Search results for: nonlinear optimization with constraints

Authors: Zheng Yuxun

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This review critically assesses the advancements and prospective developments in defect detection methodologies within the semiconductor industry, an essential domain that significantly affects the operational efficiency and reliability of electronic components. As semiconductor devices continue to decrease in size and increase in complexity, the precision and efficacy of defect detection strategies become increasingly critical. Tracing the evolution from traditional manual inspections to the adoption of advanced technologies employing automated vision systems, artificial intelligence (AI), and machine learning (ML), the paper highlights the significance of precise defect detection in semiconductor manufacturing by discussing various defect types, such as crystallographic errors, surface anomalies, and chemical impurities, which profoundly influence the functionality and durability of semiconductor devices, underscoring the necessity for their precise identification. The narrative transitions to the technological evolution in defect detection, depicting a shift from rudimentary methods like optical microscopy and basic electronic tests to more sophisticated techniques including electron microscopy, X-ray imaging, and infrared spectroscopy. The incorporation of AI and ML marks a pivotal advancement towards more adaptive, accurate, and expedited defect detection mechanisms. The paper addresses current challenges, particularly the constraints imposed by the diminutive scale of contemporary semiconductor devices, the elevated costs associated with advanced imaging technologies, and the demand for rapid processing that aligns with mass production standards. A critical gap is identified between the capabilities of existing technologies and the industry's requirements, especially concerning scalability and processing velocities. Future research directions are proposed to bridge these gaps, suggesting enhancements in the computational efficiency of AI algorithms, the development of novel materials to improve imaging contrast in defect detection, and the seamless integration of these systems into semiconductor production lines. By offering a synthesis of existing technologies and forecasting upcoming trends, this review aims to foster the dialogue and development of more effective defect detection methods, thereby facilitating the production of more dependable and robust semiconductor devices. This thorough analysis not only elucidates the current technological landscape but also paves the way for forthcoming innovations in semiconductor defect detection.

Keywords: semiconductor defect detection, artificial intelligence in semiconductor manufacturing, machine learning applications, technological evolution in defect analysis

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Authors: Rabitah Zakaria, Chan M. Luan, Nor Hakimah Ramly

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The extraction of vegetable oil using aqueous extraction process assisted by ionic extended surfactant has been investigated as an alternative to hexane extraction. However, the ionic extended surfactant has not been commercialised and its safety with respect to food processing is uncertain. Hence, food-grade non-ionic surfactants (Tween 20, Span 20, and Span 80) were proposed for the extraction of residual oil from palm-pressed mesocarp fibre. Palm-pressed mesocarp fibre contains a significant amount of residual oil ( 5-10 wt %) and its recovery is beneficial as the oil contains much higher content of vitamin E, carotenoids, and sterols compared to crude palm oil. In this study, the formulation of food-grade surfactants using a combination of high hydrophilic-lipophilic balance (HLB) surfactants and low HLB surfactants to produce micro-emulsion with very low interfacial tension (IFT) was investigated. The suitable surfactant formulation was used in the oil extraction process and the efficiency of the extraction was correlated with the IFT, droplet size and viscosity. It was found that a ternary surfactant mixture with a HLB value of 15 (82% Tween 20, 12% Span 20 and 6% Span 80) was able to produce micro-emulsion with very low IFT compared to other HLB combinations. Results suggested that the IFT and droplet size highly affect the oil recovery efficiency. Finally, optimization of the operating parameters shows that the highest extraction efficiency of 78% was achieved at 1:31 solid to liquid ratio, 2 wt % surfactant solution, temperature of 50˚C, and 50 minutes contact time.

Keywords: food-grade surfactants, aqueous extraction of residual oil, palm-pressed mesocarp fibre, interfacial tension

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Authors: Zhang Yuqi

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Service facilities are the boosters for the cultivation and development of innovative functions in innovative cluster areas. At the same time, reasonable service facilities planning can better link the internal functional blocks. This paper takes Zhejiang University Zijin Co-Maker Town as the research object, based on the combination of network data mining and field research and verification, combined with the needs of its internal innovative groups. It studies the distribution characteristics and existing problems of service facilities and then proposes a targeted planning suggestion. The main conclusions are as follows: (1) From the perspective of view, the town is rich in general life-supporting services, but lacking of provision targeted and distinctive service facilities for innovative groups; (2) From the perspective of scale structure, small-scale street shops are the main business form, lack of large-scale service center; (3) From the perspective of spatial structure, service facilities layout of each functional block is too fragile to fit the characteristics of 2aggregation- distribution' of innovation and entrepreneurial activities; (4) The goal of optimizing service facilities planning should be guided for fostering function of innovation and entrepreneurship and meet the actual needs of the innovation and entrepreneurial groups.

Keywords: the cultivation of innovative function, Zhejiang University Zijin Co-Maker Town, service facilities, network data mining, space optimization advice

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Authors: Alexander I. Dovgyallo, Evgeniy A. Zinoviev, Svetlana O. Nekrasova

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The most of works applied for the pulse tube converters contain the workflow description implemented through the use of mathematical models on stationary modes. However, the study of the thermoacoustic systems unsteady behavior in the start, stop, and acoustic load changes modes is in the particular interest. The aim of the present study was to develop a mathematical thermal excitation model of acoustic oscillations in pulse tube engine (PTE) as a small-scale scheme of pulse tube engine operating at atmospheric air. Unlike some previous works this standing wave configuration is a fully closed system. The improvements over previous mathematical models are the following: the model allows specifying any values of porosity for regenerator, takes into account the piston weight and the friction in the cylinder and piston unit, and determines the operating frequency. The numerical method is based on the relation equations between the pressure and volume velocity variables at the ends of each element of PTE which is recorded through the appropriate transformation matrix. A solution demonstrates that the PTE operation frequency is the complex value, and it depends on the piston mass and the dynamic friction due to its movement in the cylinder. On the basis of the determined frequency thermoacoustically induced heat transport and generation of acoustic power equations were solved for channel with temperature gradient on its ends. The results of numerical simulation demonstrate the features of the initialization process of oscillation and show that that generated acoustic power more than power on the steady mode in a factor of 3…4. But doesn`t mean the possibility of its further continuous utilizing due to its existence only in transient mode which lasts only for a 30-40 sec. The experiments were carried out on small-scale PTE. The results shows that the value of acoustic power is in the range of 0.7..1.05 W for the defined frequency range f = 13..18 Hz and pressure amplitudes 11..12 kPa. These experimental data are satisfactorily correlated with the numerical modeling results. The mathematical model can be straightforwardly applied for the thermoacoustic devices with variable temperatures of thermal reservoirs and variable transduction loads which are expected to occur in practical implementations of portable thermoacoustic engines.

Keywords: nonlinear processes, pulse tube engine, thermal excitation, standing wave

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Authors: Khyamling Parane, B. M. Prabhu Prasad, Basavaraj Talawar

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Fast simulations are critical in reducing time to market in CMPs and SoCs. Several simulators have been used to evaluate the performance and power consumed by Network-on-Chips. Researchers and designers rely upon these simulators for design space exploration of NoC architectures. Our experiments show that simulating large NoC topologies take hours to several days for completion. To speed up the simulations, it is necessary to investigate and optimize the hotspots in simulator source code. Among several simulators available, we choose Booksim2.0, as it is being extensively used in the NoC community. In this paper, we analyze the cache and memory system behaviour of Booksim2.0 to accurately monitor input dependent performance bottlenecks. Our measurements show that cache and memory usage patterns vary widely based on the input parameters given to Booksim2.0. Based on these measurements, the cache configuration having least misses has been identified. To further reduce the cache misses, we use software optimization techniques such as removal of unused functions, loop interchanging and replacing post-increment operator with pre-increment operator for non-primitive data types. The cache misses were reduced by 18.52%, 5.34% and 3.91% by employing above technology respectively. We also employ thread parallelization and vectorization to improve the overall performance of Booksim2.0. The OpenMP programming model and SIMD are used for parallelizing and vectorizing the more time-consuming portions of Booksim2.0. Speedups of 2.93x and 3.97x were observed for the Mesh topology with 30 × 30 network size by employing thread parallelization and vectorization respectively.

Keywords: cache behaviour, network-on-chip, performance profiling, vectorization

Procedia PDF Downloads 181

Authors: M. Derraz, M. Farhaoui

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Problem statement: In the water treatment processes, the coagulation and flocculation processes produce sludge according to the level of the water turbidity. The aluminum sulfate is the most common coagulant used in water treatment plants of Morocco as well as many countries. It is difficult to manage Sludge produced by the treatment plant. However, it can be used in the process to improve the quality of the treated water and reduce the aluminum sulfate dose. Approach: In this study, the effectiveness of sludge was evaluated at different turbidity levels (low, medium, and high turbidity) and coagulant dosage to find optimal operational conditions. The influence of settling time was also studied. A set of jar test experiments was conducted to find the sludge and aluminum sulfate dosages in order to improve the produced water quality for different turbidity levels. Results: Results demonstrated that using sludge produced by the treatment plant can improve the quality of the produced water and reduce the aluminum sulfate using. The aluminum sulfate dosage can be reduced from 40 to 50% according to the turbidity level (10, 20, and 40 NTU). Conclusions/Recommendations: Results show that sludge can be used in order to reduce the aluminum sulfate dosage and improve the quality of treated water. The highest turbidity removal efficiency is observed within 6 mg/l of aluminum sulfate and 35 mg/l of sludge in low turbidity, 20 mg/l of aluminum sulfate and 50 mg/l of sludge in medium turbidity and 20 mg/l of aluminum sulfate and 60 mg/l of sludge in high turbidity. The turbidity removal efficiency is 97.56%, 98.96%, and 99.47% respectively for low, medium and high turbidity levels.

Keywords: coagulation process, coagulant dose, sludge reuse, turbidity removal

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Authors: Ursula D. C. Resende, Yan G. Santos, Lucas M. de O. Andrade

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The recent and fast development of the internet, wireless, telecommunication technologies and low-power electronic devices has led to an expressive amount of electromagnetic energy available in the environment and the smart applications technology expansion. These applications have been used in the Internet of Things devices, 4G and 5G solutions. The main feature of this technology is the use of the wireless sensor. Although these sensors are low-power loads, their use imposes huge challenges in terms of an efficient and reliable way for power supply in order to avoid the traditional battery. The radio frequency based energy harvesting technology is especially suitable to wireless power sensors by using a rectenna since it can be completely integrated into the distributed hosting sensors structure, reducing its cost, maintenance and environmental impact. The rectenna is an equipment composed of an antenna and a rectifier circuit. The antenna function is to collect as much radio frequency radiation as possible and transfer it to the rectifier, which is a nonlinear circuit, that converts the very low input radio frequency energy into direct current voltage. In this work, a set of rectennas, mounted on a paper substrate, which can be used for the inner coating of buildings and simultaneously harvest electromagnetic energy from the environment, is proposed. Each proposed individual rectenna is composed of a 2.45 GHz patch antenna and a voltage doubler rectifier circuit, built in the same paper substrate. The antenna contains a rectangular radiator element and a microstrip transmission line that was projected and optimized by using the Computer Simulation Software (CST) in order to obtain values of S11 parameter below -10 dB in 2.45 GHz. In order to increase the amount of harvested power, eight individual rectennas, incorporating metamaterial cells, were connected in parallel forming a system, denominated Electromagnetic Wall (EW). In order to evaluate the EW performance, it was positioned at a variable distance from the internet router, and a 27 kΩ resistive load was fed. The results obtained showed that if more than one rectenna is associated in parallel, enough power level can be achieved in order to feed very low consumption sensors. The 0.12 m2 EW proposed in this work was able to harvest 0.6 mW from the environment. It also observed that the use of metamaterial structures provide an expressive growth in the amount of electromagnetic energy harvested, which was increased from 0. 2mW to 0.6 mW.

Keywords: electromagnetic energy harvesting, metamaterial, rectenna, rectifier circuit

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Authors: Taylan Kabbani, Ekrem Duman

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The design of adaptive systems that take advantage of financial markets while reducing the risk can bring more stagnant wealth into the global market. However, most efforts made to generate successful deals in trading financial assets rely on Supervised Learning (SL), which suffered from various limitations. Deep Reinforcement Learning (DRL) offers to solve these drawbacks of SL approaches by combining the financial assets price "prediction" step and the "allocation" step of the portfolio in one unified process to produce fully autonomous systems capable of interacting with its environment to make optimal decisions through trial and error. In this paper, a continuous action space approach is adopted to give the trading agent the ability to gradually adjust the portfolio's positions with each time step (dynamically re-allocate investments), resulting in better agent-environment interaction and faster convergence of the learning process. In addition, the approach supports the managing of a portfolio with several assets instead of a single one. This work represents a novel DRL model to generate profitable trades in the stock market, effectively overcoming the limitations of supervised learning approaches. We formulate the trading problem, or what is referred to as The Agent Environment as Partially observed Markov Decision Process (POMDP) model, considering the constraints imposed by the stock market, such as liquidity and transaction costs. More specifically, we design an environment that simulates the real-world trading process by augmenting the state representation with ten different technical indicators and sentiment analysis of news articles for each stock. We then solve the formulated POMDP problem using the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, which can learn policies in high-dimensional and continuous action spaces like those typically found in the stock market environment. From the point of view of stock market forecasting and the intelligent decision-making mechanism, this paper demonstrates the superiority of deep reinforcement learning in financial markets over other types of machine learning such as supervised learning and proves its credibility and advantages of strategic decision-making.

Keywords: the stock market, deep reinforcement learning, MDP, twin delayed deep deterministic policy gradient, sentiment analysis, technical indicators, autonomous agent

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Authors: Ihsan Burak Cam, Ayhan Topuz

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Turkish delight has been produced by bleaching the plain delight mix (refined sugar, water and starch) via soapwort extract and powdered sugar. Soapwort extract which contains high amount of saponin, is an additive used in Turkish delight and tahini halvah production to improve consistency, chewiness and color due to its bioactive saponin content by acting as emulsifier. In this study, soapwort powder has been produced by determining optimum process conditions of soapwort extract by using response-surface method. This extract has been enriched with saponin by reverse osmosis (contains %63 saponin in dry bases). Büchi mini spray dryer B-290 was used to produce spray-dried soapwort powder (aw=0.254) from the enriched soapwort concentrate. Processing steps optimization and saponin content enrichment of soapwort extract has been tested on Turkish Delight production. Delight samples, produced by soapwort powder and commercial extract (control), were compared in chewiness, springiness, stickiness, adhesiveness, hardness, color and sensorial characteristics. According to the results, all textural properties except hardness of delights produced by powder were found to be statistically different than control samples. Chewiness, springiness, stickiness, adhesiveness and hardness values of samples (delights produced by the powder / control delights) were determined to be 361.9/1406.7, 0.095/0.251, -120.3/-51.7, 781.9/1869.3, 3427.3g/3118.4g, respectively. According to the quality analysis that has been ran with the end products it has been determined that; there is no statistically negative effect of the soapwort extract and the soapwort powder on the color and the appearance of Turkish Delight.

Keywords: saponin, delight, soapwort powder, spray drying

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Authors: Vildan Kistik, Tuncay Can

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From a business perspective, cost and profit are two key factors for businesses. The intent of most businesses is to minimize the cost to maximize or equalize the profit, so as to provide the greatest benefit to itself. However, the physical system is very complicated because of technological constructions, rapid increase of competitive environments and similar factors. In such a system it is not easy to maximize profits or to minimize costs. Businesses must decide on the competence and competence of the personnel to be recruited, taking into consideration many criteria in selecting personnel. There are many criteria to determine the competence and competence of a staff member. Factors such as the level of education, experience, psychological and sociological position, and human relationships that exist in the field are just some of the important factors in selecting a staff for a firm. Personnel selection is a very important and costly process in terms of businesses in today's competitive market. Although there are many mathematical methods developed for the selection of personnel, unfortunately the use of these mathematical methods is rarely encountered in real life. In this study, unlike other methods, an exponential programming model was established based on the possibilities of failing in case the selected personnel was started to work. With the necessary transformations, the problem has been transformed into unconstrained Geometrical Programming problem and personnel selection problem is approached with geometric programming technique. Personnel selection scenarios for a classroom were established with the help of normal distribution and optimum solutions were obtained. In the most appropriate solutions, the personnel selection process for the classroom has been achieved with minimum cost.

Keywords: geometric programming, personnel selection, non-linear programming, operations research

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Authors: Kyu Jin Kim, Byung Kwan Oh, Hyo Seon Park

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The seismic responses-based structural health monitoring system has been performed to prevent seismic damage. Structural seismic damage of building is caused by the instantaneous stress concentration which is related with dynamic characteristic of earthquake. Meanwhile, seismic response analysis to estimate the dynamic responses of building demands significantly high computational cost. To prevent the failure of structural members from the characteristic of the earthquake and the significantly high computational cost for seismic response analysis, this paper presents an artificial neural network (ANN) based prediction model for dynamic responses of building considering specific time length. Through the measured dynamic responses, input and output node of the ANN are formed by the length of specific time, and adopted for the training. In the model, evolutionary radial basis function neural network (ERBFNN), that radial basis function network (RBFN) is integrated with evolutionary optimization algorithm to find variables in RBF, is implemented. The effectiveness of the proposed model is verified through an analytical study applying responses from dynamic analysis for multi-degree of freedom system to training data in ERBFNN.

Keywords: structural health monitoring, dynamic response, artificial neural network, radial basis function network, genetic algorithm

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Authors: Nesrine Lenchi, Salima Kebbouche-Gana, Laddada Belaid, Mohamed Lamine Khelfaoui, Mohamed Lamine Gana

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Saline lakes are extreme hypersaline environments that are considered five to ten times saltier than seawater (150 – 300 g L-1 salt concentration). Hypersaline regions differ from each other in terms of salt concentration, chemical composition and geographical location, which determine the nature of inhabitant microorganisms. In order to explore the diversity of moderate and extreme halophiles Bacteria in Chott Tinsilt (East of Algeria), an isolation program was performed. In the first time, water samples were collected from the saltern during pre-salt harvesting phase. Salinity, pH and temperature of the sampling site were determined in situ. Chemical analysis of water sample indicated that Na +and Cl- were the most abundant ions. Isolates were obtained by plating out the samples in complex and synthetic media. In this study, seven halophiles cultures of Bacteria were isolated. Isolates were studied for Gram’s reaction, cell morphology and pigmentation. Enzymatic assays (oxidase, catalase, nitrate reductase and urease), and optimization of growth conditions were done. The results indicated that the salinity optima varied from 50 to 250 g L-1, whereas the optimum of temperature range from 25°C to 35°C. Molecular identification of the isolates was performed by sequencing the 16S rRNA gene. The results showed that these cultured isolates included members belonging to the Halomonas, Staphylococcus, Salinivibrio, Idiomarina, Halobacillus Thalassobacillus and Planococcus genera and what may represent a new bacterial genus.

Keywords: bacteria, Chott, halophilic, 16S rRNA

Procedia PDF Downloads 265

Authors: K. X. Cheng, A. L. Goh, K. T. Ooi

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The effectiveness of microchannels in enhancing heat transfer has been demonstrated in the semiconductor industry. In order to tap the microscale heat transfer effects into macro geometries, overcoming the cost and technological constraints, microscale passages were created in macro geometries machined using conventional fabrication methods. A cylindrical insert was placed within a pipe, and geometrical profiles were created on the outer surface of the insert to enhance heat transfer under steady-state single-phase liquid flow conditions. However, while heat transfer coefficient values of above 10 kW/m2·K were achieved, the heat transfer enhancement was accompanied by undesirable pressure drop increment. Therefore, this study aims to address the high pressure drop issue using Constructal theory, a universal design law for both animate and inanimate systems. Two designs based on Constructal theory were developed to study the effectiveness of Constructal features in reducing the pressure drop increment as compared to parallel channels, which are commonly found in microchannel fabrication. The hydrodynamic and heat transfer performance for the Tree insert and Constructal fin (Cfin) insert were studied using experimental methods, and the underlying mechanisms were substantiated by numerical results. In technical terms, the objective is to achieve at least comparable increment in both heat transfer coefficient and pressure drop, if not higher increment in the former parameter. Results show that the Tree insert improved the heat transfer performance by more than 16 percent at low flow rates, as compared to the Tree-parallel insert. However, the heat transfer enhancement reduced to less than 5 percent at high Reynolds numbers. On the other hand, the pressure drop increment stayed almost constant at 20 percent. This suggests that the Tree insert has better heat transfer performance in the low Reynolds number region. More importantly, the Cfin insert displayed improved heat transfer performance along with favourable hydrodynamic performance, as compared to Cfinparallel insert, at all flow rates in this study. At 2 L/min, the enhancement of heat transfer was more than 30 percent, with 20 percent pressure drop increment, as compared to Cfin-parallel insert. Furthermore, comparable increment in both heat transfer coefficient and pressure drop was observed at 8 L/min. In other words, the Cfin insert successfully achieved the objective of this study. Analysis of the results suggests that bifurcation of flows is effective in reducing the increment in pressure drop relative to heat transfer enhancement. Optimising the geometries of the Constructal fins is therefore the potential future study in achieving a bigger stride in energy efficiency at much lower costs.

Keywords: constructal theory, enhanced heat transfer, microchannel, pressure drop

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Authors: Waheed Ahmad Safi, Shunichi Nakamura

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Concrete filled steel I-girder (CFIG) bridge was proposed and the bending and shear strength was confirmed by experiments. The area surrounded by the upper and lower flanges and the web is filled with concrete in CFIG, which is used to the intermediate support of a continuous girder. Three-dimensional finite element models were established to simulate the bending and shear behaviors of CFIG and to clarify the load transfer mechanism. Steel plates and filled concrete were modeled as a three-dimensional 8-node solid element and steel reinforcement bars as a three-dimensional 2-node truss element. The elements were mostly divided into the 50 x 50 mm mesh size. The non-linear stress-strain relation is assumed for concrete in compression including the softening effect after the peak, and the stress increases linearly for concrete in tension until concrete cracking but then decreases due to tension stiffening effect. The stress-strain relation for steel plates was tri-linear and that for reinforcements was bi-linear. The concrete and the steel plates were rigidly connected. The developed FEM model was applied to simulate and analysis the bending behaviors of the CFIG specimens. The vertical displacements and the strains of steel plates and the filled concrete obtained by FEM agreed very well with the test results until the yield load. The specimens collapsed when the upper flange buckled or the concrete spalled off. These phenomena cannot be properly analyzed by FEM, which produces a small discrepancy at the ultimate states. The FEM model was also applied to simulate and analysis the shear tests of the CFIG specimens. The vertical displacements and strains of steel and concrete calculated by FEM model agreed well with the test results. A truss action was confirmed by the FEM and the experiment, clarifying that shear forces were mainly resisted by the tension strut of the steel plate and the compression strut of the filled concrete acting in the diagonal direction. A trail design with the CFIG was carried out for a four-span continuous highway bridge and the design method was established. Construction cost was estimated about 12% lower than that of a conventional steel I-section girder.

Keywords: concrete filled steel I-girder, bending strength, FEM, limit states design, steel I-girder, shear strength

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Authors: Ewa M. Laskowska, Jorn Vatn

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Industry 4.0 focuses on digital optimization of industrial processes. The idea is to use extracted data in order to build a decision support model enabling use of those data for real time decision making. In terms of predictive maintenance, the desired decision support tool would be a model enabling prognostics of system's health based on the current condition of considered equipment. Within area of system prognostics and health management, a commonly used health indicator is Remaining Useful Lifetime (RUL) of a system. Because the RUL is a random variable, it has to be estimated based on available health indicators. Health indicators can be of different types and come from different sources. They can be process variables, equipment performance variables, data related to number of experienced failures, etc. The aim of this study is the analysis of performance variables of emergency shutdown valves (ESV) used in oil and gas industry. ESV is inspected periodically, and at each inspection torque and time of valve operation are registered. The data will be analyzed by means of machine learning or statistical analysis. The purpose is to investigate whether the available data could be used as a health indicator for a prognostic purpose. The second objective is to examine what is the most efficient way to incorporate the data into predictive model. The idea is to check whether the data can be applied in form of explanatory variables in Markov process or whether other stochastic processes would be a more convenient to build an RUL model based on the information coming from registered data.

Keywords: emergency shutdown valves, health indicator, prognostics, remaining useful lifetime, RUL

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Authors: Anoush Saadatmehr

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Design and build of large clear span structures have always been demanding in the construction industry targeting industrial and commercial buildings around the world. The function of these spectacular structures encompasses distinguished types of building such as aircraft and airship hangars, warehouses, bulk storage buildings, sports and recreation facilities. From an engineering point of view, there are various types of steel structure systems that are often adopted in large-span buildings like conventional trusses, space frames and cable-supported roofs. However, this paper intends to investigate and review an innovative light, economic and quickly erected large span steel structure renowned as “Stressed-Arch,” which has several advantages over the other common types of structures. This patented system integrates the use of cold-formed hollow section steel material with high-strength pre-stressing strands and concrete grout to establish an arch shape truss frame anywhere there is a requirement to construct a cost-effective column-free space for spans within the range of 60m to 180m. In this study and firstly, the main structural properties of the stressed-arch system and its components are discussed technically. These features include nonlinear behavior of truss chords during stress-erection, the effect of erection method on member’s compressive strength, the rigidity of pre-stressed trusses to overcome strict deflection criteria for cases with roof suspended cranes or specialized front doors and more importantly, the prominent lightness of steel structure. Then, the effects of utilizing pre-stressing strands to safeguard a smooth process of installation of main steel members and roof components and cladding are investigated. In conclusion, it is shown that the Stressed-Arch system not only provides an optimized light steel structure up to 30% lighter than its conventional competitors but also streamlines the process of building erection and minimizes the construction time while preventing the risks of working at height.

Keywords: large span structure, pre-stressed steel truss, stressed-arch building, stress-erection, steel structure

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Authors: Rajkumar Ghosh, Bhabani Prasad Mukhopadhyay

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The increasing demand for environmentally friendly energy solutions highlights the need to optimize solar energy systems. This study compares two types of solar energy systems: ground-mounted solar panels for drought-prone locations and rooftop solar PV installations measuring 300 sq. ft. (approx. 28 sq. m.). The electricity output of 4730 kWh/year saves ₹ 14191/year. As a clean and sustainable energy source, solar power is pivotal in reducing greenhouse gas CO2 emissions reduction by 85 tonnes in 25 years and combating climate change. This effort, "PM Suryadaya Ghar-Muft Bijli Yojana," seeks to empower Indian homes by giving free access to solar energy. The initiative is part of the Indian government's larger attempt to encourage clean and renewable energy sources while reducing reliance on traditional fossil fuels. This report reviews various installations and government reports to analyse the performance and impact of both ground-mounted and rooftop solar systems. Besides, effectiveness of government subsidy programs for residential on-grid solar systems, including the ₹78,000 incentive for systems above 3 kW. The study also looks into the subsidy schemes available for domestic agricultural grid use. Systems up to 3 kW receive ₹43,764, while systems over 10 kW receive a fixed subsidy of ₹94,822. Households can save a substantial amount of energy and minimize their reliance on grid electricity by installing the proper solar plant capacity. In terms of monthly consumption at home, the acceptable Rooftop Solar Plant capacity for households is 0-150 units (1-2 kW), 150-300 units (2-3 kW), and >300 units (above 3 kW). Ground-mounted panels, particularly in arid regions, offer benefits such as scalability and optimal orientation but face challenges like land use conflicts and environmental impact, particularly in drought-prone regions. By evaluating the distinct advantages and challenges of each system, this study aims to provide insights into their optimal applications, guiding stakeholders in making informed decisions to enhance solar energy efficiency and sustainability within regulatory constraints. This research also explores the implications of regulations, such as Italy's ban on ground-mounted solar panels on productive agricultural land, on solar energy strategies.

Keywords: sustainability, solar energy, subsidy, rooftop solar energy, renewable energy

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Authors: Wi Sung Yoo, Seung Woo Lee, Youn Kyoung Hur, Sung Hwan Kim

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Large-scale construction projects are continuously increasing, and the need for tools to monitor and evaluate the project success is emphasized. At the construction industry level, there are limitations in deriving performance evaluation factors that reflect the diversity of construction sites and systems that can objectively evaluate and manage performance. Additionally, there are difficulties in integrating structured and unstructured data generated at construction sites and deriving improvements. In this study, we propose the Key Performance Indicators (KPIs) to enable performance evaluation that reflects the increased diversity of construction sites and the unstructured data generated, and present a model for measuring performance by the derived indicators. The comprehensive performance of a unit construction site is assessed based on 6 areas (Time, Cost, Quality, Safety, Environment, Productivity) and 26 indicators. We collect performance indicator information from 30 construction sites that meet legal standards and have been successfully performed. And We apply data augmentation and optimization techniques into establishing measurement standards for each indicator. In other words, the KPI for construction site performance evaluation presented in this study provides standards for evaluating performance in six areas using institutional requirement data and document data. This can be expanded to establish a performance evaluation system considering the scale and type of construction project. Also, they are expected to be used as a comprehensive indicator of the construction industry and used as basic data for tracking competitiveness at the national level and establishing policies.

Keywords: key performance indicator, performance measurement, structured and unstructured data, data augmentation

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Authors: Peter Egri, Tamas Kis

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Scheduling problems have been studied by the algorithmic mechanism design research from the beginning. This paper is focusing on a practically important, but theoretically rather neglected field: the project scheduling problem where the jobs connected by precedence constraints compete for various nonrenewable resources, such as materials. Although the centralized problem can be solved in polynomial-time by applying the algorithm of Carlier and Rinnooy Kan from the Eighties, obtaining materials in a decentralized environment is usually far from optimal. It can be observed in practical production scheduling situations that project managers tend to cache the required materials as soon as possible in order to avoid later delays due to material shortages. This greedy practice usually leads both to excess stocks for some projects and materials, and simultaneously, to shortages for others. The aim of this study is to develop a model for the material allocation problem of a production plant, where a central decision maker—the inventory—should assign the resources arriving at different points in time to the jobs. Since the actual due dates are not known by the inventory, the mechanism design approach is applied with the projects as the self-interested agents. The goal of the mechanism is to elicit the required information and allocate the available materials such that it minimizes the maximal tardiness among the projects. It is assumed that except the due dates, the inventory is familiar with every other parameters of the problem. A further requirement is that due to practical considerations monetary transfer is not allowed. Therefore a mechanism without money is sought which excludes some widely applied solutions such as the Vickrey–Clarke–Groves scheme. In this work, a type of Serial Dictatorship Mechanism (SDM) is presented for the studied problem, including a polynomial-time algorithm for computing the material allocation. The resulted mechanism is both truthful and Pareto optimal. Thus the randomization over the possible priority orderings of the projects results in a universally truthful and Pareto optimal randomized mechanism. However, it is shown that in contrast to problems like the many-to-many matching market, not every Pareto optimal solution can be generated with an SDM. In addition, no performance guarantee can be given compared to the optimal solution, therefore this approximation characteristic is investigated with experimental study. All in all, the current work studies a practically relevant scheduling problem and presents a novel truthful material allocation mechanism which eliminates the potential benefit of the greedy behavior that negatively influences the outcome. The resulted allocation is also shown to be Pareto optimal, which is the most widely used criteria describing a necessary condition for a reasonable solution.

Keywords: material allocation, mechanism without money, polynomial-time mechanism, project scheduling

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Authors: Emilio A. Berny-Brandt, Sonia E. Ruiz

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Steel tubular towers serving as support structures for large wind turbines are subject to several hundred million stress cycles arising from the turbulent nature of the wind. This causes high-cycle fatigue which can govern tower design. The practice of maintaining the support structure after wind turbines reach its typical 20-year design life have become common, but without quantifying the changes in the reliability on the tower. There are several studies on this topic, but most of them are based on the S-N curve approach using the Miner’s rule damage summation method, the de-facto standard in the wind industry. However, the qualitative nature of Miner’s method makes desirable the use of fracture mechanics to measure the effects of fatigue in the capacity curve of the structure, which is important in order to evaluate the integrity and reliability of these towers. Temporal and spatially varying wind speed time histories are simulated based on power spectral density and coherence functions. Simulations are then applied to a SAP2000 finite element model and step-by-step analysis is used to obtain the stress time histories for a range of representative wind speeds expected during service conditions of the wind turbine. Rainflow method is then used to obtain cycle and stress range information of each of these time histories and a statistical analysis is performed to obtain the distribution parameters of each variable. Monte Carlo simulation is used here to evaluate crack growth over time in the tower base using the Paris-Erdogan equation. A nonlinear static pushover analysis to assess the capacity curve of the structure after a number of years is performed. The capacity curves are then used to evaluate the changes in reliability of a steel tower located in Oaxaca, Mexico, where wind energy facilities are expected to grow in the near future. Results show that fatigue on the tower base can have significant effects on the structural capacity of the wind turbine, especially after the 20-year design life when the crack growth curve starts behaving exponentially.

Keywords: crack growth, fatigue, Monte Carlo simulation, structural reliability, wind turbines

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Authors: N. Boontian

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Biological conversion of biomass to methane has received increasing attention in recent years. Grasses have been explored for their potential anaerobic digestion to methane. In this review, extensive literature data have been tabulated and classified. The influences of several parameters on the potential of these feedstocks to produce methane are presented. Lignocellulosic biomass represents a mostly unused source for biogas and ethanol production. Many factors, including lignin content, crystallinity of cellulose, and particle size, limit the digestibility of the hemicellulose and cellulose present in the lignocellulosic biomass. Pretreatments have used to improve the digestibility of the lignocellulosic biomass. Each pretreatment has its own effects on cellulose, hemicellulose and lignin, the three main components of lignocellulosic biomass. Solid-state anaerobic digestion (SS-AD) generally occurs at solid concentrations higher than 15%. In contrast, liquid anaerobic digestion (AD) handles feedstocks with solid concentrations between 0.5% and 15%. Animal manure, sewage sludge, and food waste are generally treated by liquid AD, while organic fractions of municipal solid waste (OFMSW) and lignocellulosic biomass such as crop residues and energy crops can be processed through SS-AD. An increase in operating temperature can improve both the biogas yield and the production efficiency, other practices such as using AD digestate or leachate as an inoculant or decreasing the solid content may increase biogas yield but have negative impact on production efficiency. Focus is placed on substrate pretreatment in anaerobic digestion (AD) as a means of increasing biogas yields using today’s diversified substrate sources.

Keywords: anaerobic digestion, lignocellulosic biomass, methane production, optimization, pretreatment

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Authors: Nicolas Plamondon, Stuart Atkinson, Shuzi Zhou

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The insurance industry is usually not the first topic one has in mind when thinking about applications of data science. However, the use of data science in the finance and insurance industry is growing quickly for several reasons, including an abundance of reliable customer data, ferocious competition requiring more accurate pricing, etc. Among the top use cases of data science, we find pricing optimization, customer segmentation, customer risk assessment, fraud detection, marketing, and triage analytics. The objective of this paper is to present an application of the generalized additive model (GAM) on a commercial automobile insurance product: an individually rated commercial automobile. These are vehicles used for commercial purposes, but for which there is not enough volume to apply pricing to several vehicles at the same time. The GAM model was selected as an improvement over GLM for its ease of use and its wide range of applications. The model was trained using the largest split of the data to determine model parameters. The remaining part of the data was used as testing data to verify the quality of the modeling activity. We used the Gini coefficient to evaluate the performance of the model. For long-term monitoring, commonly used metrics such as RMSE and MAE will be used. Another topic of interest in the insurance industry is to process of producing the model. We will discuss at a high level the interactions between the different teams with an insurance company that needs to work together to produce a model and then monitor the performance of the model over time. Moreover, we will discuss the regulations in place in the insurance industry. Finally, we will discuss the maintenance of the model and the fact that new data does not come constantly and that some metrics can take a long time to become meaningful.

Keywords: insurance, data science, modeling, monitoring, regulation, processes

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Authors: Yibeltal Chanie Manie, Zebider Asire Munyelet

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5G and 6G technology offers enhanced quality of service with high data transmission rates, which necessitates the implementation of the Internet of Things (IoT) in 5G/6G architecture. In this paper, we proposed the integration of free space optical communication (FSO) with fiber sensor networks for IoT applications. Recently, free-space optical communications (FSO) are gaining popularity as an effective alternative technology to the limited availability of radio frequency (RF) spectrum. FSO is gaining popularity due to flexibility, high achievable optical bandwidth, and low power consumption in several applications of communications, such as disaster recovery, last-mile connectivity, drones, surveillance, backhaul, and satellite communications. Hence, high-speed FSO is an optimal choice for wireless networks to satisfy the full potential of 5G/6G technology, offering 100 Gbit/s or more speed in IoT applications. Moreover, machine learning must be integrated into the design, planning, and optimization of future optical wireless communication networks in order to actualize this vision of intelligent processing and operation. In addition, fiber sensors are important to achieve real-time, accurate, and smart monitoring in IoT applications. Moreover, we proposed deep learning techniques to estimate the strain changes and peak wavelength of multiple Fiber Bragg grating (FBG) sensors using only the spectrum of FBGs obtained from the real experiment.

Keywords: optical sensor, artificial Intelligence, Internet of Things, free-space optics

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Authors: Ndidi F. Amulu, Patrick E. Amulu, Gordian O. Mbah, Callistus N. Ude

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Comparative analysis of the properties of melon seed, coconut fruit and their oil yield were evaluated in this work using standard analytical technique AOAC. The results of the analysis carried out revealed that the moisture contents of the samples studied are 11.15% (melon) and 7.59% (coconut). The crude lipid content are 46.10% (melon) and 55.15% (coconut).The treatment combinations used (leaching time, leaching temperature and solute: solvent ratio) showed significant difference (p < 0.05) in yield between the samples, with melon oil seed flour having a higher percentage range of oil yield (41.30 – 52.90%) and coconut (36.25 – 49.83%). The physical characterization of the extracted oil was also carried out. The values gotten for refractive index are 1.487 (melon seed oil) and 1.361 (coconut oil) and viscosities are 0.008 (melon seed oil) and 0.002 (coconut oil). The chemical analysis of the extracted oils shows acid value of 1.00mg NaOH/g oil (melon oil), 10.050mg NaOH/g oil (coconut oil) and saponification value of 187.00mg/KOH (melon oil) and 183.26mg/KOH (coconut oil). The iodine value of the melon oil gave 75.00mg I2/g and 81.00mg I2/g for coconut oil. A standard statistical package Minitab version 16.0 was used in the regression analysis and analysis of variance (ANOVA). The statistical software mentioned above was also used to optimize the leaching process. Both samples gave high oil yield at the same optimal conditions. The optimal conditions to obtain highest oil yield ≥ 52% (melon seed) and ≥ 48% (coconut seed) are solute - solvent ratio of 40g/ml, leaching time of 2hours and leaching temperature of 50oC. The two samples studied have potential of yielding oil with melon seed giving the higher yield.

Keywords: Coconut, Melon, Optimization, Processing

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Authors: Priyanka Bharti

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Decades ago designs were based on common sense and tradition, but after an enhancement in visualization technology and research, we are now able to comprehend the cognitive ability involved in the decoding of the visual information. However, many fields in visuals need intense research to deliver an efficient explanation for the events. Visuals are an information representation mode through images, symbols and graphics. It plays an impactful role in decision making by facilitating quick recognition, comprehension, and analysis of a situation. They enhance problem-solving capabilities by enabling the processing of more data without overloading the decision maker. As research proves that, visuals offer an improved learning environment by a factor of 400 compared to textual information. Visual information engages learners at a cognitive level and triggers the imagination, which enables the user to process the information faster (visuals are processed 60,000 times faster in the brain than text). Appropriate information, visualization, and its presentation are known to aid and intensify the decision-making process for the users. However, most literature discusses the role of visual aids in comprehension and decision making during normal conditions alone. Unlike emergencies, in a normal situation (e.g. our day to day life) users are neither exposed to stringent time constraints nor face the anxiety of survival and have sufficient time to evaluate various alternatives before making any decision. An emergency is an unexpected probably fatal real-life situation which may inflict serious ramifications on both human life and material possessions unless corrective measures are taken instantly. The situation demands the exposed user to negotiate in a dynamic and unstable scenario in the absence or lack of any preparation, but still, take swift and appropriate decisions to save life/lives or possessions. But the resulting stress and anxiety restricts cue sampling, decreases vigilance, reduces the capacity of working memory, causes premature closure in evaluating alternative options, and results in task shedding. Limited time, uncertainty, high stakes and vague goals negatively affect cognitive abilities to take appropriate decisions. More so, theory of natural decision making by experts has been understood with far more depth than that of an ordinary user. Therefore, in this study, the author aims to understand the role of visual aids in supporting rapid comprehension to take appropriate decisions during an emergency situation.

Keywords: cognition, visual, decision making, graphics, recognition

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Authors: Swapnila V. Shinde, Hemant P. Joshi, Sumit R. Dhas, Dhananjaysingh B. Rajput

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The objective of the present study is to develop hydro-dynamically balanced system for atenolol, β-blocker as a single unit floating tablet. Atenolol shows pH dependent solubility resulting into a bioavailability of 36%. Thus, site specific oral controlled release floating drug delivery system was developed. Formulation includes novice use of rate controlling polymer such as locust bean gum (LBG) in combination of HPMC K4M and gas generating agent sodium bicarbonate. Tablet was prepared by direct compression method and evaluated for physico-mechanical properties. The statistical method was utilized to optimize the effect of independent variables, namely amount of HPMC K4M, LBG and three dependent responses such as cumulative drug release, floating lag time, floating time. Graphical and mathematical analysis of the results allowed the identification and quantification of the formulation variables influencing the selected responses. To study the gastrointestinal transit of the optimized gastro-retentive formulation, in vivo gamma scintigraphy was carried out in six healthy rabbits, after radio labeling the formulation with 99mTc. The transit profiles demonstrated that the dosage form was retained in the stomach for more than 5 hrs. The study signifies the potential of the developed system for stomach targeted delivery of atenolol with improved bioavailability.

Keywords: floating tablet, factorial design, gamma scintigraphy, antihypertensive model drug, HPMC, locust bean gum

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Authors: W. Nootcharin, S. Sujittra, K. Mayuso, K. Kornphimol, M. Rawiwan

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Silver has distinct antibacterial properties and has been used as a component of commercial products with many applications. An increasing number of commercial products cause risks of silver effects for human and environment such as the symptoms of Argyria and the release of silver to the environment. Therefore, the detection of silver in the aquatic environment is important. The colorimetric chemosensor is designed by the basic of ligand interactions with a metal ion, leading to the change of signals for the naked-eyes which are very useful method to this application. Dithizone ligand is considered as one of the effective chelating reagents for metal ions due to its high selectivity and sensitivity of a photochromic reaction for silver as well as the linear backbone of dithizone affords the rotation of various isomeric forms. The present study is focused on the conformation and interaction of silver ion and silver nanoparticles (AgNPs) with dithizone using density functional theory (DFT). The interaction parameters were determined in term of binding energy of complexes and the geometry optimization, frequency of the structures and calculation of binding energies using density functional approaches B3LYP and the 6-31G(d,p) basis set. Moreover, the interaction of silver–dithizone complexes was supported by UV–Vis spectroscopy, FT-IR spectrum that was simulated by using B3LYP/6-31G(d,p) and 1H NMR spectra calculation using B3LYP/6-311+G(2d,p) method compared with the experimental data. The results showed the ion exchange interaction between hydrogen of dithizone and silver atom, with minimized binding energies of silver–dithizone interaction. However, the result of AgNPs in the form of complexes with dithizone. Moreover, the AgNPs-dithizone complexes were confirmed by using transmission electron microscope (TEM). Therefore, the results can be the useful information for determination of complex interaction using the analysis of computer simulations.

Keywords: silver nanoparticles, dithizone, DFT, NMR

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Authors: Ashok V. Sutagundar, Sunilkumar S. Manvi

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Energy, delay and bandwidth are the prime issues of wireless sensor network (WSN). Energy usage optimization and efficient bandwidth utilization are important issues in WSN. Event triggered data aggregation facilitates such optimal tasks for event affected area in WSN. Reliable delivery of the critical information to sink node is also a major challenge of WSN. To tackle these issues, we propose an event driven dynamic clustering and data aggregation scheme for WSN that enhances the life time of the network by minimizing redundant data transmission. The proposed scheme operates as follows: (1) Whenever the event is triggered, event triggered node selects the cluster head. (2) Cluster head gathers data from sensor nodes within the cluster. (3) Cluster head node identifies and classifies the events out of the collected data using Bayesian classifier. (4) Aggregation of data is done using statistical method. (5) Cluster head discovers the paths to the sink node using residual energy, path distance and bandwidth. (6) If the aggregated data is critical, cluster head sends the aggregated data over the multipath for reliable data communication. (7) Otherwise aggregated data is transmitted towards sink node over the single path which is having the more bandwidth and residual energy. The performance of the scheme is validated for various WSN scenarios to evaluate the effectiveness of the proposed approach in terms of aggregation time, cluster formation time and energy consumed for aggregation.

Keywords: wireless sensor network, dynamic clustering, data aggregation, wireless communication

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Authors: Ana Pego

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Environmental technologies were developed for decades. Clean technologies emerged a few years ago. In these perspectives, the use of cleantech technologies has become very important due the fact of new era of environmental feats. As such, the market itself has become more competitive, more collaborative towards a better use of clean technologies. This paper shows the importance of clean technologies in offshore energy sector in Portuguese market, its localization and its impact on economy. Clean technologies are directly related with renewable cluster and concomitant with economic and social resource optimization criteria, geographic aspects, climate change and soil features. Cleantech is related with regional development, socio-technical transitions in organisations. There are an economical and social combinations which allow specialisation of regions in activities, higher employment, reduce of energy costs, local knowledge spillover and, business collaboration and competitiveness. The methodology used will be quantitative (IO matrix for Portugal 2013) and qualitative (questionnaires to stakeholders). The mix of both methodologies will confirm whether the use of technologies will allow a positive impact on economic and social variables used on this model. It is expected a positive impact on Portuguese economy both in investment and employment taking in account the localization of offshore renewable activities. This means that the importance of offshore renewable investment in Portugal has a few points which should be pointed out: the increase of specialised employment, localization of specific activities in territory, and increase of value added in certain regions. The conclusion will allow researchers and organisation to compare the Portuguese model to other European regions in order to a better use of natural and human resources.

Keywords: cleantech, economic impact, localisation, territory dynamics

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Authors: Ammar Y. Alqahtani

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In order to minimize the negative impact on the environment, it is essential to manage the waste that generated from the premature disposal of end-of-life (EOL) products properly. Consequently, government and international organizations introduced new policies and regulations to minimize the amount of waste being sent to landfills. Moreover, the consumers’ awareness regards environment has forced original equipment manufacturers to consider being more environmentally conscious. Therefore, manufacturers have thought of different ways to deal with waste generated from EOL products viz., remanufacturing, reusing, recycling, or disposing of EOL products. The rate of depletion of virgin natural resources and their dependency on the natural resources can be reduced by manufacturers when EOL products are treated as remanufactured, reused, or recycled, as well as this will cut on the amount of harmful waste sent to landfills. However, disposal of EOL products contributes to the problem and therefore is used as a last option. Number of EOL need to be estimated in order to fulfill the components demand. Then, disassembly process needs to be performed to extract individual components and subassemblies. Smart products, built with sensors embedded and network connectivity to enable the collection and exchange of data, utilize sensors that are implanted into products during production. These sensors are used for remanufacturers to predict an optimal warranty policy and time period that should be offered to customers who purchase remanufactured components and products. Sensor-provided data can help to evaluate the overall condition of a product, as well as the remaining lives of product components, prior to perform a disassembly process. In this paper, a multi-period disassembly-to-order (DTO) model is developed that takes into consideration the different system uncertainties. The DTO model is solved using Nonlinear Programming (NLP) in multiple periods. A DTO system is considered where a variety of EOL products are purchased for disassembly. The model’s main objective is to determine the best combination of EOL products to be purchased from every supplier in each period which maximized the total profit of the system while satisfying the demand. This paper also addressed the impact of sensor embedded products on the cost of warranties. Lastly, this paper presented and analyzed a case study involving various simulation conditions to illustrate the applicability of the model.

Keywords: closed-loop supply chains, environmentally conscious manufacturing, product recovery, reverse logistics

Procedia PDF Downloads 125