A Discrete Process Modelling and Simulation Methodology for Industrial Systems within the Concept of Digital Twins

A generic well-defined methodology for the construction and operation of dynamic process models of discrete industrial systems following a number of well-defined steps is introduced. The sequence of steps for the application of the method as well as the necessary inputs, conditions, constraints and the results obtained are defined. The proposed methodology covers the classical offline modelling and simulation applications as well as their online counterpart, which use the physical system in the context of digital twins, with extensive data exchange and interaction with sensors, actuators and tools from other scientific fields as analytics and optimisation. The implemented process models can be used for what-if analysis, comparative evaluation of alternative scenarios and for the calculation of key performance indicators describing the behaviour of the physical systems under given conditions as well as for online monitoring, management and adjustment of the physical industrial system operations with respect to given rules and targets. An application of the proposed methodology in a discrete industrial system is presented, and interesting conclusions arise and are discussed. Finally, the open issues, limitations and future extensions of the research are considered

A Discrete Process Modelling and Simulation Methodology for Industrial Systems within the Concept of Digital Twins

A generic well-defined methodology for the construction and operation of dynamic process models of discrete industrial systems following a number of well-defined steps is introduced. The sequence of steps for the application of the method as well as the necessary inputs, conditions, constraints and the results obtained are defined. The proposed methodology covers the classical offline modelling and simulation applications as well as their online counterpart, which use the physical system in the context of digital twins, with extensive data exchange and interaction with sensors, actuators and tools from other scientific fields as analytics and optimisation. The implemented process models can be used for what-if analysis, comparative evaluation of alternative scenarios and for the calculation of key performance indicators describing the behaviour of the physical systems under given conditions as well as for online monitoring, management and adjustment of the physical industrial system operations with respect to given rules and targets. An application of the proposed methodology in a discrete industrial system is presented, and interesting conclusions arise and are discussed. Finally, the open issues, limitations and future extensions of the research are considered

Αυτόνομη πλοήγηση ηλεκτρικού οχήματος

Διπλωματική Εργασία που υπεβλήθη στην Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Ηλεκτρονικών Υπολογιστών του Πολυτεχνείου Κρήτης για την μερική ικανοποίηση των απαιτήσεων απόκτησης διπλώματοςSummarization: Autonomous driving is one of the major areas of interest for the automotive industry. This constantly evolving field requires the involvement of a wide range of engineers with complementary skills. The education of these engineers is a key issue for the further development of the field. Currently in the engineering curricula, there is a lack of related platforms that can assist the engineers to train in and further develop the required dexterities. The current practice is to use either small robotic devices or full scale prototypes in order to understand and experiment in autonomous driving principles. Each approach has disadvantages ranging from the lack of realistic conditions to the cost of the platforms and sensors being used. In this thesis we present a low-cost and open-source modular electric vehicle platform, consisting from off-the-shelf components, which can be used for experimentation and research in the area of autonomous cars. This proposed platform, an urban concept vehicle, aims to tackle the problems of realistic conditions and cost respectively. Equipped with perception sensors, such as camera, lidar and ultrasonics, as well as navigation sensors such as GPS and IMU, provides the ideal foundation for anyone dealing with autonomy - from beginners to experts. The motivation for this work was to construct and provide a functioning platform for research purposes in the domain. The functionality of the suggested system is verified by extensive experimentation in very-close-to-real traffic conditions proving reliability, robustness and easy adaptability in diverse test cases.Περίληψη: Η αυτόνομη πλοήγηση οχημάτων είναι μια από τις κύριες περιοχές ενδιαφέροντος στην αυτοκινητοβιομηχανία. Η συνεχής εξέλιξη του πεδίου αυτού προϋποθέτει την ενασχόληση ενός εύρους μηχανικών με αλληλοσυμπληρούμενες ικανότητες, η εκπαίδευση των οποίων αποτελεί κλειδί για την περαιτέρω ανάπτυξη του τομέα αυτού. Ωστόσο, πλατφόρμες ικανές να βοηθήσουν στην ανάπτυξη των απαραίτητων δεξιοτήτων δεν είναι διαθέσιμες. Συνήθως χρησιμοποιούνται είτε ρομποτικά οχήματα υπό κλίμακα, είτε πραγματικών διαστάσεων πρωτότυπα, για την κατανόηση των βασικών αρχών της αυτόνομης πλοήγησης. Σε κάθε περίπτωση υπάρχουν μειονεκτήματα όπως είναι η έλλειψη ρεαλιστικών συνθηκών ή το κόστος των οχημάτων και των αισθητήρων που χρησιμοποιούνται αντίστοιχα. Στην παρούσα εργασία, παρουσιάζεται μια πλατφόρμα ηλεκτρικού οχήματος, που μπορεί να χρησιμοποιηθεί για έρευνα στον τομέα των αυτόνομων οχημάτων. Η προτεινόμενη πλατφόρμα, ένα πρωτότυπο όχημα πόλης, αποσκοπεί στο να αντιμετωπίσει τα προβλήματα των μη ρεαλιστικών συνθηκών και του κόστους αντίστοιχα. Είναι εξοπλισμένη με αισθητήρες αντίληψης του περιβάλλοντος, όπως κάμερες, λέιζερ σαρωτή και αισθητήρες υπερήχων αλλά και αισθητήρες πλοήγησης όπως μονάδα παγκόσμιας στιγματοθέτισης αλλά και μονάδα αδρανειακής μέτρησης, και προσφέρει τα απαραίτητα εφόδια σε οποιονδήποτε σκοπεύει να ασχοληθεί με τον τομέα της αυτονομίας. Ο σκοπός της παρούσας εργασίας ήταν η κατασκευή μιας λειτουργικής πλατφόρμας που θα υποστηρίξει της ερευνητικές προσπάθειες στον τομέα αυτό. Η λειτουργικότητά της έχει επαληθευτεί μέσα από εκτεταμένα πειράματα σε -σχεδόν- πραγματικές συνθήκες αποδεικνύοντας την αξιοπιστία της και την δυνατότητα άμεσης προσαρμογής σε διαφορετικά σενάρια

Efficient Gear Ratio Selection of a Single-Speed Drivetrain for Improved Electric Vehicle Energy Consumption

The electric vehicle (EV) market has grown over the last few years and even though electric vehicles do not currently possess a high market segment, it is projected that they will do so by 2030. Currently, the electric vehicle industry is looking to resolve the issue of vehicle range, using higher battery capacities and fast charging. Energy consumption is a key issue which heavily effects charging frequency and infrastructure and, therefore, the widespread use of EVs. Although several factors that influence energy consumption of EVs have been identified, a key technology that can make electric vehicles more energy efficient is drivetrain design and development. Based on electric motors’ high torque capabilities, single-speed transmissions are preferred on many light and urban vehicles. In the context of this paper, a prototype electric vehicle is used as a test bed to evaluate energy consumption related to different gear ratio usage on single-speed transmission. For this purpose, real-time data are recorded from experimental road tests and a dynamic model of the vehicle is created and fine-tuned using dedicated software. Dynamic simulations are performed to compare and evaluate different gear ratio set-ups, providing valuable insights into their effect on energy consumption. The correlation of experimental and simulation data is used for the validation of the dynamic model and the evaluation of the results towards the selection of the optimal gear ratio. Based on the aforementioned data, we provide useful information from numerous experimental and simulation results that can be used to evaluate gear ratio effects on electric vehicles’ energy consumption and, at the same time, help to formulate evolving concepts of smart grid and EV integration

Cyber-Physical LPG Debutanizer Distillation Columns: Machine-Learning-Based Soft Sensors for Product Quality Monitoring

Refineries execute a series of interlinked processes, where the product of one unit serves as the input to another process. Potential failures within these processes affect the quality of the end products, operational efficiency, and revenue of the entire refinery. In this context, implementation of a real-time cognitive module, referring to predictive machine learning models, enables the provision of equipment state monitoring services and the generation of decision-making for equipment operations. In this paper, we propose two machine learning models: (1) to forecast the amount of pentane (C5) content in the final product mixture; (2) to identify if C5 content exceeds the specification thresholds for the final product quality. We validate our approach using a use case from a real-world refinery. In addition, we develop a visualization to assess which features are considered most important during feature selection, and later by the machine learning models. Finally, we provide insights on the sensor values in the dataset, which help to identify the operational conditions for using such machine learning models

A discrete process modelling and simulation methodology for industrial systems within the concept of digital twins

Summarization: A generic well-defined methodology for the construction and operation of dynamic process models of discrete industrial systems following a number of well-defined steps is introduced. The sequence of steps for the application of the method as well as the necessary inputs, conditions, constraints and the results obtained are defined. The proposed methodology covers the classical offline modelling and simulation applications as well as their online counterpart, which use the physical system in the context of digital twins, with extensive data exchange and interaction with sensors, actuators and tools from other scientific fields as analytics and optimisation. The implemented process models can be used for what-if analysis, comparative evaluation of alternative scenarios and for the calculation of key performance indicators describing the behaviour of the physical systems under given conditions as well as for online monitoring, management and adjustment of the physical industrial system operations with respect to given rules and targets. An application of the proposed methodology in a discrete industrial system is presented, and interesting conclusions arise and are discussed. Finally, the open issues, limitations and future extensions of the research are considered.Παρουσιάστηκε στο: Applied Science

Cyber-physical LPG debutanizer distillation columns: machine-learning-based soft sensors for product quality monitoring

Summarization: Refineries execute a series of interlinked processes, where the product of one unit serves as the input to another process. Potential failures within these processes affect the quality of the end products, operational efficiency, and revenue of the entire refinery. In this context, implementation of a real-time cognitive module, referring to predictive machine learning models, enables the provision of equipment state monitoring services and the generation of decision-making for equipment operations. In this paper, we propose two machine learning models: (1) to forecast the amount of pentane (C5) content in the final product mixture; (2) to identify if C5 content exceeds the specification thresholds for the final product quality. We validate our approach using a use case from a real-world refinery. In addition, we develop a visualization to assess which features are considered most important during feature selection, and later by the machine learning models. Finally, we provide insights on the sensor values in the dataset, which help to identify the operational conditions for using such machine learning models.Παρουσιάστηκε στο: Applied Science

Efficient gear ratio selection of a single-speed drivetrain for improved electric vehicle energy consumption

This work has been partially funded by the TUC’s internal project “TUC Eco Racing team”.Αυτό το έργο χρηματοδοτήθηκε εν μέρει από το εσωτερικό έργο του Πολυτεχνείου Κρήτης "TUC Eco Racing team".Summarization: The electric vehicle (EV) market has grown over the last few years and even though electric vehicles do not currently possess a high market segment, it is projected that they will do so by 2030. Currently, the electric vehicle industry is looking to resolve the issue of vehicle range, using higher battery capacities and fast charging. Energy consumption is a key issue which heavily effects charging frequency and infrastructure and, therefore, the widespread use of EVs. Although several factors that influence energy consumption of EVs have been identified, a key technology that can make electric vehicles more energy efficient is drivetrain design and development. Based on electric motors’ high torque capabilities, single-speed transmissions are preferred on many light and urban vehicles. In the context of this paper, a prototype electric vehicle is used as a test bed to evaluate energy consumption related to different gear ratio usage on single-speed transmission. For this purpose, real-time data are recorded from experimental road tests and a dynamic model of the vehicle is created and fine-tuned using dedicated software. Dynamic simulations are performed to compare and evaluate different gear ratio set-ups, providing valuable insights into their effect on energy consumption. The correlation of experimental and simulation data is used for the validation of the dynamic model and the evaluation of the results towards the selection of the optimal gear ratio. Based on the aforementioned data, we provide useful information from numerous experimental and simulation results that can be used to evaluate gear ratio effects on electric vehicles’ energy consumption and, at the same time, help to formulate evolving concepts of smart grid and EV integration.Presented on: Sustainabilit