Search results for: smart manufacturing

Authors: Chhavi Rana

Abstract:

The aim of the paper is to highlight the existing development in the field of big data analytics. Applications like bioinformatics, smart infrastructure projects, healthcare, and business intelligence contain voluminous and incremental data which is hard to organise and analyse and can be dealt with using the framework and model in this field of study. An organisation decision-making strategy can be enhanced by using big data analytics and applying different machine learning techniques and statistical tools to such complex data sets that will consequently make better things for society. This paper reviews the current state of the art in this field of study as well as different application domains of big data analytics. It also elaborates various frameworks in the process of analysis using different machine learning techniques. Finally, the paper concludes by stating different challenges and issues raised in existing research.

Keywords: big data, big data analytics, machine learning, review

Procedia PDF Downloads 95

Authors: Rachael Olubukola Afolabi

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Immersive technologies known as X Reality or Cross Reality that include virtual reality augmented reality, and mixed reality have pervaded into the education system at different levels from elementary school to adult learning. Instructors, instructional designers, and learning experience specialists continue to find new ways to engage students in the learning process using technology. While the progression of web technologies has enhanced digital learning experiences, analytics on learning outcomes continue to be explored to determine the relevance of these technologies in learning. Digital learning has evolved from web 1.0 (static) to 4.0 (dynamic and interactive), and this evolution of technologies has also advanced teaching methods and approaches. This paper explores how these technologies are being utilized in learning and the results that educators and learners have identified as effective learning opportunities and approaches.

Keywords: immersive technologoes, virtual reality, augmented reality, technology in learning

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Authors: Dylan Santos De Pinho, Nabil Ouerhani

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Energy consumption of machine-tools is becoming critical for machine-tool builders and end-users because of economic, ecological and legislation-related reasons. Many machine-tool builders are seeking for solutions that allow the reduction of energy consumption of machine-tools while preserving the same productivity rate and the same quality of machined parts. In this paper, we present the first results of a project conducted jointly by academic and industrial partners to reduce the energy consumption of a Swiss-Type lathe. We employ genetic algorithms to find optimal machining parameters – the set of parameters that lead to the best trade-off between energy consumption, part quality and tool lifetime. Three main machining process parameters are considered in our optimization technique, namely depth of cut, spindle rotation speed and material feed rate. These machining process parameters have been identified as the most influential ones in the configuration of the Swiss-type machining process. A state-of-the-art multi-objective genetic algorithm has been used. The algorithm combines three fitness functions, which are objective functions that permit to evaluate a set of parameters against the three objectives: energy consumption, quality of the machined parts, and tool lifetime. In this paper, we focus on the investigation of the fitness function related to energy consumption. Four different energy consumption related fitness functions have been investigated and compared. The first fitness function refers to the Kienzle cutting force model. The second fitness function uses the Material Removal Rate (RMM) as an indicator of energy consumption. The two other fitness functions are non-deterministic, learning-based functions. One fitness function uses a simple Neural Network to learn the relation between the process parameters and the energy consumption from experimental data. Another fitness function uses Lasso regression to determine the same relation. The goal is, then, to find out which fitness functions predict best the energy consumption of a Swiss-Type machining process for the given set of machining process parameters. Once determined, these functions may be used for optimization purposes – determine the optimal machining process parameters leading to minimum energy consumption. The performance of the four fitness functions has been evaluated. The Tornos DT13 Swiss-Type Lathe has been used to carry out the experiments. A mechanical part including various Swiss-Type machining operations has been selected for the experiments. The evaluation process starts with generating a set of CNC (Computer Numerical Control) programs for machining the part at hand. Each CNC program considers a different set of machining process parameters. During the machining process, the power consumption of the spindle is measured. All collected data are assigned to the appropriate CNC program and thus to the set of machining process parameters. The evaluation approach consists in calculating the correlation between the normalized measured power consumption and the normalized power consumption prediction for each of the four fitness functions. The evaluation shows that the Lasso and Neural Network fitness functions have the highest correlation coefficient with 97%. The fitness function “Material Removal Rate” (MRR) has a correlation coefficient of 90%, whereas the Kienzle-based fitness function has a correlation coefficient of 80%.

Keywords: adaptive machining, genetic algorithms, smart manufacturing, parameters optimization

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Authors: Farouk Lawan Gambo, Hamada Mohammad

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Data compression shrinks files into fewer bits than their original presentation. It has more advantage on the internet because the smaller a file, the faster it can be transferred but learning most of the concepts in data compression are abstract in nature, therefore, making them difficult to digest by some students (engineers in particular). This paper studies the learning preference of engineering students who tend to have strong, active, sensing, visual and sequential learning preferences, the paper also studies the three shift of technology-aided that learning has experienced, which mobile learning has been considered to be the feature of learning that will integrate other form of the education process. Lastly, we propose a design and implementation of mobile learning application using software engineering methodology that will enhance the traditional teaching and learning of data compression techniques.

Keywords: data compression, learning preference, mobile learning, multimedia

Procedia PDF Downloads 447

Authors: Thomas Wimmer, Bernhard Weigand

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The improvement of gas turbine efficiency is one of the main drivers of research and development in the gas turbine market. This has led to elevated gas turbine inlet temperatures beyond the melting point of the utilized materials. The turbine parts need to be actively cooled in order to withstand these harsh environments. However, the usage of compressor air as coolant decreases the overall gas turbine efficiency. Thus, coolant consumption needs to be minimized in order to gain the maximum advantage from higher turbine inlet temperatures. Therefore, sophisticated cooling designs for gas turbine parts aim to minimize coolant mass flow. New design space is accessible as additive manufacturing is maturing to industrial usage for the creation of hot gas flow path parts. By making use of this technology more efficient cooling schemes can be manufacture. In order to find such cooling schemes a generative design method is being developed. It generates cooling schemes randomly which adhere to a set of rules. These assure the sanity of the design. A huge amount of different cooling schemes are generated and implemented in a simulation environment where it is validated. Criteria for the fitness of the cooling schemes are coolant mass flow, maximum temperature and temperature gradients. This way the whole design space is sampled and a Pareto optimum front can be identified. This approach is applied to a flat plate, which resembles a simplified section of a hot gas flow path part. Realistic boundary conditions are applied and thermal barrier coating is accounted for in the simulation environment. The resulting cooling schemes are presented and compared to representative conventional cooling schemes. Further development of this method can give access to cooling schemes with an even better performance having higher complexity, which makes use of the available design space.

Keywords: additive manufacturing, cooling, gas turbine, heat transfer, heat transfer design, optimization

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Authors: Atish Bagchi, Siva Chandrasekaran

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Background: According to World Bank national accounts data, the estimated global manufacturing value-added output in 2020 was 13.74 trillion USD. These manufacturing processes are monitored, modelled, and controlled by advanced, real-time, computer-based systems, e.g., Industrial IoT, PLC, SCADA, etc. These systems measure and manipulate a set of physical variables, e.g., temperature, pressure, etc. Despite the use of IoT, SCADA etc., in manufacturing, studies suggest that unplanned downtime leads to economic losses of approximately 864 billion USD each year. Therefore, real-time, accurate detection, classification and prediction of machine behaviour are needed to minimise financial losses. Although vast literature exists on time-series data processing using machine learning, the challenges faced by the industries that lead to unplanned downtimes are: The current algorithms do not efficiently handle the high-volume streaming data from industrial IoTsensors and were tested on static and simulated datasets. While the existing algorithms can detect significant 'point' outliers, most do not handle contextual outliers (e.g., values within normal range but happening at an unexpected time of day) or subtle changes in machine behaviour. Machines are revamped periodically as part of planned maintenance programmes, which change the assumptions on which original AI models were created and trained. Aim: This research study aims to deliver a Graph Neural Network(GNN)based hybrid forecasting model that interfaces with the real-time machine control systemand can detect, predict machine behaviour and behavioural changes (anomalies) in real-time. This research will help manufacturing industries and utilities, e.g., water, electricity etc., reduce unplanned downtimes and consequential financial losses. Method: The data stored within a process control system, e.g., Industrial-IoT, Data Historian, is generally sampled during data acquisition from the sensor (source) and whenpersistingin the Data Historian to optimise storage and query performance. The sampling may inadvertently discard values that might contain subtle aspects of behavioural changes in machines. This research proposed a hybrid forecasting and classification model which combines the expressive and extrapolation capability of GNN enhanced with the estimates of entropy and spectral changes in the sampled data and additional temporal contexts to reconstruct the likely temporal trajectory of machine behavioural changes. The proposed real-time model belongs to the Deep Learning category of machine learning and interfaces with the sensors directly or through 'Process Data Historian', SCADA etc., to perform forecasting and classification tasks. Results: The model was interfaced with a Data Historianholding time-series data from 4flow sensors within a water treatment plantfor45 days. The recorded sampling interval for a sensor varied from 10 sec to 30 min. Approximately 65% of the available data was used for training the model, 20% for validation, and the rest for testing. The model identified the anomalies within the water treatment plant and predicted the plant's performance. These results were compared with the data reported by the plant SCADA-Historian system and the official data reported by the plant authorities. The model's accuracy was much higher (20%) than that reported by the SCADA-Historian system and matched the validated results declared by the plant auditors. Conclusions: The research demonstrates that a hybrid GNN based approach enhanced with entropy calculation and spectral information can effectively detect and predict a machine's behavioural changes. The model can interface with a plant's 'process control system' in real-time to perform forecasting and classification tasks to aid the asset management engineers to operate their machines more efficiently and reduce unplanned downtimes. A series of trialsare planned for this model in the future in other manufacturing industries.

Keywords: GNN, Entropy, anomaly detection, industrial time-series, AI, IoT, Industry 4.0, Machine Learning

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Authors: Monisha Isaac

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Biotechnology is a discipline which explains the use of living organisms and systems to construct a product, or we can define it as an application or technology developed to use biological systems and organisms processes for a specific use. Particularly, it includes cells and its components use for new technologies and inventions. The tools developed can be further used in diverse fields such as agriculture, industry, research and hospitals etc. The 21^st century has seen a drastic development and advancement in biotechnology in India. Significant increase in Government of India’s outlays for biotechnology over the past decade has been observed. A sectoral break up of biotechnology-based companies in India shows that most of the companies are agriculture-based companies having interests ranging from tissue culture to biopesticides. Major attention has been given by the companies in health related activities and in environmental biotechnology. The biopharmaceutical, which comprises of vaccines, diagnostic, and recombinant products is the most reliable and largest segment of the Indian Biotech industry. India has developed its vaccine markets and supplies them to various countries. Then there are the bio-services, which mainly comprise of contract researches and manufacturing services. India has made noticeable developments in the field of bio industries including manufacturing of enzymes, biofuels and biopolymers. Biotechnology is also playing a crucial and significant role in the field of agriculture. Traditional methods have been replaced by new technologies that mainly focus on GM crops, marker assisted technologies and the use of biotechnological tools to improve the quality of fertilizers and soil. It may only be a small contributor but has shown to have huge potential for growth. Bioinformatics is a computational method which helps to store, manage, arrange and design tools to interpret the extensive data gathered through experimental trials, making it important in the design of drugs.

Keywords: biotechnology, advancement, agriculture, bio-services, bio-industries, bio-pharmaceuticals

Procedia PDF Downloads 237

Authors: SangWon Han, MuWook Pyeon, Sujung Moon, DaeKyo Seo

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Recently, technologies based on three-dimensional (3D) space information are being developed and quality of life is improving as a result. Research on real-time digital map (RDM) is being conducted now to provide 3D space information. RDM is a service that creates and supplies 3D space information in real time based on location/shape detection. Research subjects on RDM include the construction of 3D space information with matching image data, complementing the weaknesses of image acquisition using multi-source data, and data collection methods using big data. Using RDM will be effective for space analysis using 3D space information in a U-City and for other space information utilization technologies.

Keywords: RDM, multi-source data, big data, U-City

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Authors: Srivatsav Sanjay Sridhar, Asvitha Raja, Prathit Kalra, Soni Gupta

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Database management is a system that keeps track of details about a person in an organisation. Not a lot of orphanages these days are shifting to a computer and program-based system, but unfortunately, most have only pen and paper-based records, which not only consumes space but it is also not eco-friendly. It comes as a hassle when one has to view a record of a person as they have to search through multiple records, and it will consume time. This program will organise all the data and can pull out any information about anyone whose data is entered. This is also a safe way of storage as physical data gets degraded over time or, worse, destroyed due to natural disasters. In this developing world, it is only smart enough to shift all data to an electronic-based storage system. The program comes with all features, including creating, inserting, searching, and deleting the data, as well as printing them.

Keywords: database, orphans, programming, C⁺⁺

Procedia PDF Downloads 156

Authors: Ozoemelam Ikechukwu Lazarus, Nizamuddin B. Zainuddi, Abdul Kafi

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Numerous studies have been carried out on Green Logistics Management Practices (GLMPs) across the globe. The study on the practices and performance of green chain practices in Africa in particular has not gained enough scholarly attention. Again, the majority of supply chain sustainability research being conducted focus on environmental sustainability. Logistics has been a major cause of supply chain resource waste and environmental damage. Many sectors of the economy that engage in logistical operations significantly rely on vehicles, which emit pollutants into the environment. Due to urbanization and industrialization, the logistical operations of manufacturing companies represent a serious hazard to the society and human life, making the sector one of the fastest expanding in the world today. Logistics companies are faced with numerous difficulties when attempting to implement logistics practices along their supply chains. In Nigeria, manufacturing companies aspire to implement reverse logistics in response to stakeholders’ requirements to reduce negative environmental consequences. However, implementing this is impeded by a criteria framework, and necessitates the careful analysis of how such criteria interact with each other in the presence of uncertainty. This study integrates most of the green logistics management practices (GLMPs) into the Nigerian firms to improve generalizability, and credibility. It examines the effect of Green Logistics Management Practices on environmental performance, social performance, market performance, and financial performance in the logistics industries. It seeks to identify the critical success factors in order to develop a model that incorporates different factors from the perspectives of the technology, organization, human and environment to inform the adoption and use of technologies for logistics supply chain social sustainability in Nigeria. It uses exploratory research approach to collect and analyse the data.

Keywords: logistics, management, sustainability, environment, operations

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Authors: Mohammed Al-Shalabi

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In recent years, the Internet of Things (IoT) has become an important technology for connecting various smart objects in many applications, especially in health monitoring applications. The IoT technology develops healthcare applications from traditional to personalized ones by making the treatment and monitoring of patients easier. Nowadays, especially during the COVID-19 pandemic, the necessity of enhancing healthcare monitoring applications to avoid the spreading of this pandemic is increased. In this paper, a mechanism is proposed to minimize direct contact with COVID-19 patients who need ventilators by using IoT technology. The proposed mechanism saves medical providers from COVID-19 infection and then minimizes the spreading of this disease.

Keywords: internet of things, healthcare monitoring, COVID-19, ventilators, thingspeak, clouds

Procedia PDF Downloads 135

Authors: Amelia Frickey, Timothy (TJ) Sheridan, Angelica Rossi, Bahar Aliakbarian

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The growth of large food supply chains demanded improved end-to-end traceability of food products, which has led to companies being increasingly interested in using smart technologies such as Radio Frequency Identification (RFID)-enabled packaging to track items. As technology is being widely used, there are several technological or economic issues that should be overcome to facilitate the adoption of this track-and-trace technology. One of the technological challenges of RFID technology is its sensitivity to different environmental form factors, including packaging materials and the content of the packaging. Although researchers have assessed the performance loss due to the proximity of water and aqueous solutions, there is still the need to further investigate the impacts of food products on the reading range of RFID tags. However, to the best of our knowledge, there are not enough studies to determine the correlation between RFID tag performance and food beverages properties. The goal of this project was to investigate the effect of the solution properties (pH and conductivity) and different packaging materials filled with food-like water-based solutions on the performance of an RFID tag. Three commercially available ultra high-frequency RFID tags were placed on three different bottles and filled with different concentrations of water-based solutions, including sodium chloride, citric acid, sucrose, and ethanol. Transparent glass, Polyethylneterephtalate (PET), and Tetrapak® were used as the packaging materials commonly used in the beverage industries. Tag readability (Theoretical Read Range, TRR) and sensitivity (Power on Tag Forward, PoF) were determined using an anechoic chamber. First, the best place to attach the tag for each packaging material was investigated using empty and water-filled bottles. Then, the bottles were filled with the food-like solutions and tested with the three different tags and the PoF and TRR at the fixed frequency of 915MHz. In parallel, the pH and conductivity of solutions were measured. The best-performing tag was then selected to test the bottles filled with wine, orange, and apple juice. Despite various solutions altering the performance of each tag, the change in tag performance had no correlation with the pH or conductivity of the solution. Additionally, packaging material played a significant role in tag performance. Each tag tested performed optimally under different conditions. This study is the first part of comprehensive research to determine the regression model for the prediction of tag performance behavior based on the packaging material and the content. More investigations, including more tags and food products, are needed to be able to develop a robust regression model. The results of this study can be used by RFID tag manufacturers to design suitable tags for specific products with similar properties.

Keywords: smart food packaging, supply chain management, food waste, radio frequency identification

Procedia PDF Downloads 114

Authors: Jae-Soo Do, Kyoung-Seok Kim

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The crisis theory of Samsung Electronics is becoming a hot topic today. Due to its performance deterioration, the share of Samsung Electronics lost its driving power. Considering the public opinion about the bad rumors circulating within the company, it is quite probable that the company is currently facing crisis. Then, what company has challenged the stronghold of Samsung Electronics? At the core of the crisis is 'Xiaomi' who snatched the first place of the market share, pushing Samsung Electronics aside in the Chinese market. In June 2010, Xiaomi, established by eight co-founders, has been showing a miraculous growth as the smart device manufacturer, taking the first place in the Chinese market and coming in fifth worldwide in just four years after its establishment. How did Xiaomi instantaneously achieve enough growth to overtake Samsung? Thus, we have conducted a comparative analysis on the competitive strategies of Samsung and Xiaomi.

Keywords: Samsung, Xiaomi, industrial attractiveness, VIRO

Procedia PDF Downloads 396

Authors: Jan Doutreloigne

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This paper describes two methods for the reduction of the peak input current during the boosting of Dickson charge pumps. Both methods are implemented in the fully integrated Dickson charge pumps of a high-voltage display driver chip for smart-card applications. Experimental results reveal good correspondence with Spice simulations and show a reduction of the peak input current by a factor of 6 during boosting

Keywords: bi-stable display driver, Dickson charge pump, high-voltage generator, peak current reduction, sub-pump boosting, variable frequency boosting

Procedia PDF Downloads 456

Authors: Joseph Marae Djouda, Ashraf Kasmi, François Hild

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Fused filament fabrication is one of the most widespread additive manufacturing techniques because of its low-cost implementation. Its initial development was based on part fabrication with thermoplastic materials. The influence of the manufacturing parameters such as the filament orientation through the nozzle, the deposited layer thickness, or the speed deposition on the mechanical properties of the parts has been widely experimentally investigated. It has been recorded the remarkable variations of the anisotropy in the function of the filament path during the fabrication process. However, there is a lack in the development of constitutive models describing the mechanical properties. In this study, integrated digital image correlation (I-DIC) is used for the identification of mechanical constitutive parameters of two configurations of ABS samples: +/-45° and so-called “oriented deposition.” In this last, the filament was deposited in order to follow the principal strain of the sample. The identification scheme based on the gap reduction between simulation and the experiment directly from images recorded from a single sample (single edge notched tension specimen) is developed. The macroscopic and mesoscopic analysis are conducted from images recorded in both sample surfaces during the tensile test. The elastic and elastoplastic models in isotropic and orthotropic frameworks have been established. It appears that independently of the sample configurations (filament orientation during the fabrication), the elastoplastic isotropic model gives the correct description of the behavior of samples. It is worth noting that in this model, the number of constitutive parameters is limited to the one considered in the elastoplastic orthotropic model. This leads to the fact that the anisotropy of the architectured 3D printed ABS parts can be neglected in the establishment of the macroscopic behavior description.

Keywords: elastic anisotropy, fused filament fabrication, Acrylonitrile butadiene styrene, I-DIC identification

Procedia PDF Downloads 126

Authors: Ozoemelam Ikechukwu Lazarus, Nizamuddin B. Zainuddin, Abdul Kafi

Abstract:

Numerous studies have been carried out on Green Logistics Management Practices (GLMPs) across the globe. The study on the practices and performance of green chain practices in Africa in particular has not gained enough scholarly attention. Again, the majority of supply chain sustainability research being conducted focus on environmental sustainability. Logistics has been a major cause of supply chain resource waste and environmental damage. Many sectors of the economy that engage in logistical operations significantly rely on vehicles, which emit pollutants into the environment. Due to urbanization and industrialization, the logistical operations of manufacturing companies represent a serious hazard to the society and human life, making the sector one of the fastest expanding in the world today. Logistics companies are faced with numerous difficulties when attempting to implement logistics practices along their supply chains. In Nigeria, manufacturing companies aspire to implement reverse logistics in response to stakeholders’ requirements to reduce negative environmental consequences. However, implementing this is impeded by a criteria framework, and necessitates the careful analysis of how such criteria interact with each other in the presence of uncertainty. This study integrates most of the green logistics management practices (GLMPs) into the Nigerian firms to improve generalizability, and credibility. It examines the effect of Green Logistics Management Practices on environmental performance, social performance, market performance, and financial performance in the logistics industries. It seeks to identify the critical success factors in order to develop a model that incorporates different factors from the perspectives of the technology, organization, human and environment to inform the adoption and use of technologies for logistics supply chain social sustainability in Nigeria. It uses exploratory research approach to collect and analyse the data.

Keywords: logistics, managemernt, suatainability, environment, operations

Procedia PDF Downloads 63

Authors: Simon Komesker, Achim Wagner, Martin Ruskowski

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In times of volatile markets with fluctuating demand and the uncertainty of global supply chains, flexible production systems are the key to an efficient implementation of a desired production program. In this publication, the authors present a holistic information concept taking into account various influencing factors for operating towards the global optimum. Therefore, a strategy for the implementation of multi-level planning for a flexible, reconfigurable production system with an alternative production concept in the automotive industry is developed. The main contribution of this work is a system structure mixing central and decentral planning and control evaluated in a simulation framework. The information system structure in current production systems in the automotive industry is rigidly hierarchically organized in monolithic systems. The production program is created rule-based with the premise of achieving uniform cycle time. This program then provides the information basis for execution in subsystems at the station and process execution level. In today's era of mixed-(car-)model factories, complex conditions and conflicts arise in achieving logistics, quality, and production goals. There is no provision for feedback loops of results from the process execution level (resources) and process supporting (quality and logistics) systems and reconsideration in the planning systems. To enable a robust production flow, the complexity of production system control is artificially reduced by the line structure and results, for example in material-intensive processes (buffers and safety stocks - two container principle also for different variants). The limited degrees of freedom of line production have produced the principle of progress figure control, which results in one-time sequencing, sequential order release, and relatively inflexible capacity control. As a result, modularly structured production systems such as modular production according to known approaches with more degrees of freedom are currently difficult to represent in terms of information technology. The remedy is an information concept that supports cross-system and cross-level information processing for centralized and decentralized decision-making. Through an architecture of hierarchically organized but decoupled subsystems, the paradigm of hybrid control is used, and a holonic manufacturing system is offered, which enables flexible information provisioning and processing support. In this way, the influences from quality, logistics, and production processes can be linked holistically with the advantages of mixed centralized and decentralized planning and control. Modular production systems also require modularly networked information systems with semi-autonomous optimization for a robust production flow. Dynamic prioritization of different key figures between subsystems should lead the production system to an overall optimum. The tasks and goals of quality, logistics, process, resource, and product areas in a cyber-physical production system are designed as an interconnected multi-agent-system. The result is an alternative system structure that executes centralized process planning and decentralized processing. An agent-based manufacturing control is used to enable different flexibility and reconfigurability states and manufacturing strategies in order to find optimal partial solutions of subsystems, that lead to a near global optimum for hybrid planning. This allows a robust near to plan execution with integrated quality control and intralogistics.

Keywords: holonic manufacturing system, modular production system, planning, and control, system structure

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Authors: Suresh Salla

Abstract:

In this paper, author explores for most sustainable last mile transport mode addressing present problems of traffic congestion, jams, pollution and travel stress. Development of energy-efficient sustainable integrated transport system(s) is/are must to make our cities more livable. Emphasis on autonomous, connected, electric, sharing system for effective utilization of systems (vehicles and public infrastructure) is on the rise. Many surface mobility innovations like PBS, Ride hailing, ride sharing, etc. are, although workable but if we analyze holistically, add to the already congested roads, difficult to ride in hostile weather, causes pollution and poses commuter stress. Sustainability of transportation is evaluated with respect to public adoption, average speed, energy consumption, and pollution. Why public prefer certain mode over others? How commute time plays a role in mode selection or shift? What are the factors play-ing role in energy consumption and pollution? Based on the study, it is clear that public prefer a transport mode which is exhaustive (i.e., less need for interchange – network is widespread) and intensive (i.e., less waiting time - vehicles are available at frequent intervals) and convenient with latest technologies. Average speed is dependent on stops, number of intersections, signals, clear route availability, etc. It is clear from Physics that higher the kerb weight of a vehicle; higher is the operational energy consumption. Higher kerb weight also demands heavier infrastructure. Pollution is dependent on source of energy, efficiency of vehicle, average speed. Mode can be made exhaustive when the unit infrastructure cost is less and can be offered intensively when the vehicle cost is less. Reliable and seamless integrated mobility till last ¼ mile (Five Minute Walk-FMW) is a must to encourage sustainable public transportation. Study shows that average speed and reliability of dedicated modes (like Metro, PRT, BRT, etc.) is high compared to road vehicles. Electric vehicles and more so battery-less or 3rd rail vehicles reduce pollution. One potential mode can be Cycleloop PRT, where commuter rides e-cycle in a dedicated path – elevated, at grade or underground. e-Bike with kerb weight per rider at 15 kg being 1/50th of car or 1/10th of other PRT systems makes it sustainable mode. Cycleloop tube will be light, sleek and scalable and can be modular erected, either on modified street lamp-posts or can be hanged/suspended between the two stations. Embarking and dis-embarking points or offline stations can be at an interval which suits FMW to mass public transit. In terms of convenience, guided e-Bike can be made self-balancing thus encouraging driverless on-demand vehicles. e-Bike equipped with smart electronics and drive controls can intelligently respond to field sensors and autonomously move reacting to Central Controller. Smart switching allows travel from origin to destination without interchange of cycles. DC Powered Batteryless e-cycle with voluntary manual pedaling makes it sustainable and provides health benefits. Tandem e-bike, smart switching and Platoon operations algorithm options provide superior through-put of the Cycleloop. Thus Cycleloop PRT will be exhaustive, intensive, convenient, reliable, speedy, sustainable, safe, pollution-free and healthy alternative mode for last mile connectivity in cities.

Keywords: cycleloop PRT, five-minute walk, lean modular infrastructure, self-balanced intelligent e-cycle

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Authors: Soumava Boral, Sanjay K. Chaturvedi, Ian Howard, Kristoffer McKee, V. N. A. Naikan

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Due to strict regulations from government to enhance the possibilities of sustainability practices in industries, and noting the advances in sustainable manufacturing practices, it is necessary that the associated processes are also sustainable. Maintenance of large scale and complex machines is a pivotal task to maintain the uninterrupted flow of manufacturing processes. Appropriate maintenance practices can prolong the lifetime of machines, and prevent associated breakdowns, which subsequently reduces different cost heads. Selection of the best maintenance strategies for such machines are considered as a burdensome task, as they require the consideration of multiple technical criteria, complex mathematical calculations, previous fault data, maintenance records, etc. In the era of the fourth industrial revolution, organizations are rapidly changing their way of business, and they are giving their utmost importance to sensor technologies, artificial intelligence, data analytics, automations, etc. In this work, the effectiveness of several maintenance strategies (e.g., preventive, failure-based, reliability centered, condition based, total productive maintenance, etc.) related to a large scale and complex gearbox, operating in a steel processing plant is evaluated in terms of economic, social, environmental and technical criteria. As it is not possible to obtain/describe some criteria by exact numerical values, these criteria are evaluated linguistically by cross-functional experts. Fuzzy sets are potential soft-computing technique, which has been useful to deal with linguistic data and to provide inferences in many complex situations. To prioritize different maintenance practices based on the identified sustainable criteria, multi-criteria decision making (MCDM) approaches can be considered as potential tools. Multi-Attributive Ideal Real Comparative Analysis (MAIRCA) is a recent addition in the MCDM family and has proven its superiority over some well-known MCDM approaches, like TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and ELECTRE (ELimination Et Choix Traduisant la REalité). It has a simple but robust mathematical approach, which is easy to comprehend. On the other side, due to some inherent drawbacks of Intuitionistic Fuzzy Sets (IFS) and Pythagorean Fuzzy Sets (PFS), recently, the use of Fermatean Fuzzy Sets (FFSs) has been proposed. In this work, we propose the novel concept of FF-MAIRCA. We obtain the weights of the criteria by experts’ evaluation and use them to prioritize the different maintenance practices according to their suitability by FF-MAIRCA approach. Finally, a sensitivity analysis is carried out to highlight the robustness of the approach.

Keywords: Fermatean fuzzy sets, Fermatean fuzzy MAIRCA, maintenance strategy selection, sustainable manufacturing, MCDM

Procedia PDF Downloads 138

Authors: Hasan Muslemani, Jeffrey Wilson, Xi Liang, Francisco Ascui, Katharina Kaesehage

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This paper aims to examine consumer behaviour towards, and the willingness to adopt, green steel use in the automotive sector, in order to identify potential barriers and opportunities for its widespread adoption. Semi-structured interviews were held with experts from global, regional and country-specific industry associations and automakers. The analysis shows there is a new shift towards lifecycle thinking in the sector, although these efforts have been voluntary and driven by customer and employee pressures rather than regulation. The paper further appraises possible demand for green steel within different vehicle types (based on size and powertrain), and shows that manufacturers of electric heavy-duty vehicles are most likely to adopt green steel in the first instance, given the amount of incorporated steel in the vehicles and the fact that lifecycle emissions lie predominantly in their manufacturing phase. A case for green advanced higher-strength steels (AHSS) can also be made in light-duty passenger vehicles, which may mitigate competition from light-weight alternative materials in terms of cost and greenness (depending on source and utilisation zones). This work builds on a wide sustainability-related literature in the automotive sector and highlights areas in need of urgent action if the sector as a whole were to meet its Paris Agreement climate targets, in particular a need to revisit current CO2 performance regulations to include Scope 1 and Scope 2 emissions, engage in educational green marketing campaigns, and explore innovative market-based mechanisms to bridge the gap between relatively-low carbon abatement costs of steelmaking and high abatement costs of vehicle manufacturing.

Keywords: Green steel, Consumer behaviour, Automotive industry, Environmental sustainability

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Authors: Sherry Ann Ganase, Sandra Sookram

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This study investigates the level of awareness to climate change and major factors that influence such awareness in Grande Riviere, Trinidad. Through the development of an Awareness Index and application of a Structural Equation Model to survey data, the findings suggest an Awareness index value of 0.459 in Grande Riviere. These results suggest that households have climate smart attitudes and behaviors but climate knowledge is lacking. This is supported by the structural equation model which shows a negative relationship between awareness and causes of climate change. The study concludes by highlighting the need for immediate action on increasing knowledge.

Keywords: awareness, climate change, climate education, index structural equation model

Procedia PDF Downloads 466

Authors: Ahmed Khattab, Bassem Shetta

Abstract:

Today, there are many branches of technology, one of them is the internet of things. In this paper, it's focused specifically on automating all the home appliances through E-mail using Node JS server, the server side stores, and processes this data. The server side contains user interface and notification system functionalities which is operated by Raspberry Pi. It will present the security requirements for the smart home. In this application, the privilege of home control including special persons to use it, using the hardware appliances through mobiles and tablets is achieved. The proposed application delivers high quality of service, long lifetime, low maintenance, fast deployment, and low power requirements with low cost needed for development.

Keywords: Raspberry Pi, E-mail, home automation, temperature sensor, PIR sensor, actuators, relay

Procedia PDF Downloads 262

Authors: Alexander Kospach, Fabian Benezeder, Jürgen Abraham

Abstract:

E-mobility poses new challenges for inverters (e.g., higher switching frequencies) in terms of thermal behavior and thermal management. Due to even higher switching frequencies, thermal losses become greater, and the cooling of critical components (like insulated gate bipolar transistor and diodes) comes into focus. New manufacturing methods, such as 3D printing, enable completely new pin-fin structures that can handle higher waste heat to meet the new thermal requirements. Based on the geometrical specifications of the industrial partner regarding the manufacturing possibilities for 3D printing, different and completely new pin-fin structures were numerically investigated for their hydraulic and thermal behavior in fundamental studies assuming an indirect liquid cooling. For the 3D computational fluid dynamics (CFD) thermal simulations OpenFOAM was used, which has as numerical method the finite volume method for solving the conjugate heat transfer problem. A steady-state solver for turbulent fluid flow and solid heat conduction with conjugate heat transfer between solid and fluid regions was used for the simulations. In total, up to fifty pinfin structures and arrangements, some of them completely new, were numerically investigated. On the basis of the results of the principal investigations, the best two pin-fin structures and arrangements for the complete module cooling of an automotive inverter were numerically investigated and compared. There are clear differences in the maximum temperatures for the critical components, such as IGTBs and diodes. In summary, it was shown that 3D pin fin structures can significantly contribute to the improvement of heat transfer and cooling of an automotive inverter. This enables in the future smaller cooling designs and a better lifetime of automotive inverter modules. The new pin fin structures and arrangements can also be applied to other cooling applications where 3D printing can be used.

Keywords: pin fin heat sink optimization, 3D printed pin fins, CFD simulation, power electronic cooling, thermal management

Procedia PDF Downloads 102

Authors: Diego Romero, Fabio Rojas, J. Alejandro Urrego

Abstract:

The sounding rockets are aerospace vehicles that were developed in the mid-20th century, and Colombia has been involved in research that was carried out with the aim of innovating with this technology. The rockets are university research programs with the collaboration of the local government, with a simple strategy, develop and reduce the greatest costs associated with the production of a kind type of technology. In this way, in this document presents the failure analysis of a solid rocket motor, with the real compatibly to reach the thermosphere with a low-cost fuel. This solid rocket motor is the latest development of the Uniandes Aerospace Project (PUA for its Spanish acronym), an undergraduate and postgraduate research group at Universidad de los Andes (Bogotá, Colombia), dedicated to incurring in this type of technology. This motor has been carried out on Candy-type solid fuel, which is a compound of potassium nitrate and sorbitol, and the investigation has allowed the production of solid motors powerful enough to reach space, and which represents a unique technological advance in Latin America and an important development in experimental rocketry.To outline the main points the explosion in a static test is an important to explore and demonstrate the ways to develop technology, methodologies, production and manufacturing, being a solid rocket motor with 30 kN of thrust. In conclusion, this analysis explores different fields such as: design, manufacture, materials, production, first fire and more, with different engineering tools with principal objective find root failure. Following the engineering analysis methodology, was possible to design a new version of motor, with learned lessons new manufacturing specification, therefore, when publishing this project, it is intended to be a reference for future research in this field and benefit the industry.

Keywords: candy propellant, candy rockets, explosion, failure analysis, static test, solid rocket motor

Procedia PDF Downloads 161

Authors: Tetsuya Yabuki, Yasunori Arizumi, Tetsuhiro Shimozato, Samy Guezouli, Hiroaki Matsusita, Masayuki Tai

Abstract:

The main object of this paper is to present the research results of the development of a hybrid stainless steel girder system for bridge construction undertaken at University of Ryukyu. In order to prevent the corrosion damage and reduce the fabrication costs, a hybrid stainless steel girder in bridge construction is developed, the stainless steel girder of which is stiffened and braced by structural carbon steel materials. It is verified analytically and experimentally that the ultimate strength of the hybrid stainless steel girder is equal to or greater than that of conventional carbon steel girder. The benefit of the life-cycle cost of the hybrid stainless steel girder is also shown.

Keywords: smart structure, hybrid stainless steel members, ultimate strength, steel bridge, corrosion prevention

Procedia PDF Downloads 378

Authors: Siddhartha Khare, Nitish Kr Boro, Omm Animesh Mishra

Abstract:

Recent food price hikes may signal the end of an era of predictable global grain crop plenty due to climate change, population expansion, and dietary changes. Food consumption will treble in 20 years, requiring enormous production expenditures. Climate and the atmosphere changed owing to rainfall and seasonal cycles in the past decade. India's tropical agricultural relies on evapotranspiration and monsoons. In places with limited resources, the global environmental change affects agricultural productivity and farmers' capacity to adjust to changing moisture patterns. Motivated by these difficulties, satellite remote sensing might be combined with near-surface imaging data (smartphones, UAVs, and PhenoCams) to enable phenological monitoring and fast evaluations of field-level consequences of extreme weather events on smallholder agriculture output. To accomplish this technique, we must digitally map all communities agricultural boundaries and crop kinds. With the improvement of satellite remote sensing technologies, a geo-referenced database may be created for rural Indian agriculture fields. Using AI, we can design digital agricultural solutions for individual farms. Main objective is to Geo-enable each farm along with their seasonal crop information by combining Artificial Intelligence (AI) with satellite and near-surface data and then prepare long term crop monitoring through in-depth field analysis and scanning of fields with satellite derived vegetation indices. We developed an AI based algorithm to understand the timelapse based growth of vegetation using PhenoCam or Smartphone based images. We developed an android platform where user can collect images of their fields based on the android application. These images will be sent to our local server, and then further AI based processing will be done at our server. We are creating digital boundaries of individual farms and connecting these farms with our smart phone application to collect information about farmers and their crops in each season. We are extracting satellite-based information for each farm from Google earth engine APIs and merging this data with our data of tested crops from our app according to their farm’s locations and create a database which will provide the data of quality of crops from their location.

Keywords: artificial intelligence, satellite remote sensing, crop monitoring, android and web application

Procedia PDF Downloads 100

Authors: M. Mischopoulou, P. Naidis, S. Kalamaras, T. Kotsopoulos, P. Samaras

Abstract:

The treatment of high strength wastewater by an Upflow Anaerobic Sludge Blanket (UASB) reactor has several benefits, such as high organic removal efficiency, short hydraulic retention time along with low operating costs. In addition, high volumes of biogas are released in these reactors, which can be utilized in several industrial facilities for energy production. This study aims at the examination of the application potential of anaerobic treatment of wastewater, with high molasses content derived from yeast manufacturing, by a lab-scale UASB reactor. The molasses wastewater and the sludge used in the experiments were collected from the wastewater treatment plant of a baker’s yeast manufacturing company. The experimental set-up consisted of a 15 L thermostated UASB reactor at 37 ◦C. Before the reactor start-up, the reactor was filled with sludge and molasses wastewater at a ratio 1:1 v/v. Influent was fed to the reactor at a flowrate of 12 L/d, corresponding to a hydraulic residence time of about 30 h. Effluents were collected from the system outlet and were analyzed for the determination of the following parameters: COD, pH, total solids, volatile solids, ammonium, phosphates and total nitrogen according to the standard methods of analysis. In addition, volatile fatty acid (VFA) composition of the effluent was determined by a gas chromatograph equipped with a flame ionization detector (FID), as an indicator to evaluate the process efficiency. The volume of biogas generated in the reactor was daily measured by the water displacement method, while gas composition was analyzed by a gas chromatograph equipped with a thermal conductivity detector (TCD). The effluent quality was greatly enhanced due to the use of the UASB reactor and high rate of biogas production was observed. The anaerobic treatment of the molasses wastewater by the UASB reactor improved the biodegradation potential of the influent, resulting at high methane yields and an effluent with better quality than the raw wastewater.

Keywords: anaerobic digestion, biogas production, molasses wastewater, UASB reactor

Procedia PDF Downloads 271

Authors: A. Sohouli, A. Suleman

Abstract:

This study focuses on reliability analysis of variable stiffness composite laminate structures to investigate the potential structural improvement compared to conventional (straight fibers) composite laminate structures. A computational framework was developed which it consists of a deterministic design step and reliability analysis. The optimization part is Discrete Material Optimization (DMO) and the reliability of the structure is computed by Monte Carlo Simulation (MCS) after using Stochastic Response Surface Method (SRSM). The design driver in deterministic optimization is the maximum stiffness, while optimization method concerns certain manufacturing constraints to attain industrial relevance. These manufacturing constraints are the change of orientation between adjacent patches cannot be too large and the maximum number of successive plies of a particular fiber orientation should not be too high. Variable stiffness composites may be manufactured by Automated Fiber Machines (AFP) which provides consistent quality with good production rates. However, laps and gaps are the most important challenges to steer fibers that effect on the performance of the structures. In this study, the optimal curved fiber paths at each layer of composites are designed in the first step by DMO, and then the reliability analysis is applied to investigate the sensitivity of the structure with different standard deviations compared to the straight fiber angle composites. The random variables are material properties and loads on the structures. The results show that the variable stiffness composite laminate structures are much more reliable, even for high standard deviation of material properties, than the conventional composite laminate structures. The reason is that the variable stiffness composite laminates allow tailoring stiffness and provide the possibility of adjusting stress and strain distribution favorably in the structures.

Keywords: material optimization, Monte Carlo simulation, reliability analysis, response surface method, variable stiffness composite structures

Procedia PDF Downloads 520

Authors: Manal Bouasria, Mohammed-Hichem Benzaama, Valérie Pralong, Yassine El Mendili

Abstract:

Reducing the quantity of cement in cementitious composites can help to reduce the environmental effect of construction materials. By-products such as ferronickel slags (FNS), fly ash (FA), and Crepidula fornicata (CR) are promising options for cement replacement. In this work, we investigated the relevance of substituting cement with FNS-CR and FA-CR on the mechanical properties of mortar and on the thermal properties of concrete. Foraging intervals ranging from 2 to 28 days, the mechanical properties are obtained by 3-point bending and compression tests. The chosen mix is used to construct a prototype in order to study the material’s hygrothermal performance. The data collected by the sensors placed on the prototype was utilized to build an artificial neural network.

Keywords: artificial neural network, cement, circular economy, concrete, by products

Procedia PDF Downloads 114

Authors: Kahlouche Amal, Touzene F. Zohra, Betatache Fatihaet Nouani Abdelouahab

Abstract:

The development of the world production of the cheese these last decades, as well as agents' greater request cheap coagulants, accentuated the search for new surrogates of the rennet. What about the interest to explore the vegetable biodiversity, the source well cheap of many naturals metabolites that the scientists today praise it (thistle, latex of fig tree, Cardoon, seeds of melon). Indeed, a big interest is concerned the search for surrogates of vegetable origin. The objective of the study is to show the possibility of extracting a protease coagulant the milk from the cake of Sunflower, available raw material and the potential source of surrogates of rennet. so, the determination of the proteolytic activity of raw extracts, the purification, the elimination of the pigments of tint of the enzymatic preparations, a better knowledge of the coagulative properties through study of the effect of certain factors (temperature, pH, concentration in CaCl2) are so many factors which contribute to value milk particularly those produced by the small ruminants of the Algerian dairy exploitations. Otherwise, extracts coagulants of vegetable origin allowed today to value traditional, in addition, although the extract coagulants of vegetable origin made it possible today to develop traditional cheeses whose Iberian peninsula is the promoter, but the test of 'pressed paste not cooked' cheese manufacturing led to the semi-scale pilot; and that, by using the enzymatic extract of sunflower (Helianthus annus) which gave satisfactory results as well to the level of outputs as on the sensory level,which, statistically,did not give any significant difference between studied cheeses. These results confirm the possibility of use of this coagulase as a substitute of rennet commercial on an industrial scale.

Keywords: characterization, cheese, Rennet, sunflower

Procedia PDF Downloads 351