Utilizing the Internet of Things to promote energy awareness and efficiency at discrete production processes: Practices and methodology

El actual contexto de fabricación, con incrementos en los precios de la energía, una creciente preocupación medioambiental y cambios continuos en los comportamientos de los consumidores, fomenta que los responsables prioricen la fabricación respetuosa con el medioambiente. El paradigma del Internet de las Cosas (IoT) promete incrementar la visibilidad y la atención prestada al consumo de energía gracias tanto a sensores como a medidores inteligentes en los niveles de máquina y de línea de producción. En consecuencia es posible y sencillo obtener datos de consumo de energía en tiempo real proveniente de los procesos de fabricación, pero además es posible analizarlos para incrementar su importancia en la toma de decisiones. Esta tesis pretende investigar cómo utilizar la adopción del Internet de las Cosas en el nivel de planta de producción, en procesos discretos, para incrementar la capacidad de uso de la información proveniente tanto de la energía como de la eficiencia energética. Para alcanzar este objetivo general, la investigación se ha dividido en cuatro sub-objetivos y la misma se ha desarrollado a lo largo de cuatro fases principales (en adelante estudios). El primer estudio de esta tesis, que se apoya sobre una revisión bibliográfica comprehensiva y sobre las aportaciones de expertos, define prácticas de gestión de la producción que son energéticamente eficientes y que se apoyan de un modo preeminente en la tecnología IoT. Este primer estudio también detalla los beneficios esperables al adoptar estas prácticas de gestión. Además, propugna un marco de referencia para permitir la integración de los datos que sobre el consumo energético se obtienen en el marco de las plataformas y sistemas de información de la compañía. Esto se lleva a cabo con el objetivo último de remarcar cómo estos datos pueden ser utilizados para apalancar decisiones en los niveles de procesos tanto tácticos como operativos. Segundo, considerando los precios de la energía como variables en el mercado intradiario y la disponibilidad de información detallada sobre el estado de las máquinas desde el punto de vista de consumo energético, el segundo estudio propone un modelo matemático para minimizar los costes del consumo de energía para la programación de asignaciones de una única máquina que deba atender a varios procesos de producción. Este modelo permite la toma de decisiones en el nivel de máquina para determinar los instantes de lanzamiento de cada trabajo de producción, los tiempos muertos, cuándo la máquina debe ser puesta en un estado de apagada, el momento adecuado para rearrancar, y para pararse, etc. Así, este modelo habilita al responsable de producción de implementar el esquema de producción menos costoso para cada turno de producción. En el tercer estudio esta investigación proporciona una metodología para ayudar a los responsables a implementar IoT en el nivel de los sistemas productivos. Se incluye un análisis del estado en que se encuentran los sistemas de gestión de energía y de producción en la factoría, así como también se proporcionan recomendaciones sobre procedimientos para implementar IoT para capturar y analizar los datos de consumo. Esta metodología ha sido validada en un estudio piloto, donde algunos indicadores clave de rendimiento (KPIs) han sido empleados para determinar la eficiencia energética. En el cuarto estudio el objetivo es introducir una vía para obtener visibilidad y relevancia a diferentes niveles de la energía consumida en los procesos de producción. El método propuesto permite que las factorías con procesos de producción discretos puedan determinar la energía consumida, el CO2 emitido o el coste de la energía consumida ya sea en cualquiera de los niveles: operación, producto o la orden de fabricación completa, siempre considerando las diferentes fuentes de energía y las fluctuaciones en los precios de la misma. Los resultados muestran que decisiones y prácticas de gestión para conseguir sistemas de producción energéticamente eficientes son posibles en virtud del Internet de las Cosas. También, con los resultados de esta tesis los responsables de la gestión energética en las compañías pueden plantearse una aproximación a la utilización del IoT desde un punto de vista de la obtención de beneficios, abordando aquellas prácticas de gestión energética que se encuentran más próximas al nivel de madurez de la factoría, a sus objetivos, al tipo de producción que desarrolla, etc. Así mismo esta tesis muestra que es posible obtener reducciones significativas de coste simplemente evitando los períodos de pico diario en el precio de la misma. Además la tesis permite identificar cómo el nivel de monitorización del consumo energético (es decir al nivel de máquina), el intervalo temporal, y el nivel del análisis de los datos son factores determinantes a la hora de localizar oportunidades para mejorar la eficiencia energética. Adicionalmente, la integración de datos de consumo energético en tiempo real con datos de producción (cuando existen altos niveles de estandarización en los procesos productivos y sus datos) es esencial para permitir que las factorías detallen la energía efectivamente consumida, su coste y CO2 emitido durante la producción de un producto o componente. Esto permite obtener una valiosa información a los gestores en el nivel decisor de la factoría así como a los consumidores y reguladores. ABSTRACT In today‘s manufacturing scenario, rising energy prices, increasing ecological awareness, and changing consumer behaviors are driving decision makers to prioritize green manufacturing. The Internet of Things (IoT) paradigm promises to increase the visibility and awareness of energy consumption, thanks to smart sensors and smart meters at the machine and production line level. Consequently, real-time energy consumption data from the manufacturing processes can be easily collected and then analyzed, to improve energy-aware decision-making. This thesis aims to investigate how to utilize the adoption of the Internet of Things at shop floor level to increase energy–awareness and the energy efficiency of discrete production processes. In order to achieve the main research goal, the research is divided into four sub-objectives, and is accomplished during four main phases (i.e., studies). In the first study, by relying on a comprehensive literature review and on experts‘ insights, the thesis defines energy-efficient production management practices that are enhanced and enabled by IoT technology. The first study also explains the benefits that can be obtained by adopting such management practices. Furthermore, it presents a framework to support the integration of gathered energy data into a company‘s information technology tools and platforms, which is done with the ultimate goal of highlighting how operational and tactical decision-making processes could leverage such data in order to improve energy efficiency. Considering the variable energy prices in one day, along with the availability of detailed machine status energy data, the second study proposes a mathematical model to minimize energy consumption costs for single machine production scheduling during production processes. This model works by making decisions at the machine level to determine the launch times for job processing, idle time, when the machine must be shut down, ―turning on‖ time, and ―turning off‖ time. This model enables the operations manager to implement the least expensive production schedule during a production shift. In the third study, the research provides a methodology to help managers implement the IoT at the production system level; it includes an analysis of current energy management and production systems at the factory, and recommends procedures for implementing the IoT to collect and analyze energy data. The methodology has been validated by a pilot study, where energy KPIs have been used to evaluate energy efficiency. In the fourth study, the goal is to introduce a way to achieve multi-level awareness of the energy consumed during production processes. The proposed method enables discrete factories to specify energy consumption, CO2 emissions, and the cost of the energy consumed at operation, production and order levels, while considering energy sources and fluctuations in energy prices. The results show that energy-efficient production management practices and decisions can be enhanced and enabled by the IoT. With the outcomes of the thesis, energy managers can approach the IoT adoption in a benefit-driven way, by addressing energy management practices that are close to the maturity level of the factory, target, production type, etc. The thesis also shows that significant reductions in energy costs can be achieved by avoiding high-energy price periods in a day. Furthermore, the thesis determines the level of monitoring energy consumption (i.e., machine level), the interval time, and the level of energy data analysis, which are all important factors involved in finding opportunities to improve energy efficiency. Eventually, integrating real-time energy data with production data (when there are high levels of production process standardization data) is essential to enable factories to specify the amount and cost of energy consumed, as well as the CO2 emitted while producing a product, providing valuable information to decision makers at the factory level as well as to consumers and regulators

Development and Implementation of a Reliable Decision Fusion and Pattern Recognition System for Object Detection and Condition Monitoring

A monitoring task of production system (bucket-wheel excavator) is investigated for the development and realization of a multisensor-based monitoring system. The objective of the monitoring system is to obtain in real time reliable decisions on the presence of target objects (large stones) in the transported material during the production process to avoid disturbances or failures of the transportation process. Due to the complexity of the considered production system, different physical effects are used for the development of the multisensor-based monitoring system. The measured signals are acquired using different sensors (five acceleration sensors, two load cells, and a laser scanner). Due to the inevitable and varying time shift between the stimulations of the individual sensors, each signal is individually subjected to preprocessing, feature extraction, and classification process. The proposed monitoring system consists of three modules: acceleration, laser scanner, and decision fusion modules. For the acceleration module which uses acceleration signals of five different acceleration sensors, two detection approaches are developed. The first approach (STFT-SVM) is based on Short-Time Fourier Transform (STFT) as feature extraction tool, Support Vector Machine (SVM) for the classification, and a novel decision fusion process to fuse the individual decisions. The second approach (CWT-SVM) is based Continuous Wavelet Transform (CWT) as feature extraction tool, Support Vector Machine (SVM) for the classification, and a rule-based decision fusion process to fuse the individual decisions. Both approaches are trained, validated, and tested using real industrial data. The developed approaches show strong improvements in detection and false alarm rates. Due to the implementation complexity and the high number of false alarms of the STFT-SVM approach in comparison to the CWT-SVM approach, the CWT-SVM-based approach is chosen for the development of the overall monitoring system. The Laser scanner module which processes the laser scanner signal consists of prefiltering, filtering, validation, and classification process. The module is validated, and successfully tested on real industrial data. The decision fusion module fuses the decisions of both detection modules in order to obtain a final reliable decision. Three fusion techniques are investigated, which are OR-logic, Bayesian Combination Rule (BCR), and the new developed decision fusion technique Basic Belief Fusion (BBF). Due to the characteristics of the considered application, the OR-Logic is chosen to perform the fusion task. For the online realization, the weightometer module is added to avoid false alarms which could be caused by acceleration module. Additionally modifications and simplification processes are performed in order to overcome the hardware limitations The proposed monitoring approach is developed for online and real time implementation, and it achieves high detection rate, with minimum false alarms rate, thus the production process disturbance is minimized

Utilizing the Internet of Things to promote energy awareness and efficiency at discrete production processes: Practices and methodology

This research aims to investigate how to utilize the Internet of Things (IoT) paradigm at the shop floor level (by providing real-time energy consumption data) to increase energy awareness and efficiency at discrete manufacturing companies

Pressure and flow characteristics of restrictive flow orifice devices.

A Restrictive Flow Orifice (RFO) can be used to enhance the safe design of a pressure system in several ways. Pressure systems frequently incorporate a regulator and relief valve to protect the downstream equipment from accidental overpressure caused by regulator failure. Analysis frequently shows that in cases of high-flow regulator failure, the downstream pressure may rise significantly above the set pressure of the relief valve. This is due to limited flow capacity of the relief valve. A different regulator or relief valve may need to be selected. A more economical solution to this problem is to use an RFO to limit the maximum system flow to acceptable limits within the flow capacity of the relief valve, thereby enhancing the overpressure protection of laboratory equipment. An RFO can also be used to limit the uncontrolled release of system fluid (gas or liquid) upon component or line failure. As an example, potential asphyxiation hazards resultant from the release of large volumes of inert gas from a 'house' nitrogen system can be controlled by the use of an RFO. This report describes a versatile new Sandia-designed RFO available from the Swagelok Company and specifies the gas flow characteristics of this device. Two sizes, 0.010 and 0.020 inch diameter RFOs are available. These sizes will allow enhanced safety for many common applications. This new RFO design are now commercially available and provide advantages over existing RFOs: a high pressure rating (6600 psig); flow through the RFO is equal for either forward or reverse directions; they minimize the potential for leakage by incorporating the highest quality threaded connections; and can enhance the safety of pressure systems

The Agent Pattern Driven Business Engineering (APBDE) approach enabled business-based systems

Agent design patterns form a new methodology used to improve the development of software agents. Agent design patterns can help by capturing solutions to common problems in agent design [Lange and Oshima, 1998].Agent design patterns are applied in different systems such as knowledge management systems, real-time systems, and network management systems. Agent design patterns for business-based systems, aim to support different ecommerce paradigms business-to-business (B2B) and business-to-consumer (B2C).In this paper, we developed an approach for extracting agent-based design patterns for B2C e-commerce to improve business-based processes.This approach is called an Agent Pattern Driven Business Engineering (APDBE).Based on this approach, we derived two agent-based commerce design patterns namely, the De-coupler Design Pattern (DecDP), and the Dynamic Design Pattern (DynDP). These design patterns are used to support selling/buying-based processes in e-commerce domain

Bibliometric Analysis of Published Literature on Industry 4.0

Research on Industry 4.0 was initiated in 2012. Since then, the term “Industry 4.0” has been widely used among researchers to further investigate the development of technologies in the related industry. Thus, the aim of this study is to analyze the scientific literature published in the field of Industry 4.0. Scopus database was utilized to collect all literature in Industry 4.0. Publish or Perish software was used to incorporate the obtained data, while VOSviewer was used for data visualization. SPSS and Microsoft Excel were employed for data analysis. The growth of publications, research productivity and citation analysis were presented using standard bibliometric analysis. Based on the search results, a total of 1256 documents were retrieved. The growth rate of literature in Industry 4.0 increased drastically year by year since 2012. Most of the articles were published in journals and conferences, mainly in English. Most of the research in Industry 4.0 was in the engineering field. Keywords of the Internet of Things (IoT) and Cyber-Physical Systems (CPS) were the most keywords used and represent the main areas of research covered in Industry 4.0. Most of the research related to Industry 4.0 was conducted in Germany and multi-authored with a mean collaboration index of 3.65 authors per article. This study presents the evolution of the scientific literature in Industry 4.0 and identifies areas of current research interests and potential directions for future research

Climatisation of a closed greenhouse in the Middle East

Cooling is an essential part of greenhouse climate control in warm climates. There are three types of cooling technique: natural ventilation, evaporative cooling and mechanical cooling. Natural ventilation can only be applied when the outside temperature does not exceed 35°C and the average daily temperature is not higher than 22°C. Above these temperatures, production will be negatively affected. Evaporative cooling can be applied when the dew-point temperature of the outside air is less than these limits. These methods of cooling work effectively in arid regions, though the water consumption is high. The third method of cooling demands a cold surface to remove the latent and sensible heat from the greenhouse. This method has been applied in the current research. This method allows optimal control of the greenhouse climate in terms of temperature and humidity, but also in terms of carbon dioxide concentration. The amount of cooling capacity required depends on the amount of solar radiation being absorbed in the greenhouse and the convective heat transfer from outside, provided the outside temperature is higher than the greenhouse air temperature. The experiment showed that roughly 50% of the solar radiation has to be cooled from the greenhouse in order to maintain its temperature. Sixty per cent of the heat being absorbed in the greenhouse is transformed into latent heat through the transpiration of the crop. The system was able to maintain the preset temperature and humidity for the greenhouse air.</p

Perceived benefits of training, individual readiness for change, and affective organizational commitment among employees of national jordanian banks

This study aimed to examine how employees' perceived benefits of training impact their level of affective organizational commitment through investigating the mediating role of individual readiness for change in National Jordanian banks. The study sample included 451 employees from 16 banks in Jordan. Stratified random sampling was used for the selection of the study participants, and data were collected using a self-administered written questionnaire. Partial least squares structural equation modelling was conducted to analyze the collected data and test the study hypotheses, which were developed according to the social exchange theory and psychological contract theory. The analysis provided strong evidence for the contentions of the social exchange theory, whereby employees' affective commitment to their banks was found to be positively influenced by their perceptions of the job-, career, and personal-related benefits of training. Moreover, individual readiness for change was shown to be positively influenced by employees' perceived benefits of training, and employees' affective organizational commitment was positively influenced by their readiness for change. Finally, individual readiness for change was found to act as a mediating variable between employees' perceived benefits of training and their level of affective commitment to their banks. The current study provides bank management teams with a comprehensive understanding of employees' affective organizational commitment as a potential outcome of training and provides evidence for the relationship between the two variables

A categorical framework of manufacturing for industry 4.0 and beyond

AbstractWith rapid advancements in industry, technology and applications, many concepts have emerged in manufacturing. It is generally known that the far-sighted term ‘Industry 4.0’ was published to highlight a new industrial revolution. Many manufacturing organizations and companies are researching this topic. However, the achievement criteria of Industry 4.0 are as yet uncertain. In addition, the technology roadmap of accomplishing Industry 4.0 is still not clear in industry nor in academia to date. This paper focuses on the fundamental conception of Industry 4.0 and the state of current manufacturing systems. It also identifies the research gaps between current manufacturing systems and Industry 4.0 requirements. The major contribution is an implementation structure of Industry 4.0, consisting of a multi-layered framework is described, and is shown how it can assist people in understanding and achieving the requirements of Industry 4.0