Search results for: optimization analysis

Authors: Xie Kefeng, Zhang He

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For the stability and control demand of offshore small floating platform, a 2-HUS/U parallel mechanism was presented as offshore platform. Inverse kinematics was obtained by institutional constraint equation, and the dynamic model of offshore 2-HUS/U parallel platform was derived based on rigid body’s Lagrangian method. The equivalent moment of inertia, damping and driving force/torque variation of offshore 2-HUS/U parallel platform were analyzed. A numerical example shows that, for parallel platform of given motion, system’s equivalent inertia changes 1.25 times maximally. During the movement of platform, they change dramatically with the system configuration and have coupling characteristics. The maximum equivalent drive torque is 800 N. At the same time, the curve of platform’s driving force/torque is smooth and has good sine features. The control system needs to be adjusted according to kinetic equation during stability and control and it provides a basis for the optimization of control system.

Keywords: 2-HUS/U platform, dynamics, Lagrange, parallel platform

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Authors: Dalicia Bouallouche, Jean-Baptiste Vioix, Stéphane Millot, Eric Busvelle

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When researchers or logistics software developers deal with vehicle routing optimization, they mainly focus on minimizing the total travelled distance or the total time spent in the tours by the trucks, and maximizing the number of visited customers. They assume that the upstream real data given to carry the optimization of a transporter tours is free from errors, like customers’ real constraints, customers’ addresses and their GPS-coordinates. However, in real transporter situations, upstream data is often of bad quality because of address geocoding errors and the irrelevance of received addresses from the EDI (Electronic Data Interchange). In fact, geocoders are not exempt from errors and could give impertinent GPS-coordinates. Also, even with a good geocoding, an inaccurate address can lead to a bad geocoding. For instance, when the geocoder has trouble with geocoding an address, it returns those of the center of the city. As well, an obvious geocoding issue is that the mappings used by the geocoders are not regularly updated. Thus, new buildings could not exist on maps until the next update. Even so, trying to optimize tours with impertinent customers GPS-coordinates, which are the most important and basic input data to take into account for solving a vehicle routing problem, is not really useful and will lead to a bad and incoherent solution tours because the locations of the customers used for the optimization are very different from their real positions. Our work is supported by a logistics software editor Tedies and a transport company Upsilon. We work with Upsilon's truck routes data to carry our experiments. In fact, these trucks are equipped with TOMTOM GPSs that continuously save their tours data (positions, speeds, tachograph-information, etc.). We, then, retrieve these data to extract the real truck routes to work with. The aim of this work is to use the experience of the driver and the feedback of the real truck tours to validate GPS-coordinates of well geocoded addresses, and bring a correction to the badly geocoded addresses. Thereby, when a vehicle makes its tour, for each visited customer, the vehicle might have trouble with finding this customer’s address at most once. In other words, the vehicle would be wrong at most once for each customer’s address. Our method significantly improves the quality of the geocoding. Hence, we achieve to automatically correct an average of 70% of GPS-coordinates of a tour addresses. The rest of the GPS-coordinates are corrected in a manual way by giving the user indications to help him to correct them. This study shows the importance of taking into account the feedback of the trucks to gradually correct address geocoding errors. Indeed, the accuracy of customer’s address and its GPS-coordinates play a major role in tours optimization. Unfortunately, address writing errors are very frequent. This feedback is naturally and usually taken into account by transporters (by asking drivers, calling customers…), to learn about their tours and bring corrections to the upcoming tours. Hence, we develop a method to do a big part of that automatically.

Keywords: driver experience feedback, geocoding correction, real truck tours

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Authors: Akash Verma, Sujit Kumar Samanta

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This study examines a discrete-time Geo/G/1 queueing-inventory system attached with (s, Q) inventory policy. Assume that the customers follow the Bernoulli process on arrival. Each customer demands a single item with arbitrarily distributed service time. The inventory is replenished by an outside supplier, and the lead time for the replenishment is determined by a geometric distribution. There is a single server and infinite waiting space in this facility. Demands must wait in the specified waiting area during a stock-out period. The customers are served on a first-come-first-served basis. With the help of the embedded Markov chain technique, we determine the joint probability distributions of the number of customers in the system and the number of items in stock at the post-departure epoch using the Matrix Analytic approach. We relate the system length distribution at post-departure and outside observer's epochs to determine the joint probability distribution at the outside observer's epoch. We use probability distributions at random epochs to determine the waiting time distribution. We obtain the performance measures to construct the cost function. The optimum values of the order quantity and reordering point are found numerically for the variety of model parameters.

Keywords: discrete-time queueing inventory model, matrix analytic method, waiting-time analysis, cost optimization

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Authors: Demisash, Abate Getnet, Gudisa, Ababo Geleta, Daba, Berhane Olani

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As consumption trends of fish are rising in Ethiopia, assessment of the environmental performance of Fisheries becomes vital. Hence, Cleaner Production Assessment was conducted on Lake Tana No.1 Fish Supply Association. This paper focuses on determining the characteristics, quantity, and setting up cleaner production options for the site with the experimental investigation. The survey analysis showed that illegal waste dumping in Lake Tana is common practice in the area, and some of the main reasons raised were they have no option than doing this for dis-charging fish wastes. Quantifying a fish waste by examination of records at the point of generation resulted in a generation rate of 72,822.61 kg per year, which is a significant amount of waste and needs management system. The result of the proximate analysis showed high free fat content of about 12.33%, and this was a good candidate for the production of biodiesel that has been set as an option for fish waste utilization. Among the different waste management options, waste reduction by product optimization, which involves biodiesel production, was chosen as a potential method. Laboratory scale experiments were performed to produce a renewable energy source from the wastes. The resulting biodiesel was characterized and found to have a density of 0.756kg/L, viscosity 0.24p, and 153°C flashpoints, which shows the product has values in compliance with the American Society for Testing and Materials (ASTM) standards.

Keywords: biodiesel, cleaner production, renewable energy, waste management

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Authors: Hamzeh Soltanali, Abbas Rohani, A. H. S. Garmabaki, Mohammad Hossein Abbaspour-Fard, Adithya Thaduri

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Toward continuous innovation and high complexity of technological systems, the automotive manufacturing industry is also under pressure to implement adequate management strategies regarding availability and productivity. In this context, evaluation of system’s performance by considering reliability, availability and maintainability (RAM) methodologies can constitute for resilient operation, identifying the bottlenecks of manufacturing process and optimization of maintenance actions. In this paper, RAM parameters are evaluated for improving the operational performance of the fluid filling process. To evaluate the RAM factors through the behavior of states defined for such process, a systematic decision framework was developed. The results of RAM analysis revealed that that the improving reliability and maintainability of main bottlenecks for each filling workstation need to be considered as a priority. The results could be useful to improve operational performance and sustainability of production process.

Keywords: automotive, performance, reliability, RAM, fluid filling process

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Authors: Sunghee Nam

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Entomopathogenic fungi is a Cordyceps species that is isolated from dead silkworm and cicada. Fungi on cicadas were described in old Chinese medicinal books and from ancient times, vegetable wasps and plant worms were widely known to have active substance and have been studied for pharmacological use. Among many fungi belonging to the genus Cordyceps, Cordyceps sinensis have been demonstrated to yield natural products possessing various biological activities and many bioactive components. Generally, It is commonly used to replenish the kidney and soothe the lung, and for the treatment of fatigue. Due to their commercial and economic importance, the demand for Cordyceps has been rapidly increased. However, a supply of Cordyceps specimen could not meet the increasing demand because of their sole dependence on field collection and habitat destruction. Because it is difficult to obtain many insect hosts in nature and the edibility of host insect needs to be verified in a pharmacological aspect. Recently, this setback was overcome that P. tenuipes was able to be cultivated in a large scale using silkworm as host. Pharmacological effects of P. tenuipes cultured on silkworm such as strengthening immune function, anti-fatigue, anti-tumor activity and controlling liver etc. have been proved. They are widely commercialized. In this study, we attempted to establish a method for stable growth inhibition of P. tenuipes on silkworm hosts and an optimal condition for synnemata formation. To determine optimum culturing conditions, temperature and light conditions were varied. The length and number of synnemata was highest at 25℃ temperature and 100~300 lux illumination. On an average, the synnemata of wild P. tenuipes measures 70 ㎜ in length and 20 in number; those of the cultured strain were relatively shorter and more in number. The number of synnemata may have increased as a result of inoculating the host with highly concentrated conidia, while the length may have decreased due to limited nutrition per individual. It is not able that changes in light illumination cause morphological variations in the synnemata. However, regulation of only light and temperature could not produce stromata like perithecia, asci, and ascospores.

Keywords: optimization of culture conditions of paecilomyces tenuipes, entomopathogenic fungi optimization of culture conditions of paecilomyces tenuipes, entomopathogenic fungi silkworm larva, bombyx mori

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Authors: Madhushi Bandara, Dharshana Kasthurirathna, Danaja Maldeniya, Mahendra Piraveenan

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Community detection is an extremely useful technique in understanding the structure and function of a social network. Louvain algorithm, which is based on Newman-Girman modularity optimization technique, is extensively used as a computationally efficient method extract the communities in social networks. It has been suggested that the nodes that are in close geographical proximity have a higher tendency of forming communities. Variants of the Newman-Girman modularity measure such as dist-modularity try to normalize the effect of geographical proximity to extract geographically dispersed communities, at the expense of losing the information about the geographically proximate communities. In this work, we propose a method to extract geographically dispersed communities while preserving the information about the geographically proximate communities, by analyzing the ‘community network’, where the centroids of communities would be considered as network nodes. We suggest that the inter-community link strengths, which are normalized over the community sizes, may be used to identify and extract the ‘overlay communities’. The overlay communities would have relatively higher link strengths, despite being relatively apart in their spatial distribution. We apply this method to the Gowalla online social network, which contains the geographical signatures of its users, and identify the overlay communities within it.

Keywords: social networks, community detection, modularity optimization, geographically dispersed communities

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Authors: Güneş Aydın, Razi Kalantari Osgouei, Murat Emre Öztürk, Ahmad Partovi Meran, Ekrem Tüfekçi

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Advanced lightweight laminated composite shells are increasingly being used in all types of modern structures, for enhancing their structural efficiency and performance. Such thin-walled structures are susceptible to buckling when subjected to various loading. This paper focuses on the buckling of cylindrical shells under axial compression and torsional loads. Effects of fiber orientation on the maximum buckling load of carbon fiber reinforced polymer (CFRP) shells are optimized. Optimum fiber angles have been calculated analytically by using MATLAB program. Numerical models have been carried out by using Finite Element Method program ABAQUS. Results from analytical and numerical analyses are also compared.

Keywords: buckling, composite, cylindrical shell, finite element, compression, torsion, MATLAB, optimization

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Authors: Joseph C. Chen, Venkata Karthik Jakka

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The main objective of this research is to optimize tensile strength and dimensional accuracy in injection molding processes using Taguchi Parameter Design. An L16 orthogonal array (OA) is used in Taguchi experimental design with five control factors at four levels each and with non-controllable factor vibration. A total of 32 experiments were designed to obtain the optimal parameter setting for the process. The optimal parameters identified for the shrinkage are shot volume, 1.7 cubic inch (A4); mold term temperature, 130 ºF (B1); hold pressure, 3200 Psi (C4); injection speed, 0.61 inch3/sec (D2); and hold time of 14 seconds (E2). The optimal parameters identified for the tensile strength are shot volume, 1.7 cubic inch (A4); mold temperature, 160 ºF (B4); hold pressure, 3100 Psi (C3); injection speed, 0.69 inch3/sec (D4); and hold time of 14 seconds (E2). The Taguchi-based optimization framework was systematically and successfully implemented to obtain an adjusted optimal setting in this research. The mean shrinkage of the confirmation runs is 0.0031%, and the tensile strength value was found to be 3148.1 psi. Both outcomes are far better results from the baseline, and defects have been further reduced in injection molding processes.

Keywords: injection molding processes, taguchi parameter design, tensile strength, high-density polyethylene(HDPE)

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Authors: Rikson Gultom

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Hate Speech and Abusive language on social media is difficult to detect, usually, it is detected after it becomes viral in cyberspace, of course, it is too late for prevention. An early detection system that has a fairly good accuracy is needed so that it can reduce conflicts that occur in society caused by postings on social media that attack individuals, groups, and governments in Indonesia. The purpose of this study is to find an early detection model on Twitter social media using machine learning that has high accuracy from several machine learning methods studied. In this study, the support vector machine (SVM), Naïve Bayes (NB), and Random Forest Decision Tree (RFDT) methods were compared with the Support Vector machine with genetic algorithm (SVM-GA), Nave Bayes with genetic algorithm (NB-GA), and Random Forest Decision Tree with Genetic Algorithm (RFDT-GA). The study produced a comparison table for the accuracy of the hate speech and abusive language detection model, and presented it in the form of a graph of the accuracy of the six algorithms developed based on the Indonesian-language Twitter dataset, and concluded the best model with the highest accuracy.

Keywords: abusive language, hate speech, machine learning, optimization, social media

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Authors: Hadis Pouyafar, D. Matin Alaghmandan

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Gallium arsenide (GaAs) solar cells are widely used in applications like spacecraft and satellites because they have a high absorption coefficient and efficiency and can withstand high-energy particles such as electrons and protons. With the energy crisis, there's a growing need for efficiency and cost-effective solar cells. GaAs cells, with their 46% efficiency compared to silicon cells 23% can be utilized in buildings to achieve nearly zero emissions. This way, we can use irradiation and convert more solar energy into electricity. III V semiconductors used in these cells offer performance compared to other technologies available. However, despite these advantages, Si cells dominate the market due to their prices. In our study, we took an approach by using software from the start to gather all information. By doing so, we aimed to design the optimal building that harnesses the full potential of solar energy. Our modeling results reveal a future; for GaAs cells, we utilized the Grasshopper plugin for modeling and optimization purposes. To assess radiation, weather data, solar energy levels and other factors, we relied on the Ladybug and Honeybee plugins. We have shown that silicon solar cells may not always be the choice for meeting electricity demands, particularly when higher power output is required. Therefore, when it comes to power consumption and the available surface area for photovoltaic (PV) installation, it may be necessary to consider efficient solar cell options, like GaAs solar cells. By considering the building requirements and utilizing GaAs technology, we were able to optimize the PV surface area.

Keywords: gallium arsenide (GaAs), optimization, sustainable building, GaAs solar cells

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Authors: Ujjal Chattaraj, Pbharat Saikumar, Thinley Dorji

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Earth is going to be a planet where no further life can sustain if people continue to pollute the environment. We need energy and fuels everyday for heating and lighting purposes in our life. It’s high time we know this problem and take measures at-least to reduce pollution and take alternative measures for everyday livelihood. Biogas is one of them. It is very essential to define and control the parameters for optimization of biogas production. Biogas plants can be made of different size, but it is very vital to make a biogas which will be cost effective, with greater efficiency (more production) and biogas plants that will sustain for a longer period of time for usage. In this research, experiments were carried out only on cow dung and Chicken manure depending on the substrates people out there (Bhutan) used. The experiment was done within 25 days and was tested for different temperatures and found out which produce more amount. Moreover, it was also statistically tested for their dependency and non-dependency which gave clear idea more on their production.

Keywords: digester, mesophilic temperature, organic manure, statistical analysis, thermophilic temperature, t-test

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Authors: Amal E. Ahmed, Levent Trabzon

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Due to the low concentration of the analytes in biological samples, the use of Biological Microelectromechanical System (Bio-MEMS) biosensors for biomolecules detection results in a minuscule output signal that is not good enough for practical applications. In response to this, a need has arisen for an optimized biosensor capable of giving high output signal in response the detection of few analytes in the sample; the ultimate goal is being able to convert the attachment of a single biomolecule into a measurable quantity. For this purpose, MEMS microcantilevers based biosensors emerged as a promising sensing solution because it is simple, cheap, very sensitive and more importantly does not need analytes optical labeling (Label-free). Among the different microcantilever transducing techniques, piezoresistive based microcantilever biosensors became more prominent because it works well in liquid environments and has an integrated readout system. However, the design of piezoresistive microcantilevers is not a straightforward problem due to coupling between the design parameters, constraints, process conditions, and performance. It was found that the parameters that can be optimized to enhance the sensitivity of Piezoresistive microcantilever-based sensors are: cantilever dimensions, cantilever material, cantilever shape, piezoresistor material, piezoresistor doping level, piezoresistor dimensions, piezoresistor position, Stress Concentration Region's (SCR) shape and position. After a systematic analyzation of the effect of each design and process parameters on the sensitivity, a step-wise optimization approach was developed in which almost all these parameters were variated one at each step while fixing the others to get the maximum possible sensitivity at the end. At each step, the goal was to optimize the parameter in a way that it maximizes and concentrates the stress in the piezoresistor region for the same applied force thus get the higher sensitivity. Using this approach, an optimized sensor that has 73.5x times higher electrical sensitivity (ΔR⁄R) than the starting sensor was obtained. In addition to that, this piezoresistive microcantilever biosensor it is more sensitive than the other similar sensors previously reported in the open literature. The mechanical sensitivity of the final senior is -1.5×10-8 Ω/Ω ⁄pN; which means that for each 1pN (10-10 g) biomolecules attach to this biosensor; the piezoresistor resistivity will decrease by 1.5×10-8 Ω. Throughout this work COMSOL Multiphysics 5.0, a commercial Finite Element Analysis (FEA) tool, has been used to simulate the sensor performance.

Keywords: biosensor, microcantilever, piezoresistive, stress concentration region (SCR)

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Authors: Magdalena Igras, Joanna Grzybowska, Mariusz Ziółko

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The study of emotions expressed in emergency phone call is presented, covering both statistical analysis of emotions configurations and an attempt to automatically classify emotions. An emergency call is a situation usually accompanied by intense, authentic emotions. They influence (and may inhibit) the communication between caller and responder. In order to support responders in their responsible and psychically exhaustive work, we studied when and in which combinations emotions appeared in calls. A corpus of 45 hours of conversations (about 3300 calls) from emergency call center was collected. Each recording was manually tagged with labels of emotions valence (positive, negative or neutral), type (sadness, tiredness, anxiety, surprise, stress, anger, fury, calm, relief, compassion, satisfaction, amusement, joy) and arousal (weak, typical, varying, high) on the basis of perceptual judgment of two annotators. As we concluded, basic emotions tend to appear in specific configurations depending on the overall situational context and attitude of speaker. After performing statistical analysis we distinguished four main types of emotional behavior of callers: worry/helplessness (sadness, tiredness, compassion), alarm (anxiety, intense stress), mistake or neutral request for information (calm, surprise, sometimes with amusement) and pretension/insisting (anger, fury). The frequency of profiles was respectively: 51%, 21%, 18% and 8% of recordings. A model of presenting the complex emotional profiles on the two-dimensional (tension-insecurity) plane was introduced. In the stage of acoustic analysis, a set of prosodic parameters, as well as Mel-Frequency Cepstral Coefficients (MFCC) were used. Using these parameters, complex emotional states were modeled with machine learning techniques including Gaussian mixture models, decision trees and discriminant analysis. Results of classification with several methods will be presented and compared with the state of the art results obtained for classification of basic emotions. Future work will include optimization of the algorithm to perform in real time in order to track changes of emotions during a conversation.

Keywords: acoustic analysis, complex emotions, emotion recognition, machine learning

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Authors: Federico Pittino, Thomas Arnold

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The shredding of waste materials is a key step in the recycling process towards the circular economy. Industrial shredders for waste processing operate in very harsh operating conditions, leading to the need for frequent maintenance of critical components. Maintenance optimization is particularly important also to increase the machine’s efficiency, thereby reducing the operational costs. In this work, a monitoring system has been developed and deployed on an industrial shredder located at a waste recycling plant in Austria. The machine has been monitored for one year, and methods for predictive maintenance have been developed for two key components: the cutting knives and the drive belt. The large amount of collected data is leveraged by statistical machine learning techniques, thereby not requiring very detailed knowledge of the machine or its live operating conditions. The results show that, despite the wide range of operating conditions, a reliable estimate of the optimal time for maintenance can be derived. Moreover, the trade-off between the cost of maintenance and the increase in power consumption due to the wear state of the monitored components of the machine is investigated. This work proves the benefits of real-time monitoring system for the efficient operation of industrial shredders.

Keywords: predictive maintenance, circular economy, industrial shredder, cost optimization, statistical machine learning

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Authors: Assad Maalouf, Lunjin Lu, James Lynott

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We present a taint analysis that can automatically detect when string operations result in a string that is free of taints, where all the tainted patterns have been removed. This is an improvement on the conservative behavior of previous taint analyzers, where a string operation on a tainted string always leads to a tainted string unless the operation is manually marked as a sanitizer. The taint analysis is built on top of a string analysis that uses finite state automata to approximate the sets of values that string variables can take during the execution of a program. The proposed approach has been implemented as an extension of FlowDroid and experimental results show that the resulting taint analyzer is much more precise than the original FlowDroid.

Keywords: android, static analysis, string analysis, taint analysis

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Authors: R. M. Shabbir Ahmed, Mohamed Haneef, A. R. Anwar Khan

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Analysis of stresses plays important role in the optimization of structures. Prior stress estimation helps in better design of the products. Composites find wide usage in the industrial and home applications due to its strength to weight ratio. Especially in the air craft industry, the usage of composites is more due to its advantages over the conventional materials. Composites are mainly made of orthotropic materials having unequal strength in the different directions. Composite materials have the drawback of delamination and debonding due to the weaker bond materials compared to the parent materials. So proper analysis should be done to the composite joints before using it in the practical conditions. In the present work, a composite plate with elastic pin is considered for analysis using finite element software Ansys. Basically the geometry is built using Ansys software using top down approach with different Boolean operations. The modelled object is meshed with three dimensional layered element solid46 for composite plate and solid element (Solid45) for pin material. Various combinations are considered to find the strength of the composite joint under uniaxial loading conditions. Due to symmetry of the problem, only quarter geometry is built and results are presented for full model using Ansys expansion options. The results show effect of pin diameter on the joint strength. Here the deflection and load sharing of the pin are increasing and other parameters like overall stress, pin stress and contact pressure are reducing due to lesser load on the plate material. Further material effect shows, higher young modulus material has little deflection, but other parameters are increasing. Interference analysis shows increasing of overall stress, pin stress, contact stress along with pin bearing load. This increase should be understood properly for increasing the load carrying capacity of the joint. Generally every structure is preloaded to increase the compressive stress in the joint to increase the load carrying capacity. But the stress increase should be properly analysed for composite due to its delamination and debonding effects due to failure of the bond materials. When results for an isotropic combination is compared with composite joint, isotropic joint shows uniformity of the results with lesser values for all parameters. This is mainly due to applied layer angle combinations. All the results are represented with necessasary pictorial plots.

Keywords: bearing force, frictional force, finite element analysis, ANSYS

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Authors: Ankit Shah, R. K. Shrivastava

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Flood and drought are two main features of hydrology which affect the human life. Floods are natural disasters which cause millions of rupees’ worth of damage each year in India and the whole world. Flood causes destruction in form of life and property. An accurate estimate of the flood damage potential is a key element to an effective, nationwide flood damage abatement program. Also, the increase in demand of water due to increase in population, industrial and agricultural growth, has let us know that though being a renewable resource it cannot be taken for granted. We have to optimize the use of water according to circumstances and conditions and need to harness it which can be done by construction of hydraulic structures. For their safe and proper functioning of hydraulic structures, we need to predict the flood magnitude and its impact. Hydraulic structures play a key role in harnessing and optimization of flood water which in turn results in safe and maximum use of water available. Mainly hydraulic structures are constructed on ungauged sites. There are two methods by which we can estimate flood viz. generation of Unit Hydrographs and Flood Frequency Analysis. In this study, Regional Flood Frequency Analysis has been employed. There are many methods for estimating the ‘Regional Flood Frequency Analysis’ viz. Index Flood Method. National Environmental and Research Council (NERC Methods), Multiple Regression Method, etc. However, none of the methods can be considered universal for every situation and location. The Narmada basin is located in Central India. It is drained by most of the tributaries, most of which are ungauged. Therefore it is very difficult to estimate flood on these tributaries and in the main river. As mentioned above Artificial Neural Network (ANN)s and Multiple Regression Method is used for determination of Regional flood Frequency. The annual peak flood data of 20 sites gauging sites of Narmada Basin is used in the present study to determine the Regional Flood relationships. Homogeneity of the considered sites is determined by using the Index Flood Method. Flood relationships obtained by both the methods are compared with each other, and it is found that ANN is more reliable than Multiple Regression Method for the present study area.

Keywords: artificial neural network, index flood method, multi layer perceptrons, multiple regression, Narmada basin, regional flood frequency

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Authors: F. Hamidi, N. Amiri, H. Mishmast Nehi

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The Bilevel Programming (BP) model has been presented for a decision making process that consists of two decision makers in a hierarchical structure. In fact, BP is a model for a static two person game (the leader player in the upper level and the follower player in the lower level) wherein each player tries to optimize his/her personal objective function under dependent constraints; this game is sequential and non-cooperative. The decision making variables are divided between the two players and one’s choice affects the other’s benefit and choices. In other words, BP consists of two nested optimization problems with two objective functions (upper and lower) where the constraint region of the upper level problem is implicitly determined by the lower level problem. In real cases, the coefficients of an optimization problem may not be precise, i.e. they may be interval. In this paper we develop an algorithm for solving interval bilevel linear fractional programming problems. That is to say, bilevel problems in which both objective functions are linear fractional, the coefficients are interval and the common constraint region is a polyhedron. From the original problem, the best and the worst bilevel linear fractional problems have been derived and then, using the extended Charnes and Cooper transformation, each fractional problem can be reduced to a linear problem. Then we can find the best and the worst optimal values of the leader objective function by two algorithms.

Keywords: best and worst optimal solutions, bilevel programming, fractional, interval coefficients

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Authors: Shoumodip Roy, Ankit Singhania, M. K. G. Choudhury, Santanu Mallick, M. K. Agarwal, R. V. Ramna, Uttam Singh

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One of the key corporate goals of Tata Steel company is to demonstrate Environment Leadership. Decreasing specific CO₂ emission is one of the key steps to achieve the stated corporate goal. At any Blast Furnace, specific CO₂ emission is directly proportional to fuel intake. To reduce the fuel intake at G Blast Furnace, an initial benchmarking exercise was carried out with international and domestic Blast Furnaces to determine the potential for improvement. The gap identified during the exercise revealed that the benchmark Blast Furnaces operated with superior raw material quality than that in G Blast Furnace. However, since the raw materials to G Blast Furnace are sourced from the captive mines, improvement in the raw material quality was out of scope. Therefore, trials were taken with different operating regimes, to identify the key process parameters, which on optimization could significantly reduce the fuel intake in G Blast Furnace. The key process parameters identified from the trial were the Stoichiometric Oxygen Ratio, Melting Capacity ratio and the burden distribution inside the furnace. These identified process parameters were optimized to bridge the gap in fuel intake at G Blast Furnace, thereby reducing specific CO₂ emission to benchmark levels. This paradigm shift enabled to lower the fuel intake by 70kg per ton of liquid iron produced, thereby reducing the specific CO₂ emission by 15 percent.

Keywords: benchmark, blast furnace, CO₂ emission, fuel rate

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Authors: Nour Wehbe

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The Lebanese electricity sector is at the heart of a deep crisis. Electricity in Lebanon is supplied by Électricité du Liban (EdL) which has to suffer from technical and financial deficiencies for decades and proved to be insufficient and deficient as the demand still exceeds the supply. As a result, backup generation is widespread throughout Lebanon. The sector costs massive government resources and, on top of it, consumers pay massive additional amounts for satisfying their electrical needs. While the developed countries have been investing in renewable energy for the past two decades, the Lebanese government realizes the importance of adopting such energy sourcing strategies for the upgrade of the electricity sector in the country. The diversification of the national electricity generation mix has increased considerably in Lebanon's energy planning agenda, especially that a detailed review of the energy potential in Lebanon has revealed a great potential of solar and wind energy resources, a considerable potential of biomass resource, and an important hydraulic potential in Lebanon. This paper presents a review of the energy status of Lebanon, and illustrates a detailed review of the EDL structure with the existing problems and recommended solutions. In addition, scenarios reflecting implementation of policy projects are presented, and conclusions are drawn on the usefulness of a proposed evaluation methodology and the effectiveness of the adopted new energy policy for the electrical sector in Lebanon.

Keywords: EdL Electricite du Liban, portfolio optimization, electricity generation mix, mean-variance approach

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Authors: Natheer Alatawneh

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The finite element analysis of magnetic fields in electromagnetic devices shows that the machine cores experience different flux patterns including alternating and rotating fields. The rotating fields are generated in different configurations range between circular and elliptical with different ratios between the major and minor axis of the flux locus. Experimental measurements on electrical steel exposed to different flux patterns disclose different magnetic losses in the samples under test. Consequently, electric machines require special attention during the cores loss calculation process to consider the flux patterns. In this study, a circular rotational single sheet tester is employed to measure the core losses in electric steel sample of M36G29. The sample was exposed to alternating field, circular field, and elliptical fields with axis ratios of 0.2, 0.4, 0.6 and 0.8. The measured data was implemented on 6-4 switched reluctance motor at three different frequencies of interest to the industry as 60 Hz, 400 Hz, and 1 kHz. The results disclose a high margin of error that may occur during the loss calculations if the flux patterns issue is neglected. The error in different parts of the machine associated with considering the flux patterns can be around 50%, 10%, and 2% at 60Hz, 400Hz, and 1 kHz, respectively. The future work will focus on the optimization of machine geometrical shape which has a primary effect on the flux pattern in order to minimize the magnetic losses in machine cores.

Keywords: alternating core losses, electric machines, finite element analysis, rotational core losses

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Authors: Soheila Sadeghi

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Accurate cost prediction is essential for effective project management and decision-making in the construction industry. This study aims to develop a cost prediction model for construction projects using Machine Learning techniques and Particle Swarm Optimization (PSO). The research utilizes a comprehensive dataset containing project cost estimates, actual costs, resource details, and project performance metrics from a road reconstruction project. The methodology involves data preprocessing, feature selection, and the development of an Artificial Neural Network (ANN) model optimized using PSO. The study investigates the impact of various input features, including cost estimates, resource allocation, and project progress, on the accuracy of cost predictions. The performance of the optimized ANN model is evaluated using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared. The results demonstrate the effectiveness of the proposed approach in predicting project costs, outperforming traditional benchmark models. The feature selection process identifies the most influential variables contributing to cost variations, providing valuable insights for project managers. However, this study has several limitations. Firstly, the model's performance may be influenced by the quality and quantity of the dataset used. A larger and more diverse dataset covering different types of construction projects would enhance the model's generalizability. Secondly, the study focuses on a specific optimization technique (PSO) and a single Machine Learning algorithm (ANN). Exploring other optimization methods and comparing the performance of various ML algorithms could provide a more comprehensive understanding of the cost prediction problem. Future research should focus on several key areas. Firstly, expanding the dataset to include a wider range of construction projects, such as residential buildings, commercial complexes, and infrastructure projects, would improve the model's applicability. Secondly, investigating the integration of additional data sources, such as economic indicators, weather data, and supplier information, could enhance the predictive power of the model. Thirdly, exploring the potential of ensemble learning techniques, which combine multiple ML algorithms, may further improve cost prediction accuracy. Additionally, developing user-friendly interfaces and tools to facilitate the adoption of the proposed cost prediction model in real-world construction projects would be a valuable contribution to the industry. The findings of this study have significant implications for construction project management, enabling proactive cost estimation, resource allocation, budget planning, and risk assessment, ultimately leading to improved project performance and cost control. This research contributes to the advancement of cost prediction techniques in the construction industry and highlights the potential of Machine Learning and PSO in addressing this critical challenge. However, further research is needed to address the limitations and explore the identified future research directions to fully realize the potential of ML-based cost prediction models in the construction domain.

Keywords: cost prediction, construction projects, machine learning, artificial neural networks, particle swarm optimization, project management, feature selection, road reconstruction

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Authors: Rabab M. Ramadan, Elaraby A. Elgallad

Abstract:

With extensive application, the performance of unimodal biometrics systems has to face a diversity of problems such as signal and background noise, distortion, and environment differences. Therefore, multimodal biometric systems are proposed to solve the above stated problems. This paper introduces a bimodal biometric recognition system based on the extracted features of the human palm print and iris. Palm print biometric is fairly a new evolving technology that is used to identify people by their palm features. The iris is a strong competitor together with face and fingerprints for presence in multimodal recognition systems. In this research, we introduced an algorithm to the combination of the palm and iris-extracted features using a texture-based descriptor, the Scale Invariant Feature Transform (SIFT). Since the feature sets are non-homogeneous as features of different biometric modalities are used, these features will be concatenated to form a single feature vector. Particle swarm optimization (PSO) is used as a feature selection technique to reduce the dimensionality of the feature. The proposed algorithm will be applied to the Institute of Technology of Delhi (IITD) database and its performance will be compared with various iris recognition algorithms found in the literature.

Keywords: iris recognition, particle swarm optimization, feature extraction, feature selection, palm print, the Scale Invariant Feature Transform (SIFT)

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Authors: Gaurav Kumar Sinha

Abstract:

In an era marked by increasing concerns about energy sustainability, this research endeavors to address the pressing challenge of energy consumption in buildings and industries. This study delves into the transformative potential of AWS services in optimizing energy efficiency. The research is founded on the recognition that effective management of energy consumption is imperative for both environmental conservation and economic viability. Buildings and industries account for a substantial portion of global energy use, making it crucial to develop advanced techniques for analysis and reduction. This study sets out to explore the integration of AWS services with big data analytics to provide innovative solutions for energy consumption analysis. Leveraging AWS's cloud computing capabilities, scalable infrastructure, and data analytics tools, the research aims to develop efficient methods for collecting, processing, and analyzing energy data from diverse sources. The core focus is on creating predictive models and real-time monitoring systems that enable proactive energy management. By harnessing AWS's machine learning and data analytics capabilities, the research seeks to identify patterns, anomalies, and optimization opportunities within energy consumption data. Furthermore, this study aims to propose actionable recommendations for reducing energy consumption in buildings and industries. By combining AWS services with metrics-driven insights, the research strives to facilitate the implementation of energy-efficient practices, ultimately leading to reduced carbon emissions and cost savings. The integration of AWS services not only enhances the analytical capabilities but also offers scalable solutions that can be customized for different building and industrial contexts. The research also recognizes the potential for AWS-powered solutions to promote sustainable practices and support environmental stewardship.

Keywords: energy consumption analysis, big data analytics, AWS services, energy efficiency

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Authors: Priyal Motwani

Abstract:

The current market for bamboo products in India is about Rs. 21000 crores and is highly unorganised and fragmented. In this study, we have closely analysed the structure and functions of a major bamboo craft based organisation in Kerela, India and elaborated about its value chain, product mix, pricing strategy and supply chain, collaborations and competitive landscape. We have identified six major bottlenecks that are prevalent in such organisations, based on the Indian context, in relation to their product mix, asset management, and supply chain- corresponding waste management and retail network. The study has identified that the target customers for the bamboo based products and alternative revenue streams (eco-tourism, microenterprises, training), by carrying out secondary and primary research (5000 sample space), that can boost the existing revenue by 150%. We have then recommended an optimum product mix-covering premium, medium and low valued processing, for medium sized bamboo based organisations, in accordance with their capacity to maximize their revenue potential. After studying such organisations and their counter parts, the study has established an optimum retail network, considering B2B, B2C physical and online retail, to maximize their sales to their target groups. On the basis of the results obtained from the analysis of the future and present trends, our study gives recommendations to improve the revenue potential of bamboo based organisation in India and promote sustainability.

Keywords: bamboo, bottlenecks, optimization, product mix, retail network, value chain

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Authors: Mohsen Azarmjoo, Navid Sharifi, Zahra Alikhani Koopaei

Abstract:

This article aims to conserve energy and optimize the performance of solar water heaters using modern modeling systems. In this study, a large-scale solar water heater is modeled using an AVR microcontroller, which is a digital processor from the AVR microcontroller family. This mechatronic system will be used to analyze the performance and design of solar collectors, with the ultimate goal of improving the efficiency of the system being used. The findings of this research provide insights into optimizing the performance of solar water heaters. By manipulating the arrangement of solar panels and controlling the water flow through them using the AVR microcontroller, researchers can identify the optimal configurations and operational protocols to achieve the desired temperature and flow conditions. These findings can contribute to the development of more efficient and sustainable heating and cooling systems. This article investigates the optimization of solar water heater performance. It examines the impact of solar panel layout on system efficiency and explores methods of controlling water flow to achieve the desired temperature and flow conditions. The results of this research contribute to the development of more sustainable heating and cooling systems that rely on renewable energy sources.

Keywords: energy conservation, solar water heaters, solar cooling, simulation, mechatronics

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Authors: Abate Getnet Demisash, Beshatu Taye Hatew, Ababo Geleta Gudisa

Abstract:

As consumption trends of fish are rising in Ethiopia, assessment of the environmental performance of Fisheries becomes vital. Hence, Cleaner Production Assessment was conducted on Lake Tana No.1 Fish Supply Association. This paper focuses on determining the characteristics, quantity and setting up cleaner production option for the site with experimental investigation. The survey analysis showed that illegal waste dumping in Lake Tana is common practice in the area and some of the main reasons raised were they have no option than doing this for discharging fish wastes. Quantifying a fish waste by examination of records at the point of generation resulted in generation rate of 72,822.61 kg per year which is a significant amount of waste and needs management system. The result of the proximate analysis showed high free fat content of about 12.33% and this was a good candidate for the production of biodiesel that has been set as an option for fish waste utilization. Among the different waste management options, waste reduction by product optimization which involves biodiesel production was chosen as a potential method. Laboratory scale experiments were performed to produce renewable energy source from the wastes. The resulting biodiesel was characterized and found to have a density of 0.756kg/L, viscosity 0.24p and 153°C flash points which shows the product has values in compliance with American Society for Testing and Materials (ASTM) standards.

Keywords: biodiesel, cleaner production, renewable energy, clean energy, waste to energy

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Authors: Zohreh Khojasteh-Ghamari, Takashi Irohara

Abstract:

One of the efficient ways in supply chain risk management is avoiding the interruption in Supply Chain (SC) before it occurs. Although the majority of the organizations focus on their first-tier suppliers to avoid risk in the SC, studies show that in only 60 percent of the disruption cases the reason is first tier suppliers. In the 40 percent of the SC disruptions, the reason is downstream SC, which is the second tier and lower. Due to the increasing complexity and interrelation of modern supply chains, the SC elements have become difficult to trace. Moreover, studies show that there is a vital need to better understand the integration of risk and visibility, especially in the context of multiple objectives. In this study, we propose a multi-objective programming model to avoid disruption in SC. The objective of this study is evaluating the effect of downstream SCV on managing supply chain risk. We propose a multi-objective mathematical programming model with the objective functions of minimizing the total cost and maximizing the downstream supply chain visibility (SCV). The decision variable is supplier selection. We assume there are several manufacturers and several candidate suppliers. For each manufacturer, our model proposes the best suppliers with the lowest cost and maximum visibility in downstream supply chain. We examine the applicability of the model by numerical examples. We also define several scenarios for datasets and observe the tendency. The results show that minimum visibility in downstream SC is needed to have a safe SC network.

Keywords: downstream supply chain, optimization, supply chain risk, supply chain visibility

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Authors: Inderpreet Kaur, Sukhjit Kaur, Balwinder Singh Sohi

Abstract:

The design proposed in this paper mainly focuses on implementation of a single feed compact rectangular microstrip patch antenna (MSA) for single band application. The antenna presented here also works in dual band but its best performance has been obtained when optimised to work in single band mode. In this paper, a new feeding structure is applied in the patch antenna design to overcome undesirable features of the earlier multilayer feeding structures while maintaining their interesting features.To make the proposed antenna more efficient the optimization of the antenna design parameters have been done using HFSS’s optometric. For the proposed antenna one resonant frequency has been obtained at 6.03GHz, with Bandwidth of 167MHz and return loss of -33.82db. The characteristics of the designed structure are investigated by using FEM based electromagnetic solver.

Keywords: bandwidth, retun loss, parasitic lines, microstrip antenna

Procedia PDF Downloads 448