Search results for: product optimization

Authors: Julius Denafas, Irina Kliopova, Gintaras Denafas

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Three opportunities for implementation of industrial ecology principles in the real industrial production of c-Si solar cells and modules are presented in this study. It includes: material flow dematerialisation, product modification and industrial symbiosis. Firstly, it is shown how the collaboration between R&D institutes and industry helps to achieve significant reduction of material consumption by a) refuse from phosphor silicate glass cleaning process and b) shortening of silicon nitride coating production step. Secondly, it was shown how the modification of solar module design can reduce the CO2 footprint for this product and enhance waste prevention. It was achieved by implementing a frameless glass/glass solar module design instead of glass/backsheet with aluminium frame. Such a design change is possible without purchasing new equipment and without loss of main product properties like efficiency, rigidity and longevity. Thirdly, industrial symbiosis in the solar cell production is possible in such case when manufacturing waste (silicon wafer and solar cell breakage) also used solar modules are collected, sorted and supplied as raw-materials to other companies involved in the production chain of c-Si solar cells. The obtained results showed that solar cells produced from recycled silicon can have a comparable electrical parameters like produced from standard, commercial silicon wafers. The above mentioned work was performed at solar cell producer Soli Tek R&D in the frame of H2020 projects CABRISS and Eco-Solar.

Keywords: manufacturing, process optimisation, recycling, solar cells, solar modules, waste prevention

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Authors: Mia Françoise

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This report aims to develop a strategy for assisting short-term investors to benefit from the current economic climate in South Africa by utilizing technical analysis techniques and predictive analytics. As part of this research, value investing and technical analysis principles will be combined to maximize returns for South African investors while optimizing volatility. As an emerging market, South Africa offers many opportunities for high growth in sectors where other developed countries cannot grow at the same rate. Investing in South African companies with significant growth potential can be extremely rewarding. Although the risk involved is more significant in countries with less developed markets and infrastructure, there is more room for growth in these countries. According to recent research, the offshore market is expected to outperform the local market over the long term; however, short-term investments in the local market will likely be more profitable, as the Johannesburg Stock Exchange is predicted to outperform the S&P500 over the short term. The instabilities in the economy contribute to increased market volatility, which can benefit investors if appropriately utilized. Price prediction and portfolio optimization comprise the two primary components of this methodology. As part of this process, statistics and other predictive modeling techniques will be used to predict the future performance of stocks listed on the Johannesburg Stock Exchange. Following predictive data analysis, Modern Portfolio Theory, based on Markowitz's Mean-Variance Theorem, will be applied to optimize the allocation of assets within an investment portfolio. By combining different assets within an investment portfolio, this optimization method produces a portfolio with an optimal ratio of expected risk to expected return. This methodology aims to provide a short-term investment with a stock portfolio that offers the best risk-to-return profile for stocks listed on the JSE by combining price prediction and portfolio optimization.

Keywords: financial stocks, optimized asset allocation, prediction modelling, South Africa

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Authors: Christoph Opperer

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The rapid urbanization of modern societies and the need for sustainable urban development demand innovative solutions that meet both individual and collective needs while addressing environmental concerns. To address these challenges, this paper presents a study that explores the potential of spatial and energetic/ecological optimization to enhance the performance of urban settlements, focusing on both architectural and urban scales. The study focuses on the application of biological principles and self-organization processes in urban planning and design, aiming to achieve a balance between ecological performance, architectural quality, and individual living conditions. The research adopts a case study approach, focusing on a 10-hectare brownfield site in the south of Vienna. The site is surrounded by a small-scale built environment as an appropriate starting point for the research and design process. However, the selected urban form is not a prerequisite for the proposed design methodology, as the findings can be applied to various urban forms and densities. The methodology used in this research involves dividing the overall building mass and program into individual small housing units. A computational model has been developed to optimize the distribution of these units, considering factors such as solar exposure/radiation, views, privacy, proximity to sources of disturbance (such as noise), and minimal internal circulation areas. The model also ensures that existing vegetation and buildings on the site are preserved and incorporated into the optimization and design process. The model allows for simultaneous optimization at two scales, architectural and urban design, which have traditionally been addressed sequentially. This holistic design approach leads to individual and collective benefits, resulting in urban environments that foster a balance between ecology and architectural quality. The results of the optimization process demonstrate a seemingly random distribution of housing units that, in fact, is a densified hybrid between traditional garden settlements and allotment settlements. This urban typology is selected due to its compatibility with the surrounding urban context, although the presented methodology can be extended to other forms of urban development and density levels. The benefits of this approach are threefold. First, it allows for the determination of ideal housing distribution that optimizes solar radiation for each building density level, essentially extending the concept of sustainable building to the urban scale. Second, the method enhances living quality by considering the orientation and positioning of individual functions within each housing unit, achieving optimal views and privacy. Third, the algorithm's flexibility and robustness facilitate the efficient implementation of urban development with various stakeholders, architects, and construction companies without compromising its performance. The core of the research is the application of global and local optimization strategies to create efficient design solutions. By considering both, the performance of individual units and the collective performance of the urban aggregation, we ensure an optimal balance between private and communal benefits. By promoting a holistic understanding of urban ecology and integrating advanced optimization strategies, our methodology offers a sustainable and efficient solution to the challenges of modern urbanization.

Keywords: sustainable development, self-organization, ecological performance, solar radiation and exposure, daylight, visibility, accessibility, spatial distribution, local and global optimization

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Authors: Kevin Worrall, Euan McGookin

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The use of optimization algorithms is common across a large number of diverse fields. This work presents the use of a hybrid optimization algorithm applied to a mobile robot tasked with carrying out a search of an unknown environment. The algorithm is then applied to the multiple robots case, which results in a reduction in the time taken to carry out the search. The hybrid algorithm is a Random Search Algorithm fused with a Tabu mechanism. The work shows that the algorithm locates the desired points in a quicker time than a brute force search. The Tabu Random algorithm is shown to work within a simulated environment using a validated mathematical model. The simulation was run using three different environments with varying numbers of targets. As an algorithm, the Tabu Random is small, clear and can be implemented with minimal resources. The power of the algorithm is the speed at which it locates points of interest and the robustness to the number of robots involved. The number of robots can vary with no changes to the algorithm resulting in a flexible algorithm.

Keywords: algorithms, control, multi-agent, search and rescue

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Authors: Letizia Marchetti, Federica Annunzi, Federico Fiorini, Cristiano Nicolella

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Automotive shredder residue (ASR) is a mixture of waste that makes up 20-25% of end-of-life vehicles. For many years, ASR was commonly disposed of in landfills or incinerated, causing serious environmental problems. Nowadays, thermochemical treatments are a promising alternative, although the heterogeneity of ASR still poses some challenges. One of the emerging thermochemical treatments for ASR is pyrolysis, which promotes the decomposition of long polymeric chains by providing heat in the absence of an oxidizing agent. In this way, pyrolysis promotes the conversion of ASR into solid, liquid, and gaseous phases. This work aims to improve the performance of a two-step pyrolysis process. After the characterization of the analysed ASR, the focus is on determining the effects of residence time on product yields and gas composition. A batch experimental setup that reproduces the entire process was used. The setup consists of three sections: the pyrolysis section (made of two reactors), the separation section, and the analysis section. Two different residence times were investigated to find suitable conditions for the first sample of ASR. These first tests showed that the products obtained were more sensitive to residence time in the second reactor. Indeed, slightly increasing residence time in the second reactor managed to raise the yield of gas and carbon residue and decrease the yield of liquid fraction. Then, to test the versatility of the setup, the same conditions were applied to a different sample of ASR coming from a different chemical plant. The comparison between the two ASR samples shows that similar product yields and compositions are obtained using the same setup.

Keywords: automotive shredder residue, experimental tests, heterogeneity, product yields, two-step pyrolysis

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Authors: Jin Gang Lee, Hyun-Soo Lee, Moonseo Park

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The utilization of BIM and IFC allows project participants to collaborate across different areas by consistently sharing interoperable product information represented in a model. Comments or markups generated during the coordination process can be categorized as communication information, which can be shared in less standardized manner. It can be difficult to manage and reuse such information compared to the product information in a model. The present study proposes an interoperable coordination method using BCF (the BIM Collaboration Format) for managing and sharing the communication information during BIM based coordination process. A management function for coordination in the BIM collaboration system is developed to assess its ability to share the communication information in BIM collaboration projects. This approach systematically links communication information during the coordination process to the building model and serves as a type of storage system for retrieving knowledge created during BIM collaboration projects.

Keywords: design coordination, building information model, BIM collaboration format, industry foundation classes

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Authors: Giuseppe Timperio, Robert De Souza

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The combination of factors such as temperamental climate change, rampant urbanization of risk exposed areas, political and social instabilities, is posing an alarming base for the further growth of number and magnitude of humanitarian crises worldwide. Given the unique features of humanitarian supply chain such as unpredictability of demand in space, time, and geography, spike in the number of requests for relief items in the first days after the calamity, uncertain state of logistics infrastructures, large volumes of unsolicited low-priority items, a proactive approach towards design of disaster response operations is needed to achieve high agility in mobilization of emergency supplies in the immediate aftermath of the event. This paper is an attempt in that direction, and it provides decision makers with crucial strategic insights for a more effective network design for disaster response. Decision sciences and ICT are integrated to analyse the robustness and resilience of a prepositioned network of emergency strategic stockpiles for a real-life case about Indonesia, one of the most vulnerable countries in Asia-Pacific, with the model being built upon a rich set of quantitative data. At this aim, a network optimization approach was implemented, with several what-if scenarios being accurately developed and tested. Findings of this study are able to support decision makers facing challenges related with disaster relief chains resilience, particularly about optimal configuration of supply chain facilities and optimal flows across the nodes, while considering the network structure from an end-to-end in-country distribution perspective.

Keywords: disaster preparedness, humanitarian logistics, network optimization, resilience

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Authors: Farideh Tabatabei-Yazdi, Fereshteh Falah, Alireza Vasiee

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Nowadays, production of functional foods has become very essential. Inulin is one of the most functional hydrocolloid compounds used in such products. In the present study, the production of a synbiotic yogurt containing 1, 2.5, and 5% (w/v) inulin has been investigated. The yogurt was fermented with Lactobacillus brevis PML1 derived from Tarkhineh, an Iranian cereal-dairy fermented food. Furthermore, the physicochemical properties, antioxidant activity, sensory attributes, and microbial viability properties were investigated on the 0th, 7th, and 14th days of storage after fermentation. The viable cells of L. brevis PML1 reached 108 CFU/g, and the product resisted to simulated digestive juices. Moreover, the synbiotic yogurt impressively increased the production of antimicrobial compounds and had the most profound antimicrobial effect on S. typhimurium. The physiochemical properties were in the normal range, and the fat content of the synbiotic yogurt was reduced remarkably. The antioxidant capacity of the fermented yogurt was significantly increased (p<0:05), which was equal to those of DPPH (69:18±1:00%) and BHA (89:16±2:00%). The viability of L. brevis PML1 was increased during storage. Sensory analysis showed that there were significant differences in terms of the impressive parameters between the samples and the control (p<0:05). Addition of 2.5% inulin not only improved the physical properties but also retained the viability of the probiotic after 14 days of storage, in addition to the viability of L. brevis with a viability count above 6 log CFU/g in the yogurt. Therefore, a novel synbiotic product containing L. brevis PML1, which can exert the desired properties, can be used as a suitable carrier for the delivery of the probiotic strain, exerting its beneficial health effects.

Keywords: functional food, lactobacillus brevis, symbiotic yogurt, physiochemical properties

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Authors: Jiayuan Wu. Lu Hu

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With the development of the sharing economy and mobile technology, carsharing becomes more popular. In this paper, we focus on the joint optimization of one-way station-based carsharing systems. We model the problem as an integer linear program with six elements: station locations, station capacity, fleet size, initial vehicle allocation, vehicle relocation, and demand selection. A greedy-based heuristic is proposed to address the model. Firstly, initialization based on the location variables relaxation using Gurobi solver is conducted. Then, according to the profit margin and demand satisfaction of each station, the number of stations is downsized iteratively. This method is applied to real data from Chengdu, Sichuan taxi data, and it’s efficient when dealing with a large scale of candidate stations. The result shows that with vehicle relocation and demand selection, the profit and demand satisfaction of carsharing systems are increased.

Keywords: one-way carsharing, location, vehicle relocation, demand selection, greedy algorithm

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Authors: Mutiara Effendi

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Nunukan Island is located on North Kalimantan of Indonesia. The region is one of Indonesia’s cross-border with Malaysia. In conjunction with its strategic geographic location, its potential as the new oil and gas resources has brought many researchers to do their studies here. The research area consists of claystone which criss-crossed with quarts sandstone. There are also rocks claystone-grained which are the weathering product of basaltic volcanic rocks. In some places, there are argillic clays which are the hydrothermal-altered product of Sei Apok ancient volcano. Geochemical study was established to learn the origin of the claystones, whether it came from weathering, hydrothermal alteration, or both. The samples used in this research are fresh rock, weathering rocks, hydrothermally-altered rock, and claystones. Chemical compositions of each sample were determined and their relations was studied. The studies encompass major and minor elements analysis using X-Ray Fluoresence (XRF) method and trace elements analysis, specifically rare earth elements, using Inductively Coupled Plasma-Mass Spectrometry (ICP-MS) method. The results were plotted on certain graphics to learn about the trend and the relations of each sample and element. Any changes in chemical compositions, like increase and decrease of elements or species, was analysed to learn about geological phenomenon that happens during the formation of claystones. The result of this study shows that claystones of Nunukan Island have relation with volcanic rocks of its surrounding area. Its chemical composition profile corresponds to weathering product of volcanic rocks rather than hydrothermally-altered product. The general profile also resembles claystone minerals of illite or montmorillonite, especially in the existence of aluminum, iron, potassium, and magnesium. Both minerals are formed in basic condition and commonly happen to shales. It is consistent with the fact that claystone was found mixing with shales and silt to clay grained mudstones in field exploration. Even though the general profile is much alike, the amount of each elements is not precisely the same as theoretically claystone mineral compositions because the mineral have not formed completely yet.

Keywords: claystone, geochemistry, ICP-MS, XRF

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Authors: Thirunavoukkarasu Manikkannan, Karpanai Selvan Balasubramanian

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Textile industry is the one of the most important industrial sector in India. It accounts for 5% of total Gross Domestic Product (GDP) in the country. A Textile industry consumes large quantities of water (~250 m3/ton of product) and they generate almost ~90% of wastewater from its consumption. The problem is alarming and requires proper treatment process to acquire dual benefit of Zero Liquid Discharge and no contamination to the environment. Here we describe the process by which the textile wastewater can be reused. We have collected the textile wastewater in and around Ayyampettai area of Tamilnadu, India. Among different microalgal strains used, Desodesmus sp. collected at Manali, Chennai, Tamilnadu, India was able to lessen the colour of the waste water in 12-15 hrs of its growth, COD around 81.7%, Dissolved solid reduction was 28 ± 0.5 %, Suspended solid was reduced to 40.5 ± 0.3 %, Dye degradation was 50-78%. Further, Desodesmus sp. able to achieve the biomass of 0.9 ± 0.2 g/L (dry weight) in two weeks’ time, the Chl a content was 11 mg/L. It infers that this algal strain able to utilize the textile wastewater as source for growth and algal biomass production.

Keywords: Desodesmus sp., microalgae, textile, treatment, wastewater

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Authors: Vidyarini Dwita, Rebecca Fanany

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This study will discuss the growth of the market for smartphone technology in Indonesia. This country, with the world’s fourth largest population, has a reputation as the social media capital of the world, and this reputation is largely justified. The penetration of social media is high in Indonesia which has one of the largest global markets. Most Indonesian users of Facebook, Twitter and other social media platforms access the sites from their mobile phones. Indonesia is expected to continue to be a major market for digital mobile devices, such as smartphone and tablets that can access the internet. The aim of this study to describe the way responses of Indonesian consumers to smartphone advertising using English and Indonesian will impact on their perceptions of the prestige and product image of the advertised items and thus influence consumer intention to purchase the item. Advertising for smartphones and similar products is intense and dynamic and often draws on the social attitudes of Indonesians with respect to linguistic and cultural content and especially appeals to their desire to be part of global mainstream culture. The study uses a qualitative method based on in-depth interviews with 30 participants. Content analysis is employed to analyse the responses of Indonesian consumers to smartphone advertising that uses English and Indonesian text. Its findings indicate that consumers’ impressions of English and Indonesian slogans influence their attitudes toward smartphones, suggesting that linguistic context plays a role in influencing consumer purchases.

Keywords: consumer purchases, marketing communication, product image, smartphone advertising, sociolinguistic

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Authors: Muhammad Fezan Afzal, Asad Abbas, Imran Khan, Salma Imtiaz

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Software Product Line (SPL) paradigm is used for the development of the family of software products that share common and variable features. Feature model is a domain of SPL that consists of common and variable features with predefined relationships and constraints. Multiple SPLs consist of a number of similar common and variable features, such as mobile phones and Tabs. Reusability of common and variable features from the different domains of SPL is a complex task due to the external relationships and constraints of features in the feature model. To increase the reusability of feature model resources from domain engineering, it is required to manage the commonality of features at the level of SPL application development. In this research, we have proposed an approach that combines multiple SPLs into a single domain and converts them to a common feature model. Extracting the common features from different feature models is more effective, less cost and time to market for the application development. For extracting features from multiple SPLs, the proposed framework consists of three steps: 1) find the variation points, 2) find the constraints, and 3) combine the feature models into a single feature model on the basis of variation points and constraints. By using this approach, reusability can increase features from the multiple feature models. The impact of this research is to reduce the development of cost, time to market and increase products of SPL.

Keywords: software product line, feature model, variability management, multi-SPLs

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Authors: Wenqian Shi, Xie He, Ziyin Huang, Zi Yu

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The dramatic changes in the global economic situation have produced dramatic changes to companies’ supply chain systems. A variety of opportunities and challenges make the traditional manufacturing industry feel pressured, and the manufacturing industry must seek a new way out as soon as possible. This paper presents a case study of the advanced planning and scheduling technology problem encountered by an electrical and electronics manufacturer. The objective is to seek the minimum cost of production planning and order management. Digitalization is applied to the problem, and the results demonstrate that significant production performances can be achieved in the face of the existing production of each link and order management systems to analyze and optimize. This paper can also provide some practical implications in various manufacturing industries. Finally, future research directions are discussed.

Keywords: advanced planning and scheduling, case study, production planning, supply chain optimization

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Authors: Jesús Antonio Puente Fernández, Luis Javier Garcia Villalba

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New trends on streaming videos such as series or films require a high demand of network resources. This fact results in a huge problem within traditional IP networks due to the rigidity of its architecture. In this way, Software Defined Networks (SDN) is a new concept of network architecture that intends to be more flexible and it simplifies the management in networks with respect to the existing ones. These aspects are possible due to the separation of control plane (controller) and data plane (switches). Taking the advantage of this separated control, it is easy to deploy a monitoring tool independent of device vendors since the existing ones are dependent on the installation of specialized and expensive hardware. In this paper, we propose a framework that optimizes the traffic monitoring in SDN networks that decreases the number of monitoring queries to improve the network traffic and also reduces the overload. The performed experiments (with and without the optimization) using a video streaming delivery between two hosts demonstrate the feasibility of our monitoring proposal.

Keywords: optimization, monitoring, software defined networking, statistics, query

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Authors: Fanqiang Kong, Chending Bian

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In this work, we exploit two assumed properties of the abundances of the observed signatures (endmembers) in order to reconstruct the abundances from hyperspectral data. Joint-sparsity is the first property of the abundances, which assumes the adjacent pixels can be expressed as different linear combinations of same materials. The second property is rank-deficiency where the number of endmembers participating in hyperspectral data is very small compared with the dimensionality of spectral library, which means that the abundances matrix of the endmembers is a low-rank matrix. These assumptions lead to an optimization problem for the sparse unmixing model that requires minimizing a combined l_2,p-norm and nuclear norm. We propose a variable splitting and augmented Lagrangian algorithm to solve the optimization problem. Experimental evaluation carried out on synthetic and real hyperspectral data shows that the proposed method outperforms the state-of-the-art algorithms with a better spectral unmixing accuracy.

Keywords: hyperspectral unmixing, joint-sparse, low-rank representation, abundance estimation

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Authors: Stefano Zaninotto, Geoffrey Farrugia, Johan Debattista, Jason Gauci

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The need to reduce fuel and noise during taxi operations in the airports with a scenario of constantly increasing air traffic has resulted in an effort by the aerospace industry to move towards electric taxiing. In fact, this is one of the problems that is currently being addressed by SESAR JU and two main solutions are being proposed. With the first solution, electric motors are installed in the main (or nose) landing gear of the aircraft. With the second solution, manned or unmanned electric tow trucks are used to tow aircraft from the gate to the runway (or vice-versa). The presence of the tow trucks results in an increase in vehicle traffic inside the airport. Therefore, it is important to design the system in a way that the workload of Air Traffic Control (ATC) is not increased and the system assists ATC in managing all ground operations. The aim of this work is to develop an electric taxiing system, based on the use of autonomous tow trucks, which optimizes aircraft ground operations while keeping ATC in the loop. This system will consist of two components: an optimization tool and a Graphical User Interface (GUI). The optimization tool will be responsible for determining the optimal path for arriving and departing aircraft; allocating a tow truck to each taxiing aircraft; detecting conflicts between aircraft and/or tow trucks; and proposing solutions to resolve any conflicts. There are two main optimization strategies proposed in the literature. With centralized optimization, a central authority coordinates and makes the decision for all ground movements, in order to find a global optimum. With the second strategy, called decentralized optimization or multi-agent system, the decision authority is distributed among several agents. These agents could be the aircraft, the tow trucks, and taxiway or runway intersections. This approach finds local optima; however, it scales better with the number of ground movements and is more robust to external disturbances (such as taxi delays or unscheduled events). The strategy proposed in this work is a hybrid system combining aspects of these two approaches. The GUI will provide information on the movement and status of each aircraft and tow truck, and alert ATC about any impending conflicts. It will also enable ATC to give taxi clearances and to modify the routes proposed by the system. The complete system will be tested via computer simulation of various taxi scenarios at multiple airports, including Malta International Airport, a major international airport, and a fictitious airport. These tests will involve actual Air Traffic Controllers in order to evaluate the GUI and assess the impact of the system on ATC workload and situation awareness. It is expected that the proposed system will increase the efficiency of taxi operations while reducing their environmental impact. Furthermore, it is envisaged that the system will facilitate various controller tasks and improve ATC situation awareness.

Keywords: air traffic control, electric taxiing, autonomous tow trucks, graphical user interface, ground operations, multi-agent, route optimization

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Authors: Changqiao Wu, Guoqing Ding, Xin Chen

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In this paper, ways of modeling dynamic measurement systems are discussed. Specially, for linear system with single-input single-output, it could be modeled with shallow neural network. Then, gradient based optimization algorithms are used for searching the proper coefficients. Besides, method with normal equation and second order gradient descent are proposed to accelerate the modeling process, and ways of better gradient estimation are discussed. It shows that the mathematical essence of the learning objective is maximum likelihood with noises under Gaussian distribution. For conventional gradient descent, the mini-batch learning and gradient with momentum contribute to faster convergence and enhance model ability. Lastly, experimental results proved the effectiveness of second order gradient descent algorithm, and indicated that optimization with normal equation was the most suitable for linear dynamic models.

Keywords: dynamic system modeling, neural network, normal equation, second order gradient descent

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Authors: Dejan Tanikić, Miodrag Manić, Jelena Đoković, Saša Kalinović

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This paper deals with the determination of the optimum machining parameters, according to the measured and modelled data of the cutting temperature and surface roughness, during the turning of the AISI 4140 steel. The high cutting temperatures are unwanted occurences in the metal cutting process. They impact negatively on the quality of the machined part. The machining experiments were performed using different cutting regimes (cutting speed, feed rate and depth of cut), with different values of the workpiece hardness, which causes different values of the measured cutting temperature as well as the measured surface roughness. The temperature and surface roughness data were modelled after that using Response Surface Methodology (RSM). The obtained RSM models are used in the process of optimization of the cutting regimes using the Genetic Algorithms (GA) tool, which enables the metal cutting process in the optimum conditions.

Keywords: genetic algorithms, machining parameters, response surface methodology, turning process

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Authors: Manendra Singh Parihar, Sandip Ghosh Chowdhury

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Austenitic stainless steels are widely used and give a good combination of properties. When this steel is plastically deformed, a phase transformation of the metastable Face Centred Cubic Austenite to the stable Body Centred Cubic (α’) or to the Hexagonal close packed (ԑ) martensite may occur, leading to the enhancement in the mechanical properties like strength. The work was based on variant selection and corresponding texture analysis for the strain induced martensitic transformation during deformation of the parent austenite FCC phase to form the product HCP and the BCC martensite phases separately, obeying their respective orientation relationships. The automated method for reconstruction of the parent phase orientation using the EBSD data of the product phase orientation is done using the MATLAB and TSL-OIM software. The method of triplets was used which involves the formation of a triplet of neighboring product grains having a common variant and linking them using a misorientation-based criterion. This led to the proper reconstruction of the pre-transformation phase orientation data and thus to its microstructure and texture. The computational speed of current method is better compared to the previously used methods of reconstruction. The reconstruction of austenite from ԑ and α’ martensite was carried out for multiple samples and their IPF images, pole figures, inverse pole figures and ODFs were compared. Similar type of results was observed for all samples. The comparison gives the idea for estimating the correct sequence of the transformation i.e. γ → ε → α’ or γ → α’, during deformation of AISI 304 austenitic stainless steel.

Keywords: variant selection, reconstruction, EBSD, austenitic stainless steel, martensitic transformation

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Authors: Ujwal Kishor Zore, Shankar Balajirao Kausley, Aniruddha Bhalchandra Pandit

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There are some important design parameters of activated sludge process (ASP) for wastewater treatment and they must be optimally defined to have the optimized plant working. To know them, developing a mathematical model is a way out as it is nearly commensurate the real world works. In this study, a mathematical model was developed for ASP, solved under activated sludge model no 1 (ASM 1) conditions and MATLAB tool was used to solve the mathematical equations. For its real-life validation, the developed model was tested for the inputs from the municipal wastewater treatment plant and the results were quite promising. Additionally, the most cardinal assumptions required to design the treatment plant are discussed in this paper. With the need for computerization and digitalization surging in every aspect of engineering, this mathematical model developed might prove to be a boon to many biological wastewater treatment plants as now they can in no time know the design parameters which are required for a particular type of wastewater treatment.

Keywords: waste water treatment, activated sludge process, mathematical modeling, optimization

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Authors: Mohammad Sadeghian, Mohsen Sadeghian

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In this paper, planning catamaran and mono-hull vessels resistance and trim in calm waters were considered. Hydrodynamic analysis of fast mono-hull planning vessel was also investigated. For hull form geometry optimization, numerical methods of different parameters were used for this type of vessels. Hull material was selected as carbon fiber composite. Exact architectural aspects were specified and stability calculations were performed, as well. Hydrodynamic calculations to extract the resistance force using semi-analytical methods and numerical modeling were carried out. Free surface numerical analysis of vessel in designed draft using finite volume method and double phase were evaluated and verified by experimental tests.

Keywords: fast vessel, hydrostatic and hydrodynamic optimization, free surface flow, computational fluid dynamics

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Authors: Kyoungwoo Park, Gil Dong Kim, Seong Joon Moon, Ho Kil Lee

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The Urea-SCR is one of the most efficient technologies to reduce NOx emissions in diesel engines. In the present work, the computational prediction of internal flow and spray characteristics in the Urea-SCR system was carried out by using 3D-CFD simulation to evaluate NH3 uniformity index (NH3 UI) and its activation time according to the official New European Driving Cycle (NEDC). The number of nozzle and its diameter, two types of injection directions, and penetration length were chosen as the design variables. The optimal solutions were obtained by coupling the CFD analysis with Taguchi method. The L16 orthogonal array and small-the-better characteristics of the Taguchi method were used, and the optimal values were confirmed to be valid with 95% confidence and 5% significance level through analysis of variance (ANOVA). The results show that the optimal solutions for the NH3 UI and activation time (NH3 UI 0.22) are obtained by 0.41 and 0,125 second, respectively, and their values are improved by 85.0% and 10.7%, respectively, compared with those of the base model.

Keywords: computational fluid dynamics, NH3 uniformity index, optimization, Taguchi method, Urea-SCR system, UWS injector

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Authors: Abhishek Jain, Kavish Kejriwal, V. Balaji Rao, Abhigna Chavda

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Increasing environmental and social concerns are forcing companies to take a fresh view of the impact of supply chain operations on environment and society when designing a supply chain. A challenging task in today’s food industry is the distribution of high-quality food items throughout the food supply chain. Improper storage and unwanted transportation are the major hurdles in food supply chain and can be tackled by making dynamic storage facility location decisions with the distribution network. Since food supply chain in India is one of the biggest supply chains in the world, the companies should also consider environmental impact caused by the supply chain. This project proposes a multi-objective optimization model by integrating sustainability in decision-making, on distribution in a food supply chain network (SCN). A Multi-Objective Mixed-Integer Linear Programming (MOMILP) model between overall cost and environmental impact caused by the SCN is formulated for the problem. The goal of MOMILP is to determine the pareto solutions for overall cost and environmental impact caused by the supply chain. This is solved by using GAMS with CPLEX as third party solver. The outcomes of the project are pareto solutions for overall cost and environmental impact, facilities to be operated and the amount to be transferred to each warehouse during the time horizon.

Keywords: multi-objective mixed linear programming, food supply chain network, GAMS, multi-product, multi-period, environment

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Authors: Madia Safdar, G. Amjad Hussain, Mashhood Ahmad

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One of the major challenges of today’s era is peak demand which causes stress on the transmission lines and also raises the cost of energy generation and ultimately higher electricity bills to the end users, and it was used to be managed by the supply side management. However, nowadays this has been withdrawn because of existence of potential in the demand side management (DSM) having its economic and- environmental advantages. DSM in domestic load can play a vital role in reducing the peak load demand on the network provides a significant cost saving. In this paper the potential of demand response (DR) in reducing the peak load demands and electricity bills to the electric users is elaborated. For this purpose the domestic appliances are modeled in MATLAB Simulink and controlled by a module called energy management controller. The devices are categorized into controllable and uncontrollable loads and are operated according to real-time tariff pricing pattern instead of fixed time pricing or variable pricing. Energy management controller decides the switching instants of the controllable appliances based on the results from optimization algorithms. In GAMS software, the MILP (mixed integer linear programming) algorithm is used for optimization. In different cases, different constraints are used for optimization, considering the comforts, needs and priorities of the end users. Results are compared and the savings in electricity bills are discussed in this paper considering real time pricing and fixed tariff pricing, which exhibits the existence of potential to reduce electricity bills and peak loads in demand side management. It is seen that using real time pricing tariff instead of fixed tariff pricing helps to save in the electricity bills. Moreover the simulation results of the proposed energy management system show that the gained power savings lie in high range. It is anticipated that the result of this research will prove to be highly effective to the utility companies as well as in the improvement of domestic DR.

Keywords: controllable and uncontrollable domestic loads, demand response, demand side management, optimization, MILP (mixed integer linear programming)

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Authors: Sadaf Sahar, Usman Qamar, Sadaf Ayaz

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In the software development lifecycle, the quality prediction techniques hold a prime importance in order to minimize future design errors and expensive maintenance. There are many techniques proposed by various researchers, but with the increasing complexity of the software lifecycle model, it is crucial to develop a flexible system which can cater for the factors which in result have an impact on the quality of the end product. These factors include properties of the software development process and the product along with its operation conditions. In this paper, a neural network (perceptron) based software quality prediction technique is proposed. Using this technique, the stakeholders can predict the quality of the resulting software during the early phases of the lifecycle saving time and resources on future elimination of design errors and costly maintenance. This technique can be brought into practical use using successful training.

Keywords: software quality, fuzzy logic, perception, prediction

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Authors: Sebastian Kehne, Alexander Epple, Werner Herfs

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A new method for optimal selection of components for multi-axes forward-feed drive systems is proposed in which the choice of motors, gear boxes and ball screw drives is optimized. Essential is here the synchronization of electrical and mechanical frequency behavior of all axes because even advanced controls (like H∞-controls) can only control a small part of the mechanical modes – namely only those of observable and controllable states whose value can be derived from the positions of extern linear length measurement systems and/or rotary encoders on the motor or gear box shafts. Further problems are the unknown processing forces like cutting forces in machine tools during normal operation which make the estimation and control via an observer even more difficult. To start with, the open source Modelica Feed Drive Library which was developed at the Laboratory for Machine Tools, and Production Engineering (WZL) is extended from one axis design to the multi axes design. It is capable to simulate the mechanical, electrical and thermal behavior of permanent magnet synchronous machines with inverters, different gear boxes and ball screw drives in a mechanical system. To keep the calculation time down analytical equations are used for field and torque producing equivalent circuit, heat dissipation and mechanical torque at the shaft. As a first step, a small machine tool with a working area of 635 x 315 x 420 mm is taken apart, and the mechanical transfer behavior is measured with an impulse hammer and acceleration sensors. With the frequency transfer functions, a mechanical finite element model is built up which is reduced with substructure coupling to a mass-damper system which models the most important modes of the axes. The model is modelled with Modelica Feed Drive Library and validated by further relative measurements between machine table and spindle holder with a piezo actor and acceleration sensors. In a next step, the choice of possible components in motor catalogues is limited by derived analytical formulas which are based on well-known metrics to gain effective power and torque of the components. The simulation in Modelica is run with different permanent magnet synchronous motors, gear boxes and ball screw drives from different suppliers. To speed up the optimization different black-box optimization methods (Surrogate-based, gradient-based and evolutionary) are tested on the case. The objective that was chosen is to minimize the integral of the deviations if a step is given on the position controls of the different axes. Small values are good measures for a high dynamic axes. In each iteration (evaluation of one set of components) the control variables are adjusted automatically to have an overshoot less than 1%. It is obtained that the order of the components in optimization problem has a deep impact on the speed of the black-box optimization. An approach to do efficient black-box optimization for multi-axes design is presented in the last part. The authors would like to thank the German Research Foundation DFG for financial support of the project “Optimierung des mechatronischen Entwurfs von mehrachsigen Antriebssystemen (HE 5386/14-1 | 6954/4-1)” (English: Optimization of the Mechatronic Design of Multi-Axes Drive Systems).

Keywords: ball screw drive design, discrete optimization, forward feed drives, gear box design, linear drives, machine tools, motor design, multi-axes design

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Authors: Kemka Ihemelandu, Chukwuemeka Ihemelandu

Abstract:

Melanoma remains a public health crisis, with incidence rates increasing rapidly in the past decades. Improving diagnostic accuracy to decrease misdiagnosis using Artificial intelligence (AI) continues to be documented. Unfortunately, unintended racially biased outcomes, a product of lack of diversity in the dataset used, with a noted class imbalance favoring lighter vs. darker skin tone, have increasingly been recognized as a problem.Resulting in noted limitations of the accuracy of the Convolutional neural network (CNN)models. CNN models are prone to biased output due to biases in the dataset used to train them. Our aim in this study was the optimization of convolutional neural network algorithms to mitigate bias in the automated diagnosis of melanoma. We hypothesized that our proposed training algorithms based on a data augmentation method to optimize the diagnostic accuracy of a CNN classifier by generating new training samples from the original ones will reduce bias in the automated diagnosis of melanoma. We applied geometric transformation, including; rotations, translations, scale change, flipping, and shearing. Resulting in a CNN model that provided a modifiedinput data making for a model that could learn subtle racial features. Optimal selection of the momentum and batch hyperparameter increased our model accuracy. We show that our augmented model reduces bias while maintaining accuracy in the automated diagnosis of melanoma.

Keywords: bias, augmentation, melanoma, convolutional neural network

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Authors: Daniela Tapusi, Adrian Andronic, Naomi Tufan, Ruxandra Erbașu, Ioana Teodorescu

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The proposed theme of this article falls within the policy of reducing carbon emissions imposed by the ‘Green New Deal’ by replacing structural elements made of energy-intensive materials with ecological materials. In this sense, wood has many qualities (high strength/mass and stiffness/mass ratio, low specific gravity, recovery/recycling) that make it competitive with classic building materials. The topological optimization of the linear glulam elements, resulting from different types of analysis (Finite Element Method, simple regression on metamodels), tests on models or by Monte-Carlo simulation, leads to a material reduction of more than 10%. This article proposes a method of obtaining topologically optimized shapes for different types of glued laminated timber beams. The results obtained will constitute the database for AI training.

Keywords: timber, glued laminated timber, artificial-intelligence, environment, carbon emissions

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Authors: Vithaya Intraphimol

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The purpose of this study is to investigate the relationship between market orientation and business performance through innovations that include product innovation and process innovation. Auto parts and accessories companies in Thailand were used as sample for this investigation. Survey research with structured questionnaire was used as the key instrument in collecting the data. The structural equation modeling (SEM) was assigned test the hypotheses. The sample size in this study requires the minimum sample size of 200. The result found that competitor orientation, and interfunctional coordination has an effect on product innovation. Moreover, interfunctional coordination has an effect on process innovation, and return on asset. This indicates that within- firm coordination has crucial to firms’ performances. The implication for practice, firms should support interfunctional coordination that members of different functional areas of an organization communicate and work together for the creation of value to target buyers they may have better profitability.

Keywords: auto parts industry, business performance, innovations, market orientation

Procedia PDF Downloads 292