DATA DRIVEN MODELING FOR ENGINEERS

Big data and the algorithms used in data science, together with the corresponding process and its technology tools, have important implications for addressing climate change. From machine learning algorithms to data visualization, data science methods are used to investigate and better understand climate change and its various effects on land, sea, food, etc.Data science is a powerful approach which is capable of helping practitioners, and policy-makers understand the uncertainties and ambiguities inherent in data, to identify interventions, strategies, and solutions that realize the benefits for humanity and the environment, and to evaluate the multiple– and sometimes conflicting–goals of decision-makers. In this MOOC course, we introduce methods pertaining to the growing field of data science and apply them to issues relevant to climate change. Topics Data science Analytics as a process Data-driven decisions Climate change Applications of data science in climate change Course content Understand data science Learn about the sources of big data Understand the basics of climate change, its impacts and sustainable development goals Get to know data-driven decisions and how they are made Highlight some climate change challenges that are directly or indirectly related to data science Apply data science knowledge and skills to make climate change related decisions Learn how others have used data science in association with addressing climate change problems You will learnBy the end of the course, you will be able to: obtain and analyze datasets; make data-driven decisions; identify and address climate change challenges using data science Who is the course for?This course is designed for those who want to improve their analytics and data-driven decision-making skills, with an emphasis on utilizing such skills for addressing climate change challenges. The course will also be useful for practitioners and policy-makers as they can benefit from understanding the uncertainties and ambiguities inherent in data and using it to identify interventions, strategies, and solutions that realize benefits for humanity and the environment.

The information and communication technology (ICT) sector is responsible for approx. 1.8-2.8% of the global greenhouse gas (GHG) emissions in 2020, and software is both part of the problems and the solutions. Traditional software engineering principles and techniques do not consider the climate, environment, and sustainability aspects in building and using software for any purpose. We, software engineers, developers, researchers, climate scientists, and various other related stakeholders, need to think about how we can reduce the carbon footprint due to building and using software-intensive systems. Green and sustainable software engineering is an emerging concept that can help reduce the carbon footprint related to software. In this introductory course, we will introduce the concept of green and sustainable software engineering and the engineering process to build green and sustainable software. Topics Sustainable and green computing Sustainable and green software engineering Process Energy efficient computing Sustainability issues in Scientific computing You will learnBy the end of the course, you will be able to: analyze the green and sustainability issues in traditional software engineering, identify and incorporate key elements to be included in the software engineering process to make the software green and sustainable, and use techniques to make your software code energy efficient. Who is the course for?This course is designed for those who are software developers, managers and software related policy makers, or have knowledge about software development, and want to consider the green and sustainability aspects in their everyday life. Also, this course will be useful for computational scientists who build green software and want to know more about these aspects in software engineering. However, this is an introductory course, and it will show a path for life-long learning to build more in-depth knowledge in each concept introduced in this course.

Fiber-optic sensing technologies are fast evolving and have entered in a large domain of our industry. Today all geothermal fields, water dams, railroads and to some lesser extent mines are equipped with fiber-optic cables to allow not only digital data transmission but also to interrogate fiber cables for information such as temperature changes or values (leakage issues or fractured rocks) but also strain measurements that can be indicators of soil failure or movements. When conducted in a controlled manner, artificial signals can be generated to help image the subsurface for mineral exploration, mine tailing characterization and for geothermal field development work by mapping faults and thermal fluids. There are other applications such as traffic monitoring that can also be done using this technology. Given its vast applications in the green transition, fiber-optic sensing is one of the most advanced technologies to be implemented in a wide range of fossil-free energy systems, hence, of a great importance to learn about their pros and cons and possibilities. Course content The course will have the following content: Introduction to DAS DAS Interrogators for temperature and strain measurements Fiber optic cables and their health conditions (hands-on with fiber-cable microscopes and fusion splicers) Design of a fiber-optic survey (surface and borehole) Parameter testing such as gauge length, laser pulse and width Field trials at a mine tailing test site or a mineral exploration borehole Work with the data and reporting Course design Hybrid and blended including hands-on practices.  This course takes about 30 hours of study to complete. You will learn By taking the course the participants are intended to learn about: Fiber-optic cables and their specifications including how to check their health and splice them DAS interrogators and their interior designs for fiber-optic sensing applications Design surface and borehole experiments Read and work with the data (hands-on) Who is the course for? The course will be given to a broad range of participants from engineering to geoscience backgrounds including university students but also participants from the industry. Participants can be from construction industry, road administration, energy sector (e.g., water dams), mining and defence workers. The course will be run within the newly established Smart Exploration Research Center involving tech companies such as BitSimNow Part of Prevas who are also expert in PFGA and fiber-related technologies. A prerequisite to the course is prior knowledge on different problems in the energy sector but some knowledge with Matlab and/or Python programming. The course can continue as an industry offer through the SERC-center as a multidisciplinary course at Uppsala University and for industry participants. 

Batteries and battery technology are vital for achieving sustainable transportation and climate-neutral goals. As concerns over retired batteries are growing and companies in the battery or electric vehicle ecosystem need appropriate business strategies and framework to work with.This course aims to help participants with a deep understanding of battery circularity within the context of circular business models. You will gain the knowledge and skills necessary to design and implement circular business models and strategies in the battery and electric vehicle industry, considering both individual company specific and ecosystem-wide perspectives. You will also gain the ability to navigate the complexities of transitioning towards circularity and green transition in the industry.The course includes a project work to develop a digitally enabled circular business model based on real-world problems. Course content Battery second life and circularity Barriers and enablers of battery circularity Circular business models Ecosystem management Pathways for circular transformation Design principles for battery circularity Role of advanced digital technologies Learning outcomes After completing the course, you will be able to: Describe the concept of battery circularity and its importance in achieving sustainability goals. Examine and explain the characteristics and differences of different types of circular business models and required collaboration forms in the battery- and electric vehicle- industry. Analyze key factors that are influencing design and implement circular business models based on specific individual company and its ecosystem contexts. Analyze key stakeholders and develop ecosystem management strategies for designing and implementing circular business models. Explain the role of digitalization, design, and policies to design and implement circular business models. Plan and design a digitally enabled circular business model that is suitable for a given battery circularity problem. Examples of professional roles that will benefit from this course are sustainability managers, battery technology engineers, business development managers, circular developers, product developers, environmental engineers, material engineers, supply chain engineers or managers, battery specialists, circular economy specialists, etc. This course is given by Mälardalen university in cooperation with Luleå University of Technology Study effort: 80 hrs

Why markets for electricity? How do they function? This introductory course explains how incentives shape outcomes in the electricity market. It brings out the implications for businesses and society of electricity pricing in the shadow of the energy transition. The course aims to provide a comprehensive overview of the electricity market's role in ensuring an efficient electricity supply and addressing key public questions, such as What is the purpose of the electricity market? Why do electricity prices vary by location? How can electricity prices surge despite low production costs? Are there alternative ways to sell electricity? Why is international electricity trading important? The course emphasizes the role of economic incentives in shaping market behavior and addresses critical issues such as market power and its consequences. You will also explore the inefficiencies stemming from unpriced aspects of energy supply and the role of regulation in mitigating these inefficiencies. As the global push toward decarbonization accelerates, the course delves into the challenges posed by large-scale electrification, the implications of climate legislation for energy systems, and the impact of protectionist national policies. The course offers a comprehensive introduction to the electricity market, provides you with analytical tools for independent analysis and brings you to the forefront of current energy policy debate. The course will enable you to Describe the interaction between the electricity system and the electricity market. Explain how the electricity market can increase the efficiency of electricity supply, e.g. with respect to market integration. Show how market power reduces the efficiency of the electricity market. Categorize fundamental market imperfections and describe their solutions. Explain economic and political challenges associated with the green transition. Apply economic tools to analyze the electricity market and examine how changes to the electricity system and regulation affect market outcomes. Target group This course is designed for engineers and managers eager to enhance their understanding of electricity markets within the context of the industrial green energy transition. The purpose is to increase the understanding of the scope of the electricity market and its role in achieving efficient electricity supply. Study effort: 80 hrs

Understanding and optimizing battery performance is crucial for advancing electrification, sustainable mobility, and renewable energy systems. This course provides a comprehensive overview of battery performance, ageing processes, and modelling techniques to improve efficiency, reliability, and service life. Participants will explore battery operation from a whole-system perspective, including its integration in electric vehicles (EVs), charging infrastructure, and energy grids. The course covers both physics-based and data-driven modelling approaches at the cell, module, and pack levels, equipping learners with tools to monitor, predict, and optimize battery performance in real-world applications. Through this course, you will gain the ability to assess battery health, model degradation, and evaluate second-life applications from both technical and economic standpoints. Course content Battery fundamentals and degradation mechanisms Battery modelling Battery monitoring and diagnostics Operational strategies for battery systems Techno-economic performance assessment Battery second-life applications   You will learn to: Explain the principles of battery operation and degradation mechanisms. Develop battery performance models using both physics-based and data-driven approaches. Apply methods for State of Health (SOH) estimation and Remaining Useful Life (RUL) prediction. Analyze key factors influencing battery lifespan economics in different applications. Evaluate battery second-life potential and identify suitable applications. Target group: Professionals in energy, automotive, R&D, or sustainability roles Engineers and data scientists transitioning into battery technologies Technical specialists working with electrification, battery management systems, or energy storage