Search results for: optimal volume

Authors: Zhenyu Zhang, Hsi-Hsien Wei

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Highway network systems play a vital role in disaster response for disaster-damaged areas. Damaged bridges in such network systems can impede disaster response by disrupting transportation of rescue teams or humanitarian supplies. Therefore, emergency inspection and repair of bridges to quickly collect damage information of bridges and recover the functionality of highway networks is of paramount importance to disaster response. A widely used measure of a network’s capability to recover from disasters is resilience. To enhance highway network resilience, plenty of studies have developed various repair scheduling methods for the prioritization of bridge-repair tasks. These methods assume that repair activities are performed after the damage to a highway network is fully understood via inspection, although inspecting all bridges in a regional highway network may take days, leading to the significant delay in repairing bridges. In reality, emergency repair activities can be commenced as soon as the damage data of some bridges that are crucial to emergency response are obtained. Given that emergency bridge inspection and repair (EBIR) activities are executed simultaneously in the response phase, the real-time interactions between these activities can occur – the blockage of highways due to repair activities can affect inspection routes which in turn have an impact on emergency repair scheduling by providing real-time information on bridge damages. However, the impact of such interactions on the optimal emergency inspection routes (EIR) and emergency repair schedules (ERS) has not been discussed in prior studies. To overcome the aforementioned deficiencies, this study develops a routing and scheduling model for EBIR while accounting for real-time inspection-repair interactions to maximize highway network resilience. A stochastic, time-dependent integer program is proposed for the complex and real-time interacting EBIR problem given multiple inspection and repair teams at locations as set post-disaster. A hybrid genetic algorithm that integrates a heuristic approach into a traditional genetic algorithm to accelerate the evolution process is developed. Computational tests are performed using data from the 2008 Wenchuan earthquake, based on a regional highway network in Sichuan, China, consisting of 168 highway bridges on 36 highways connecting 25 cities/towns. The results show that the simultaneous implementation of bridge inspection and repair activities can significantly improve the highway network resilience. Moreover, the deployment of inspection and repair teams should match each other, and the network resilience will not be improved once the unilateral increase in inspection teams or repair teams exceeds a certain level. This study contributes to both knowledge and practice. First, the developed mathematical model makes it possible for capturing the impact of real-time inspection-repair interactions on inspection routing and repair scheduling and efficiently deriving optimal EIR and ERS on a large and complex highway network. Moreover, this study contributes to the organizational dimension of highway network resilience by providing optimal strategies for highway bridge management. With the decision support tool, disaster managers are able to identify the most critical bridges for disaster management and make decisions on proper inspection and repair strategies to improve highway network resilience.

Keywords: disaster management, emergency bridge inspection and repair, highway network, resilience, uncertainty

Procedia PDF Downloads 108

Authors: C. H. Chang, R. Z. Qiu, C. W. Tsao, Y. H. Cheng, C. H. Chen, W. J. Hsueh

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Characteristics of photoluminescence (PL) in a resonant quasi-periodic double-period quantum wells (DPQW) are demonstrated. The maximum PL intensity in the DPQW is remarkably greater than that in a traditional periodic QW (PQW) under the Bragg or anti-Bragg conditions. The optimal PL spectrum in the DPQW has an asymmetrical form instead of the symmetrical form in the PQW. Moreover, there are two large values of PL intensity in the DPQW, which also differs from the PQW.

Keywords: Photoluminescence, quantum wells, quasiperiodic structure

Procedia PDF Downloads 715

Authors: Kamyar Tolouei, Ehsan Moosavi

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In open-pit mines, the long-term production scheduling optimization problem (LTPSOP) is a complicated problem that contains constraints, large datasets, and uncertainties. Uncertainty in the output is caused by several geological, economic, or technical factors. Due to its dimensions and NP-hard nature, it is usually difficult to find an ideal solution to the LTPSOP. The optimal schedule generally restricts the ore, metal, and waste tonnages, average grades, and cash flows of each period. Past decades have witnessed important measurements of long-term production scheduling and optimal algorithms since researchers have become highly cognizant of the issue. In fact, it is not possible to consider LTPSOP as a well-solved problem. Traditional production scheduling methods in open-pit mines apply an estimated orebody model to produce optimal schedules. The smoothing result of some geostatistical estimation procedures causes most of the mine schedules and production predictions to be unrealistic and imperfect. With the expansion of simulation procedures, the risks from grade uncertainty in ore reserves can be evaluated and organized through a set of equally probable orebody realizations. In this paper, to synthesize grade uncertainty into the strategic mine schedule, a stochastic integer programming framework is presented to LTPSOP. The objective function of the model is to maximize the net present value and minimize the risk of deviation from the production targets considering grade uncertainty simultaneously while satisfying all technical constraints and operational requirements. Instead of applying one estimated orebody model as input to optimize the production schedule, a set of equally probable orebody realizations are applied to synthesize grade uncertainty in the strategic mine schedule and to produce a more profitable and risk-based production schedule. A mixture of metaheuristic procedures and mathematical methods paves the way to achieve an appropriate solution. This paper introduced a hybrid model between the augmented Lagrangian relaxation (ALR) method and the metaheuristic algorithm, the Harris Hawks optimization (HHO), to solve the LTPSOP under grade uncertainty conditions. In this study, the HHO is experienced to update Lagrange coefficients. Besides, a machine learning method called Random Forest is applied to estimate gold grade in a mineral deposit. The Monte Carlo method is used as the simulation method with 20 realizations. The results specify that the progressive versions have been considerably developed in comparison with the traditional methods. The outcomes were also compared with the ALR-genetic algorithm and ALR-sub-gradient. To indicate the applicability of the model, a case study on an open-pit gold mining operation is implemented. The framework displays the capability to minimize risk and improvement in the expected net present value and financial profitability for LTPSOP. The framework could control geological risk more effectively than the traditional procedure considering grade uncertainty in the hybrid model framework.

Keywords: grade uncertainty, metaheuristic algorithms, open-pit mine, production scheduling optimization

Procedia PDF Downloads 100

Authors: Mohd Sabri Minhat, Nurul Adilla Mohd Subha

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

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

Procedia PDF Downloads 156

Authors: Mustafa Saeed Omar

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The formula which contains the maximum increase of mean bond length, melting entropy and critical particle radius is used to calculate lattice volume in nanoscale size crystals of Ga₂Se₃. This compound belongs to the binary group of III₂VI₃. The critical radius is calculated from the values of the first surface atomic layer height which is equal to 0.336nm. The size-dependent mean bond length is calculated by using an equation-free from fitting parameters. The size-dependent lattice parameter then is accordingly used to calculate the size-dependent lattice volume. The lattice size in the nanoscale region increases to about 77.6 A³, which is up to four times of its bulk state value 19.97 A³. From the values of the nanosize scale dependence of lattice volume, the nanoscale size dependence of melting temperatures is calculated. The melting temperature decreases with the nanoparticles size reduction, it becomes zero when the radius reaches to its critical value. Bulk melting temperature for Ga₂Se₃, for example, has values of 1293 K. From the size-dependent melting temperature and mean bond length, the size-dependent lattice thermal expansion is calculated. Lattice thermal expansion decreases with the decrease of nanoparticles size and reaches to its minimum value as the radius drops down to about 5nm.

Keywords: Ga₂Se₃, lattice volume, lattice thermal expansion, melting point, nanoparticles

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Authors: Miroslav Hyza, Petr Rulik, Vojtech Bednar, Jan Sury

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An early and reliable detection of an increased radioactivity level in the atmosphere is one of the key aspects of atmospheric radiological monitoring. Although the standard laboratory procedures provide detection limits as low as few µBq/m³, their major drawback is the delayed result reporting: typically a few days. This issue is the main objective of the HAMRAD project, which gave rise to a prototype of an autonomous monitoring device. It is based on the idea of sequential aerosol sampling using a carrousel sample changer combined with a gamma-ray spectrometer. In our hardware configuration, the air is drawn through a filter positioned on the carrousel so that it could be rotated into the measuring position after a preset sampling interval. Filter analysis is performed via a 50% HPGe detector inside an 8.5cm lead shielding. The spectrometer output signal is then analyzed using DSP electronics and Gamwin software with preset nuclide libraries and other analysis parameters. After the counting, the filter is placed into a storage bin with a capacity of 250 filters so that the device can run autonomously for several months depending on the preset sampling frequency. The device is connected to a central server via GPRS/GSM where the user can view monitoring data including raw spectra and technological data describing the state of the device. All operating parameters can be remotely adjusted through a simple GUI. The flow rate is continuously adjustable up to 10 m³/h. The main challenge in spectrum analysis is the natural background subtraction. As detection limits are heavily influenced by the deposited activity of radon decay products and the measurement time is fixed, there must exist an optimal sample decay time (delayed spectrum acquisition). To solve this problem, we adopted a simple procedure based on sequential spectrum acquisition and optimal partial spectral sum with respect to the detection limits for a particular radionuclide. The prototyped device proved to be able to detect atmospheric contamination at the level of mBq/m³ per an 8h sampling.

Keywords: aerosols, atmosphere, atmospheric radioactivity monitoring, autonomous sampler

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Authors: Ashok K. Satapathy, Prerana Nashine

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Radiative heat transfer in participating medium was anticipated using the finite volume method. The radiative transfer equations are formulated for absorbing and anisotropically scattering and emitting medium. The solution strategy is discussed and the conditions for computational stability are conferred. The equations have been solved for transient radiative medium and transient radiation incorporated with transient conduction. Results have been obtained for irradiation and corresponding heat fluxes for both the cases. The solutions can be used to conclude incident energy and surface heat flux. Transient solutions were obtained for a slab of heat conducting in slab by thermal radiation. The effect of heat conduction during the transient phase is to partially equalize the internal temperature distribution. The solution procedure provides accurate temperature distributions in these regions. A finite volume procedure with variable space and time increments is used to solve the transient energy equation. The medium in the enclosure absorbs, emits, and anisotropically scatters radiative energy. The incident radiations and the radiative heat fluxes are presented in graphical forms. The phase function anisotropy plays a significant role in the radiation heat transfer when the boundary condition is non-symmetric.

Keywords: participating media, finite volume method, radiation coupled with conduction, heat transfer

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Authors: Mohamad H. Atyeh, Ahmad Khaldi

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The main objective of this research is to measure the relationship between the Kuwait stock Exchange (KSE) index and its two marketing sectors after the new market classification. The findings of this research are important for Public economic policy makers as they need to know if the new system (new classification) is efficient and to what level, to monitor the markets and intervene with appropriate measures. The data used are the daily index of the whole Kuwaiti market and the daily closing price, number of deals and volume of shares traded of two marketing sectors (consumer goods and consumer services) for the period from the 13th of May 2012 till the 12th of December 2016. The results indicate a positive direct impact of the closing price, volume and deals indexes of the consumer goods and the consumer services companies on the overall KSE index, volume and deals of the Kuwaiti stock market (KSE).

Keywords: correlation, market capitalization, Kuwait Stock Exchange (KSE), marketing sectors, stock performance

Procedia PDF Downloads 324

Authors: Rachid Ziteuni, Selt Omar

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In this paper, we propose a new polynomial metaheuristic elaboration (tabu search) for solving scheduling problems. This method allows us to solve the scheduling problem of n tasks on m identical parallel machines with unavailability periods. This problem is NP-complete in the strong sens and finding an optimal solution appears unlikely. Note that all data in this problem are integer and deterministic. The performance criterion to optimize in this problem which we denote Pm/N-c/summs of (wjCj) is the weighted sum of the end dates of tasks.

Keywords: scheduling, parallel identical machines, unavailability periods, metaheuristic, tabu search

Procedia PDF Downloads 328

Authors: J. Ritonja

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Available commercial applications of power system stabilizers assure optimal damping of synchronous generator’s oscillations only in a small part of operating range. Parameters of the power system stabilizer are usually tuned for the selected operating point. Extensive variations of the synchronous generator’s operation result in changed dynamic characteristics. This is the reason that the power system stabilizer tuned for the nominal operating point does not satisfy preferred damping in the overall operation area. The small-signal stability and the transient stability of the synchronous generators have represented an attractive problem for testing different concepts of the modern control theory. Of all the methods, the adaptive control has proved to be the most suitable for the design of the power system stabilizers. The adaptive control has been used in order to assure the optimal damping through the entire synchronous generator’s operating range. The use of the adaptive control is possible because the loading variations and consequently the variations of the synchronous generator’s dynamic characteristics are, in most cases, essentially slower than the adaptation mechanism. The paper shows the development and the application of the self-tuning power system stabilizer based on recursive least square identification method and linear quadratic regulator. Identification method is used to calculate the parameters of the Heffron-Phillips model of the synchronous generator. On the basis of the calculated parameters of the synchronous generator’s mathematical model, the synthesis of the linear quadratic regulator is carried-out. The identification and the synthesis are implemented on-line. In this way, the self-tuning power system stabilizer adapts to the different operating conditions. A purpose of this paper is to contribute to development of the more effective power system stabilizers, which would replace currently used linear stabilizers. The presented self-tuning power system stabilizer makes the tuning of the controller parameters easier and assures damping improvement in the complete operating range. The results of simulations and experiments show essential improvement of the synchronous generator’s damping and power system stability.

Keywords: adaptive control, linear quadratic regulator, power system stabilizer, recursive least square identification

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Authors: Liping Qiu, Xiaofeng Zhang

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The ion chromatography method for the rapid determination of four anions (F⁻、Cl⁻、SO₄²⁻、NO₃⁻) in burial ground poles was optimized. The L₉(₃⁴) orthogonal test was used to determine the optimal parameters of sample pretreatment: accurately weigh 2.000g of sample, add 10mL of ultrapure water, and extract for 40min under the conditions of shaking temperature 40℃ and shaking speed 180 r·min-1. The eluent was 25 mmol/L KOH solution, the analytical column was Ion Pac® AS11-SH (250 mm × 4.0 mm), and the purified filtrate was measured by a conductivity detector. Under this method, the detection limit of each ion is 0.066～0.078mg/kg, the relative standard deviation is 0.86%～2.44% (n=7), and the recovery rate is 94.6～101.9.

Keywords: ion chromatography, tomb, anion (F⁻, Cl⁻, SO₄²⁻, NO₃⁻), environmental protection

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Authors: M. Derraz, M.Farhaoui

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In the water treatment processes, the determination of the optimal dose of the coagulant is an issue of particular concern. Coagulant dosing is correlated to raw water quality which depends on some parameters (turbidity, ph, temperature, conductivity…). The objective of this study is to provide water treatment operators with a tool that enables to predict and replace, sometimes, the manual method (jar testing) used in this plant to predict the optimum coagulant dose. The model is constructed using actual process data for a water treatment plant located in the middle of Morocco (Meknes).

Keywords: coagulation process, aluminum sulfate, model, coagulant dose

Procedia PDF Downloads 273

Authors: Azam Zare

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Separation flow control for performance enhancement over airfoils at high incidence angle has become an increasingly important topic. This work details the characteristics of an efficient feedback control of the turbulent subsonic flow over NACA23012 airfoil using forced reduced‐order model based on the proper orthogonal decomposition/Galerkin projection and perturbation method on the compressible Reynolds Averaged Navier‐Stokes equations. The forced reduced‐order model is used in the optimal control of the turbulent separated flow over a NACA23012 airfoil at Mach number of 0.2, Reynolds number of 5×106, and high incidence angle of 24° using blowing/suction controlling jets. The Spallart-Almaras turbulence model is implemented for high Reynolds number calculations. The main shortcoming of the POD/Galerkin projection on flow equations for controlling purposes is that the blowing/suction controlling jet velocity does not show up explicitly in the resulting reduced order model. Combining perturbation method and POD/Galerkin projection on flow equations introduce a forced reduced‐order model that can predict the time-varying influence of the blowing/suction controlling jet velocity. An optimal control theory based on forced reduced‐order system is used to design a control law for a nonlinear reduced‐order model, which attempts to minimize the vorticity content in the turbulent flow field over NACA23012 airfoil. Numerical simulations were performed to help understand the behavior of the controlled suction jet at 12% to 18% chord from leading edge and a pair of blowing/suction jets at 15% to 18% and 24% to 30% chord from leading edge, respectively. Analysis of streamline profiles indicates that the blowing/suction jets are efficient in removing separation bubbles and increasing the lift coefficient up to 22%, while the perturbation method can predict the flow field in an accurate Manner.

Keywords: flow control, POD, Galerkin projection, separation

Procedia PDF Downloads 147

Authors: Michael Vieth, Aric Schadler, Hubert Ballard, J. A. Bauer, Pratibha Thakkar

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Introduction: Congenital diaphragmatic hernia (CDH) is a condition characterized by the herniation of abdominal contents into the thoracic cavity requiring postnatal surgical repair. Previous literature suggests improved CDH outcomes at high-volume regional referral centers compared to low-volume centers. The purpose of this study was to examine CDH outcomes at Kentucky Children’s Hospital (KCH), a low-volume center, compared to the Congenital Diaphragmatic Hernia Study Group (CDHSG). Methods: A retrospective chart review was performed at KCH from 2007-2019 for neonates with CDH, and then subdivided into two cohorts: those requiring ECMO therapy and those not requiring ECMO therapy. Basic demographic data and measures of mortality and morbidity including ventilator days and length of stay were compared to the CDHSG. Measures of morbidity for the ECMO cohort including duration of ECMO, clinical bleeding, intracranial hemorrhage, sepsis, need for continuous renal replacement therapy (CRRT), need for sildenafil at discharge, timing of surgical repair, and total ventilator days were collected. Statistical analysis was performed using IBM SPSS Statistics version 28. One-sample t-tests and one-sample Wilcoxon Signed Rank test were utilized as appropriate.Results: There were a total of 27 neonatal patients with CDH at KCH from 2007-2019; 9 of the 27 required ECMO therapy. The birth weight and gestational age were similar between KCH and the CDHSG (2.99 kg vs 2.92 kg, p =0.655; 37.0 weeks vs 37.4 weeks, p =0.51). About half of the patients were inborn in both cohorts (52% vs 56%, p =0.676). KCH cohort had significantly more Caucasian patients (96% vs 55%, p=<0.001). Unadjusted mortality was similar in both groups (KCH 70% vs CDHSG 72%, p =0.857). Using ECMO utilization (KCH 78% vs CDHSG 52%, p =0.118) and need for surgical repair (KCH 95% vs CDHSG 85%, p =0.060) as proxy for severity, both groups’ mortality were comparable. No significant difference was noted for pulmonary outcomes such as average ventilator days (KCH 43.2 vs. CDHSG 17.3, p =0.078) and home oxygen dependency (KCH 44% vs. CDHSG 24%, p =0.108). Average length of hospital stay for patients treated at KCH was similar to CDHSG (64.4 vs 49.2, p=1.000). Conclusion: Our study demonstrates that outcome in CDH patients is independent of center’s case volume status. Management of CDH with a standardized approach in a low-volume center can yield similar outcomes. This data supports the treatment of patients with CDH at low-volume centers as opposed to transferring to higher-volume centers.

Keywords: ECMO, case volume, congenital diaphragmatic hernia, congenital diaphragmatic hernia study group, neonate

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Authors: Mustapha Kamel Mihoubi, Hocine Dahmani

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The purpose of work is to study the phenomenon of agitation of storm waves at basin caused by different directions of waves relative to the current provision thrown numerical model based on the equation in shallow water using Boussinesq model MIKE 21 BW. According to the diminishing effect of penetration of a wave optimal solution will be available to be reproduced in reduced model. Another alternative arrangement throws will be proposed to reduce the agitation and the effects of the swell reflection caused by the penetration of waves in the harbor.

Keywords: agitation, Boussinesq equations, combination, harbor

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Authors: Anna Avramenko, Heikki Haario

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This paper reports the development and application of a 2D depth-averaged model. The main goal of this contribution is to apply the depth averaged equations to a wind park model in which the treatment of the geometry, introduced on the mathematical model by the mass and momentum source terms. The depth-averaged model will be used in future to find the optimal position of wind turbines in the wind park. K-E and 2D LES turbulence models were consider in this article. 2D CFD simulations for one hill was done to check the depth-averaged model in practise.

Keywords: depth-averaged equations, numerical modeling, CFD, wind park model

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Authors: Albert Kwabena Osei Konadu

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Pterocarpus erinaceus, also known as Rosewood, is tropical wood, endemic in forest savannah transition zones within the middle and northern portion of Ghana. Its economic viability has made it increasingly popular and in high demand, leading to widespread conservation concerns. Ghana’s forest resource management regime for these ecozones is mainly on conservation and very little on resource utilization. Consequently, commercial logging management standards are at teething stage and not fully developed, leading to a deficiency in the monitoring of logging operations and quantification of harvested trees volumes. Tree information form (TIF); a volume estimation and tracking regime, has proven to be an effective, sustainable management tool for regulating timber resource extraction in the high forest zones of the country. This work aims to generate TIF that can track and capture requisite parameters to accurately estimate the volume of harvested rosewood within forest savannah transition zones. Tree information forms were created on three scenarios of individual billets, stacked billets and conveying vessel basis. These TIFs were field-tested to deduce the most viable option for the tracking and estimation of harvested volumes of rosewood using the smallian and cubic volume estimation formula. Overall, four districts were covered with individual billets, stacked billets and conveying vessel scenarios registering mean volumes of 25.83m3,45.08m3 and 32.6m3, respectively. These adduced volumes were validated by benchmarking to assigned volumes of the Forestry Commission of Ghana and known standard volumes of conveying vessels. The results did indicate an underestimation of extracted volumes under the quotas regime, a situation that could lead to unintended overexploitation of the species. The research revealed conveying vessels route is the most viable volume estimation and tracking regime for the sustainable management of the Pterocarpous erinaceus species as it provided a more practical volume estimate and data extraction protocol.

Keywords: convention on international trade in endangered species, cubic volume formula, forest transition savannah zones, pterocarpus erinaceus, smallian’s volume formula, tree information form

Procedia PDF Downloads 95

Authors: Belcher Fulele, W. A. A. Ddamba

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Study of solvent systems contributes to the understanding of intermolecular interactions that occur in binary mixtures. These interactions involves among others strong dipole-dipole interactions and weak van de Waals interactions which are of significant application in pharmaceuticals, solvent extractions, design of reactors and solvent handling and storage processes. Binary mixtures of solvents can thus be used as a model to interpret thermodynamic behavior that occur in a real solution mixture. Densities of pure DFM, n-alkanes (n-pentane, n-hexane, n-heptane and n-octane) and amides (N-methylformamide, N-ethylformamide, N,N-dimethylformamide and N,N-dimethylacetamide) as well as their [DFM + ((C5-C8) n-alkane or amide)] binary mixtures over the entire composition range, have been reported at temperature 293.15, 298.15 and 303.15 K and atmospheric pressure. These data has been used to derive the thermodynamic properties: the excess molar volume of solution, apparent molar volumes, excess partial molar volumes, limiting excess partial molar volumes, limiting partial molar volumes of each component of a binary mixture. The results are discussed in terms of possible intermolecular interactions and structural effects that occur in the binary mixtures. The variation of excess molar volume with DFM composition for the [DFM + (C5-C7) n-alkane] binary mixture exhibit a sigmoidal behavior while for the [DFM + n-octane] binary system, positive deviation of excess molar volume function was observed over the entire composition range. For each of the [DFM + (C5-C8) n-alkane] binary mixture, the excess molar volume exhibited a fall with increase in temperature. The excess molar volume for each of [DFM + (NMF or NEF or DMF or DMA)] binary system was negative over the entire DFM composition at each of the three temperatures investigated. The negative deviations in excess molar volume values follow the order: DMA > DMF > NEF > NMF. Increase in temperature has a greater effect on component self-association than it has on complex formation between molecules of components in [DFM + (NMF or NEF or DMF or DMA)] binary mixture which shifts complex formation equilibrium towards complex to give a drop in excess molar volume with increase in temperature. The Prigogine-Flory-Patterson model has been applied at 298.15 K and reveals that the free volume is the most important contributing term to the excess experimental molar volume data for [DFM + (n-pentane or n-octane)] binary system. For [DFM + (NMF or DMF or DMA)] binary mixture, the interactional term and characteristic pressure term contributions are the most important contributing terms in describing the sign of experimental excess molar volume. The mixture systems contributed to the understanding of interactions of polar solvents with proteins (amides) with non-polar solvents (alkanes) in biological systems.

Keywords: alkanes, amides, excess thermodynamic parameters, Prigogine-Flory-Patterson model

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Authors: Mohanad Al-Behadili, Xiang Song, Djamila Ouelhadj, Alex Fraess-Ehrfeld

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In off-shore wind farms, turbine blade inspection accessibility under various sea states is very challenging and greatly affects the downtime of wind turbines. Maintenance of any offshore system is not an easy task due to the restricted logistics and accessibility. The multirotor unmanned helicopter is of increasing interest in inspection applications due to its manoeuvrability and payload capacity. These advantages increase when many of them are deployed simultaneously in a swarm. Hence this paper proposes a drone swarm framework for inspecting offshore wind turbine blades and nacelles so as to reduce downtime. One of the big challenges of this task is that when operating a drone swarm, an individual drone may not have enough power to fly and communicate during missions and it has no capability of refueling due to its small size. Once the drone power is drained, there are no signals transmitted and the links become intermittent. Vessels equipped with 5G masts and small power units are utilised as platforms for drones to recharge/swap batteries. The research work aims at designing a smart energy management system, which provides automated vessel and drone routing and recharging plans. To achieve this goal, a novel mathematical optimisation model is developed with the main objective of minimising the number of drones and vessels, which carry the charging stations, and the downtime of the wind turbines. There are a number of constraints to be considered, such as each wind turbine must be inspected once and only once by one drone; each drone can inspect at most one wind turbine after recharging, then fly back to the charging station; collision should be avoided during the drone flying; all wind turbines in the wind farm should be inspected within the given time window. We have developed a real-time Ant Colony Optimisation (ACO) algorithm to generate real-time and near-optimal solutions to the drone swarm routing problem. The schedule will generate efficient and real-time solutions to indicate the inspection tasks, time windows, and the optimal routes of the drones to access the turbines. Experiments are conducted to evaluate the quality of the solutions generated by ACO.

Keywords: drone swarm, routing, scheduling, optimisation model, ant colony optimisation

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Authors: Juan M. Rodriguez-Diaz

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The precision of parameter estimators in the Gaussian linear model is traditionally accounted by the variance-covariance matrix of the asymptotic distribution. However, this measure can underestimate the true variance, specially for small samples. Traditionally, optimal design theory pays attention to this variance through its relationship with the model's information matrix. For this reason it seems convenient, at least in some cases, adapt the optimality criteria in order to get the best designs for the actual variance structure, otherwise the loss in efficiency of the designs obtained with the traditional approach may be very important.

Keywords: correlated observations, information matrix, optimality criteria, variance-covariance matrix

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Authors: Pejman Hosseiniun, Rose Shayeghi, Alireza Farzaneh, Abolghasem Ghasempour

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Using LED lamps as lighting resources with new technology in designing lighting systems has been studied in this article. In this respect a history of LED emergence, its different manufacturing methods and technologies were revised, then their structure, light production line, its application and benefits in lighting industry has been evaluated. Finally, there is a comparison between these lamps and ordinary lamps to assess light parameters as well as energy consumption using DIALux software. Considering the results of analogies LED lamps have lower consumption and more lighting yield, therefore they are more economically feasible. Color variety, longer usage lap (circa 10 years) and compatibility with DC voltages are other LED lamps perquisites.

Keywords: LED, lighting efficiency, lighting intensity, luminance

Procedia PDF Downloads 589

Authors: Robert Lenzie

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Transcranial electrical stimulation (tES) is significant in the research literature. However, the effects of tES on brain activity are still poorly understood at the surface level, the Brodmann Area level, and the impact on neural networks. Using a method like electroencephalography (EEG) in conjunction with tES might make it possible to comprehend the brain response and mechanisms behind published observed alterations in more depth. Using a method to directly see the effect of tES on EEG may offer high temporal resolution data on the brain activity changes/modulations brought on by tES that correlate to various processing stages within the brain. This paper provides unpublished information on a cutting-edge methodology that may reveal details about the dynamics of how the human brain works beyond what is now achievable with existing methods.

Keywords: tACS, frequency, EEG, optimal

Procedia PDF Downloads 77

Authors: Sandra Cecilia Muhirirwe, Bart Van Der Bruggen, Violet Kisakye

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Economic analysis of Rainwater harvesting (RWH) systems is vital in search of a cost-effective solution to water unreliability, especially in low-income countries. There is little literature focusing on the financial aspects of RWH for dairy farmers. The main purpose was to assess the economic viability of rainwater harvesting for diary framers in the Rwenzori region. The study focused on the use of rainwater harvesting systems from the rooftop and collection in above surface tanks. Daily rainfall time series for 12 years was obtained across nine gauging stations. The daily water balance equation was used for optimal sizing of the tank. Economic analysis of the investment was carried out based on the life cycle costs and the accruing benefits for the period of 15 years. Roof areas were varied from 75m2 as the minimum required area to 500m2 while maintaining the same number of cattle and keeping the daily water demand constant. The results show that the required rainwater tank sizes are very large and may be impractical to install due to the strongly varying terrain and the initial cost of investment. In all districts, there is a significant reduction of the volume of the required tank with an increasing collection area. The results further show that increasing the collection area has a minor effect on reducing the required tank size. Generally, for all rainfall areas, the reliability increases with an increase in the roof area. The results indicate that 100% reliability can only be realized with very large collection areas that are impractical to install. The estimated benefits outweigh the cost of investment. The Present Net Value shows that the investment is economically viable and investment with a short payback of a maximum of 3 years for all the time series in the study area.

Keywords: dairy cattle, optimisation, rainwater harvesting, economic analysis

Procedia PDF Downloads 200

Authors: H. Al-Qassem, L. Cheng, Y. Pan

Abstract:

In this paper, we establish sharp bounds for oscillatory singular integrals with an arbitrary real polynomial phase P. Our kernels are allowed to be rough both on the unit sphere and in the radial direction. We show that the bounds grow no faster than log (deg(P)), which is optimal and was first obtained by Parissis and Papadimitrakis for kernels without any radial roughness. Our results substantially improve many previously known results. Among key ingredients of our methods are an L¹→L² sharp estimate and using extrapolation.

Keywords: oscillatory singular integral, rough kernel, singular integral, orlicz spaces, block spaces, extrapolation, L^{p} boundedness

Procedia PDF Downloads 456

Authors: Alexis Winfield, Carly Sugar, Barbara Fenesi

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The COVID-19 pandemic uprooted regular routines forcing many children to learn from home, requiring many adults to work from home, and cutting families off from support outside the home. Public health restrictions associated with the pandemic caused widespread psychological distress, including depression and anxiety, increased fear, panic, and stress. These trends are particularly concerning for families raising neuroatypical children, such as those with Attention-Deficit Hyperactivity Disorder (ADHD), as these children are already more likely than their typically developing peers to experience comorbid mental health issues and to experience greater distress when required to stay indoors. Families with children who have ADHD are also at greater risk for experiencing heightened familial stress due to the challenges associated with managing ADHD behavioural symptoms, greater parental discord and divorce, and greater financial difficulties compared to other families. The current study engaged families comprised of at least one child diagnosed with ADHD to elucidate 1) the unique ways that the COVID-19 pandemic affected their mental health and 2) the specific barriers these families faced to maintaining optimal mental wellbeing. A total of 33 participants (15 parent-child dyads) engaged in virtual interviews. Content analysis revealed that the most frequently identified mental health effects for families were increased child anxiety and disconnectedness, as well as deteriorating parental mental health. The most frequently identified barriers to maintaining optimal mental well-being were lack of routine, lack of social interaction and social support, and uncertainty and fear. Findings underscore areas of need during times of large-scale social isolation, bring voice to the families of children with ADHD, and contribute to our understanding of the pandemic’s impact on the wellbeing of vulnerable families. This work contributes to a growing body of research aimed at creating safeguards to support mental wellbeing for vulnerable families during times of crisis.

Keywords: attention-deficit hyperactivity disorder, COVID-19, mental health, vulnerable families

Procedia PDF Downloads 284

Authors: Chao-Ton Su, Li-Fei Chen

Abstract:

The parameter design is crucial to improving the uniformity of a product or process. In the product design stage, parameter design aims to determine the optimal settings for the parameters of each element in the system, thereby minimizing the functional deviations of the product. In the process design stage, parameter design aims to determine the operating settings of the manufacturing processes so that non-uniformity in manufacturing processes can be minimized. The parameter design, trying to minimize the influence of noise on the manufacturing system, plays an important role in the high-tech companies. Taiwan has many well-known high-tech companies, which show key roles in the global economy. Quality remains the most important factor that enables these companies to sustain their competitive advantage. In Taiwan however, many high-tech companies face various quality problems. A common challenge is related to root causes and defect patterns. In the R&D stage, root causes are often unknown, and defect patterns are difficult to classify. Additionally, data collection is not easy. Even when high-volume data can be collected, data interpretation is difficult. To overcome these challenges, high-tech companies in Taiwan use more advanced quality improvement tools. In addition to traditional statistical methods and quality tools, the new trend is the application of powerful tools, such as neural network, fuzzy theory, data mining, industrial engineering, operations research, and innovation skills. In this study, several examples of optimizing the parameter settings for the manufacturing process in Taiwan’s tech companies will be presented to illustrate proposed approach’s effectiveness. Finally, a discussion of using traditional experimental design versus the proposed approach for process optimization will be made.

Keywords: quality engineering, parameter design, neural network, genetic algorithm, experimental design

Procedia PDF Downloads 143

Authors: Jae-Hyun Ro, Yong-Jun Kim, Chang-Bin Ha, Hyoung-Kyu Song

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In multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems, many detection schemes have been developed to improve the error performance and to reduce the complexity. Maximum likelihood (ML) detection has optimal error performance but it has very high complexity. Thus, this paper proposes reduced complexity of ML detection combined with decision feedback equalizer (DFE). The error performance of the proposed detection scheme is higher than the conventional DFE. But the complexity of the proposed scheme is lower than the conventional ML detection.

Keywords: detection, DFE, MIMO-OFDM, ML

Procedia PDF Downloads 606

Authors: Pramod Ghadge, Swadesh Srivastava

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Revenue generation of a logistics company depends on how the logistics cost of a shipment is calculated. Logistics cost of a shipment is a function of distance & speed of the shipment travel in a particular network, its volumetric size and dead weight. Logistics billing is based mainly on the consumption of the scarce resource (space or weight carrying capacity of a carrier). Shipment’s size or deadweight is a function of product and packaging weight, dimensions and flexibility. Hence, to arrive at a standard methodology to compute accurate cost to bill the customer, the interplay among above mentioned physical attributes along with their measurement plays a key role. This becomes even more complex for an ecommerce company, like Flipkart, which caters to shipments from both warehouse and marketplace in an unorganized non-standard market like India. In this paper, we will explore various methodologies to define a standard way of billing the non-standard shipments across a wide range of size, shape and deadweight. Those will be, usage of historical volumetric/dead weight data to arrive at a factor which can be used to compute the logistics cost of a shipment, also calculating the real/contour volume of a shipment to address the problem of irregular shipment shapes which cannot be solved by conventional bounding box volume measurements. We will also discuss certain key business practices and operational quality considerations needed to bring standardization and drive appropriate ownership in the ecosystem.

Keywords: contour volume, logistics, real volume, volumetric weight

Procedia PDF Downloads 261

Authors: Hao Yan, Xiaobing Zhang

Abstract:

The working mechanism of miniaturized solid rocket motors (SRMs) is not yet fully understood. It is imperative to explore its unique features. However, there are many disadvantages to using common multi-objective evolutionary algorithms (MOEAs) in predicting the parameters of the miniaturized SRM during its conceptual design phase. Initially, the design variables and objectives are constrained in a lumped parameter model (LPM) of this SRM, which leads to local optima in MOEAs. In addition, MOEAs require a large number of calculations due to their population strategy. Although the calculation time for simulating an LPM just once is usually less than that of a CFD simulation, the number of function evaluations (NFEs) is usually large in MOEAs, which makes the total time cost unacceptably long. Moreover, the accuracy of the LPM is relatively low compared to that of a CFD model due to its assumptions. CFD simulations or experiments are required for comparison and verification of the optimal results obtained by MOEAs with an LPM. The conceptual design phase based on MOEAs is a lengthy process, and its results are not precise enough due to the above shortcomings. An artificial neural network (ANN) based parameter prediction is proposed as a way to reduce time costs and improve prediction accuracy. In this method, an ANN is used to build a surrogate model that is trained with a 3D numerical simulation. In design, the original LPM is replaced by a surrogate model. Each case uses the same MOEAs, in which the calculation time of the two models is compared, and their optimization results are compared with 3D simulation results. Using the surrogate model for the parameter prediction process of the miniaturized SRMs results in a significant increase in computational efficiency and an improvement in prediction accuracy. Thus, the ANN-based surrogate model does provide faster and more accurate parameter prediction for an initial design scheme. Moreover, even when the MOEAs converge to local optima, the time cost of the ANN-based surrogate model is much lower than that of the simplified physical model LPM. This means that designers can save a lot of time during code debugging and parameter tuning in a complex design process. Designers can reduce repeated calculation costs and obtain accurate optimal solutions by combining an ANN-based surrogate model with MOEAs.

Keywords: artificial neural network, solid rocket motor, multi-objective evolutionary algorithm, surrogate model

Procedia PDF Downloads 87

Authors: N. Y. H. Konagano

Abstract:

Riverside population of Brazilian amazon suffers drinking water scarcity, seeking alternative water resources such as well and rivers, ordinary polluted. Although Amazon Region holds high annual river inflow and enough available of underground water, human activities have compromised the conservation of water resources. In addition, decentralized rural households make difficult to access of potable water. Main objective is to analyze quantitatively the potential of rainwater harvesting to human consumption at Marupaúba community, located in northeast of Amazon region, Brazil. Methods such as historical rainfall data series of municipality of Tomé-Açu at Pará state were obtained from Hydrological Information System of National Water Agency (ANA). Besides, Rippl method was used to calculate, mainly, volume of the reservoir based on difference of water demand and volume available through rainwater using as references two houses (CA I and CA II) as model of rainwater catchment and supply. Results presented that, from years 1984 to 2017, average annual precipitation was 2.607 mm, average maximum precipitation peak was 474 mm on March and average minimum peak on September was 44 mm. All months, of a year, surplus volume of water have presented in relation to demand, considering catchment area (CA) I = 134.4m² and demand volume =0.72 m³/month; and, CA II = 81.84 m² and demand volume = 0.48 m³/month. Based on results, it is concluded that it is feasible to use rainwater for the supply of the rural community Marupaúba, since the access of drinking water is a human right and the lack of this resource compromises health and daily life of human beings.

Keywords: Amazon Region, rainwater harvesting, rainwater resource, rural community

Procedia PDF Downloads 145