Search results for: hybrid optimization

Authors: Oluwaseun A. Oyetade, Roelof J. Kriek

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The development of effective catalysts for the oxygen evolution reaction (OER) is of great importance to combat energy-related concerns in the environment. Herein, we report a one-step solvothermal method employed for the fabrication of nickel selenide hybrids (NiSe-Ni₃Se₂) and a series of nickel selenide hybrid/multiwalled carbon nanotube composites (NiSe-Ni₃Se₂/MWCNT) as electrocatalysts for OER in alkaline media. The catalytic activities of these catalysts were investigated via several electrochemical characterization techniques, such as linear sweep voltammetry, chronoamperometric studies at constant potential, electrochemical surface area determination, and Tafel slope calculation, under alkaline conditions. Morphological observations demonstrated the agglomeration of non-uniform NiSe-Ni₃Se₂ microspheres around carbon nanotubes (CNTs), demonstrating the successful synthesis of NiSe-Ni₃Se₂/MWCNT nanocomposites. Among the tested electrocatalysts, the 20% NiSe-Ni₃Se₂/MWCNT nanocomposite demonstrated the highest activity, exhibiting an overpotential of 325 mV to achieve a current density of 10 mA.cm⁻² in 0.1 mol.dm⁻³ KOH solution. The NiSe-Ni₃Se₂/MWCNT nanocomposites showed improved activity toward OER compared to bare NiSe-Ni₃Se₂ hybrids and MWCNTs, exhibiting an overpotential of 528, 392 and 434 mV for 10%, 30% and 50% NiSe-Ni₃Se₂/MWCNT nanocomposites, respectively. These results compare favourably to the overpotential of noble catalysts, such as RuO₂ and IrO₂. Our results imply that the addition of MWCNTs increased the activity of NiSe-Ni₃Se₂ hybrids due to an increased number of catalytic sites, dispersion of NiSe-Ni₃Se₂ hybrid nanoparticles, and electronic conductivity of the nanocomposites. These nanocomposites also demonstrated better long-term stability compared to NiSe-Ni₃Se₂ hybrids and MWCNTs. Hence, NiSe-Ni₃Se₂/MWCNT nanocomposites possess the potential as effective electrocatalysts for OER in alkaline media.

Keywords: carbon nanotubes, electrocatalysts, nanocomposites, nickel selenide hybrids, oxygen evolution reaction

Procedia PDF Downloads 115

Authors: Tamrat Balcha Balla

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Background: Tablets are still the most preferred means of drug delivery. The search for new and improved direct compression tablet excipients is an area of research focus. Taro Boloso-I is a variety of Colocasia esculenta (L. Schott) yielding 67% more than the other varieties (Godare) in Ethiopia. This study aimed to enhance the flowability while keeping the compressibility and compactibility of the pregelatinized Taro Boloso-I starch. Methods: Central composite design was used for the optimization of two factors which were the temperature and duration of pregelatinization against 5 responses. The responses were angle of repose, Hausner ratio, Kawakita compressibility index, mean yield pressure and tablet breaking force. Results and Discussions: An increase in both temperature and time resulted in decrease in the angle of repose. The increase in temperature was shown to decrease the Hausner ratio and to decrease the Kawakita compressibility index. The mean yield pressure was observed to increase with increasing levels of both temperature and time. The pregelatinized (optimized) Taro Boloso-I starch could show desired flow property and compressibility. Conclusions: Pregelatinized Taro Boloso - I starch could be regarded as a potential direct compression excipient in terms of flowability, compressibility and compactibility.

Keywords: starch, compression, pregelatinization, Taro Boloso-I

Procedia PDF Downloads 91

Authors: D. Dannehl, Z. Taylor, J. Suhl, L. Miranda, R., Ulrichs, C., Salazar, E. Fitz-Rodriguez, I. Lopez-Cruz, A. Rojano-Aguilar, G. Navas-Gomez, U. Schmidt

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Growing environmental and sustainability concerns have driven continual modernization of horticultural practices, especially for urban farming. Controlled environment and soilless production methods are increasing in popularity because of their efficient resource use and intensive cropping capabilities. However, some popular substrates used for hydroponic cultivation, particularly rock wool, represent a large environmental burden in regard to their manufacture and disposal. Substrate-less hydroponic systems are effective in producing short cropping cycle plants such as lettuce or herbs, but less information is available for the production of plants with larger root-systems and longer cropping times. Here, we investigated the viability of a hybrid aeroponic/nutrient film technique (AP/NFT) system for the cultivation of greenhouse tomatoes (Solanum lycopersicum ‘Panovy’). The plants grown in the AP/NFT system had a more compact phenotype, accumulated more Na⁺ and less P and S than the rock wool grown counterparts. Due to forced irrigation interruptions, we propose that the differences observed were cofounded by the differing severity of water-stress for plants with and without substrate. They may also be caused by a higher root zone temperature predominant in plants exposed to AP/NFT. However, leaf area, stem diameter, and number of trusses did not differ significantly. The same was found for leaf pigments and plant photosynthetic efficiency. Overall, the AP/NFT system appears to be viable for the production of greenhouse tomato, enabling the environment to be relieved by way of lessening rock wool usage.

Keywords: closed aeroponic systems, fruit quality, nutrient dynamics, substrate waste reduction, urban farming systems, water savings

Procedia PDF Downloads 254

Authors: M. H. Shwehdi, S. Raja Mohammad

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Recently energy savings have become more pronounced as a result of the world financial crises as well the unstable oil prices. Certainly all entities needs to adapt Energy Conservation and Management Strategies due to high monthly consumption of their spread locations and advancements of its telecom systems. These system improvements necessitate the establishment of more exchange centers as well provide energy savings. This paper investigates the impact of HVAC System Characteristics, Operational Strategies, the impact of Envelope Thermal Characteristics, and energy conservation measures. These are classified under three types of measures i.e. Zero-Investment; Low-Investment and High-Investment Energy Conservation Measures. The study shows that the Energy Conservation Measures (ECMs) pertaining to the HVAC system characteristics and operation represent the highest potential for energy reduction, attention should be given to window thermal and solar radiation characteristics when large window areas are used. The type of glazing system needs to be carefully considered in the early design phase of future buildings. Paper will present the thermal optimization of different size centers in the two hot-dry and hot-humid Saudi Arabian city of Al Khobar, East province.

Keywords: energy conservation, optimization, thermal design, intermittent operation, exchange centers, hot-humid climate, Saudi Arabia

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Authors: James M. Wakiru, Liliane Pintelon, Peter Muchiri, Peter Chemweno

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The fast-changing business and operational environment are forcing organizations to adopt asset performance management strategies, not only to reduce costs but also maintain operational and production policies while addressing demand. To attain optimal asset performance management, a framework that ensures a continuous and systematic approach to analyzing an organization’s current maturity level and expected improvement regarding asset maintenance processes, strategies, technologies, capabilities, and systems is essential. Moreover, this framework while addressing maintenance-intensive organizations should consider the diverse business, operational and technical context (often dynamic) an organization is in and realistically prescribe or relate to the appropriate tools and systems the organization can potentially employ in the respective level, to improve and attain their maturity goals. This paper proposes an asset maintenance maturity model to assess the current capabilities, strength and weaknesses of maintenance processes an organization is using and analyze gaps for improvement via structuring set levels of achievement. At the epicentre of the proposed framework is the utilization of maintenance objective selected by an organization for various maintenance optimization programs. The framework adapts the Capability Maturity Model of assessing the maintenance process maturity levels in the organization.

Keywords: asset maintenance, maturity models, maintenance objectives, optimization

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Authors: Erwanda Nuryahya, Aas Nurasyiah, Sri Yayu Ninglasari

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Indonesia is the fourth most populous country that still has many troubles in its development. One of the problems which is very important and unresolved is poverty. Limited job opportunity is one unresolved cause of it until today. The purpose of making this scientific paper is to know benefits of E-Money Paytren Application to enhance its partners’ income, owned by company Veritra Sentosa International. The methodology used here is the quantitative and qualitative descriptive method by case study approach. The data used are primary and secondary data. The primary data is obtained from interviews and observation to company Veritra Sentosa International and the distribution of 400 questionnaires to Paytren partner. Secondary data is obtained from the literature study and documentary. The result is that the Paytren with a system direct sales tiered syariah proven able to enhance its partners’ income. Therefore, the Optimization Financial Technology through E-Money Paytren Application should be utilized by Indonesians because it is proven that it is able to increase the income of the partners. Therefore, Paytren Application is very useful for the government, the sharia financial industry, and society in reducing poverty in Indonesia.

Keywords: e-money PayTren application, financial technology, poverty, direct sales tiered Sharia

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Authors: Kemka C. Ihemelandu, Chukwuemeka U. Ihemelandu

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The incidence of melanoma has been increasing rapidly over the past two decades, making melanoma a current public health crisis. Unfortunately, even as screening efforts continue to expand in an effort to ameliorate the death rate from melanoma, there is a need to improve diagnostic accuracy to decrease misdiagnosis. Artificial intelligence (AI) a new frontier in patient care has the ability to improve the accuracy of melanoma diagnosis. Convolutional neural network (CNN) a form of deep neural network, most commonly applied to analyze visual imagery, has been shown to outperform the human brain in pattern recognition. However, there are noted limitations with the accuracy of the CNN models. Our aim in this study was the optimization of convolutional neural network algorithms for the automated diagnosis of melanoma. We hypothesized that Optimal selection of the momentum and batch hyperparameter increases model accuracy. Our most successful model developed during this study, showed that optimal selection of momentum of 0.25, batch size of 2, led to a superior performance and a faster model training time, with an accuracy of ~ 83% after nine hours of training. We did notice a lack of diversity in the dataset used, with a noted class imbalance favoring lighter vs. darker skin tone. Training set image transformations did not result in a superior model performance in our study.

Keywords: melanoma, convolutional neural network, momentum, batch hyperparameter

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Authors: Gerald Gwamba

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Formation and oil well sand production is one of the greatest and oldest concerns for the Oil and gas industry. The production of sand particles may vary from very small and limited amounts to far elevated levels which has the potential to block or plug the pore spaces near the perforated points to blocking production from surface facilities. Therefore, the timely and reliable investigation of conditions leading to the onset or quantifying sanding while producing is imperative. The challenges of sand production are even more elevated while producing in Waxy and Heavy wells with Clay Fine sands (WHFC). Existing research argues that both waxy and heavy hydrocarbons exhibit far differing characteristics with waxy more paraffinic while heavy crude oils exhibit more asphaltenic properties. Moreover, the combined effect of WHFC conditions presents more complexity in production as opposed to individual effects that could be attributed to a consolidation of a surmountable opposing force. However, research on a combined high WHFC system could depict a better representation of the surmountable effect which in essence is more comparable to field conditions where a one-sided view of either individual effects on sanding has been argued to some extent misrepresentative of actual field conditions since all factors act surmountably. In recognition of the limited customized research on sand production studies with the combined effect of WHFC however, our research seeks to apply the Design of Experiments (DOE) methodology based on latest literature to analyze the relationship between various interparticle factors in relation to selected sand control methods. Our research aims to unearth a better understanding of how the combined effect of interparticle factors including: strength, cementation, particle size and production rate among others could better assist in the design of an optimal sand control system for the WHFC well conditions. In this regard, we seek to answer the following research question: How does the combined effect of interparticle factors affect the optimization of sand control systems for WHFC wells? Results from experimental data collection will inform a better justification for a sand control design for WHFC. In doing so, we hope to contribute to earlier contrasts arguing that sand production could potentially enable well self-permeability enhancement caused by the establishment of new flow channels created by loosening and detachment of sand grains. We hope that our research will contribute to future sand control designs capable of adapting to flexible production adjustments in controlled sand management. This paper presents results which are part of an ongoing research towards the authors' PhD project in the optimization of sand control systems for WHFC wells.

Keywords: waxy-heavy oils, clay-fine sands, sand control optimization, interparticle factors, design of experiments

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Authors: Ramiro Gustavo Ramirez Camacho, Waldir De Oliveira, Eraldo Cruz Dos Santos, Edna Raimunda Da Silva, Tania Marie Arispe Angulo, Carlos Eduardo Alves Da Costa, Tânia Cristina Alves Dos Reis

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It presents a preliminary design methodology of an axial fan based on the lift wing theory and the potential vortex hypothesis. The literature considers a study of acoustic and engineering expertise to model a fan with low noise. Axial fans with inadequate intake geometry, often suffer poor condition of the flow at the entrance, varying from velocity profiles spatially asymmetric to swirl floating with respect to time, this produces random forces acting on the blades. This produces broadband gust noise which in most cases triggers the tonal noise. The analysis of the axial flow fan will be conducted for the solution of the Navier-Stokes equations and models of turbulence in steady and transitory (RANS - URANS) 3-D, in order to find an efficient aerodynamic design, with low noise and suitable for industrial installation. Therefore, the process will require the use of computational optimization methods, aerodynamic design methodologies, and numerical methods as CFD- Computational Fluid Dynamics. The objective is the development of the methodology of the construction axial fan, provide of design the geometry of the blade, and evaluate aerodynamic performance

Keywords: Axial fan design, CFD, Preliminary Design, Optimization

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Authors: Zafer Yüce, Paşa Yayla, Alev Taşkın

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There are heaps of analytical methods to estimate the crack growth life of a component. Soft computing methods have an increasing trend in predicting fatigue life. Their ability to build complex relationships and capability to handle huge amounts of data are motivating researchers and industry professionals to employ them for challenging problems. This study focuses on soft computing methods, especially random forest regressors and artificial neural networks with hyperparameter optimization algorithms such as grid search and random grid search, to estimate the crack growth life of an aircraft wing splice joint under variable amplitude loading. TensorFlow and Scikit-learn libraries of Python are used to build the machine learning models for this study. The material considered in this work is 7050-T7451 aluminum, which is commonly preferred as a structural element in the aerospace industry, and regarding the crack type; corner crack is used. A finite element model is built for the joint to calculate fastener loads and stresses on the structure. Since finite element model results are validated with analytical calculations, findings of the finite element model are fed to AFGROW software to calculate analytical crack growth lives. Based on Fighter Aircraft Loading Standard for Fatigue (FALSTAFF), 90 unique fatigue loading spectra are developed for various load levels, and then, these spectrums are utilized as inputs to the artificial neural network and random forest regression models for predicting crack growth life. Finally, the crack growth life predictions of the machine learning models are compared with analytical calculations. According to the findings, a good correlation is observed between analytical and predicted crack growth lives.

Keywords: aircraft, fatigue, joint, life, optimization, prediction.

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Authors: Vimanyu Chadha, Ranganath M. Singari

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Drilling composite materials is a frequently practiced machining process during assembling in various industries such as automotive and aerospace. However, drilling of glass fiber reinforced plastic (GFRP) composites is significantly affected by damage tendency of these materials under cutting forces such as thrust force and torque. The aim of this paper is to investigate the influence of the various cutting parameters such as cutting speed and feed rate; subsequently also to study the influence of number of layers on delamination produced while drilling a GFRP composite. A plan of experiments, based on Taguchi techniques, was instituted considering drilling with prefixed cutting parameters in a hand lay-up GFRP material. The damage induced associated with drilling GFRP composites were measured. Moreover, Analysis of Variance (ANOVA) was performed to obtain minimization of delamination influenced by drilling parameters and number layers. The optimum drilling factor combination was obtained by using the analysis of signal-to-noise ratio. The conclusion revealed that feed rate was the most influential factor on the delamination. The best results of the delamination were obtained with composites with a greater number of layers at lower cutting speeds and feed rates.

Keywords: analysis of variance, delamination, design optimization, drilling, glass fiber reinforced plastic composites, Taguchi method

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Authors: Ermias A. Tegegn, Million Meshesha

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Human Immunodeficiency Virus and Acquired Immunodeficiency Syndrome (HIV/AIDS) is a major cause of death for most African countries. Ethiopia is one of the seriously affected countries in sub Saharan Africa. Previously in Ethiopia, having HIV/AIDS was almost equivalent to a death sentence. With the introduction of Antiretroviral Therapy (ART), HIV/AIDS has become chronic, but manageable disease. The study focused on a data mining technique to predict future living status of HIV/AIDS patients at the time of drug regimen change when the patients become toxic to the currently taking ART drug combination. The data is taken from University of Gondar Hospital ART program database. Hybrid methodology is followed to explore the application of data mining on ART program dataset. Data cleaning, handling missing values and data transformation were used for preprocessing the data. WEKA 3.7.9 data mining tools, classification algorithms, and expertise are utilized as means to address the research problem. By using four different classification algorithms, (i.e., J48 Classifier, PART rule induction, Naïve Bayes and Neural network) and by adjusting their parameters thirty-two models were built on the pre-processed University of Gondar ART program dataset. The performances of the models were evaluated using the standard metrics of accuracy, precision, recall, and F-measure. The most effective model to predict the status of HIV patients with drug regimen substitution is pruned J48 decision tree with a classification accuracy of 98.01%. This study extracts interesting attributes such as Ever taking Cotrim, Ever taking TbRx, CD4 count, Age, Weight, and Gender so as to predict the status of drug regimen substitution. The outcome of this study can be used as an assistant tool for the clinician to help them make more appropriate drug regimen substitution. Future research directions are forwarded to come up with an applicable system in the area of the study.

Keywords: HIV drug regimen, data mining, hybrid methodology, predictive model

Procedia PDF Downloads 133

Authors: Terkimbi Vange, Obed Abimiku, Lateef Lekan Bello, Lucky Omoigui

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In 2014 and 2015, eight maize inbred lines resistant to Striga hermonthica (Del) Benth were crossed in 8 x 8 half diallel (Griffing method 11, model 1). The eight parent inbred lines were planted out in a Randomized Complete Block Design (RCBD) with three replications at two different Striga infested environments (Lafia and Makurdi) during the late cropping season. The objectives were to determine the combining ability of Striga resistant maize inbred lines and identify suitable inbreds for hybrids development. The lines were used to estimate general combining ability (GCA), and specific combining ability (SCA) effects for Striga related parameters such as Striga shoot counts, Striga damage rating (SDR), plant height and grain yield and other agronomic traits. The result of combined ANOVA revealed that mean squares were highly significant for all traits except Striga damage rating (SDR1) at 8WAS and Striga emergence count (STECOI) at 8WAS. Mean squares for SCA were significantly low for all traits. TZSTR190 was the highest yielding parent, and TZSTR166xTZST190 was the highest yielding hybrid (cross). Parent TZSTR166, TZEI188, TZSTR190 and TZSTR193 shows significant (p < 0.05) positive GCA effects for grain yield while the rest had negative GCA effects for grain yield. Parent TZSTR166, TZEI188, TZSTR190, and TZSTR193 could be used for initiating hybrid development. Also, TZSTR166xTZSTR190 cross was the best specific combiner followed by TZEI188xTZSTR193, TZEI80xTZSTR193, and TZSTR190xTZSTR193. TZSTR166xTZSTR190 and TZSTR190xTZSTR193 had the highest SCA effects. However, TZEI80 and TZSTR190 manifested a high positive SCA effect with TZSTR166 indicating that these two inbreds combined better with TZSTR166.

Keywords: combining ability, Striga hermonthica, resistance, grain yield

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Authors: Patrick May Mukonki

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KOV pit (Kamoto Oliveira Virgule) is located 10 km from Kolwezi town, one of the mineral rich town in the Lualaba province of the Democratic Republic of Congo. The KOV pit is currently operating under the Katanga Mining Limited (KML), a Glencore-Gecamines (a State Owned Company) join venture. Recently, the mine optimization process provided a life of mine of approximately 10 years withnice pushbacks using the Datamine NPV Scheduler software. In previous KOV pit studies, we recently outlined the impact of the accuracy of the geological information on a long-term mine plan for a big copper mine such as KOV pit. The approach taken, discussed three main scenarios and outlined some weaknesses on the geological information side, and now, in this paper that we are going to develop here, we are going to highlight, as an overview, those weaknesses, strengths and opportunities, in a global SWOT analysis. The approach we are taking here is essentially descriptive in terms of steps taken to optimize KOV pit and, at every step, we categorized the challenges we faced to have a better tradeoff between what we called strengths and what we called weaknesses. The same logic is applied in terms of the opportunities and threats. The SWOT analysis conducted in this paper demonstrates that, despite a general poor ore body definition, and very rude ground water conditions, there is room for improvement for such high grade ore body.

Keywords: mine planning, mine optimization, mine scheduling, SWOT analysis

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Authors: Nguyen Quang Kim, Nguyen Thu Hien, Nguyen Thien Dung

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Flood disasters are increasing worldwide in both frequency and magnitude. Every year in Vietnam, flood causes great damage to people, property, and environmental degradation. The flood risk management policy in Vietnam is currently updated. The planning of flood mitigation strategies is reviewed to make a decision how to reach sustainable flood risk reduction. This paper discusses the basic approach where the measures of flood protection are chosen based on minimizing the present value of expected monetary expenses, total residual risk and costs of flood control measures. This approach will be proposed and demonstrated in a case study for flood risk management in Vinh Phuc province of Vietnam. Research also proposed the framework to find a solution of optimal protection level and optimal measures of the flood. It provides an explicit economic basis for flood risk management plans and interactive effects of options for flood damage reduction. The results of the case study are demonstrated and discussed which would provide the processing of actions helped decision makers to choose flood risk reduction investment options.

Keywords: drainage plan, flood planning, flood risk, residual risk, risk optimization

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Authors: Abbas Ahmadi, Bijan Raie, Mohammad Reza Neyshabouri, Mohammad Ali Ghorbani, Farrokh Asadzadeh

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In recent years, vast areas of Urmia Lake in Dasht-e-Tabriz has dried up leading to saline sediments exposure on the surface lake coastal areas being highly susceptible to wind erosion. This study was conducted to investigate wind erosion and its relevance to soil physicochemical properties and also modeling of wind erodibility (WE) using artificial intelligence techniques. For this purpose, 96 soil samples were collected from 0-5 cm depth in 414000 hectares using stratified random sampling method. To measure the WE, all samples (<8 mm) were exposed to 5 different wind velocities (9.5, 11, 12.5, 14.1 and 15 m s-1 at the height of 20 cm) in wind tunnel and its relationship with soil physicochemical properties was evaluated. According to the results, WE varied within the range of 76.69-9.98 (g m-2 min-1)/(m s-1) with a mean of 10.21 and coefficient of variation of 94.5% showing a relatively high variation in the studied area. WE was significantly (P<0.01) affected by soil physical properties, including mean weight diameter, erodible fraction (secondary particles smaller than 0.85 mm) and percentage of the secondary particle size classes 2-4.75, 1.7-2 and 0.1-0.25 mm. Results showed that the mean weight diameter, erodible fraction and percentage of size class 0.1-0.25 mm demonstrated stronger relationship with WE (coefficients of determination were 0.69, 0.67 and 0.68, respectively). This study also compared efficiency of multiple linear regression (MLR), gene expression programming (GEP), artificial neural network (MLP), artificial neural network based on genetic algorithm (MLP-GA) and artificial neural network based on whale optimization algorithm (MLP-WOA) in predicting of soil wind erodibility in Dasht-e-Tabriz. Among 32 measured soil variable, percentages of fine sand, size classes of 1.7-2.0 and 0.1-0.25 mm (secondary particles) and organic carbon were selected as the model inputs by step-wise regression. Findings showed MLP-WOA as the most powerful artificial intelligence techniques (R2=0.87, NSE=0.87, ME=0.11 and RMSE=2.9) to predict soil wind erodibility in the study area; followed by MLP-GA, MLP, GEP and MLR and the difference between these methods were significant according to the MGN test. Based on the above finding MLP-WOA may be used as a promising method to predict soil wind erodibility in the study area.

Keywords: wind erosion, erodible fraction, gene expression programming, artificial neural network

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Authors: Anjian Chen, Joseph C. Chen

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This paper studies a case where the targeted surface roughness of fused deposition modeling (FDM) additive manufacturing process is improved. The process is designing to reduce or eliminate the defects and improve the process capability index Cp and Cpk for an FDM additive manufacturing process. The baseline Cp is 0.274 and Cpk is 0.654. This research utilizes the Taguchi methodology, to eliminate defects and improve the process. The Taguchi method is used to optimize the additive manufacturing process and printing parameters that affect the targeted surface roughness of FDM additive manufacturing. The Taguchi L9 orthogonal array is used to organize the parameters' (four controllable parameters and one non-controllable parameter) effectiveness on the FDM additive manufacturing process. The four controllable parameters are nozzle temperature [°C], layer thickness [mm], nozzle speed [mm/s], and extruder speed [%]. The non-controllable parameter is the environmental temperature [°C]. After the optimization of the parameters, a confirmation print was printed to prove that the results can reduce the amount of defects and improve the process capability index Cp from 0.274 to 1.605 and the Cpk from 0.654 to 1.233 for the FDM additive manufacturing process. The final results confirmed that the Taguchi methodology is sufficient to improve the surface roughness of FDM additive manufacturing process.

Keywords: additive manufacturing, fused deposition modeling, surface roughness, six-sigma, Taguchi method, 3D printing

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Authors: Taiheng Zhang, Hongbin Zhao

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Compressed air energy storage become increasingly vital for solving intermittency problem of some renewable energies. In this study, a new hybrid system on a combination of compressed air energy storage (CAES), solid oxide fuel cell (SOFC), gas turbine (GT), and organic Rankine cycle (ORC) is proposed. In the new system, excess electricity during off-peak time is utilized to compress air. Then, the compressed air is stored in compressed air storage tank. During peak time, the compressed air enters the cathode of SOFC directly instead of combustion chamber of traditional CAES. There is no air compressor consumption of SOFC-GT in peak demand, so SOFC- GT can generate power with high-efficiency. In addition, the waste heat of exhaust from GT is recovered by applying an ORC. Three different organic working fluid (R123, R601, R601a) of ORC are chosen to evaluate system performance. Based on Aspen plus and Engineering Equation Solver (EES) software, energy and exergoeconomic analysis are used to access the viability of the combined system. Besides, the effect of two parameters (fuel flow and ORC turbine inlet pressure) on energy efficiency is studied. The effect of low-price electricity at off-peak hours on thermodynamic criteria (total unit exergy cost of products and total cost rate) is also investigated. Furthermore, for three different organic working fluids, the results of round-trip efficiency, exergy efficiency, and exergoeconomic factors are calculated and compared. Based on thermodynamic performance and exergoeconomic performance of different organic working fluids, the best suitable working fluid will be chosen. In conclusion, this study can provide important guidance for system efficiency improvement and viability.

Keywords: CAES, SOFC, ORC, energy and exergoeconomic analysis, organic working fluids

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Authors: Ali Shareef, Aliha Shareef, Yifeng Zhu

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Energy minimization is of great importance in wireless sensor networks in extending the battery lifetime. One of the key activities of nodes in a WSN is communication and the routing of their data to a centralized base-station or sink. Routing using the shortest path to the sink is not the best solution since it will cause nodes along this path to fail prematurely. We propose a cross-layer energy efficient routing protocol Optrix that utilizes a convex formulation to maximize the lifetime of the network as a whole. We further propose, Optrix-BW, a novel convex formulation with bandwidth constraint that allows the channel conditions to be accounted for in routing. By considering this key channel parameter we demonstrate that Optrix-BW is capable of congestion control. Optrix is implemented in TinyOS, and we demonstrate that a relatively large topology of 40 nodes can converge to within 91% of the optimal routing solution. We describe the pitfalls and issues related with utilizing a continuous form technique such as convex optimization with discrete packet based communication systems as found in WSNs. We propose a routing controller mechanism that allows for this transformation. We compare Optrix against the Collection Tree Protocol (CTP) and we found that Optrix performs better in terms of convergence to an optimal routing solution, for load balancing and network lifetime maximization than CTP.

Keywords: wireless sensor network, Energy Efficient Routing

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Authors: Barbara Urasinska-Wojcik, Neil Chilton, Peter Todd, Christopher Elsworthy, Gregory J. Gibbons

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The recent increase in the application of Additive Manufacturing (AM) of products has resulted in new demands on capability. The ability to integrate both form and function within printed objects is the next frontier in the 3D printing area. To move beyond prototyping into low volume production, we demonstrate a UK-designed and built AM hybrid system that combines polymer based structural deposition with digital deposition of electrically conductive elements. This hybrid manufacturing system is based on a multi-planar build approach to improve on many of the limitations associated with AM, such as poor surface finish, low geometric tolerance, and poor robustness. Specifically, the approach involves a multi-planar Material Extrusion (ME) process in which separated build stations with up to 5 axes of motion replace traditional horizontally-sliced layer modeling. The construction of multi-material architectures also involved using multiple print systems in order to combine both ME and digital deposition of conductive material. To demonstrate multi-material 3D printing, three thermoplastics, acrylonitrile butadiene styrene (ABS), polyamide 6,6/6 copolymers (CoPA) and polyamide 12 (PA) were used to print specimens, on top of which our high viscosity Ag-particulate ink was printed in a non-contact process, during which drop characteristics such as shape, velocity, and volume were assessed using a drop watching system. Spectroscopic analysis of these 3D printed materials in the IR region helped to determine the optimum in-situ curing system for implementation into the AM system to achieve improved adhesion and surface refinement. Thermal Analyses were performed to determine the printed materials glass transition temperature (Tg), stability and degradation behavior to find the optimum annealing conditions post printing. Electrical analysis of printed conductive tracks on polymer surfaces during mechanical testing (static tensile and 3-point bending and dynamic fatigue) was performed to assess the robustness of the electrical circuits. The tracks on CoPA, ABS, and PA exhibited low electrical resistance, and in case of PA resistance values of tracks remained unchanged across hundreds of repeated tensile cycles up to 0.5% strain amplitude. Our developed AM printer has the ability to fabricate fully functional objects in one build, including complex electronics. It enables product designers and manufacturers to produce functional saleable electronic products from a small format modular platform. It will make 3D printing better, faster and stronger.

Keywords: additive manufacturing, conductive tracks, hybrid 3D printer, integrated manufacture

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Authors: Martin Doran, Roy Sterritt, George Wilkie

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NASA, ESA, and NSSC space agencies have plans to put planetary rovers on Mars in 2020. For these future planetary rovers to succeed, they will heavily depend on sensors to detect obstacles. This will also become of vital importance in the future, if rovers become less dependent on commands received from earth-based control and more dependent on self-configuration and self-decision making. These planetary rovers will face harsh environments and the possibility of hardware failure is high, as seen in missions from the past. In this paper, we focus on using Autonomic principles where self-healing, self-optimization, and self-adaption are explored using the MAPE-K model and expanding this model to encapsulate the attributes such as Awareness, Analysis, and Adjustment (AAA-3). In the experimentation, a Pioneer P3-DX research robot is used to simulate a planetary rover. The sonar sensors on the P3-DX robot are used to simulate the sensors on a planetary rover (even though in reality, sonar sensors cannot operate in a vacuum). Experiments using the P3-DX robot focus on how our software system can be adapted with the loss of sonar sensor functionality. The autonomic manager system is responsible for the decision making on how to make use of remaining ‘enabled’ sonars sensors to compensate for those sonar sensors that are ‘disabled’. The key to this research is that the robot can still detect objects even with reduced sonar sensor capability.

Keywords: autonomic, self-adaption, self-healing, self-optimization

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Authors: S. M. Anisuzzaman, Sariah Abang, Awang Bono, D. Krishnaiah, N. M. Ismail, G. B. Sandrison

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Wax and asphaltene are high molecular weighted compounds that contribute to the stability of crude oil at a dispersed state. Transportation of crude oil along pipelines from the oil rig to the refineries causes fluctuation of temperature which will lead to the coagulation of wax and flocculation of asphaltenes. This paper focuses on the prevention of wax and asphaltene precipitate deposition on the inner surface of the pipelines by using a wax inhibitor and an asphaltene dispersant. The novelty of this prevention method is the combination of three substances; a wax inhibitor dissolved in a wax inhibitor solvent and an asphaltene solvent, namely, ethylene-vinyl acetate (EVA) copolymer dissolved in methylcyclohexane (MCH) and toluene (TOL) to inhibit the precipitation and deposition of wax and asphaltene. The objective of this paper was to optimize the percentage composition of each component in this inhibitor which can maximize the viscosity reduction of crude oil. The optimization was divided into two stages which are the laboratory experimental stage in which the viscosity of crude oil samples containing inhibitor of different component compositions is tested at decreasing temperatures and the data optimization stage using response surface methodology (RSM) to design an optimizing model. The results of experiment proved that the combination of 50% EVA + 25% MCH + 25% TOL gave a maximum viscosity reduction of 67% while the RSM model proved that the combination of 57% EVA + 20.5% MCH + 22.5% TOL gave a maximum viscosity reduction of up to 61%.

Keywords: asphaltene, ethylene-vinyl acetate, methylcyclohexane, toluene, wax

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Authors: Amir Hossein Hejazi, Nima Amjady

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In recent years, due to environmental issues traditional energy sources had been replaced by renewable ones. Wind energy as the fastest growing renewable energy shares a considerable percent of energy in power electricity markets. With this fast growth of wind energy worldwide, owners and operators of wind farms, transmission system operators, and energy traders need reliable and secure forecasts of wind energy production. In this paper, a new forecasting strategy is proposed for short-term wind power prediction based on Echo State Networks (ESN). The forecast engine utilizes state-of-the-art training process including dynamical reservoir with high capability to learn complex dynamics of wind power or wind vector signals. The study becomes more interesting by incorporating prediction of wind direction into forecast strategy. The Big Bang-Big Crunch (BB-BC) evolutionary optimization algorithm is adopted for adjusting free parameters of ESN-based forecaster. The proposed method is tested by real-world hourly data to show the efficiency of the forecasting engine for prediction of both wind vector and wind power output of aggregated wind power production.

Keywords: wind power forecasting, echo state network, big bang-big crunch, evolutionary optimization algorithm

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Authors: Sara Camino, Fernando Vega, Mercedes Cano, Benito Navarrete, José A. Camino

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Carbon capture and storage (CCS) technologies should play a relevant role towards low-carbon systems in the European Union by 2030. Partial oxy-combustion emerges as a promising CCS approach to mitigate anthropogenic CO₂ emissions. Its advantages respect to other CCS technologies rely on the production of a higher CO₂ concentrated flue gas than these provided by conventional air-firing processes. The presence of more CO₂ in the flue gas increases the driving force in the separation process and hence it might lead to further reductions of the energy requirements of the overall CO₂ capture process. A higher CO₂ concentrated flue gas should enhance the CO₂ capture by chemical absorption in solvent kinetic and CO₂ cyclic capacity. They have impact on the performance of the overall CO₂ absorption process by reducing the solvent flow-rate required for a specific CO₂ removal efficiency. Lower solvent flow-rates decreases the reboiler duty during the regeneration stage and also reduces the equipment size and pumping costs. Moreover, R&D activities in this field are focused on novel solvents and blends that provide lower CO₂ absorption enthalpies and therefore lower energy penalties associated to the solvent regeneration. In this respect, sterically hindered amines are considered potential solvents for CO₂ capture. They provide a low energy requirement during the regeneration process due to its molecular structure. However, its absorption kinetics are slow and they must be promoted by blending with faster solvents such as monoethanolamine (MEA) and piperazine (PZ). In this work, the kinetic behavior of two sterically hindered amines were studied under partial oxy-combustion conditions and compared with MEA. A lab-scale semi-batch reactor was used. The CO₂ composition of the synthetic flue gas varied from 15%v/v – conventional coal combustion – to 60%v/v – maximum CO₂ concentration allowable for an optimal partial oxy-combustion operation. Firstly, 2-amino-2-methyl-1-propanol (AMP) showed a hybrid behavior with fast kinetics and a low enthalpy of CO₂ absorption. The second solvent was Isophrondiamine (IF), which has a steric hindrance in one of the amino groups. Its free amino group increases its cyclic capacity. In general, the presence of higher CO₂ concentration in the flue gas accelerated the CO₂ absorption phenomena, producing higher CO₂ absorption rates. In addition, the evolution of the CO2 loading also exhibited higher values in the experiments using higher CO₂ concentrated flue gas. The steric hindrance causes a hybrid behavior in this solvent, between both fast and slow kinetic solvents. The kinetics rates observed in all the experiments carried out using AMP were higher than MEA, but lower than the IF. The kinetic enhancement experienced by AMP at a high CO2 concentration is slightly over 60%, instead of 70% – 80% for IF. AMP also improved its CO₂ absorption capacity by 24.7%, from 15%v/v to 60%v/v, almost double the improvements achieved by MEA. In IF experiments, the CO₂ loading increased around 10% from 15%v/v to 60%v/v CO₂ and it changed from 1.10 to 1.34 mole CO₂ per mole solvent, more than 20% of increase. This hybrid kinetic behavior makes AMP and IF promising solvents for partial oxy–combustion applications.

Keywords: absorption, carbon capture, partial oxy-combustion, solvent

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Authors: Hamid Sharif, Nazish Saleem Abbas, Muhammad Haris Jamil

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An adaptive filter is a filter that self-adjusts its transfer function according to an optimization algorithm driven by an error signal. Because of the complexity of the optimization algorithms, most adaptive filters are digital filters. Adaptive filtering constitutes one of the core technologies in digital signal processing and finds numerous application areas in science as well as in industry. Adaptive filtering techniques are used in a wide range of applications, including adaptive noise cancellation and echo cancellation. Acoustic echo cancellation is a common occurrence in today’s telecommunication systems. The signal interference caused by acoustic echo is distracting to both users and causes a reduction in the quality of the communication. In this paper, we review different techniques of adaptive filtering to reduce this unwanted echo. In this paper, we see the behavior of techniques and algorithms of adaptive filtering like Least Mean Square (LMS), Normalized Least Mean Square (NLMS), Variable Step-Size Least Mean Square (VSLMS), Variable Step-Size Normalized Least Mean Square (VSNLMS), New Varying Step Size LMS Algorithm (NVSSLMS) and Recursive Least Square (RLS) algorithms to reduce this unwanted echo, to increase communication quality.

Keywords: adaptive acoustic, echo cancellation, LMS algorithm, adaptive filter, normalized least mean square (NLMS), variable step-size least mean square (VSLMS)

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Authors: Nixon Kuruvila, H. V. Ravindra

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Wire-cut Electro Discharge Machining (WEDM) is a special form of conventional EDM process in which electrode is a continuously moving conductive wire. The present study aims at determining parametric influence and optimum process parameters of Wire-EDM using Taguchi’s Technique and Genetic algorithm. The variation of the performance parameters with machining parameters was mathematically modeled by Regression analysis method. The objective functions are Dimensional Accuracy (DA) and Material Removal Rate (MRR). Experiments were designed as per Taguchi’s L16 Orthogonal Array (OA) where in Pulse-on duration, Pulse-off duration, Current, Bed-speed and Flushing rate have been considered as the important input parameters. The matrix experiments were conducted for the material Oil Hardened Non Shrinking Steel (OHNS) having the thickness of 40 mm. The results of the study reveals that among the machining parameters it is preferable to go in for lower pulse-off duration for achieving over all good performance. Regarding MRR, OHNS is to be eroded with medium pulse-off duration and higher flush rate. Finally, the validation exercise performed with the optimum levels of the process parameters. The results confirm the efficiency of the approach employed for optimization of process parameters in this study.

Keywords: dimensional accuracy (DA), regression analysis (RA), Taguchi method (TM), volumetric material removal rate (VMRR)

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Authors: Pia Kastl

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Potentials of blended learning are the flexibility of learning and the possibility to get in touch with lecturers and fellow students on site. By combining face-to-face sessions with digital self-learning units, the learning process can be optimized, and learning success increased. To examine wether blended learning outperforms online and face-to-face teaching, a theory-based questionnaire survey was conducted. The results show that the interaction and cooperation among students is poorly provided in blended learning, and face-to-face teaching performs better in this respect. The aim of this article is to identify concrete suggestions students have for improving cooperation and interaction in blended learning courses. For this purpose, interviews were conducted with students from various academic disciplines in face-to-face, online, or blended learning courses (N= 60). The questions referred to opinions and suggestions for improvement regarding the course design of the respective learning environment. The analysis was carried out by qualitative content analysis. The results show that students perceive the interaction as beneficial to their learning. They verbalize their knowledge and are exposed to different perspectives. In addition, emotional support is particularly important in exam phases. Interaction and cooperation were primarily enabled in the face-to-face component of the courses studied, while there was very limited contact with fellow students in the asynchronous component. Forums offered were hardly used or not used at all because the barrier to asking a question publicly is too high, and students prefer private channels for communication. This is accompanied by the disadvantage that the interaction occurs only among people who already know each other. Creating contacts is not fostered in the blended learning courses. Students consider optimization possibilities as a task of the lecturers in the face-to-face sessions: Here, interaction and cooperation should be encouraged through get-to-know-you rounds or group work. It is important here to group the participants randomly to establish contact with new people. In addition, sufficient time for interaction is desired in the lecture, e.g., in the context of discussions or partner work. In the digital component, students prefer synchronous exchange at a fixed time, for example, in breakout rooms or an MS Teams channel. The results provide an overview of how interaction and cooperation can be implemented in blended learning courses. Positive design possibilities are partly dependent on subject area and course. Future studies could tie in here with a course-specific analysis.

Keywords: blended learning, higher education, hybrid teaching, qualitative research, student learning

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Authors: Mohammad Hossein Mohammadi Sanjani, Ashknaz Oraee, Arian Amirnia, Atena Taheri, Mohammadreza Arabi, Mahmud Fotuhi-Firuzabad

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Ferroresonance effects cause overvoltage in medium voltage transformers and isolators used in electrical networks. Ferroresonance effects are nonlinear and occur between the network capacitor and the nonlinear inductance of the voltage transformer during saturation. This phenomenon is unwanted for transformers since it causes overheating, introduction of high dynamic forces in primary coils, and rise of voltage in primary coils for the voltage transformer. Furthermore, it results in electrical and thermal failure of the transformer. Expansion of distribution lines, design of the transformer in smaller sizes, and the increase of harmonics in distribution networks result in an increase of ferroresonance. There is limited literature available to improve the effects of ferroresonance; therefore, optimizing its effects for voltage transformers is of great importance. In this study, comprehensive modeling of a medium voltage block-type voltage transformer is performed. In addition, a recent model is proposed to improve the performance of voltage transformers during the occurrence of ferroresonance using damping oscillations. Also, transformer design optimization is presented in this study to show further improvements in the performance of the voltage transformer. The recently proposed model is experimentally tested and verified on a medium voltage transformer in the laboratory, and simulation results show a large reduction of the effects of ferroresonance.

Keywords: optimization, voltage transformer, ferroresonance, modeling, damper

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Authors: Pierre-Marie Basset, Gabriel Reboul, Binh DangVu, Sébastien Mercier

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Rotorcraft provides invaluable services thanks to their Vertical Take-Off and Landing (VTOL), hover and low speed capabilities. Yet their use is still often limited by their cost and environmental impact, especially noise and energy consumption. One of the main brakes to the expansion of the use of rotorcraft for urban missions is the environmental impact. The first main concern for the population is the noise. In order to develop the transversal competency to assess the rotorcraft environmental footprint, a collaboration has been launched between six research departments within ONERA. The progress in terms of models and methods are capitalized into the numerical workshop C.R.E.A.T.I.O.N. “Concepts of Rotorcraft Enhanced Assessment Through Integrated Optimization Network”. A typical mission for which the environmental impact issue is of great relevance has been defined. The first milestone is to perform the pre-sizing of a reference helicopter for this mission. In a second milestone, an alternate rotorcraft concept has been defined: a tandem rotorcraft with optional propulsion. The key design trends are given for the pre-sizing of this rotorcraft aiming at a significant reduction of the global environmental impact while still giving equivalent flight performance and safety with respect to the reference helicopter. The models and methods have been improved for catching sooner and more globally, the relative variations on the environmental impact when changing the rotorcraft architecture, the pre-design variables and the operation parameters.

Keywords: environmental impact, flight performance, helicopter, multi objectives multidisciplinary optimization, rotorcraft

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Authors: Sokkhey Phauk, Takeo Okazaki

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The challenging task in educational institutions is to maximize the high performance of students and minimize the failure rate of poor-performing students. An effective method to leverage this task is to know student learning patterns with highly influencing factors and get an early prediction of student learning outcomes at the timely stage for setting up policies for improvement. Educational data mining (EDM) is an emerging disciplinary field of data mining, statistics, and machine learning concerned with extracting useful knowledge and information for the sake of improvement and development in the education environment. The study is of this work is to propose techniques in EDM and integrate it into a web-based system for predicting poor-performing students. A comparative study of prediction models is conducted. Subsequently, high performing models are developed to get higher performance. The hybrid random forest (Hybrid RF) produces the most successful classification. For the context of intervention and improving the learning outcomes, a feature selection method MICHI, which is the combination of mutual information (MI) and chi-square (CHI) algorithms based on the ranked feature scores, is introduced to select a dominant feature set that improves the performance of prediction and uses the obtained dominant set as information for intervention. By using the proposed techniques of EDM, an academic performance prediction system (APPS) is subsequently developed for educational stockholders to get an early prediction of student learning outcomes for timely intervention. Experimental outcomes and evaluation surveys report the effectiveness and usefulness of the developed system. The system is used to help educational stakeholders and related individuals for intervening and improving student performance.

Keywords: academic performance prediction system, educational data mining, dominant factors, feature selection method, prediction model, student performance

Procedia PDF Downloads 94