The document discusses applying energy optimization techniques to large industrial systems through integrated modelling and simulation using a modular "cube" approach, where individual production processes, machines, buildings, and energy systems are represented as interconnected cubes that can be simulated together to analyze energy usage and identify optimization opportunities. Measurement data is used to develop data-driven models of machines and other systems within the cubes to enable simulation of a full production facility and evaluation of different energy scenarios.
Role of Building Automation related to Renewable Energy in nZEB’sLeonardo ENERGY
Building automation has a key role to play in the implementation of nearly zero-energy buildings (nZEB). Building automation is the connector of all the single requirements for nZEB, such as a well-insulated and airtight building shell, efficient HVAC system and a high share of renewable energy. That is the main conclusion of this study, prepared by Ecofys for the Leonardo ENERGY initiative.
Main functions of building automation
The study identifies the most important building automation functions in an nZEB as follows:
1. Central, concerted control of all energy related components
Building automation taps all internal saving potential and assures that the whole system can work with highest efficiency. While sophisticated central systems with producer-independent compatibility are commonly available, they need to be developed and supported further to arrive at a necessary standard requirement for nearly zero-energy buildings.
2. Monitoring and providing feedback
Building automation guarantees that the demanded and calculated (low) energy demand of the nearly zero-energy is met. That is one of the key aspects in meeting climate goals.
Next to that, building automation encourages users to save energy. The expected saving potential of this indirect efficiency measure are estimated to be up to 30%.
3. Load shifting and storage management
Building automation increases the coverage rates of renewable energy on site (mainly PV). The expected increase of total coverage rate by building automation without additional storage is estimated to be up to 5% in southern European regions.
Also, building automation increases free cooling potentials in central and southern European regions. Free cooling potentials are often still untapped, but necessary to reduce cooling demand significantly.
Thirdly, building automation increases grid stability. The challenge to maintain grid stability will become larger with increased penetration of renewable energies.
4. Ensuring the thermal comfort
Ensuring thermal comfort is especially important at highly efficient but slow reacting systems, like concrete activation or floor heating. Industry needs to develop and improve control mechanisms, which are specialized to control slow reacting systems in nearly zero-energy buildings (e.g. by using weather forecasts).
Action required
To ensure that the indicated potentials of building automation are achieved different actions from different stakeholders will be necessary. As indicated in chapter 6 some of those need to originate from policies, e.g. to develop an adequate regulatory basis or to create awareness. Others need to come from industry to provide suitable products.
Finally, many aspects of building automation will need to be further investigated before their full benefits can be reaped. Testing and monitoring of realized nearly zero energy buildings with inte-grated building automation systems wi
N-SIDE provides optimization solutions for energy flexibility and decision making using advanced analytics. They combine expertise in mathematics, business engineering, and computer science. Their approach includes descriptive mathematical models, predictive advanced forecasts, and prescriptive efficient algorithms to generate optimal decisions. They have developed optimization solutions for market coupling, energy flexibility, microgrids, and innovative projects on energy flexibility.
The document discusses Granollers' plans to develop low-carbon heating and cooling networks through the Eco Congost project. It aims to reduce fossil energy consumption in the city's industrial parks by generating energy from renewable sources like biogas and distributing steam and hot water through a district heating system. The city has collected data on energy sources, demand, and infrastructure to help model and plan the optimal heating network configuration through the EU-funded THERMOS project.
This document summarizes a solar thermal district heating project in Freiburg-Gutleutmatten, Germany. The project involves installing decentralized solar thermal collectors and storage on 38 buildings to provide summer heat demand. A biogas-fired CHP plant and gas boilers provide backup heating through a district heating network. Project partners include the City of Freiburg, Fraunhofer Institute, and badenovaWÄRMEPLUS GmbH, which built and operates the system. The decentralized solar thermal is expected to meet over 25% of annual heat demand and allow shutdown of the district heating network in summer months.
Justyna Rybicka discuss using WITNESS software to model support decision maki...Lanner
Presenting at the Lanner predictive simulation conference, 2016, Justyna Rybicka from Cranfield University explores the use of Lanner simulation software WITNESS to model support decision making tool for flexible manufacturing system optimisation.
This document provides an overview and instructions for using the Energy Distribution Support Tool (EDST) to analyze and distribute a company's energy consumption data. The EDST is an Excel-based tool that allows users to input technical data on machinery, lighting, compressed air and other energy consuming systems. It then calculates and distributes the electrical and thermal energy consumption across different production processes, segments and time periods. The document outlines the key steps to setup and use the EDST, including inputting company and production data, technical specifications for energy systems, and hours of operation. It provides examples of how to analyze the energy use for different segments. The goal of the tool is to help companies understand where and how energy is being consumed to identify
D. Anagnostakis, J.M. Ritchie and T. Lim explore how Lanner predictive simula...Lanner
Presenting at the Lanner predictive simulation conference, 2016, D. Anagnostakis, J.M. Ritchie and T. Lim explore how Lanner predictive simulation software WITNESS can help improve the environmental impact of a manufacturing system.
Role of Building Automation related to Renewable Energy in nZEB’sLeonardo ENERGY
Building automation has a key role to play in the implementation of nearly zero-energy buildings (nZEB). Building automation is the connector of all the single requirements for nZEB, such as a well-insulated and airtight building shell, efficient HVAC system and a high share of renewable energy. That is the main conclusion of this study, prepared by Ecofys for the Leonardo ENERGY initiative.
Main functions of building automation
The study identifies the most important building automation functions in an nZEB as follows:
1. Central, concerted control of all energy related components
Building automation taps all internal saving potential and assures that the whole system can work with highest efficiency. While sophisticated central systems with producer-independent compatibility are commonly available, they need to be developed and supported further to arrive at a necessary standard requirement for nearly zero-energy buildings.
2. Monitoring and providing feedback
Building automation guarantees that the demanded and calculated (low) energy demand of the nearly zero-energy is met. That is one of the key aspects in meeting climate goals.
Next to that, building automation encourages users to save energy. The expected saving potential of this indirect efficiency measure are estimated to be up to 30%.
3. Load shifting and storage management
Building automation increases the coverage rates of renewable energy on site (mainly PV). The expected increase of total coverage rate by building automation without additional storage is estimated to be up to 5% in southern European regions.
Also, building automation increases free cooling potentials in central and southern European regions. Free cooling potentials are often still untapped, but necessary to reduce cooling demand significantly.
Thirdly, building automation increases grid stability. The challenge to maintain grid stability will become larger with increased penetration of renewable energies.
4. Ensuring the thermal comfort
Ensuring thermal comfort is especially important at highly efficient but slow reacting systems, like concrete activation or floor heating. Industry needs to develop and improve control mechanisms, which are specialized to control slow reacting systems in nearly zero-energy buildings (e.g. by using weather forecasts).
Action required
To ensure that the indicated potentials of building automation are achieved different actions from different stakeholders will be necessary. As indicated in chapter 6 some of those need to originate from policies, e.g. to develop an adequate regulatory basis or to create awareness. Others need to come from industry to provide suitable products.
Finally, many aspects of building automation will need to be further investigated before their full benefits can be reaped. Testing and monitoring of realized nearly zero energy buildings with inte-grated building automation systems wi
N-SIDE provides optimization solutions for energy flexibility and decision making using advanced analytics. They combine expertise in mathematics, business engineering, and computer science. Their approach includes descriptive mathematical models, predictive advanced forecasts, and prescriptive efficient algorithms to generate optimal decisions. They have developed optimization solutions for market coupling, energy flexibility, microgrids, and innovative projects on energy flexibility.
The document discusses Granollers' plans to develop low-carbon heating and cooling networks through the Eco Congost project. It aims to reduce fossil energy consumption in the city's industrial parks by generating energy from renewable sources like biogas and distributing steam and hot water through a district heating system. The city has collected data on energy sources, demand, and infrastructure to help model and plan the optimal heating network configuration through the EU-funded THERMOS project.
This document summarizes a solar thermal district heating project in Freiburg-Gutleutmatten, Germany. The project involves installing decentralized solar thermal collectors and storage on 38 buildings to provide summer heat demand. A biogas-fired CHP plant and gas boilers provide backup heating through a district heating network. Project partners include the City of Freiburg, Fraunhofer Institute, and badenovaWÄRMEPLUS GmbH, which built and operates the system. The decentralized solar thermal is expected to meet over 25% of annual heat demand and allow shutdown of the district heating network in summer months.
Justyna Rybicka discuss using WITNESS software to model support decision maki...Lanner
Presenting at the Lanner predictive simulation conference, 2016, Justyna Rybicka from Cranfield University explores the use of Lanner simulation software WITNESS to model support decision making tool for flexible manufacturing system optimisation.
This document provides an overview and instructions for using the Energy Distribution Support Tool (EDST) to analyze and distribute a company's energy consumption data. The EDST is an Excel-based tool that allows users to input technical data on machinery, lighting, compressed air and other energy consuming systems. It then calculates and distributes the electrical and thermal energy consumption across different production processes, segments and time periods. The document outlines the key steps to setup and use the EDST, including inputting company and production data, technical specifications for energy systems, and hours of operation. It provides examples of how to analyze the energy use for different segments. The goal of the tool is to help companies understand where and how energy is being consumed to identify
D. Anagnostakis, J.M. Ritchie and T. Lim explore how Lanner predictive simula...Lanner
Presenting at the Lanner predictive simulation conference, 2016, D. Anagnostakis, J.M. Ritchie and T. Lim explore how Lanner predictive simulation software WITNESS can help improve the environmental impact of a manufacturing system.
How costs affect deployment of low carbon technologies - analysis with JRC-EU...IEA-ETSAP
The document discusses using the JRC-EU-TIMES energy system optimization model to analyze the impacts of technology costs and assumptions on the deployment of low carbon technologies in Europe. The model can explore scenarios assessing different technology sensitivities to provide insights for targeting research and innovation efforts. Examples analyzed include the impacts of solar PV costs and the role of geothermal with and without carbon capture and storage. The model results can indicate potentially cost-effective research and innovation investment levels to achieve breakthrough technology performance levels.
A framework for dynamic pricing electricity consumption patterns via time ser...Asoka Korale
Clustering individual household electricity consumption patterns enables a utility to design pricing plans catered to groups of households in a particular locality to more accurately reflect the cost of supply at a particular time of day.
In this paper we model each time series as an Autoregressive Moving Average (ARMA) process with an optimal model order determined by the Akaike Information Criterion when the parameters estimated by the Hannan-Rissanen algorithm converge. The estimated model has the representation of a transfer function with a frequency response defined by the ARMA parameters. We use the frequency response as the means to further refine the within cluster profiling and classification of the objects.
Through our modeling we are also able to identify instances where the consumption behavior exhibits patterns that are uncharacteristic or not in line with the behavior or consumption profiles of the other households in a particular locality providing insights in to potential faults, fraud or illegal activity.
This document summarizes a project to simulate and optimize the machining process for an optical mount. It includes:
- Building a toolpath for machining the mount using specified tooling and simulating the process to evaluate cycle time.
- Estimating tool life using Taylor's model and calculating the number of parts each tool can machine.
- Estimating total original machining cost of $8.84 including material, labor, tools, equipment, and overhead costs.
- Generating a cost model and optimizing speeds and feeds to minimize cost, resulting in an optimized cost of $8.30.
This paper experiments with different heuristic approaches to solve a real facility layout problem at a furniture manufacturing company. Five layout modeling techniques are applied to the problem: graph theory, CRAFT, optimum sequence, BLOCPLAN, and genetic algorithm. The resulting layouts are evaluated based on total area, flow times distance, and adjacency percentage. The best layout is selected using the analytic hierarchy process and is found to improve upon the existing layout, demonstrating the effectiveness of formal modeling approaches for real industrial problems.
This document summarizes the services of a technology-focused company that provides computational mechanics and simulation tools. They offer modeling, simulation, analysis, optimization and evaluation across various technical areas including multiphysics simulation, multibody dynamics, and total design. Their methodology uses commercial and research simulation packages along with custom software development. They can help companies with product design, validation, independent engineering and operational improvements through simulation.
Simcenter engineering solutions for intake and exhaustAlfredo De Seta
This document discusses multi-physics solutions for automotive intake and exhaust systems. It summarizes trends driving innovation like electrification and emissions regulations. It then discusses Simcenter Engineering & Consulting's approach to balancing engine performance and efficiency using system simulation. Their solutions also address reducing NOx emissions through 3D CFD SCR modeling, optimizing NVH performance, identifying loads from testing for model improvement, and evaluating exhaust fatigue from thermal and mechanical loads.
Computer integrated manufacturing (CIM) is the integration of all enterprise operations and activities around a common corporate data repository through the use of integrated systems and data communications coupled with new managerial philosophies. CIM is not a product that can be purchased and installed, but rather a way of thinking and solving problems through the use of computers for on-line automation, optimization, and integration of the total manufacturing system from design to production. Flexible manufacturing systems (FMS) bridge the gap between high-production transfer lines and programmable but low-production numerical control machines by allowing for medium part variety and medium production volumes. FMS consist of computer-controlled machines connected by an automated material handling system.
STREAM-0D: a new vision for Zero-Defect ManufacturingFulvio Bernardini
This slide deck has been extracted from STREAM-0D's webinar, that was held on March 27th, 2020.
The presentation aims at providing insights on the STREAM-0D solution, showcasing goals and results of the project and real case scenarios applications in automotive production lines: brake boosters, tapered-roller bearings and rubber car sealings.
STREAM-0D project has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 723082.
4. Luca Ferrarini (POLIMI, Italy) - Pilot Case 1: The Reality of Working with...Cassandra Project
This document describes a pilot case study conducted at a shopping center in Italy. The study had two parts: 1) in-simulation modeling and testing of the building and 2) an in-field behavioral program with retail shops. For part 1, thermal and electrical models of the building were developed and different control strategies and demand response programs were tested. For part 2, a web application was used to provide shops with real-time energy use data and monetary incentives were given to reduce consumption. The overall goal was to evaluate demand response and feedback programs in the commercial sector using the CASSANDRA software platform.
Building Simulation, Its Role, Softwares & Their LimitationsPrasad Thanthratey
A presentation on Building Simulation, Its Role, Softwares & Their Limitations for the course of Energy Efficient Architecture from students of 5th Semester Architecture at VNIT, Nagpur (Aug-December 2015)
Induction Motors Matching Permanent Magnet Performances at Lower Costsfernando nuño
Due to a continued concern on the external dependence of permanent magnets in Europe, induction technology is being pushed beyond its limits to maximise performance.
With novel materials, material characterisation and multi-domain design, power-speed capability of laminated rotor induction motors can match that typically associated with surface permanent magnet machines, at a fraction of the cost.
This session reviews the findings relating to lower cost induction motors, highlighting how they can successfully be used as an alternative to permanent magnets.
The document discusses various considerations for process design and facility layout including process selection based on factors like volume and variety, different types of process layouts including job shop, batch, repetitive, and continuous processes, and considerations for automation and layout types including product, process, fixed-position, group technology, and cellular layouts. It also covers concepts like line balancing, parallel workstations, and designing layouts for different environments like warehouses, retail stores, and offices.
This document discusses process design and facility layout. It begins by explaining different process types like job shops, batch processing, repetitive/assembly, and continuous processing. It then discusses factors to consider for process selection like product variety and volume. Different layout types are described like product layouts, process layouts, fixed-position layouts, and combination layouts. Cellular layouts and group technology layouts are also covered. The document concludes with a discussion of line balancing to optimize workstation efficiency.
Overview of solutions for machine monitoringIvan Zgela
Presentation showing condition monitoring solutions for rotating machines from KONČAR Institute. The solutions are divided in two big groups:
1. Compact stationary monitoring solutions
2. Portable monitoring and diagnostic instruments
Overall 13 solutions are presented with description of market, applications, value propositions, etc.
Using Modelica and FMI to evaluate requirements compliance early in system d...Modelon
This document discusses automated requirements validation using the Functional Mockup Interface (FMI) and Modelica. It describes how requirements can be formalized, translated into executable monitors, and automatically checked by simulating FMI models. This allows validating that system designs meet requirements early in development and catching any failures through continuous validation as the models evolve.
Smart energy management systems provide benefits such as energy efficiency, cost savings, load management, and power quality monitoring. They involve installing hardware like power analyzers to monitor energy usage throughout a facility. The data is visualized using software which allows users to track consumption, identify inefficiencies, set alarms, and detect power quality issues. This helps optimize energy distribution, reduce costs, and prevent production disruptions. The document discusses an implementation at a car manufacturing plant that identified power quality problems, saved on energy bills and maintenance costs, and provided a return on investment within 1.4 years.
This document discusses the potential for smart 5G heat networks to reduce costs and carbon emissions compared to traditional heat networks. It summarizes a demonstration project that found smart networks could reduce required installation capacity by up to 50% through load shifting and optimized control. However, sizing standards are still an issue and overestimate hot water loads. The project concluded smart controls worked as expected and could lower operating costs. Next steps proposed include developing an open operating platform and national sizing standards based on primary data to advance smart heat networks.
How costs affect deployment of low carbon technologies - analysis with JRC-EU...IEA-ETSAP
The document discusses using the JRC-EU-TIMES energy system optimization model to analyze the impacts of technology costs and assumptions on the deployment of low carbon technologies in Europe. The model can explore scenarios assessing different technology sensitivities to provide insights for targeting research and innovation efforts. Examples analyzed include the impacts of solar PV costs and the role of geothermal with and without carbon capture and storage. The model results can indicate potentially cost-effective research and innovation investment levels to achieve breakthrough technology performance levels.
A framework for dynamic pricing electricity consumption patterns via time ser...Asoka Korale
Clustering individual household electricity consumption patterns enables a utility to design pricing plans catered to groups of households in a particular locality to more accurately reflect the cost of supply at a particular time of day.
In this paper we model each time series as an Autoregressive Moving Average (ARMA) process with an optimal model order determined by the Akaike Information Criterion when the parameters estimated by the Hannan-Rissanen algorithm converge. The estimated model has the representation of a transfer function with a frequency response defined by the ARMA parameters. We use the frequency response as the means to further refine the within cluster profiling and classification of the objects.
Through our modeling we are also able to identify instances where the consumption behavior exhibits patterns that are uncharacteristic or not in line with the behavior or consumption profiles of the other households in a particular locality providing insights in to potential faults, fraud or illegal activity.
This document summarizes a project to simulate and optimize the machining process for an optical mount. It includes:
- Building a toolpath for machining the mount using specified tooling and simulating the process to evaluate cycle time.
- Estimating tool life using Taylor's model and calculating the number of parts each tool can machine.
- Estimating total original machining cost of $8.84 including material, labor, tools, equipment, and overhead costs.
- Generating a cost model and optimizing speeds and feeds to minimize cost, resulting in an optimized cost of $8.30.
This paper experiments with different heuristic approaches to solve a real facility layout problem at a furniture manufacturing company. Five layout modeling techniques are applied to the problem: graph theory, CRAFT, optimum sequence, BLOCPLAN, and genetic algorithm. The resulting layouts are evaluated based on total area, flow times distance, and adjacency percentage. The best layout is selected using the analytic hierarchy process and is found to improve upon the existing layout, demonstrating the effectiveness of formal modeling approaches for real industrial problems.
This document summarizes the services of a technology-focused company that provides computational mechanics and simulation tools. They offer modeling, simulation, analysis, optimization and evaluation across various technical areas including multiphysics simulation, multibody dynamics, and total design. Their methodology uses commercial and research simulation packages along with custom software development. They can help companies with product design, validation, independent engineering and operational improvements through simulation.
Simcenter engineering solutions for intake and exhaustAlfredo De Seta
This document discusses multi-physics solutions for automotive intake and exhaust systems. It summarizes trends driving innovation like electrification and emissions regulations. It then discusses Simcenter Engineering & Consulting's approach to balancing engine performance and efficiency using system simulation. Their solutions also address reducing NOx emissions through 3D CFD SCR modeling, optimizing NVH performance, identifying loads from testing for model improvement, and evaluating exhaust fatigue from thermal and mechanical loads.
Computer integrated manufacturing (CIM) is the integration of all enterprise operations and activities around a common corporate data repository through the use of integrated systems and data communications coupled with new managerial philosophies. CIM is not a product that can be purchased and installed, but rather a way of thinking and solving problems through the use of computers for on-line automation, optimization, and integration of the total manufacturing system from design to production. Flexible manufacturing systems (FMS) bridge the gap between high-production transfer lines and programmable but low-production numerical control machines by allowing for medium part variety and medium production volumes. FMS consist of computer-controlled machines connected by an automated material handling system.
STREAM-0D: a new vision for Zero-Defect ManufacturingFulvio Bernardini
This slide deck has been extracted from STREAM-0D's webinar, that was held on March 27th, 2020.
The presentation aims at providing insights on the STREAM-0D solution, showcasing goals and results of the project and real case scenarios applications in automotive production lines: brake boosters, tapered-roller bearings and rubber car sealings.
STREAM-0D project has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 723082.
4. Luca Ferrarini (POLIMI, Italy) - Pilot Case 1: The Reality of Working with...Cassandra Project
This document describes a pilot case study conducted at a shopping center in Italy. The study had two parts: 1) in-simulation modeling and testing of the building and 2) an in-field behavioral program with retail shops. For part 1, thermal and electrical models of the building were developed and different control strategies and demand response programs were tested. For part 2, a web application was used to provide shops with real-time energy use data and monetary incentives were given to reduce consumption. The overall goal was to evaluate demand response and feedback programs in the commercial sector using the CASSANDRA software platform.
Building Simulation, Its Role, Softwares & Their LimitationsPrasad Thanthratey
A presentation on Building Simulation, Its Role, Softwares & Their Limitations for the course of Energy Efficient Architecture from students of 5th Semester Architecture at VNIT, Nagpur (Aug-December 2015)
Induction Motors Matching Permanent Magnet Performances at Lower Costsfernando nuño
Due to a continued concern on the external dependence of permanent magnets in Europe, induction technology is being pushed beyond its limits to maximise performance.
With novel materials, material characterisation and multi-domain design, power-speed capability of laminated rotor induction motors can match that typically associated with surface permanent magnet machines, at a fraction of the cost.
This session reviews the findings relating to lower cost induction motors, highlighting how they can successfully be used as an alternative to permanent magnets.
The document discusses various considerations for process design and facility layout including process selection based on factors like volume and variety, different types of process layouts including job shop, batch, repetitive, and continuous processes, and considerations for automation and layout types including product, process, fixed-position, group technology, and cellular layouts. It also covers concepts like line balancing, parallel workstations, and designing layouts for different environments like warehouses, retail stores, and offices.
This document discusses process design and facility layout. It begins by explaining different process types like job shops, batch processing, repetitive/assembly, and continuous processing. It then discusses factors to consider for process selection like product variety and volume. Different layout types are described like product layouts, process layouts, fixed-position layouts, and combination layouts. Cellular layouts and group technology layouts are also covered. The document concludes with a discussion of line balancing to optimize workstation efficiency.
Overview of solutions for machine monitoringIvan Zgela
Presentation showing condition monitoring solutions for rotating machines from KONČAR Institute. The solutions are divided in two big groups:
1. Compact stationary monitoring solutions
2. Portable monitoring and diagnostic instruments
Overall 13 solutions are presented with description of market, applications, value propositions, etc.
Using Modelica and FMI to evaluate requirements compliance early in system d...Modelon
This document discusses automated requirements validation using the Functional Mockup Interface (FMI) and Modelica. It describes how requirements can be formalized, translated into executable monitors, and automatically checked by simulating FMI models. This allows validating that system designs meet requirements early in development and catching any failures through continuous validation as the models evolve.
Smart energy management systems provide benefits such as energy efficiency, cost savings, load management, and power quality monitoring. They involve installing hardware like power analyzers to monitor energy usage throughout a facility. The data is visualized using software which allows users to track consumption, identify inefficiencies, set alarms, and detect power quality issues. This helps optimize energy distribution, reduce costs, and prevent production disruptions. The document discusses an implementation at a car manufacturing plant that identified power quality problems, saved on energy bills and maintenance costs, and provided a return on investment within 1.4 years.
This document discusses the potential for smart 5G heat networks to reduce costs and carbon emissions compared to traditional heat networks. It summarizes a demonstration project that found smart networks could reduce required installation capacity by up to 50% through load shifting and optimized control. However, sizing standards are still an issue and overestimate hot water loads. The project concluded smart controls worked as expected and could lower operating costs. Next steps proposed include developing an open operating platform and national sizing standards based on primary data to advance smart heat networks.
Automation is the use of technology to control and operate production processes. It is used to increase productivity, address labor shortages and high labor costs, improve quality, and reduce manufacturing time. Common machines used in automation include numerically controlled machines, robotics, and automated quality inspection systems. Automation can be applied in continuous processes like chemical plants, mass production like automobiles, batch production like books, and job shop production like prototypes. While automation focuses on reducing unit production time, CAD/CAM additionally aims to reduce design time. The benefits of automation include increased productivity, reduced time, less floor space needed, and less human fatigue, but the costs can be high initially and development costs unpredictable.
ICME THE BACKBONE FOR LIGHTWEIGHT VEHICLE DEVELOPMENTiQHub
1) The document discusses how integrated computational material engineering (ICME) can be used for lightweight vehicle development by connecting real materials data to virtual testing and simulation.
2) Hexagon is a leader in ICME and materials digitization, helping customers develop new materials, optimize designs, predict defects, and reduce costs and time.
3) Case studies are presented showing how Hexagon's ICME solutions improved correlation between simulation and testing for various vehicle components like suspension arms, engine flaps, oil pans, and front ends.
The French Cluster MEDEE and several of its members (L2EP, LSEE, URIA and LAMIH) are involved in an ambitious R&D French project called CE2I “Integrated & Smart Power Converters” (budget: €10M). This multidisciplinary project will create the electric motor of the 21st century.
CE2I develops a system integrating a reliable electromagnetic actuator and a static converter based on large gap components.
This package will be fault-tolerant, remote programmable and will have to meet the requirements of compactness, eco-efficiency and modularity (plug and play) to satisfy a large range of applications such as on board systems, renewable energy production, electrical mobility and industrial process reliability.
CE2I has already received a lot of support from industrial stakeholders.
The presentation will give you an overview of CE2I.
Similar to Roessler, Hafner - Modelling and Simulation in Industrial Applications: Applying energy optimization to large scale systems (20)
This talk provides a critical view on employing machine learning / deep learning methods in algorithmic trading. We highlight the particular challenges that we meet in this domain along with approaches to tackle some of these challenges in practice. Even though experience has shown that algorithmic trading using advanced machine learning can be successful, the crucial issue remains that predictive patterns utilizing market inefficiencies quickly become void as soon as competing market participants use them too. The conclusion is that the crucial advantage is – and has always been – to know more and to be faster than competitors.
Our Speaker: Dr. Ulrich Bodenhofer
MSc (applied math, Johannes Kepler University, Linz, Austria, 1996)
PhD (applied math, Johannes Kepler University, Linz, Austria, 1998)
Since June 2018: Chief Artificial Intelligence Officer at QUOMATIC.AI (Linz, Austria)
The consumer product landscape, particularly among e-commerce firms, includes a bevy of subscription-based business models. Internet and mobile phone subscriptions are now commonplace and joining the ranks are dietary supplements, meals, clothing, cosmetics and personal grooming products.
Standard metrics to diagnose a healthy consumer-brand relationship typically include customer purchase frequency and ultimately, retention of the customer demonstrated by regular purchases. If a brand notices that a customer isn’t purchasing, it may consider targeting the customer with discount offers or deploying a tailored messaging campaign in the hope that the customer will return and not “churn”.The churn diagnosis, however, becomes more complicated for subscription-based products, many of which offer multiple delivery frequencies and the ability to pause a subscription. Brands with subscription-based products need to have some reliable measure of churn propensity so they can further isolate the factors that lead to churn and preemptively identify at-risk customers.
Since the worldwide outbreak of the COVID-19 pandemic, experts all around the globe are working heavily to establish reliable forecasts for the spread of the disease. Hereby they allow decision-makers to roughly plan ahead and inform the population with estimates of what might still lie ahead. Yet, the huge jungle of different models, data and results is confusing and difficult to overlook: what models are reliable, which results can be trusted and what are the secrets behind these models?
OUR SPEAKER
Dr. Martin Bicher is chief developer of the COVID-19 modeling team around Dr. Niki Popper in dwh simulation-services GmbH, which currently supports decision-makers all around Austria with simulated forecasts, scenarios and policy evaluations. Moreover, he is a postdoctoral researcher at the Institute of Information Systems Engineering at TU Wien where he finished his PhD in Technical Mathematics.
State-of-the-art time-series prediction with continuous-time recurrent neural networks.
Neural networks with continuous-time hidden state representations have become unprecedentedly popular within the machine learning community. This is due to their strong approximation capability in modeling time-series, their adaptive computation modality, their memory and parameter efficiency. In this talk Ramin will discuss how this family of neural networks work and why they realize attractive degrees of generalizability across different application domains.
OUR SPEAKER
Ramin Hasani, PhD, Machine Learning Scientist at TU Wien, expert in robotics, including previously being a scholar MIT CSAL, presents technical aspects of continuous-time neural networks.
As more and more machines are supplied with machine learning algorithms, the question arises who is liable in cases of damage? Who is liable in case of accidents involving an autonomous driving car? Is there a difference when an autonomous lawnmower causes damage to the neighbour's property? Public interest in those questions is high, whereas legal opinions are rare and court decisions are missing. Daniel will show why it can be difficult to fit machine learning-based applications in the existing legal liability system, and what the future might look like.
- Marek Danis is an experienced data scientist and trainer who has worked for Schlumberger Oilfield Services and the Digital Transformation Team. He has a MSc from Texas A&M University Mays Business School and specializes in QHSE (Quality, Health, Safety and Environment) analytics.
- Marek runs his own consulting company in Austria focusing on QHSE analytics and using data science to decrease risk and increase business outcomes. He has developed a strong understanding of data analytics applications in corporate environments.
Kaggle is one of the largest online communities for data scientists specifically known for their competitions where participants aim to solve data science challenges. Kaggle has a long history of varying types of competitions from different areas such as medicine, finance, scientific research, or sports focusing on different types of data and prediction problems such as tabular data, time series, NLP, or computer vision.
NLP in a Bank: Automated Document Reading: Yevgen Kolesnyk / Patrik Zatko / D...Vienna Data Science Group
Despite the fast pace of digitalization happening in the modern world, core processes in the banking area are still based on printed documents to a large extent. Document processing, therefore, consumes a significant amount of manpower and processing time, as well as an increasing operating risk level of the bank by being prone to human errors. In this session, you will learn how automated document processing can create a great opportunity to modernize and simplify the way modern banks work, reduce associated operation risk level, as well as reduce time and costs spent within a given process area.
The analysis of movement is an important research topic in, for example, geography, ecology, visual analytics, GIScience as well as in application domains such as urban, maritime, and aviation research. Movement data analysis requires tools for the manipulation and visualization of movement or trajectory data. This talk presents the new Python library MovingPandas.org
Armin Rabitsch's presentation on the importance of social media in the electi...Vienna Data Science Group
This document summarizes Election.Watch.EU's social media monitoring efforts for the 2019 European Parliament elections. It monitored Facebook, Twitter, and YouTube from September 1-30 to analyze traffic and topics on party and politician accounts over time, as well as the impact of Facebook advertising. Election.Watch.EU partnered with data science groups and observed in 28 EU member states, making 16 recommendations including regulating political campaigns on social media and platforms providing data access to observers. It found right-wing populist movements successfully used social media and some countries introduced legislation and oversight for online campaigns.
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Roessler, Hafner - Modelling and Simulation in Industrial Applications: Applying energy optimization to large scale systems
1. Modelling and Simulation in Industrial
Applications
Applying Energy Optimization to
Large Scale Systems
DI Matthias Rößler
DI Irene Hafner
dwh simulation services
2. Current Situation
• Energy Consumption Austria
• Challenges regarding energy efficiency
– no holistic view on production process with respect to resource
consumption
– highly complex matter
– lack of expertise on energy systems in enterprises
– lack of knowledge of possibilities
2.1%
28.7%
32.9%
12.4%
23.9%
Agriculture
Manufacturing
Transport
Service Sector
Private Households
Statistik Austria, 2011
2
3. Energy Optimization -
Motivation
• Energy consumption in production industry
approx. 40% of total energy consumption
in industrialized nations
• Potential for reduction:
30-65% (depending on sector)
Increase of energy costs
Tougher regulations
Rising ecological awareness
Importance of energy efficiency in the industrial sector
3
6. INFO - Approach
Analysis
Modelling
Integrated Simulation
... aiming energy optimization in production facilities
... to achieve economic and ecologic goals
Optimization
Fields of Optimization
Energy
System
Production
System
MachineProcess Building
6
7. INFO
Partial Model: Machines
Analysis
Modelling
Integrated Simulation
... aiming energy optimization in production facilities
... to achieve economic and ecologic goals
Optimization
Fields of Optimization
Energy
System
Production
System
MachineProcess Building
7
8. INFO
Partial Model: Machines
• machines
– machine tools
– laser cutters
– ovens
– compressors
• production scenario
– modelling a load profile via SAP data of a
representative production week
• considered energy flows (3 thermal zones)
– electric
– diffuse heat emission
– recoverable heat
8
9. INFO
Machine Tools
initial approach
• technological
• focus on
modelling
individual tasks
of machine tools
• what is possible
from the
technological
point of view?
Step Back and focus
on 15 minute average
values
• approach from
the opposite
direction
• which values
are required to
generate the
desired output?
data based machine
model
• model based on
measured data
• easily
parameterized
• modular built,
hence flexible
• available
production data
from respective
enterprise are
essential
9
measurements
•AMS (Stiwa): Hermle
C40, C32
•Anger Machining: HCX
BA 1035, HCX BA 1110
•CNC Profi (DMG): DMU
65
•EMCO: Maxxturn 45
•Krause Mauser: Invers
BAZ for Daimler
•Hoerbiger: Stama/MC
334 Twin
10. initial approach
• technological
• focus on
modelling
individual tasks
of machine tools
• what is possible
from the
technological
point of view?
Step Back and focus
on 15 minute average
values
• approach from
the opposite
direction
• which values
are required to
generate the
desired output?
data based machine
model
• model based on
measured data
• easily
parameterized
• modular built,
hence flexible
• available
production data
from respective
enterprise are
essential
measurements
•AMS (Stiwa): Hermle
C40, C32
•Anger Machining: HCX
BA 1035, HCX BA 1110
•CNC Profi (DMG): DMU
65
•EMCO: Maxxturn 45
•Krause Mauser: Invers
BAZ for Daimler
•Hoerbiger: Stama/MC
334 Twin
INFO
Machine Tools
3,000
5,000
7,000
9,000
11,000
13,000
468 470 472 474 476 478 480 482 484
El.Power[W]
Zeit [s]
Leistung ohne
Werkstück
Leistung mit
Werkstück
slowing-down process
of the approaching
cutting unit
approaching the workpiece
without tool usage
tool usage (drilling)
tool usage (finish drilling) drill move out and approach
tothe next drilling
short move out of the drill
(ejection of chippings)
Power without
workpiece
Power with
workpiece
10
11. INFO
Machine Tools
initial approach
• technological
• focus on
modelling
individual tasks
of machine tools
• what is possible
from the
technological
point of view?
Step Back and focus
on 15 minute average
values
• approach from
the opposite
direction
• which values
are required to
generate the
desired output?
data based machine
model
• model based on
measured data
• easily
parameterized
• modular built,
hence flexible
• available
production data
from respective
enterprise are
essential
measurements
•AMS (Stiwa): Hermle
C40, C32
•Anger Machining: HCX
BA 1035, HCX BA 1110
•CNC Profi (DMG): DMU
65
•EMCO: Maxxturn 45
•Krause Mauser: Invers
BAZ for Daimler
•Hoerbiger: Stama/MC
334 Twin
location
building
production chain
machine
process
T
O
P
D
O
W
N
B
O
T
T
O
M
U
P
compressor
model
machine tool model
physical background and
measurement
oven and laser
model
11
0
50
100
150
200
Mon Tue Wed Thu Fri Sat Sun Mon
elektrischeLeistunginkW
Maschinenpark Shedhalle Kompressorencompressorsmachines
electricpowerinkW
12. INFO
Machine Tools
initial approach
• technological
• focus on
modelling
individual tasks
of machine tools
• what is possible
from the
technological
point of view?
Step Back and focus
on 15 minute average
values
• approach from
the opposite
direction
• which values
are required to
generate the
desired output?
data based machine
model
• model based on
measured data
• easily
parameterized
• modular built,
hence flexible
• available
production data
from respective
enterprise are
essential
measurements
•AMS (Stiwa): Hermle
C40, C32
•Anger Machining: HCX
BA 1035, HCX BA 1110
•CNC Profi (DMG): DMU
65
•EMCO: Maxxturn 45
•Krause Mauser: Invers
BAZ for Daimler
•Hoerbiger: Stama/MC
334 Twin
0
20
40
60
80
100
120
140
160
180
200
Mon Tue Wed Thu Fri Sat Sun Mon
electricpowerinkW
Messung Modell
• 25 machine tools in the
production hall
• comparison
measurement/model
measurement model 12
13. INFO
Partial Model: Building
Analysis
Modelling
Integrated Simulation
... aiming energy optimization in production facilities
... to achieve economic and ecologic goals
Optimization
Fields of Optimization
Energy
System
Production
System
MachineProcess Building
13
14. INFO – Building III
Output
daylight
dependent
control of
- artificial light
- shading
heat output/
cooling capacity
zone
temperature
Building ModelInput
weather data
waste heat
people/
devices
waste heat
machines
14
15. INFO
Partial Model: Energy System
Analysis
Modelling
Integrated Simulation
... aiming energy optimization in production facilities
... to achieve economic and ecologic goals
Optimization
Fields of Optimization
Energy
System
Production
System
MachineProcess Building
15
19. INFO: Specific Aims
Optimization based on simulation
• increase of energy efficiency
• inclusion of new carriers of energy
• manual comparison of specific scenarios
• no automatic optimization
Formalization of the model structure – reference model
• independent of specific implementation and
simulation environment
component based black-box approach, modularization
• illustration of dynamic dependencies and feedbacks
connection of variables and interface definition
• integration of planning and simulation
19
20. INFO: Approach
Theoretical Modelling Technical Integration
Goal: integrated dynamic simulation
• overall system not implementable in one simulator
– different modelling approaches
– gravely differing dynamics (time constants)
• several fields of expertise
• dynamic coupling
Coupling of well-established simulation tools
Co-Simulation
20
21. INFO: Overall Simulation
coupling
framework
economic and
ecologic
evaluation
static input
temperature
solar radiation
waste heat of people and
devices
electricity consumption
of devices
energy
consumption
CO2 emission
machine model
building model
energy system
model
e.g. waste heat
reuseable/diffuse
electricity
consumption of
machines
weather data
diffuse waste heat
machines
waste heat of people and
devices
room temperatures
air change rate
heating and cooling
demands
room temperatures
air change rate
heating and cooling
demands
reuseable waste heat of
machines
energy consumption
CO2 emission
21
22. INFO – Co-Simulation
• cooperative simulation with control of data exchange via
framework
• individual simulators calculate system parts independently
– different solver algorithms
– different time steps
• data exchange between simulators via framework at
previously defined points in time
• different ways of data exchange
– Strong Coupling: iterative data exchange in every step
– Loose Coupling: extrapolation between synchronization references required
…
22
23. INFO – Co-Simulation
Loose Coupling (Jacobi Type)
System 1
System 2
Jacobi Type:
Model Problem:
Extrapolation of y1 and y2
23
System 1:
System 2:
24. INFO – Co-Simulation
Loose Coupling (Gauß-Seidl Type)
Gauß-Seidl Type:
System 1
System 2
Extrapolation of y2
Interpolation of y1
24
Model Problem: System 1:
System 2:
25. INFO – Co-Simulation
Consistency
• consistency error measures the error of the
numeric method in one step
• consistency error in loose coupling co-simulation:
• ODE solver of first order: consistency order
maintained
• solver of higher order: lower consistency order
… consistency error of the method in a mono-simulation
… Lipschitz constant of the “right side“ from
… coefficient from the second characteristic polynomial
25
26. INFO – Co-Simulation
BCVTB I
• Building Controls Virtual Test Bed
• open-source software platform (developed at Lawrence
Berkeley National Laboratory, University of California)
• middleware for run-time coupling of different simulation
environments
• software components (clients) are executed in parallel
26
27. INFO – Co-Simulation
BCVTB II
• communication via BSD sockets and network protocol
(inter-process communication)
• Loose Coupling (Jacobi Type) with equidistant time steps
• in INFO: combination of
– MATLAB: data-based models
– EnergyPlus: thermal building simulation
– Dymola: component-based modelling of technical equipment
27
28. INFO – Co-Simulation
Simulation control framework
BCVTB
Machine Simulation
MATLAB/Excel
Building Simulation
EnergyPlus
Energy System Simulation
Dymola
Post - Processing
MATLAB
28
29. INFO - Results
• scenarios for different HVAC systems –
performance prediction
• energy performance certificate
• lifecycle cost-benefit analysis
• roadmap for energy efficient production
Energy Efficient
Production
29
31. Software Tool-Chain, embedded in operational automation
systems:
BaMa-Optimization: optimization of line operation
regarding the goals energy, time, costs, quality
optimized operational management strategy
identification of main potential savings
BaMa-Prediction: prediction of energy demands of the
whole facility based on production plan, operational
management and prediction data
BaMa-Monitoring: aggregation and visualisation of
resource demands
BaMa - Goals
31
32. BaMa - Approach
• Modularisation of the system „production facility“
partitioning according to energetic reasons
separation into manageable parts
systematically approaching the high system complexity
modular approach allows flexibility
• consistent terminus: „cube“
32
33. BaMa – Cubes I
Cubes are clearly confined units basic modules for system
analysis
integration of different points of view and system areas
(machines, building services, building, logistics) in one system
general Cube specification
Cubes bundle information and resource flows (energy, material,
costs, etc.) within identical balance borders
transparency und analysis of energy flows
new modular technology allows optimal connection of the real and
the virtual system
real production facility
machine
building
services
building
logistics
energy, material and
information flow
33
34. modelling hitherto modelling with Cube approach
equal system boundaries
modular, expandable and easy to apply to special areas
in practise
concurrent consideration of energy flows and material
flows in one system
overlapping/non-equal system boundaries, hence
redundancies
different models for energy flow, material flow and
costs
concurrent consideration of flows not possible
BaMa – Cubes II
34
Mass balance
Energy balance
Time balance
Cost balance
production
machine
production
machine
air
compressor
waste disposal
production
process
Cube
production
machine
Cube
production
machine
Cube
air
compressor
Cube
waste disposal
Mass balance
Energy balance
Time balance
Cost balance
production
process
information and
resource flow
35. BaMa
Cubes: Interfaces
cubes have uniformly defined interfaces
flexibility, modularising, exchangeability
connections and interactions
between cubes
material flow
energy flow
information flow
diffuse waste heat, recoverable heat
CO2 share
balance equations at cube borders
monitoringdata
controlaction
energyflow
energyflow
work piece,
baking goods, etc.
discretized
footprint (costs, CO2)
material flow material flow
parameters:
dimensions
power characteristics
efficiency
etc.
production plan
operating mode
control signal
etc.
energy demand
operational state
etc.
power: electric, thermal, etc.
exergy measure
CO2 share
work piece,
baking goods, etc.
updated footprint
35
36. BaMa Toolchain
• Cubes also help with the description in the simulation environment
• Cubes have a virtual „counterpart“ - based on simulation models and
measured data
• Cube view supports reusability in implementation
control
status
User Interface
BaMa - Virtual Cubes
real production facility
machine
building
services
building
logistics
energy, material
and information flow
virtual system
Virtual cube
machine
Virtual cube
building
services
Virtual cube
building
Virtual cube
logistics
information flow
36
38. BaMa – Cube Classes
„Cube“
machine,
production process
value-adding
non-value-
adding
building
building hull
thermal zone
energy system,
building services
energy converter
energy storage
energy networks
logistics
transport system
handling system
storage system
38
40. BaMa – DEV&DESS II
Formalism
• building on systems-theoretical basics
• allows the description of hierarchically structured systems
• DEVS: description of purely event based (and hence time-discrete) systems
• DESS: description of causal continuous systems
• DEV&DESS: suitable for hybrid systems supporting continuous as well as discrete
changes in system states
Implementation
• event scheduling required
• zero-crossing detection for(real) State Events desired
• numerical solving of differential equations can be realised in the model
• data models can be included
40
41. BaMa – DEV&DESS III
Cube
guarantees
consistency in the cube description
technical feasibility
requirements for sustainable implementation
scientific acceptance
41
42. Real Cube
Model
(verbal, conceptual, physical, mathematical)
Formal Cube Description
DEV&DESS Formulation of the Cube
DEV&DESS Implementation of the Cube
= virtual Cube
BaMa – Cube Workflow I
42
46. Formal Cube Description
...
Bedarf el. Leistung (PelB)
Anforderung Entität (Ereq)
Elektrische Leistung (Pel)
Entität (E)
Entität(E)
Abfall (EA)
Umgebungstemperatur (Tu)
Nicht nutzbare Abwärme
(QAW)
Nutzbare Abwärme (Qrec)
Produktionsplan (Pplan)
Heizleistung (PH)
Haltedauer (tB)
Solltemperatur (Tsoll)
Zweipunktregler Hysterese (H)
Volumen Ofen (V)
Wärmedurchgang Ofenwand (UA)
Wärmekapazität Luft (cpL)
Dichte Luft (rhoL)
Abwärmenutzung (eta)
Abfallmenge (alpha)
Parameter:
Zustandsgrößen:
Betriebszustand (p): standby,
aufheizen, warten, halten
Heizzustand (h): on, off
Masse der Entität im Ofen (m)
Wärmekap. der Entität im Ofen (cp)
Temperatur im Ofen (T)
BaMa – Cube Workflow V
46
47. DEV&DESS Formulation of the Cube
Name Kürzel Einheit Datentyp Wertebereich
Entität E Entität
Attribut: Masse E.m kg Skalar > 0
Attribut: Temperatur E.T K Skalar > 0
Attribut: Wärmekap. E.cp J/(kg*K) Skalar > 0
Name Kürzel Einheit Datentyp Wertebereich
Entität E Entität
Attribut: Masse E.m kg Skalar > 0
Attribut: Temperatur E.T K Skalar > 0
Attribut: Wärmekap. E.cp J/(kg*K) Skalar > 0
Abfall EA Entität
Attribut: Masse EA.m kg Skalar > 0
Attribut: Temperatur EA.T K Skalar > 0
Attribut: Wärmekap. EA.cp J/(kg*K) Skalar > 0
Materialflüsse
Eingänge:
Ausgänge:
BaMa – Cube Workflow VI
47
48. DEV&DESS Formulation of the Cube
Ausgang
• wird nur bei Beendigung des Betriebszustands "halten"
ausgegeben
• Unterscheidung: Entstehung von Abfall
BaMa
Cube Workflow VII
48
50. BaMa - Optimization
• scenario: production plans, operational conditions
(constraints, initial solution)
• optimization selects control variables (production plan)
• target function: evaluating the current simulation results
for the chosen parameters
• selection of new parameters for next simulation run
• iteration to find the most suitable production plan for the
respective scenario within a given time span
Scenario
control variables
optimization
target function
parameters feedback
modified parameters
Simulation
50
51. BaMa - Optimization
Target Function
• weighing of different criteria:
– on-time delivery, storage
– total energy cost
– throughput time
– idle period
– …
delayed delivery, storage costs (on-time delivery)
total throughput time
total energy: costs – CO2
total number: DESIRED - ACTUAL
lot throughput
weights (adjustable)
51
52. production
BaMa - Carbon Footprint of
Products (CFP)
evaluation of environmental sustainability of a product throughout its whole life
cycle
comparison to other products
identification of pollution during life cycle
reduction of pollutant emissions
CO2-footprint of a product
resources utilization disposal
52
53. CFP from
heating/cooling of
storerooms
BaMa - CFP Method
exemplary tasks at an up-to-date CFP calculation
consideration of
stand-by and setup
times
energy for building
services
energy input of
machines apportioned
to machines
energy for
transport systems
ventilation,
illumination,… of
the building
53
54. BaMa - Results
• modular approach for high flexibility
• carbon footprint of products
• automated optimization of production plans
• aims: effecitivity regarding
– energy
– costs
– resources
– CFP
• proof of concept with six use cases in several
production facilities from different fields
54
55. Conclusion
• energy efficiency: increasing need for simulation
based solutions
• two different approaches
– co-simulation
(quasi) arbitrary amount of participating simulators
most suitable software for every partial system
individual solvers/time steps for partial systems
loss of accuracy
– DEV&DESS formalism
monolithic approach (one simulator)
no accuracy loss
need to formalize (adapt model description)
55