Search results for: mathematical modeling membrane bioreactor

Authors: Jeet Shende, Leonid Shpanin, Misko Abramiuk, Mattew Goodwin, Nicholas Pickett

Abstract:

Energy harvesting on board the Unmanned Ariel Vehicle (UAV) is one of the most rapidly growing emerging technologies and consists of the collection of small amounts of energy, for different applications, from unconventional sources that are incidental to the operation of the parent system or device. Different energy harvesting techniques have already been investigated in the multirotor drones, where the energy collected comes from the systems surrounding ambient environment and typically involves the conversion of solar, kinetic, or thermal energies into electrical energy. The energy harvesting from the vibrated propeller using the piezoelectric components inside the propeller has also been proven to be feasible. However, the impact on the UAV flight performance using this technology has not been investigated. In this contribution the impact on the multirotor drone operation has been investigated at different flight control configurations which support the efficient performance of the propeller vibration energy harvesting. The industrially made MANTIS X8-PRO octocopter frame kit was used to explore the octocopter operation which was modelled using SolidWorks 3D CAD package for simulation studies. The octocopter flight control strategy is developed through integration of the SolidWorks 3D CAD software and MATLAB/Simulink simulation environment for evaluation of the octocopter behaviour under different simulated flight modes and octocopter geometries. Analysis of the two modelled octocopter geometries and their flight performance is presented via graphical representation of simulated parameters. The possibility of not using the landing gear in octocopter geometry is demonstrated. The conducted study evaluates the octocopter’s flight control technique and its impact on the energy harvesting mechanism developed on board the octocopter. Finite Element Analysis (FEA) simulation results of the modelled octocopter in operation are presented exploring the performance of the octocopter flight control and structural configurations. Applications of both octocopter structures and their flight control strategy are discussed.

Keywords: energy harvesting, flight control modelling, object modeling, unmanned aerial vehicle

Procedia PDF Downloads 78

Authors: M. Ntšolo, D. Kalumba, N. Lefu, G. Letlatsa

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Rock mass damage due to shear tectonic activity has been investigated largely in geoscience where fluid transport is of major interest. However, little has been studied on the effect of shear zones on rock mass behavior and its impact on stability of rock slopes. At Letšeng Diamonds open pit mine in Lesotho, the shear zone composed of sheared kimberlite material, calcite and altered basalt is forming part of the haul ramp into the main pit cut 3. The alarming rate at which the shear zone is deteriorating has triggered concerns about both local and global stability of pit the walls. This study presents the numerical modelling of the open pit slope affected by shear zone at Letšeng Diamond Mine (LDM). Analysis of the slope involved development of the slope model by using a two-dimensional finite element code RS2. Interfaces between shear zone and host rock were represented by special joint elements incorporated in the finite element code. The analysis of structural geological mapping data provided a good platform to understand the joint network. Major joints including shear zone were incorporated into the model for simulation. This approach proved successful by demonstrating that continuum modelling can be used to evaluate evolution of stresses, strain, plastic yielding and failure mechanisms that are consistent with field observations. Structural control due to geological shear zone structure proved to be important in its location, size and orientation. Furthermore, the model analyzed slope deformation and sliding possibility along shear zone interfaces. This type of approach can predict shear zone deformation and failure mechanism, hence mitigation strategies can be deployed for safety of human lives and property within mine pits.

Keywords: numerical modeling, open pit mine, shear zone, slope stability

Procedia PDF Downloads 299

Authors: Juan José Mesas, Luis Sainz

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The motivation for the development of this paper lies in the need for energy networks to reduce losses, improve performance, optimize their operation and try to benefit from the interconnection capacity with other networks enabled for other energy carriers. These interconnections generate interdependencies between some energy networks and others, which requires suitable models and methods for their analysis. Traditionally, the modeling and study of energy networks have been carried out independently for each energy carrier. Thus, there are well-established models and methods for the steady-state analysis of electrical networks, gas networks, and thermal networks separately. What is intended is to extend and combine them adequately to be able to face in an integrated way the steady-state analysis of networks with multiple energy carriers. Firstly, the added value of multi-energy networks, their operation, and the basic principles that characterize them are explained. In addition, two current aspects of great relevance are exposed: the storage technologies and the coupling elements used to interconnect one energy network with another. Secondly, the characteristic equations of the different energy networks necessary to carry out the steady-state analysis are detailed. The electrical network, the natural gas network, and the thermal network of heat and cold are considered in this paper. After the presentation of the equations, a particular case of the steady-state analysis of a specific multi-energy network is studied. This network is represented graphically, the interconnections between the different energy carriers are described, their technical data are exposed and the equations that have previously been presented theoretically are formulated and developed. Finally, the two iterative numerical resolution methods considered in this paper are presented, as well as the resolution procedure and the results obtained. The pros and cons of the application of both methods are explained. It is verified that the results obtained for the electrical network (voltages in modulus and angle), the natural gas network (pressures), and the thermal network (mass flows and temperatures) are correct since they comply with the distribution, operation, consumption and technical characteristics of the multi-energy network under study.

Keywords: coupling elements, energy carriers, multi-energy networks, steady-state analysis

Procedia PDF Downloads 81

Authors: Adel A. Ghobbar

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The airline industry is continuously challenging how to safely increase the service life of the aircraft with limited maintenance budgets. Operators are looking for the most qualified maintenance providers of aircraft components, offering the finest customer service. Component owner and maintenance provider is offering an Abacus agreement (Aircraft Component Leasing) to increase the efficiency and productivity of the customer service. To increase the customer service, the current focus on No Fault Found (NFF) units must change into the focus on Early Failure (EF) units. Since the effect of EF units has a significant impact on customer satisfaction, this needs to increase the reliability of EF units at minimal cost, which leads to the goal of this paper. By identifying the reliability of early failure (EF) units with regards to No Fault Found (NFF) units, in particular, the root cause analysis with an integrated cost analysis of EF units with the use of a failure mode analysis tool and a cost model, there will be a set of EF maintenance improvements. The data used for the investigation of the EF units will be obtained from the Pentagon system, an Enterprise Resource Planning (ERP) system used by Fokker Services. The Pentagon system monitors components, which needs to be repaired from Fokker aircraft owners, Abacus exchange pool, and commercial customers. The data will be selected on several criteria’s: time span, failure rate, and cost driver. When the selected data has been acquired, the failure mode and root cause analysis of EF units are initiated. The failure analysis approach tool was implemented, resulting in the proposed failure solution of EF. This will lead to specific EF maintenance improvements, which can be set-up to decrease the EF units and, as a result of this, increasing the reliability. The investigated EFs, between the time period over ten years, showed to have a significant reliability impact of 32% on the total of 23339 unscheduled failures. Since the EFs encloses almost one-third of the entire population.

Keywords: supportability, no fault found, FMEA, early failure, availability, operational reliability, predictive model

Procedia PDF Downloads 129

Authors: Sherif Fayez Lewis Ghaly

Abstract:

up to datefacupupdated planning has the mission of designing merchandise, plant life, procedures, enterprise, regions, and the development of a up to date. The requirements for up-to-date planning and the constructing of a updated have changed in recent years. everyday restructuring is turning inupupdated greater essential up-to-date hold the competitiveness of a manufacturing facilityupdated. restrictions in new regions, shorter existence cycles of product and manufacturing generation up-to-date a VUCA global (Volatility, Uncertainty, Complexity & Ambiguity) up-to-date greater frequent restructuring measures inside a manufacturing facilityupdated. A virtual up-to-date model is the making plans basis for rebuilding measures and up-to-date an fundamental up-to-date. short-time period rescheduling can now not be handled through on-web site inspections and manual measurements. The tight time schedules require 3177227fc5dac36e3e5ae6cd5820dcaa making plans fashions. updated the high variation fee of facup-to-dateries defined above, a method for rescheduling facupdatedries on the idea of a modern-day digital up to datery dual is conceived and designed for sensible software in updated restructuring projects. the point of interest is on rebuild approaches. The purpose is up-to-date preserve the planning basis (virtual up-to-date model) for conversions within a up to datefacupupdated updated. This calls for the application of a methodology that reduces the deficits of present techniques. The goal is up-to-date how a digital up to datery version may be up to date up to date during ongoing up to date operation. a method up-to-date on phoup to dategrammetry technology is presented. the focus is on developing a easy and fee-powerful up to date tune the numerous adjustments that arise in a manufacturing unit constructing in the course of operation. The method is preceded with the aid of a hardware and software assessment up-to-date become aware of the most cost effective and quickest version.

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

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Authors: Raheleh Farzanmanesh, Christopher J. Weston

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Mangroves are forest ecosystems located in the inter-tidal regions of tropical and subtropical coastlines that provide many valuable economic and ecological benefits for millions of people, such as preventing coastal erosion, providing breeding, and feeding grounds, improving water quality, and supporting the well-being of local communities. In addition, mangroves capture and store high amounts of carbon in biomass and soils that play an important role in combating climate change. The decline in mangrove area has prompted government and private sector interest in mangrove conservation and restoration projects to achieve multiple Sustainable Development Goals, from reducing poverty to improving life on land. Mangrove aboveground biomass plays an essential role in the global carbon cycle, climate change mitigation and adaptation by reducing CO2 emissions. However, little information is available about the effectiveness of mangrove sustainable management on mangrove change area and aboveground biomass (AGB). Here, we proposed a method for mapping, modeling, and assessing mangrove area and AGB in two Global Environment Facility (GEF) blue forests projects based on Sentinel-2 Level 1C imagery during their conservation lifetime. The SVR regression model was used to estimate AGB in Tahiry Honko project in Madagascar and the Abu Dhabi Blue Carbon Demonstration Project (Abu Dhabi Emirates. The results showed that mangrove forests and AGB declined in the Tahiry Honko project, while in the Abu Dhabi project increased after the conservation initiative was established. The results provide important information on the impact of mangrove conservation activities and contribute to the development of remote sensing applications for mapping and assessing mangrove forests in blue carbon initiatives.

Keywords: blue carbon, mangrove forest, REDD+, aboveground biomass, Sentinel-2

Procedia PDF Downloads 74

Authors: Ruichong Zhang, Fadi Sawaged, Lotfi Gargab

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This paper introduces a wave-based approach for system identification of high-rise building structures with a pair of seismic recordings, which can be used to evaluate structural integrity and detect damage in post-earthquake structural condition assessment. The fundamental of the approach is based on wave features of generalized impulse and frequency response functions (GIRF and GFRF), i.e., wave responses at one structural location to an impulsive motion at another reference location in time and frequency domains respectively. With a pair of seismic recordings at the two locations, GFRF is obtainable as Fourier spectral ratio of the two recordings, and GIRF is then found with the inverse Fourier transformation of GFRF. With an appropriate continuous model for the structure, a closed-form solution of GFRF, and subsequent GIRF, can also be found in terms of wave transmission and reflection coefficients, which are related to structural physical properties above the impulse location. Matching the two sets of GFRF and/or GIRF from recordings and the model helps identify structural parameters such as wave velocity or shear modulus. For illustration, this study examines ten-story Millikan Library in Pasadena, California with recordings of Yorba Linda earthquake of September 3, 2002. The building is modelled as piecewise continuous layers, with which GFRF is derived as function of such building parameters as impedance, cross-sectional area, and damping. GIRF can then be found in closed form for some special cases and numerically in general. Not only does this study reveal the influential factors of building parameters in wave features of GIRF and GRFR, it also shows some system-identification results, which are consistent with other vibration- and wave-based results. Finally, this paper discusses the effectiveness of the proposed model in system identification.

Keywords: wave-based approach, seismic responses of buildings, wave propagation in structures, construction

Procedia PDF Downloads 234

Authors: Titus A. Beu

Abstract:

Many modern gene-delivery protocols rely on condensed complexes of DNA with polycations to introduce the genetic payload into cells by endocytosis. In particular, polyethyleneimine (PEI) stands out by a high buffering capacity (enabling the efficient condensation of DNA) and relatively simple fabrication. Realistic computational studies can offer essential insights into the formation process of DNA-PEI polyplexes, providing hints on efficient designs and engineering routes. We present comprehensive computational investigations of solvated PEI and DNA-PEI polyplexes involving calculations at three levels: ab initio, all-atom (AA), and coarse-grained (CG) molecular mechanics. In the first stage, we developed a rigorous AA CHARMM (Chemistry at Harvard Macromolecular Mechanics) force field (FF) for PEI on the basis of accurate ab initio calculations on protonated model pentamers. We validated this atomistic FF by matching the results of extensive molecular dynamics (MD) simulations of structural and dynamical properties of PEI with experimental data. In a second stage, we developed a CG MARTINI FF for PEI by Boltzmann inversion techniques from bead-based probability distributions obtained from AA simulations and ensuring an optimal match between the AA and CG structural and dynamical properties. In a third stage, we combined the developed CG FF for PEI with the standard MARTINI FF for DNA and performed comprehensive CG simulations of DNA-PEI complex formation and condensation. Various technical aspects which are crucial for the realistic modeling of DNA-PEI polyplexes, such as options of treating electrostatics and the relevance of polarizable water models, are discussed in detail. Massive CG simulations (with up to 500 000 beads) shed light on the mechanism and provide time scales for DNA polyplex formation independence of PEI chain size and protonation pattern. The DNA-PEI condensation mechanism is shown to primarily rely on the formation of DNA bundles, rather than by changes of the DNA-strand curvature. The gained insights are expected to be of significant help for designing effective gene-delivery applications.

Keywords: DNA condensation, gene-delivery, polyethylene-imine, molecular dynamics.

Procedia PDF Downloads 120

Authors: Afshin Shahbazi, Hadi Shadi Naghadeh, Ahmad Khodadadi Darban

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In the Magnetically Assisted Chemical Separation (MACS) process, tiny ferromagnetic particles coated with solvent extractant are used to selectively separate radionuclides and hazardous metals from aqueous waste streams. The contaminant-loaded particles are then recovered from the waste solutions using a magnetic field. In the present study, Cyanex272 or C272 (bis (2,4,4-trimethylpentyl) phosphinic acid) coated magnetic particles are being evaluated for the possible application in the extraction of Thorium (IV) from nuclear waste streams. The uptake behaviour of Th(IV) from nitric acid solutions was investigated by batch studies. Adsorption of Thorium (IV) from aqueous solution onto adsorbent was investigated in a batch system. Adsorption isotherm and adsorption kinetic studies of Thorium (IV) onto nanoparticles coated Cyanex272 were carried out in a batch system. The factors influencing Thorium (IV) adsorption were investigated and described in detail, as a function of the parameters such as initial pH value, contact time, adsorbent mass, and initial Thorium (IV) concentration. Magnetically Assisted Chemical Separation (MACS) process adsorbent showed best results for the fast adsorption of Th (IV) from aqueous solution at aqueous phase acidity value of 0.5 molar. In addition, more than 80% of Th (IV) was removed within the first 2 hours, and the time required to achieve the adsorption equilibrium was only 140 minutes. Langmuir and Frendlich adsorption models were used for the mathematical description of the adsorption equilibrium. Equilibrium data agreed very well with the Langmuir model, with a maximum adsorption capacity of 48 mg.g-1. Adsorption kinetics data were tested using pseudo-first-order, pseudo-second-order and intra-particle diffusion models. Kinetic studies showed that the adsorption followed a pseudo-second-order kinetic model, indicating that the chemical adsorption was the rate-limiting step.

Keywords: Thorium (IV) adsorption, MACS process, magnetic nanoparticles, Cyanex272

Procedia PDF Downloads 342

Authors: Richard Ren

Abstract:

Air pollution is a serious danger to international well-being and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.

Keywords: air quality prediction, air pollution, artificial intelligence, machine learning algorithms

Procedia PDF Downloads 130

Authors: Keunhong Chae, Seokho Yoon

Abstract:

Recently, the global navigation satellite system (GNSS) such as Galileo and GPS is employing more satellites to provide a higher degree of accuracy for the location service, thus calling for a more efficient signaling scheme among the satellites used in the overall GNSS network. In that the network throughput is improved, the spatial diversity can be one of the efficient signaling schemes; however, it requires multiple antenna that could cause a significant increase in the complexity of the GNSS. Thus, a diversity scheme called the cooperative signaling was proposed, where the virtual multiple-input multiple-output (MIMO) signaling is realized with using only a single antenna in the transmit satellite of interest and with modeling the neighboring satellites as relay nodes. The main drawback of the cooperative signaling is that the relay nodes receive the transmitted signal at different time instants, i.e., they operate in an asynchronous way, and thus, the overall performance of the GNSS network could degrade severely. To tackle the problem, several modified cooperative signaling schemes were proposed; however, all of them are difficult to implement due to a signal decoding at the relay nodes. Although the implementation at the relay nodes could be simpler to some degree by employing the time-reversal and conjugation operations instead of the signal decoding, it would be more efficient if we could implement the operations of the relay nodes at the source node having more resources than the relay nodes. So, in this paper, we propose a novel cooperative signaling scheme, where the data signals are combined in a unique way at the source node, thus obviating the need of the complex operations such as signal decoding, time-reversal and conjugation at the relay nodes. The numerical results confirm that the proposed scheme provides the same performance in the cooperative diversity and the bit error rate (BER) as the conventional scheme, while reducing the complexity at the relay nodes significantly. Acknowledgment: This work was supported by the National GNSS Research Center program of Defense Acquisition Program Administration and Agency for Defense Development.

Keywords: global navigation satellite network, cooperative signaling, data combining, nodes

Procedia PDF Downloads 281

Authors: T. Jayanthi, K. Velusamy, H. Seetha, S. A. V. Satya Murty

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Verification and Validation of Simulated Process Model is the most important phase of the simulator life cycle. Evaluation of simulated process models based on Verification and Validation techniques checks the closeness of each component model (in a simulated network) with the real system/process with respect to dynamic behaviour under steady state and transient conditions. The process of Verification and validation helps in qualifying the process simulator for the intended purpose whether it is for providing comprehensive training or design verification. In general, model verification is carried out by comparison of simulated component characteristics with the original requirement to ensure that each step in the model development process completely incorporates all the design requirements. Validation testing is performed by comparing the simulated process parameters to the actual plant process parameters either in standalone mode or integrated mode. A Full Scope Replica Operator Training Simulator for PFBR - Prototype Fast Breeder Reactor has been developed at IGCAR, Kalpakkam, INDIA named KALBR-SIM (Kalpakkam Breeder Reactor Simulator) wherein the main participants are engineers/experts belonging to Modeling Team, Process Design and Instrumentation and Control design team. This paper discusses the Verification and Validation process in general, the evaluation procedure adopted for PFBR operator training Simulator, the methodology followed for verifying the models, the reference documents and standards used etc. It details out the importance of internal validation by design experts, subsequent validation by external agency consisting of experts from various fields, model improvement by tuning based on expert’s comments, final qualification of the simulator for the intended purpose and the difficulties faced while co-coordinating various activities.

Keywords: Verification and Validation (V&V), Prototype Fast Breeder Reactor (PFBR), Kalpakkam Breeder Reactor Simulator (KALBR-SIM), steady state, transient state

Procedia PDF Downloads 266

Authors: Mahmoud B. Rokaya

Abstract:

The study of opinion mining and sentiment analysis includes analysis of opinions, sentiments, evaluations, attitudes, and emotions. The rapid growth of social media, social networks, reviews, forum discussions, microblogs, and Twitter, leads to a parallel growth in the field of sentiment analysis. The field of sentiment analysis tries to develop effective tools to make it possible to capture the trends of people. There are two approaches in the field, lexicon-based and corpus-based methods. A lexicon-based method uses a sentiment lexicon which includes sentiment words and phrases with assigned numeric scores. These scores reveal if sentiment phrases are positive or negative, their intensity, and/or their emotional orientations. Creation of manual lexicons is hard. This brings the need for adaptive automated methods for generating a lexicon. The proposed method generates dynamic lexicons based on the corpus and then classifies text using these lexicons. In the proposed method, different approaches are combined to generate lexicons from text. The proposed method classifies the tweets into 5 classes instead of +ve or –ve classes. The sentiment classification problem is written as an optimization problem, finding optimum sentiment lexicons are the goal of the optimization process. The solution was produced based on mathematical programming approaches to find the best lexicon to classify texts. A genetic algorithm was written to find the optimal lexicon. Then, extraction of a meta-level feature was done based on the optimal lexicon. The experiments were conducted on several datasets. Results, in terms of accuracy, recall and F measure, outperformed the state-of-the-art methods proposed in the literature in some of the datasets. A better understanding of the Arabic language and culture of Arab Twitter users and sentiment orientation of words in different contexts can be achieved based on the sentiment lexicons proposed by the algorithm.

Keywords: social media, Twitter sentiment, sentiment analysis, lexicon, genetic algorithm, evolutionary computation

Procedia PDF Downloads 190

Authors: Vera Wilden, Benno Hoffmeister, Markus Feldmann

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Glued laminated timber (glulam) is a preferred choice for long span girders, e.g., for gyms or storage halls. While the material provides sufficient strength to resist the bending moments, large spans lead to increased slenderness of such members and to a higher susceptibility to stability issues, in particular to lateral torsional buckling (LTB). Rules for the determination of the ultimate LTB resistance are provided by Eurocode 5. The verifications of the resistance may be performed using the so called equivalent member method or by means of theory 2nd order calculations (direct method), considering equivalent imperfections. Both methods have significant limitations concerning their applicability; the equivalent member method is limited to rather simple cases; the direct method is missing detailed provisions regarding imperfections and requirements for numerical modeling. In this paper, the results of a test series on slender glulam beams in three- and four-point bending are presented. The tests were performed in an innovative, newly developed testing rig, allowing for a very precise definition of loading and boundary conditions. The load was introduced by a hydraulic jack, which follows the lateral deformation of the beam by means of a servo-controller, coupled with the tested member and keeping the load direction vertically. The deformation-controlled tests allowed for the identification of the ultimate limit state (governed by elastic stability) and the corresponding deformations. Prior to the tests, the structural and geometrical imperfections were determined and used later in the numerical models. After the stability tests, the nearly undamaged members were tested again in pure bending until reaching the ultimate moment resistance of the cross-section. These results, accompanied by numerical studies, were compared to resistance values obtained using both methods according to Eurocode 5.

Keywords: experimental tests, glued laminated timber, lateral torsional buckling, numerical simulation

Procedia PDF Downloads 240

Authors: Yaniv Knop, Alva Peled

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Over the past few years there has been a growing interest in the development of Portland Composite Cement, by partial replacement of the clinker with mineral additives. The motivations to reduce the clinker content are threefold: (1) Ecological - due to lower emission of CO2 to the atmosphere; (2) Economical - due to cost reduction; and (3) Scientific\Technology – improvement of performances. Among the mineral additives being used and investigated, limestone is one of the most attractive, as it is considered natural, available, and with low cost. The goal of the research is to develop green cement, by partial replacement of the clinker with limestone powder while improving the performances of the cement paste. This work studied blended cements with three limestone powder particle diameters: smaller than, larger than, and similarly sized to the clinker particle. Blended cement with limestone consisting of one particle size distribution and limestone consisting of a combination of several particle sizes were studied and compared in terms of hydration rate, hydration degree, and water demand to achieve normal consistency. The performances of these systems were also compared with that of the original cement (without added limestone). It was found that the ability to replace an active material with an inert additive, while achieving improved performances, can be obtained by increasing the packing density of the cement-based particles. This may be achieved by replacing the clinker with limestone powders having a combination of several different particle size distributions. Mathematical and physical models were developed to simulate the setting history from initial to final setting time and to predict the packing density of blended cement with limestone having different sizes and various contents. Besides the effect of limestone, as inert additive, on the packing density of the blended cement, the influence of the limestone particle size on three different chemical reactions were studied; hydration of the cement, carbonation of the calcium hydroxide and the reactivity of the limestone with the hydration reaction products. The main results and developments will be presented.

Keywords: packing density, hydration degree, limestone, blended cement

Procedia PDF Downloads 287

Authors: May Haggag, Ezzat Fahmy, Mohamed Abdel-Mooty, Sherif Safar

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Sandwich sections have a very complex nature due to variability of behavior of different materials within the section. Cracking, crushing and yielding capacity of constituent materials enforces high complexity of the section. Furthermore, slippage between the different layers adds to the section complex behavior. Conventional methods implemented in current industrial guidelines do not account for the above complexities. Thus, a throughout study is needed to understand the true behavior of the sandwich panels thus, increase the ability to use them effectively and efficiently. The purpose of this paper is to conduct numerical investigation using ANSYS software for the structural behavior of sandwich wall section under eccentric loading. Sandwich walls studied herein are composed of two RC faces, a foam core and linking shear connectors. Faces are modeled using solid elements and reinforcement together with connectors are modeled using link elements. The analysis conducted herein is nonlinear static analysis incorporating material nonlinearity, crashing and crushing of concrete and yielding of steel. The model is validated by comparing it to test results in literature. After validation, the model is used to establish extensive parametric analysis to investigate the effect of three key parameters on the axial force bending moment interaction diagram of the walls. These parameters are the concrete compressive strength, face thickness and number of shear connectors. Furthermore, the results of the parametric study are used to predict a coefficient that links the interaction diagram of a solid wall to that of a sandwich wall. The equation is predicted using the parametric study data and regression analysis. The predicted α was used to construct the interaction diagram of the investigated wall and the results were compared with ANSYS results and showed good agreement.

Keywords: sandwich walls, interaction diagrams, numerical modeling, eccentricity, reinforced concrete

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Authors: Oyatayo Kehinde Taofik, Ndabula Christopher

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Drainage basins are a central theme in the green economy. The rising challenges in flooding, erosion or sediment transport and sedimentation threaten the green economy. This has led to increasing emphasis on quantitative analysis of drainage basin parameters for better understanding, estimation and prediction of fluvial responses and, thus associated hazards or disasters. This can be achieved through direct measurement, characterization, parameterization, or modeling. This study applied the Remote Sensing and Geographic Information System approach of parameterization and characterization of the morphometric variables of Katsina – Ala basin using a 30 m resolution Shuttle Radar Topographic Mission (SRTM) Digital Elevation Model (DEM). This was complemented with topographic and hydrological maps of Katsina-Ala on a scale of 1:50,000. Linear, areal and relief parameters were characterized. The result of the study shows that Ala and Udene sub-watersheds are 4th and 5th order basins, respectively. The stream network shows a dendritic pattern, indicating homogeneity in texture and a lack of structural control in the study area. Ala and Udene sub-watersheds have the following values for elongation ratio, circularity ratio, form factor and relief ratio: 0.48 / 0.39 / 0.35/ 9.97 and 0.40 / 0.35 / 0.32 / 6.0. They also have the following values for drainage texture and ruggedness index of 0.86 / 0.011 and 1.57 / 0.016. The study concludes that the two sub-watersheds are elongated, suggesting that they are susceptible to erosion and, thus higher sediment load in the river channels, which will dispose the watersheds to higher flood peaks. The study also concludes that the sub-watersheds have a very coarse texture, with good permeability of subsurface materials and infiltration capacity, which significantly recharge the groundwater. The study recommends that efforts should be put in place by the Local and State Governments to reduce the size of paved surfaces in these sub-watersheds by implementing a robust agroforestry program at the grass root level.

Keywords: erosion, flood, mitigation, morphometry, watershed

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Authors: Yin Ma, Xun Wang, Kelsey Austin

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Purpose – Building upon career construction theory and the conservation of resources theory, we developed a moderated mediation model to examine how the perceived university support impact students’ career adapting responses, namely, crystallization, exploration, decision and preparation, via the mediator career adaptability and moderator perceived parental support. Design/methodology/approach – The multi-stage sampling strategy was employed and survey data were collected. Structural equation modeling was used to perform the analysis. Findings – Perceived university support could directly promote students’ career adaptability, and promote three career adapting responses, namely, exploration, decision and preparation. It could also impact four career adapting responses via mediation effect of career adaptability. Its impact on students’ career adaptability can greatly increase when students’ receive parental related career support. Research limitations/implications – The cross-sectional design limits causal inference. Conducted in China, our findings should be cautiously interpreted in other countries due to cultural differences. Practical implications – University support is vital to students’ career adaptability and supports from parents can enhance this process. University-home collaboration is necessary to promote students’ career adapting responses. For students, seeking and utilizing as much supporting resources as possible is vital for their human resources development. On an organizational level, universities could benefit from our findings by introducing the practices which ask students to rate the career-related courses and encourage them to chat with parents regularly. Originality/ value – Using recently developed scale, current work contributes to the literature by investigating the impact of multiple contextual factors on students’ career adapting response. It also provide the empirical support for the role of human intervention in fostering career adapting responses.

Keywords: career adapability, university and parental support, China studies, sociology of education

Procedia PDF Downloads 66

Authors: Kurias George, Ildefonso Cuesta Romeo, Clara Salueña Pérez, Jordi Sole Olle

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With the growing prevalence of wind energy there is a need, for modeling techniques to evaluate the impact of wind farms on meteorology and oceanography. This study presents an approach that utilizes the WRF (Weather Research and Forecasting )with that include a Wind Farm Parametrization model to simulate the dynamics around Parc Tramuntana project, a offshore wind farm to be located near the Gulf of Roses off the coast of Barcelona, Catalonia. The model incorporates parameterizations for wind turbines enabling a representation of the wind field and how it interacts with the infrastructure of the wind farm. Current results demonstrate that the model effectively captures variations in temeperature, pressure and in both wind speed and direction over time along with their resulting effects on power output from the wind farm. These findings are crucial for optimizing turbine placement and operation thus improving efficiency and sustainability of the wind farm. In addition to focusing on atmospheric interactions, this study delves into the wake effects within the turbines in the farm. A range of meteorological parameters were also considered to offer a comprehensive understanding of the farm's microclimate. The model was tested under different horizontal resolutions and farm layouts to scrutinize the wind farm's effects more closely. These experimental configurations allow for a nuanced understanding of how turbine wakes interact with each other and with the broader atmospheric and oceanic conditions. This modified approach serves as a potent tool for stakeholders in renewable energy, environmental protection, and marine spatial planning. environmental protection and marine spatial planning. It provides a range of information regarding the environmental and socio economic impacts of offshore wind energy projects.

Keywords: weather research and forecasting, wind turbine wake effects, environmental impact, wind farm parametrization, sustainability analysis

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Authors: Marta Błażkiewicz, Adam Konefał

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Currently, boron-neutron therapy is carried out mainly with the use of a neutron beam generated in research nuclear reactors. This fact limits the possibility of realization of a BNCT in centers distant from the above-mentioned reactors. Moreover, the number of active nuclear reactors in operation in the world is decreasing due to the limited lifetime of their operation and the lack of new installations. Therefore, the possibilities of carrying out boron-neutron therapy based on the neutron beam from the experimental reactor are shrinking. However, the use of nuclear power reactors for BNCT purposes is impossible due to the infrastructure not intended for radiotherapy. Therefore, a serious challenge is to find ways to perform boron-neutron therapy based on neutrons generated outside the research nuclear reactor. This work meets this challenge. Its goal is to develop a BNCT technique based on commonly available neutron sources such as Cf-252 and PuBe, which will enable the above-mentioned therapy in medical centers unrelated to nuclear research reactors. Advances in the field of neutron source fabrication make it possible to achieve strong neutron fluxes. The current stage of research focuses on the development of virtual models of the above-mentioned sources using the Monte Carlo simulation method. In this study, the GEANT4 tool was used, including the model for simulating neutron-matter interactions - High Precision Neutron. Models of neutron sources were developed on the basis of experimental verification based on the activation detectors method with the use of indium foil and the cadmium differentiation method allowing to separate the indium activation contribution from thermal and resonance neutrons. Due to the large number of factors affecting the result of the verification experiment, the 10% discrepancy between the simulation and experiment results was accepted.

Keywords: BNCT, virtual models, neutron sources, monte carlo, GEANT4, neutron activation detectors, gamma spectroscopy

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Authors: Mohammad Hossein Mohammadi Sanjani, Ashknaz Oraee, Oriol Gomis Bellmunt, Vinicius Albernaz Lacerda Freitas

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The use of distributed and renewable sources in power systems has grown significantly, recently. One the most popular sources are wind farms which have grown massively. However, ¬wind farms are connected to the grid, this can cause problems such as reduced voltage stability, frequency fluctuations and reduced dynamic stability. Variable speed generators (asynchronous) are used due to the uncontrollability of wind speed specially Doubley Fed Induction Generators (DFIG). The most important disadvantage of DFIGs is its sensitivity to voltage drop. In the case of faults, a large volume of reactive power is induced therefore, use of FACTS devices such as SVC and STATCOM are suitable for improving system output performance. They increase the capacity of lines and also passes network fault conditions. In this paper, in addition to modeling the reactive power control system in a DFIG with converter, FACTS devices have been used in a DFIG wind turbine to improve the stability of the power system containing two synchronous sources. In the following paper, recent optimal control systems have been designed to minimize fluctuations caused by system disturbances, for FACTS devices employed. For this purpose, a suitable method for the selection of nine parameters for MPSH-phase-post-phase compensators of reactive power compensators is proposed. The design algorithm is formulated ¬¬as an optimization problem searching for optimal parameters in the controller. Simulation results show that the proposed controller Improves the stability of the network and the fluctuations are at desired speed.

Keywords: renewable energy sources, optimization wind power plant, stability, reactive power compensator, double-feed induction generator, optimal control, genetic algorithm

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Authors: Scott Nielsen, Luca Longanesi, Chris Chuck

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Palm oil is obtained from the flesh and kernel of the fruit of oil palms and is the most productive and inexpensive oil crop. The global demand for palm oil is approximately 75 million metric tonnes, a 29% increase in global production of palm oil since 2016. This expansion of oil palm cultivation has resulted in mass deforestation, vast biodiversity destruction and increasing net greenhouse gas emissions. One possible alternative is to produce a saturated oil, similar to palm, from microbes such as oleaginous yeast. The yeasts can be cultured on sugars derived from second-generation sources and do not compete with tropical forests for land. One highly promising oleaginous yeast for this application is Metschnikowia pulcherrima. However, recent techno-economic modeling has shown that cell lysis and standard lipid extraction are major contributors to the cost of the oil. Typical cell disruption techniques to extract either single cell oils or proteins have been based around bead-beating, homogenization and acid lysis. However, these can have a detrimental effect on lipid quality and are energy-intensive. In this study, a vortex separator, which produces high sheer with minimal energy input, was investigated as a potential low energy method of lysing cells. This was compared to four more traditional methods (thermal lysis, acid lysis, alkaline lysis, and osmotic lysis). For each method, the yeast loading was also examined at 1 g/L, 10 g/L and 100 g/L. The quality of the cell disruption was measured by optical cell density, cell counting and the particle size distribution profile comparison over a 2-hour period. This study demonstrates that the vortex separator is highly effective at lysing the cells and could potentially be used as a simple apparatus for lipid recovery in an oleaginous yeast process. The further development of this technology could potentially reduce the overall cost of microbial lipids in the future.

Keywords: palm oil substitute, metschnikowia pulcherrima, cell disruption, cell lysis

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Authors: Shereif. H. Mahmoud

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Egypt has limited water resources, and it will be under water stress by the year 2030. Therefore, Egypt should consider natural and non-conventional water resources to overcome such a problem. Rain harvesting is one solution. This Paper presents a geographic information system (GIS) methodology - based on decision support system (DSS) that uses remote sensing data, filed survey, and GIS to identify potential RWH areas. The input into the DSS includes a map of rainfall surplus, slope, potential runoff coefficient (PRC), land cover/use, soil texture. In addition, the outputs are map showing potential sites for RWH. Identifying suitable RWH sites implemented in the ArcGIS model environment using the model builder of ArcGIS 10.1. Based on Analytical hierarchy process (AHP) analysis taking into account five layers, the spatial extents of RWH suitability areas identified using Multi-Criteria Evaluation (MCE). The suitability model generated a suitability map for RWH with four suitability classes, i.e. Excellent, Moderate, Poor, and unsuitable. The spatial distribution of the suitability map showed that the excellent suitable areas for RWH concentrated in the northern part of Egypt. According to their averages, 3.24% of the total area have excellent and good suitability for RWH, while 45.04 % and 51.48 % of the total area are moderate and unsuitable suitability, respectively. The majority of the areas with excellent suitability have slopes between 2 and 8% and with an intensively cultivated area. The major soil type in the excellent suitable area is loam and the rainfall range from 100 up to 200 mm. Validation of the used technique depends on comparing existing RWH structures locations with the generated suitability map using proximity analysis tool of ArcGIS 10.1. The result shows that most of exiting RWH structures categorized as successful.

Keywords: rainwater harvesting (RWH), geographic information system (GIS), analytical hierarchy process (AHP), multi-criteria evaluation (MCE), decision support system (DSS)

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Authors: Luis Amador Jimenez, Md. Shohel Amin

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Pavement performance models are based on projections of observed traffic loads, which makes uncertain to study funding strategies in the long run if history does not repeat. Neural networks can be used to estimate deterioration rates but the learning rate and momentum have not been properly investigated, in addition, economic evolvement could change traffic flows. This study addresses both issues through a case study for roads of Montreal that simulates traffic for a period of 50 years and deals with the measurement error of the pavement deterioration model. Travel demand models are applied to simulate annual average daily traffic (AADT) every 5 years. Accumulated equivalent single axle loads (ESALs) are calculated from the predicted AADT and locally observed truck distributions combined with truck factors. A back propagation Neural Network (BPN) method with a Generalized Delta Rule (GDR) learning algorithm is applied to estimate pavement deterioration models capable of overcoming measurement errors. Linear programming of lifecycle optimization is applied to identify M&R strategies that ensure good pavement condition while minimizing the budget. It was found that CAD 150 million is the minimum annual budget to good condition for arterial and local roads in Montreal. Montreal drivers prefer the use of public transportation for work and education purposes. Vehicle traffic is expected to double within 50 years, ESALS are expected to double the number of ESALs every 15 years. Roads in the island of Montreal need to undergo a stabilization period for about 25 years, a steady state seems to be reached after.

Keywords: pavement management system, traffic simulation, backpropagation neural network, performance modeling, measurement errors, linear programming, lifecycle optimization

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Authors: Lydia Novozhilova, Vladimir Urazhdin

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An optimization problem for reinforced thin-walled vessels under uniform external pressure is considered. The conventional approaches to optimization generally start with pre-defined geometric parameters of the vessels, and then employ analytic or numeric calculations and/or experimental testing to verify functionality, such as stability under the projected conditions. The proposed approach consists of two steps. First, the feasibility domain will be identified in the multidimensional parameter space. Every point in the feasibility domain defines a design satisfying both geometric and functional constraints. Second, an objective function defined in this domain is formulated and optimized. The broader applicability of the suggested methodology is maximized by implementing the Support Vector Machines (SVM) classification algorithm of machine learning for identification of the feasible design region. Training data for SVM classifier is obtained using the Simulation package of SOLIDWORKS®. Based on the data, the SVM algorithm produces a curvilinear boundary separating admissible and not admissible sets of design parameters with maximal margins. Then optimization of the vessel parameters in the feasibility domain is performed using the standard algorithms for the constrained optimization. As an example, optimization of a ring-stiffened closed cylindrical thin-walled vessel with semi-spherical caps under high external pressure is implemented. As a functional constraint, von Mises stress criterion is used but any other stability constraint admitting mathematical formulation can be incorporated into the proposed approach. Suggested methodology has a good potential for reducing design time for finding optimal parameters of thin-walled vessels under uniform external pressure.

Keywords: design parameters, feasibility domain, von Mises stress criterion, Support Vector Machine (SVM) classifier

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Authors: Nkwenti Mbelli Njah

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This research was motivated by a project requested by AXA on the topic of pensions payable under the workers compensation (WC) line of business. There are two types of pensions: the compulsorily recoverable and the not compulsorily recoverable. A pension is compulsorily recoverable for a victim when there is less than 30% of disability and the pension amount per year is less than six times the minimal national salary. The law defines that the mathematical provisions for compulsory recoverable pensions must be calculated by applying the following bases: mortality table TD88/90 and rate of interest 5.25% (maybe with rate of management). To manage pensions which are not compulsorily recoverable is a more complex task because technical bases are not defined by law and much more complex computations are required. In particular, companies have to predict the amount of payments discounted reflecting the mortality effect for all pensioners (this task is monitored monthly in AXA). The purpose of this research was thus to develop a stochastic model for the future mortality of the worker’s compensation pensioners of both the Portuguese market workers and AXA portfolio. Not only is past mortality modeled, also projections about future mortality are made for the general population of Portugal as well as for the two portfolios mentioned earlier. The global model was split in two parts: a stochastic model for population mortality which allows for forecasts, combined with a point estimate from a portfolio mortality model obtained through three different relational models (Cox Proportional, Brass Linear and Workgroup PLT). The one-year death probabilities for ages 0-110 for the period 2013-2113 are obtained for the general population and the portfolios. These probabilities are used to compute different life table functions as well as the not compulsorily recoverable reserves for each of the models required for the pensioners, their spouses and children under 21. The results obtained are compared with the not compulsory recoverable reserves computed using the static mortality table (TD 73/77) that is currently being used by AXA, to see the impact on this reserve if AXA adopted the dynamic tables.

Keywords: compulsorily recoverable, life table functions, relational models, worker’s compensation pensioners

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Authors: M. Varin, A. M. Dubois, R. Gadbois-Langevin, B. Chalghaf

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Good knowledge of the physical environment is essential for an integrated forest planning. This information enables better forecasting of operating costs, determination of cutting volumes, and preservation of ecologically sensitive areas. The use of satellite images in stereoscopic pairs gives the capacity to generate high precision 3D models, which are scale-adapted for harvesting operations. These models could represent an alternative to 3D LiDAR data, thanks to their advantageous cost of acquisition. The objective of the study was to assess the quality of stereo-derived canopy height models (CHM) in comparison to a traditional LiDAR CHM and ground tree-height samples. Two study sites harboring two different forest stand types (broadleaf and conifer) were analyzed using stereo pairs and tri-stereo images from the WorldView-3 satellite to calculate CHM. Acquisition of multispectral images from an Unmanned Aerial Vehicle (UAV) was also realized on a smaller part of the broadleaf study site. Different algorithms using two softwares (PCI Geomatica and Correlator3D) with various spatial resolutions and band selections were tested to select the 3D modeling technique, which offered the best performance when compared with LiDAR. In the conifer study site, the CHM produced with Corelator3D using only the 50-cm resolution panchromatic band was the one with the smallest Root-mean-square deviation (RMSE: 1.31 m). In the broadleaf study site, the tri-stereo model provided slightly better performance, with an RMSE of 1.2 m. The tri-stereo model was also compared to the UAV, which resulted in an RMSE of 1.3 m. At individual tree level, when ground samples were compared to satellite, lidar, and UAV CHM, RMSE were 2.8, 2.0, and 2.0 m, respectively. Advanced analysis was done for all of these cases, and it has been noted that RMSE is reduced when the canopy cover is higher when shadow and slopes are lower and when clouds are distant from the analyzed site.

Keywords: very high spatial resolution, satellite imagery, WorlView-3, canopy height models, CHM, LiDAR, unmanned aerial vehicle, UAV

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Authors: Peng Zhang, Lihui Zheng, Xiangchun Wang, Xiaopan Kou

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Quantitative research on the main control factors of lost circulation has few considerations and single data source. Using Unmanned Intervention Algorithm to find the main control factors of lost circulation adopts all measurable parameters. The degree of lost circulation is characterized by the loss rate as the objective function. Geological, engineering and fluid data are used as layers, and 27 factors such as wellhead coordinates and WOB are used as dimensions. Data classification is implemented to determine function independent variables. The mathematical equation of loss rate and 27 influencing factors is established by multiple regression method, and the undetermined coefficient method is used to solve the undetermined coefficient of the equation. Only three factors in t-test are greater than the test value 40, and the F-test value is 96.557%, indicating that the correlation of the model is good. The funnel viscosity, final shear force and drilling time were selected as the main control factors by elimination method, contribution rate method and functional method. The calculated values of the two wells used for verification differ from the actual values by -3.036m3/h and -2.374m3/h, with errors of 7.21% and 6.35%. The influence of engineering factors on the loss rate is greater than that of funnel viscosity and final shear force, and the influence of the three factors is less than that of geological factors. Quantitatively calculate the best combination of funnel viscosity, final shear force and drilling time. The minimum loss rate of lost circulation wells in Shunbei area is 10m3/h. It can be seen that man-made main control factors can only slow down the leakage, but cannot fundamentally eliminate it. This is more in line with the characteristics of karst caves and fractures in Shunbei fault solution oil and gas reservoir.

Keywords: drilling and completion, drilling fluid, lost circulation, loss rate, main controlling factors, unmanned intervention algorithm

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Authors: Adel Francis

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Most of scheduling methods and software apply the critical path logic. This logic schedule activities, apply constraints between these activities and try to optimize and level the allocated resources. The extensive use of this logic produces a complex an erroneous network hard to present, follow and update. Planning and management building projects should tackle the coordination of works and the management of limited spaces, traffic, and supplies. Activities cannot be performed without the resources available and resources cannot be used beyond the capacity of workplaces. Otherwise, workspace congestion will negatively affect the flow of works. The objective of the space planning is to link the spatial and temporal aspects, promote efficient use of the site, define optimal site occupancy rates, and ensures suitable rotation of the workforce in the different spaces. The Chronographic scheduling modelling belongs to this category and models construction operations as well as their processes, logical constraints, association and organizational models, which help to better illustrate the schedule information using multiple flexible approaches. The model defined three categories of areas (punctual, surface and linear) and four different layers (space creation, systems, closing off space, finishing, and reduction of space). The Chronographical modelling is a more complete communication method, having the ability to alternate from one visual approach to another by manipulation of graphics via a set of parameters and their associated values. Each individual approach can help to schedule a certain project type or specialty. Visual communication can also be improved through layering, sheeting, juxtaposition, alterations, and permutations, allowing for groupings, hierarchies, and classification of project information. In this way, graphic representation becomes a living, transformable image, showing valuable information in a clear and comprehensible manner, simplifying the site management while simultaneously utilizing the visual space as efficiently as possible.

Keywords: building projects, chronographic modelling, CPM, critical path, precedence diagram, scheduling

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Authors: Anik Sarminingsih, Krishna V. Pradana

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The failure of flood control system can be caused by various factors, such as not considering the uncertainty of designed flood causing the capacity of the flood control system is exceeded. The presence of the uncertainty factor is recognized as a serious issue in hydrological studies. Uncertainty in hydrological analysis is influenced by many factors, starting from reading water elevation data, rainfall data, selection of method of analysis, etc. In hydrological modeling selection of models and parameters corresponding to the watershed conditions should be evaluated by the hydraulic model in the river as a drainage channel. River cross-section capacity is the first defense in knowing the reliability of the flood control system. Reliability of river capacity describes the potential magnitude of flood risk. Case study in this research is Wulan River in Central Java. This river occurring flood almost every year despite some efforts to control floods such as levee, floodway and diversion. The flood-affected areas include several sub-districts, mainly in Kabupaten Kudus and Kabupaten Demak. First step is analyze the frequency of discharge observation from Klambu weir which have time series data from 1951-2013. Frequency analysis is performed using several distribution frequency models such as Gumbel distribution, Normal, Normal Log, Pearson Type III and Log Pearson. The result of the model based on standard deviation overlaps, so the maximum flood discharge from the lower return periods may be worth more than the average discharge for larger return periods. The next step is to perform a hydraulic analysis to evaluate the reliability of river capacity based on the flood discharge resulted from several methods. The selection of the design flood discharge of flood control system is the result of the method closest to bankfull capacity of the river.

Keywords: design flood, hydrological model, reliability, uncertainty, Wulan river

Procedia PDF Downloads 294