosis. Firstly, medical terms are vectorized into Lexical Semantic Vectors (LSV), which are concatenated with the embedded word vectors of word2vec to enrich the feature representation. Secondly, the semantic distribution of medical terms serves as Semantic Decision Guide (SDG) for the optimization of deep learning models. The study evaluate the performance of LSV-SDG-CNN model on four kinds of Chinese EMR datasets. Additionally, CNN, LSV-CNN, and SDG-CNN are designed as baseline models for comparison. The experimental results show that LSV-SDG-CNN model outperforms baseline models on four kinds of Chinese EMR datasets. The best configuration of the model yielded an F1 score of 86.20%. The results clearly demonstrate that CNN has been effectively guided and optimized by lexical-semantic knowledge, and LSV-SDG-CNN model improves the disease classification accuracy with a clear margin.

Keywords: convolutional neural network, electronic medical record, feature representation, lexical semantics, semantic decision

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Authors: Ram V. Mohan, John Rivas-Murillo, Ahmed Mohamed, Wayne D. Hodo

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Cementitious materials, an excellent example of highly complex, heterogeneous material systems, are cement-based systems that include cement paste, mortar, and concrete that are heavily used in civil infrastructure; though commonly used are one of the most complex in terms of the material morphology and structure than most materials, for example, crystalline metals. Processes and features occurring at the nanometer sized morphological structures affect the performance, deformation/failure behavior at larger length scales. In addition, cementitious materials undergo chemical and morphological changes gaining strength during the transient hydration process. Hydration in cement is a very complex process creating complex microstructures and the associated molecular structures that vary with hydration. A fundamental understanding can be gained through multi-scale level modeling for the behavior and properties of cementitious materials starting from the material chemistry level atomistic scale to further explore their role and the manifested effects at larger length and engineering scales. This predictive modeling enables the understanding, and studying the influence of material chemistry level changes and nanomaterial additives on the expected resultant material characteristics and deformation behavior. Atomistic-molecular dynamic level modeling is required to couple material science to engineering mechanics. Starting at the molecular level a comprehensive description of the material’s chemistry is required to understand the fundamental properties that govern behavior occurring across each relevant length scale. Material chemistry level models and molecular dynamics modeling and simulations are employed in our work to describe the molecular-level chemistry features of calcium-silicate-hydrate (CSH), one of the key hydrated constituents of cement paste, their associated deformation and failure. The molecular level atomic structure for CSH can be represented by Jennite mineral structure. Jennite has been widely accepted by researchers and is typically used to represent the molecular structure of the CSH gel formed during the hydration of cement clinkers. This paper will focus on our recent work on the shear and compressive deformation and failure behavior of CSH represented by Jennite mineral structure that has been widely accepted by researchers and is typically used to represent the molecular structure of CSH formed during the hydration of cement clinkers. The deformation and failure behavior under shear and compression loading deformation in traditional hydrated CSH; effect of material chemistry changes on the predicted stress-strain behavior, transition from linear to non-linear behavior and identify the on-set of failure based on material chemistry structures of CSH Jennite and changes in its chemistry structure will be discussed.

Keywords: cementitious materials, deformation, failure, material chemistry modeling

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Authors: Walid Tawfik

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The interaction of high intensity ultrashort laser pulses with plasma generates many significant applications, including soft x-ray lasers, time-resolved laser induced plasma spectroscopy LIPS, and laser-driven accelerators. The development in producing of femtosecond down to ten femtosecond optical pulses has facilitates scientists with a vital tool in a variety of ultrashort phenomena, such as high field physics, femtochemistry and high harmonic generation HHG. In this research, we generate a two-octave-wide ultrashort supercontinuum pulses with an optical spectrum extending from 3.5 eV (ultraviolet) to 1.3 eV (near-infrared) using a capillary fiber filled with neon gas. These pulses are formed according to nonlinear self-phase modulation in the neon gas as a nonlinear medium. The investigations of the created pulses were made using spectral phase interferometry for direct electric-field reconstruction (SPIDER). A complete description of the output pulses was considered. The observed characterization of the produced pulses includes the beam profile, the pulse width, and the spectral bandwidth. After reaching optimization conditions, the intensity of the reconstructed pulse autocorrelation function was applied for the shorts pulse duration to achieve transform limited ultrashort pulses with durations below 6-fs energies up to 600μJ. Moreover, the effect of neon pressure variation on the pulse width was examined. The nonlinear self-phase modulation realized to be increased with the pressure of the neon gas. The observed results may lead to an advanced method to control and monitor ultrashort transit interaction in femtochemistry.

Keywords: supercontinuum, ultrafast, SPIDER, ultra-broadband

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Authors: Pia Wetzl

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The learning process of students can be optimized by combining online teaching with face-to-face sessions. So-called blended learning offers extensive flexibility as well as contact opportunities with fellow students and teachers. Furthermore, learning can be individualized and self-regulated. The aim of this article is to investigate which factors are necessary for blended learning to be successful. Semi-structured interviews were conducted with students (N = 60) and lecturers (N = 21) from different disciplines at two German universities. The questions focused on the perception of online, face-to-face and blended learning courses. In addition, questions focused on possible optimization potential and obstacles to practical implementation. The results show that on-site presence is very important for blended learning to be successful. If students do not get to know each other on-site, there is a risk of loneliness during the self-learning phases. This has a negative impact on motivation. From the perspective of the lecturers, the willingness of the students to participate in the sessions on-site is low. Especially when there is no obligation to attend, group work is difficult to implement because the number of students attending is too low. Lecturers would like to see more opportunities from the university and its administration to enforce attendance. In their view, this is the only way to ensure the success of blended learning. In addition, they see the conception of blended learning courses as requiring a great deal of time, which they are not always willing to invest. More incentives are necessary to keep the lecturers motivated to develop engaging teaching material. The study identifies factors that can help teachers conceptualize blended learning. It also provides specific implementation advice and identifies potential impacts. This catalogue has great value for the future-oriented development of courses at universities. Future studies could test its practical use.

Keywords: blended learning, higher education, teachers, student learning, qualitative research

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Authors: Luis Guiliana, Andrea Osorio

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The lack of comprehension of the reservoir geomechanics conditions may cause operational problems that cost to the industry billions of dollars per year. The drilling operations at the Ceuta Field, Area 2 South, Maracaibo Lake, have been very expensive due to problems associated with drilling. The principal objective of this investigation is to develop a 3D geomechanical model in this area, in order to optimize the future drillings in the field. For this purpose, a 1D geomechanical model was built at first instance, following the workflow of the MEM (Mechanical Earth Model), this consists of the following steps: 1) Data auditing, 2) Analysis of drilling events and structural model, 3) Mechanical stratigraphy, 4) Overburden stress, 5) Pore pressure, 6) Rock mechanical properties, 7) Horizontal stresses, 8) Direction of the horizontal stresses, 9) Wellbore stability. The 3D MEM was developed through the geostatistic model of the Eocene C-SUP VLG-3676 reservoir and the 1D MEM. With this data the geomechanical grid was embedded. The analysis of the results threw, that the problems occurred in the wells that were examined were mainly due to wellbore stability issues. It was determined that the stress field change as the stratigraphic column deepens, it is normal to strike-slip at the Middle Miocene and Lower Miocene, and strike-slipe to reverse at the Eocene. In agreement to this, at the level of the Eocene, the most advantageous direction to drill is parallel to the maximum horizontal stress (157º). The 3D MEM allowed having a tridimensional visualization of the rock mechanical properties, stresses and operational windows (mud weight and pressures) variations. This will facilitate the optimization of the future drillings in the area, including those zones without any geomechanics information.

Keywords: geomechanics, MEM, drilling, stress

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Authors: Devendra Sillu, Shekhar Agnihotri

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The 'nano-biocatalyst' utilizes an ordered assembling of enzyme on to nanomaterial carriers to catalyze desirable biochemical kinetics and substrate selectivity. The current study describes an inter-disciplinary approach for converting agriculture waste, sugarcane bagasse into D-glucose exploiting halloysite nanotubes (HNTs) decorated cellulase enzyme as nano-biocatalytic system. Cellulase was successfully immobilized on HNTs employing polydopamine as an eco-friendly crosslinker while iron oxide nanoparticles were attached to facilitate magnetic recovery of material. The characterization studies (UV-Vis, TEM, SEM, and XRD) displayed the characteristic features of both cellulase and magnetic HNTs in the resulting nanocomposite. Various factors (i.e., working pH, temp., crosslinker conc., enzyme conc.) which may influence the activity of biocatalytic system were investigated. The experimental design was performed using Response Surface Methodology (RSM) for process optimization. Analyses data demonstrated that the nanobiocatalysts retained 80.30% activity even at elevated temperature (55°C) and excellent storage stabilities after 10 days. The repeated usage of system revealed a remarkable consistent relative activity over several cycles. The immobilized cellulase was employed to decompose agro-waste and the maximum decomposition rate of 67.2 % was achieved. Conclusively, magnetic HNTs can serve as a potential support for enzyme immobilization with long term usage, good efficacy, reusability and easy recovery from solution.

Keywords: halloysite nanotubes, enzyme immobilization, cellulase, response surface methodology, magnetic recovery

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Authors: Filip Buruiana, Alexander Hall, Reimar Hofmann, Thomas Hofmann, Silviu Ganceanu, Alexandru Tudorica

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In-memory column-stores make interactive analysis feasible for many big data scenarios. PowerDrill is a system used internally at Google for exploration in logs data. Even though it is a highly parallelized column-store and uses in memory caching, interactive response times cannot be achieved for all datasets (note that it is common to analyze data with 50 billion records in PowerDrill). In this paper, we investigate two orthogonal approaches to optimize performance at the expense of an acceptable loss of accuracy. Both approaches can be implemented as outer wrappers around existing database engines and so they should be easily applicable to other systems. For the first optimization we show that memory is the limiting factor in executing queries at speed and therefore explore possibilities to improve memory efficiency. We adapt some of the theory behind data sketches to reduce the size of particularly expensive fields in our largest tables by a factor of 4.5 when compared to a standard compression algorithm. This saves 37% of the overall memory in PowerDrill and introduces a 0.4% relative error in the 90th percentile for results of queries with the expensive fields. We additionally evaluate the effects of using sampling on accuracy and propose a simple heuristic for annotating individual result-values as accurate (or not). Based on measurements of user behavior in our real production system, we show that these estimates are essential for interpreting intermediate results before final results are available. For a large set of queries this effectively brings down the 95th latency percentile from 30 to 4 seconds.

Keywords: big data, in-memory column-store, high-performance SQL queries, approximate SQL queries

Procedia PDF Downloads 260