Enhancing Future Skills and Entrepreneurship

This open access book presents the proceedings of the 3rd Indo-German Conference on Sustainability in Engineering held at Birla Institute of Technology and Science, Pilani, India, on September 16–17, 2019. Intended to foster the synergies between research and education, the conference is one of the joint activities of the BITS Pilani and TU Braunschweig conducted under the auspices of Indo-German Center for Sustainable Manufacturing, established in 2009. The book is divided into three sections: engineering, education and entrepreneurship, covering a range of topics, such as renewable energy forecasting, design & simulation, Industry 4.0, and soft & intelligent sensors for energy efficiency. It also includes case studies on lean and green manufacturing, and life cycle analysis of ceramic products, as well as papers on teaching/learning methods based on the use of learning factories to improve students’problem-solving and personal skills. Moreover, the book discusses high-tech ideas to help the large number of unemployed engineering graduates looking for jobs become tech entrepreneurs. Given its broad scope, it will appeal to academics and industry professionals alike

A framework for automation of data recording, modelling, and optimal statistical control of production lines

Unarguably, the automation of data collection and subsequent statistical treatment enhance the quality of industrial management systems. The rise of accessible digital technologies has enabled the introduction of the Industry 4.0 pillars in Cariri local companies. Particularly, such practice positively contributes to the triple bottom line of sustainable development: People, Environment, and Economy. The present work aims to provide a general automated framework for data recording and statistical control of conveyor belts in production lines. The software has been developed in three layers: graphical user interface, in PHP language; database collection, search, and safeguard, in MySQL; computational statistics, in R; and hardware control, in C. The computational statistics are based on the combination of artificial neural nets and autoregressive integrated and moving average models, via minimal variance method. The hardware components are composed by open source hardware as Arduino based boards and modular or industrial sensors. Specifically, the embedded system is designed to constantly monitor and record a number of measurable characteristics of the conveyor belts (e.g. electric consumption and temperature), via a number of sensors, allowing both the computation of statistical control metrics and the evaluation of the quality of the production system. As a case study, the project makes use of a laminated limestone production line, located at the Mineral Technology Center, Nova Olinda, Ceará state, Brazil.Indiscutivelmente, a automação da coleta de dados e o subsequente tratamento estatístico aumentam a qualidade dos sistemas de gestão industrial. O surgimento de tecnologias digitais acessíveis possibilitou a introdução dos pilares da Indústria 4.0 nas empresas locais do Cariri. Particularmente, tal prática contribui positivamente para o triplo resultado do desenvolvimento sustentável: Pessoas, Meio Ambiente e Economia. O presente trabalho tem como objetivo fornecer um Framework geral automatizado para registro de dados e controle estatístico de esteiras transportadoras em linhas de produção. O software foi desenvolvido em três camadas: interface gráfica do usuário, em linguagem PHP; coleta, pesquisa e proteção de banco de dados em MySQL; estatística computacional, em R; e controle de hardware, em C. As estatísticas computacionais são baseadas na combinação de redes neurais artificiais e modelos autorregressivos integrados e de média móvel, via método de mínima variância. Os componentes de hardware são compostos por hardware open source como placas baseadas em Arduino e sensores modulares ou industriais. Especificamente, o sistema embarcado é projetado para monitorar e registrar constantemente uma série de características mensuráveis das esteiras transportadoras (por exemplo, consumo elétrico e temperatura), por meio de uma série de sensores, permitindo tanto o cálculo de métricas de controle estatístico quanto a avaliação da qualidade do sistema de produção. Como estudo de caso, o projeto utiliza uma linha de produção de calcário laminado, localizada no Centro de Tecnologia Mineral, Nova Olinda, Ceará, Brasil

latent Dirichlet allocation method-based nowcasting approach for prediction of silver price

Silver is a metal that offers significant value to both investors and companies. The purpose of this study is to make an estimation of the price of silver. While making this estimation, it is planned to include the frequency of searches on Google Trends for the words that affect the silver price. Thus, it is aimed to obtain a more accurate estimate. First, using the Latent Dirichlet Allocation method, the keywords to be analyzed in Google Trends were collected from various articles on the Internet. Mining data from Google Trends combined with the information obtained by LDA is the new approach this study took, to predict the price of silver. No study has been found in the literature that has adopted this approach to estimate the price of silver. The estimation was carried out with Random Forest Regression, Gaussian Process Regression, Support Vector Machine, Regression Trees and Artificial Neural Networks methods. In addition, ARIMA, which is one of the traditional methods that is widely used in time series analysis, was also used to benchmark the accuracy of the methodology. The best MSE ratio was obtained as 0,000227131 ± 0.0000235205 by the Regression Trees method. This score indicates that it would be a valid technique to estimate the price of "Silver" by using Google Trends data using the LDA method

Flexible Automation and Intelligent Manufacturing: The Human-Data-Technology Nexus

This is an open access book. It gathers the first volume of the proceedings of the 31st edition of the International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2022, held on June 19 – 23, 2022, in Detroit, Michigan, USA. Covering four thematic areas including Manufacturing Processes, Machine Tools, Manufacturing Systems, and Enabling Technologies, it reports on advanced manufacturing processes, and innovative materials for 3D printing, applications of machine learning, artificial intelligence and mixed reality in various production sectors, as well as important issues in human-robot collaboration, including methods for improving safety. Contributions also cover strategies to improve quality control, supply chain management and training in the manufacturing industry, and methods supporting circular supply chain and sustainable manufacturing. All in all, this book provides academicians, engineers and professionals with extensive information on both scientific and industrial advances in the converging fields of manufacturing, production, and automation

Predicting the Future

Due to the increased capabilities of microprocessors and the advent of graphics processing units (GPUs) in recent decades, the use of machine learning methodologies has become popular in many fields of science and technology. This fact, together with the availability of large amounts of information, has meant that machine learning and Big Data have an important presence in the field of Energy. This Special Issue entitled “Predicting the Future—Big Data and Machine Learning” is focused on applications of machine learning methodologies in the field of energy. Topics include but are not limited to the following: big data architectures of power supply systems, energy-saving and efficiency models, environmental effects of energy consumption, prediction of occupational health and safety outcomes in the energy industry, price forecast prediction of raw materials, and energy management of smart buildings

Assessing Atmospheric Pollution and Its Impacts on the Human Health

This reprint contains articles published in the Special Issue entitled "Assessing Atmospheric Pollution and Its Impacts on the Human Health" in the journal Atmosphere. The research focuses on the evaluation of atmospheric pollution by statistical methods on the one hand, and on the other hand, on the evaluation of the relationship between the level of pollution and the extent of its effect on the population's health, especially on pulmonary diseases

Flexible Automation and Intelligent Manufacturing: The Human-Data-Technology Nexus

This is an open access book. It gathers the first volume of the proceedings of the 31st edition of the International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2022, held on June 19 – 23, 2022, in Detroit, Michigan, USA. Covering four thematic areas including Manufacturing Processes, Machine Tools, Manufacturing Systems, and Enabling Technologies, it reports on advanced manufacturing processes, and innovative materials for 3D printing, applications of machine learning, artificial intelligence and mixed reality in various production sectors, as well as important issues in human-robot collaboration, including methods for improving safety. Contributions also cover strategies to improve quality control, supply chain management and training in the manufacturing industry, and methods supporting circular supply chain and sustainable manufacturing. All in all, this book provides academicians, engineers and professionals with extensive information on both scientific and industrial advances in the converging fields of manufacturing, production, and automation

Material and energy flows of the iron and steel industry: status quo, challenges and perspectives

Integrated analysis and optimization of material and energy flows in the iron and steel industry have drawn considerable interest from steelmakers, energy engineers, policymakers, financial firms, and academic researchers. Numerous publications in this area have identified their great potential to bring significant benefits and innovation. Although much technical work has been done to analyze and optimize material and energy flows, there is a lack of overview of material and energy flows of the iron and steel industry. To fill this gap, this work first provides an overview of different steel production routes. Next, the modelling, scheduling and interrelation regarding material and energy flows in the iron and steel industry are presented by thoroughly reviewing the existing literature. This study selects eighty publications on the material and energy flows of steelworks, from which a map of the potential of integrating material and energy flows for iron and steel sites is constructed. The paper discusses the challenges to be overcome and the future directions of material and energy flow research in the iron and steel industry, including the fundamental understandings of flow mechanisms, the dynamic material and energy flow scheduling and optimization, the synergy between material and energy flows, flexible production processes and flexible energy systems, smart steel manufacturing and smart energy systems, and revolutionary steelmaking routes and technologies

Data Mining in Smart Grids

Effective smart grid operation requires rapid decisions in a data-rich, but information-limited, environment. In this context, grid sensor data-streaming cannot provide the system operators with the necessary information to act on in the time frames necessary to minimize the impact of the disturbances. Even if there are fast models that can convert the data into information, the smart grid operator must deal with the challenge of not having a full understanding of the context of the information, and, therefore, the information content cannot be used with any high degree of confidence. To address this issue, data mining has been recognized as the most promising enabling technology for improving decision-making processes, providing the right information at the right moment to the right decision-maker. This Special Issue is focused on emerging methodologies for data mining in smart grids. In this area, it addresses many relevant topics, ranging from methods for uncertainty management, to advanced dispatching. This Special Issue not only focuses on methodological breakthroughs and roadmaps in implementing the methodology, but also presents the much-needed sharing of the best practices. Topics include, but are not limited to, the following: Fuzziness in smart grids computing Emerging techniques for renewable energy forecasting Robust and proactive solution of optimal smart grids operation Fuzzy-based smart grids monitoring and control frameworks Granular computing for uncertainty management in smart grids Self-organizing and decentralized paradigms for information processin

Production Optimization Indexed to the Market Demand Through Neural Networks

Connectivity, mobility and real-time data analytics are the prerequisites for a new model of intelligent production management that facilitates communication between machines, people and processes and uses technology as the main driver. Many works in the literature treat maintenance and production management in separate approaches, but there is a link between these areas, with maintenance and its actions aimed at ensuring the smooth operation of equipment to avoid unnecessary downtime in production. With the advent of technology, companies are rushing to solve their problems by resorting to technologies in order to fit into the most advanced technological concepts, such as industries 4.0 and 5.0, which are based on the principle of process automation. This approach brings together database technologies, making it possible to monitor the operation of equipment and have the opportunity to study patterns of data behavior that can alert us to possible failures. The present thesis intends to forecast the pulp production indexed to the stock market value.The forecast will be made by means of the pulp production variables of the presses and the stock exchange variables supported by artificial intelligence (AI) technologies, aiming to achieve an effective planning. To support the decision of efficient production management, in this thesis algorithms were developed and validated with from five pulp presses, as well as data from other sources, such as steel production and stock exchange, which were relevant to validate the robustness of the model. This thesis demonstrated the importance of data processing methods and that they have great relevance in the model input since they facilitate the process of training and testing the models. The chosen technologies demonstrated good efficiency and versatility in performing the prediction of the values of the variables of the equipment, also demonstrating robustness and optimization in computational processing. The thesis also presents proposals for future developments, namely in further exploration of these technologies, so that there are market variables that can calibrate production through forecasts supported on these same variables.Conectividade, mobilidade e análise de dados em tempo real são pré-requisitos para um novo modelo de gestão inteligente da produção que facilita a comunicação entre máquinas, pessoas e processos, e usa a tecnologia como motor principal. Muitos trabalhos na literatura tratam a manutenção e a gestão da produção em abordagens separadas, mas existe uma correlação entre estas áreas, sendo que a manutenção e as suas políticas têm como premissa garantir o bom funcionamento dos equipamentos de modo a evitar paragens desnecessárias na linha de produção. Com o advento da tecnologia há uma corrida das empresas para solucionar os seus problemas recorrendo às tecnologias, visando a sua inserção nos conceitos tecnológicos, mais avançados, tais como as indústrias 4.0 e 5.0, as quais têm como princípio a automatização dos processos. Esta abordagem junta as tecnologias de sistema de informação, sendo possível fazer o acompanhamento do funcionamento dos equipamentos e ter a possibilidade de realizar o estudo de padrões de comportamento dos dados que nos possam alertar para possíveis falhas. A presente tese pretende prever a produção da pasta de papel indexada às bolsas de valores. A previsão será feita por via das variáveis da produção da pasta de papel das prensas e das variáveis da bolsa de valores suportadas em tecnologias de artificial intelligence (IA), tendo como objectivo conseguir um planeamento eficaz. Para suportar a decisão de uma gestão da produção eficiente, na presente tese foram desenvolvidos algoritmos, validados em dados de cinco prensas de pasta de papel, bem como dados de outras fontes, tais como, de Produção de Aço e de Bolsas de Valores, os quais se mostraram relevantes para a validação da robustez dos modelos. A presente tese demonstrou a importância dos métodos de tratamento de dados e que os mesmos têm uma grande relevância na entrada do modelo, visto que facilita o processo de treino e testes dos modelos. As tecnologias escolhidas demonstraram uma boa eficiência e versatilidade na realização da previsão dos valores das variáveis dos equipamentos, demonstrando ainda robustez e otimização no processamento computacional. A tese apresenta ainda propostas para futuros desenvolvimentos, designadamente na exploração mais aprofundada destas tecnologias, de modo a que haja variáveis de mercado que possam calibrar a produção através de previsões suportadas nestas mesmas variáveis