Linköping Studies in Science and Technology, Dissertation No Nawzad Mardan - PDF

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Linköping Studies in Science and Technology, Dissertation No Combining simulation and optimization for improved decision support on energy efficiency in industry Nawzad Mardan Division of Energy

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Linköping Studies in Science and Technology, Dissertation No Combining simulation and optimization for improved decision support on energy efficiency in industry Nawzad Mardan Division of Energy Systems Department of Management and Engineering Linköping Institute of Technology SE , Linköping, Sweden Copyright 2012 Nawzad Mardan ISBN ISSN Printed in Sweden by LiU-Tryck, Linköping 2012 ii Abstract Industrial production systems in general are very complex and there is a need for decision support regarding management of the daily production as well as regarding investments to increase energy efficiency and to decrease environmental effects and overall costs. Simulation of industrial production as well as energy systems optimization may be used in such complex decision-making situations. The simulation tool is most powerful when used for design and analysis of complex production processes. This tool can give very detailed information about how the system operates, for example, information about the disturbances that occur in the system, such as lack of raw materials, blockages or stoppages on a production line. Furthermore, it can also be used to identify bottlenecks to indicate where work in process, material, and information are being delayed. The energy systems optimization tool can provide the company management additional information for the type of investment studied. The tool is able to obtain more basic data for decision-making and thus also additional information for the production-related investment being studied. The use of the energy systems optimization tool as investment decision support when considering strategic investments for an industry with complex interactions between different production units seems greatly needed. If not adopted and used, the industry may face a risk of costly reinvestments. Although these decision-making tools individually give good results, the possibility to use them in combination increases the reliability of the results, enhances the possibility to find optimal solutions, promises improved analyses, and a better basis for decisions in industry. The energy systems optimization tool can be used to find the optimal result and the simulation tool can be used to find out whether the solution from the optimization tool is possible to run at the site. In this thesis, the discrete event simulation and energy systems optimization tools have been combined. Three Swedish industrial case studies are included: The new foundry at Volvo Powertrain in Skövde, Arla Foods dairy in Linköping and the SKF foundry in Katrineholm. Results from these cases show possibilities to decrease energy use and idling, to increase production, to combine existing and new production equipment and to decrease loss of products. For an existing industrial system, it is always preferable to start with the optimization tool remind rather than the simulation tool since it takes less time to build the optimization model and obtain results than it does to build the corresponding simulation modeling. While, for a nonexistent system, it is in general a good idea to use both the simulation and the optimization tool remind simultaneously, because there are many uncertain data that are difficult to estimate, by using only one of them. An iterative working process may follow where both tools are used. There is a need for future work to further develop structured working processes and to improve the model to e.g. take production related support processes into account. To adapt the results in industries, improve the user friendliness of the tool and the understanding of the underlying modeling developments of the optimization tool remind will be necessary. iii iv Sammanfattning Industriella system i allmänhet är mycket komplexa och det finns ett behov av beslutsstöd vid hantering av den dagliga produktionen, liksom beslut om investeringar för att öka energieffektiviteten och minska miljöpåverkan och kostnader. Simulering av industriell produktion och energisystemoptimering kan användas som beslutsstöd i sådana komplexa beslutssituationer. Simuleringsverktyg är mest kraftfullt när det används för design och analys av komplexa produktionsprocesser. Verktyget kan ge mycket detaljerad information om hur systemet fungerar, till exempel information om de störningar som inträffar i systemet såsom brist på råvaror, blockeringar eller avbrott på en produktionslinje. Dessutom kan verktyget användas för att identifiera flaskhalsar för att indikera var arbete, material och information är försenade. Energisystemoptimeringsverktyget kan ge företagsledningen ytterligare information om en eventuell studerad investering. Verktyget kan ge mer underlag för att fatta beslut och därmed ge mer information för den produktionsrelaterade investeringen som studeras. Behovet av användningen av energisystemoptimeringsverktyg som investeringsbeslutsstöd när man överväger strategiska investeringar för en industri med komplexa interaktioner mellan olika produktionsenheter bedöms vara stort. Om inte kan industrin istället möta en risk för kostsamma reinvesteringar. Även om dessa verktyg kan vara beslutsstöd var för sig och ge bra resultat, så medföljer möjligheten att kombinera dessa verktyg att tillförlitligheten av resultaten ökar, såväl som möjligheten att hitta optimala lösningar, bättre analyser och ett bättre underlag för beslut inom industrin. Optimeringsverktyget kan användas för att hitta det optimala resultatet och simuleringsverktyg kan användas för att ta reda på om lösningen från optimeringsverktyget är möjlig att realisera i verklig drift. I den här avhandlingen har diskret händelsestyrd simulering och energisystemoptimeringsverktyg kombinerats. Tre svenska industriella fallstudier är inkluderade: Volvo Powertrains nya gjuteri i Skövde, Arla Foods mejeri i Linköping och SKF-gjuteriet i Katrineholm. Resultat från dessa fall visar på möjligheterna att minska energianvändningen och tomgångsförlusterna, att öka produktionen, att kombinera ny och befintlig produktionsutrustning på ett effektivare sätt, och att minska kassation av produkter. För ett befintligt industriellt system är det alltid mer effektivt att börja med optimeringsverktyget remind snarare än simuleringsverktyg - eftersom det tar mindre tid att bygga en optimeringsmodell och få resultat, än det gör för att bygga en motsvarande simuleringsmodell. För ett icke-existerande system är det i allmänhet ett effektivare tillvägagångssätt att använda både simulerings och optimeringsverktyg remind samtidigt, eftersom det finns många osäkra data som är svåra att uppskatta, med hjälp av endast ett av verktygen. En iterativ arbetsprocess kan följa där båda verktyg används. Det finns ett behov av fortsatt arbete bl. a. av att utveckla strukturerade arbetssätt och att kunna integrera produktionsrelaterade stödprocesser i modelleringen. För att anpassa resultaten för industrin, och förbättra användarvänligheten av verktyget, utvecklingen av optimeringsverktyget remind kommer att behövas. v vi List of appended papers Paper I Nawzad Mardan, Magnus Karlsson, Petter Solding. Benefits of integration of energy systems optimization and discrete event simulation. Submitted to Energy Systems Paper II Nawzad Mardan, Roger Klahr. Combining optimisation and simulation in an energy systems analysis of a Swedish iron foundry. Energy, 44(1): , Paper III Nawzad Mardan, Magnus Karlsson, Roger Klahr. Industrial decision-making for energy efficiency combining optimization and simulation. In Proceedings of the 24 rd International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems, Novi Sad, Serbia, pp , Paper IV Patrik Thollander, Nawzad Mardan, Magnus Karlsson. Optimization as investment decision support in a Swedish medium-sized iron foundry a move beyond traditional energy auditing. Applied Energy, 86(4): , Paper V Petter Solding, Damir Petku, Nawzad Mardan. Using simulation for more sustainable production systems - methodologies and case studies. International Journal of Sustainable Engineering, 2(2): , 2009 Paper VI Magnus Karlsson, Nawzad Mardan. Timing and sizing of investments in industrial processes the use of an optimization tool. In Proceedings of the 23 rd International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems, Lausanne, Switzerland, Book 4, pp , Paper VII Magnus Karlsson, Nawzad Mardan. Considering start-ups and shutdowns using an optimisation tool including a dairy production planning case study. Submitted to Applied Energy. vii viii Pêŝkeŝe be xepekanm ix x Acknowledgement First and foremost, I want to extend my sincere thanks to my supervisor, Mats Söderström, for his contribution of encouraging and fruitful discussions throughout the work. Your good advice in industrial issues have been very helpful. I would also like to express my gratitude to my co-supervisor, Magnus Karlsson. You have been a so encouraging when conducting MIND studies; sincere thanks for all the hours of critiquing my early drafts of various papers and the discussion surrounding them. I would also like to thank Helene Lidestam for her valuable and constructive comments on a draft of the thesis. I would also like to thank all the co-authors of appended papers, Damir Petku, Petter Solding and Roger Klahr. Thank you to all my colleagues at the Division of Energy Systems, and especially thank you to Patrik Thollander for good co-operation and the kind support I have received from you. I would also like to express my appreciation to Tomas Haakon at the Volvo Powertrain in Skövde, Marja Andersson and Åke Eriksson at the SKF foundry in Katrineholm and Fredrik Stig Larsson at the ArlaFoods dairy in Linköping and everyone else involved in the research related to the dairy and foundry industries. The financial support from the Swedish Energy Agency is greatly acknowledged. I express my great thanks to the God for your goodness and for the strength you gave me to finish this work. I am very grateful to my wife Tara for her love, support, understanding and patience, which made this dissertation possible. I dedicate this dissertation to her and to our lovely children. Great thanks also to my lovely children Anya, Aland and Alina, who have enriched my life and for being the light of my life. Finally I would like to thank my parents, as well as my sisters and brothers, for always believing in me, and for bringing other things than research into my life. Linköping, 1 August 2012 Nawzad Mardan xi xii Thesis outline This thesis gives an introduction to, and a summary of, the seven appended papers Chapter 1 describes the background together with the aim and research questions of the thesis, as well as presents a brief overview of the research collaboration. The chapter ends with an overview of the included papers and co-author statement. Chapter 2 gives a brief description of Decision Support and presents different decision support tools in industrial energy systems. Chapter 3 gives a brief description of modeling. The chapter continues with a presentation of methods that are applied in the thesis and gives a short description of how and why the methods are combined. Lastly, it presents a brief description of how a problem should be formulated, how an objective should be defined, and finally how data should be collected when the methods are combined. Chapter 4 provides a summary of the results from the case studies in accordance with the stated research questions. Chapter 5 presents a summary of the conclusions drawn from the papers included in the thesis. Chapter 6 presents some suggestions or ideas for future research. xiii xiv Table of contents 1 Introduction Background Aim and research questions Scope and delimitation Research collaboration Paper overview Co-author statements Other publications not included in the thesis Decision support Decision support in general Decision support in industrial energy systems Mathematical programming Simulation Method Modeling Optimization with remind Discrete event simulation Method combination Problem formulation Setting of objectives Data collection Results Research question General case study xv 4.1.2 General method application Research question The Volvo Powertrain foundry The Arla Foods dairy The SKF foundry Research quesion Modelling investment problems Modelling production planning Concluding discussion Further research References xvi 1 Introduction In this chapter, the thesis background is described together with the aim and research questions. A brief overview of the research collaboration is presented. An overview of the appended papers in combination with a short summary of the paper and co-author statements is given. A list of publications not included in the thesis is also given. 1.1 Background Increasing energy prices in recent years as well as uncertainty concerning future prices have played an important role on the increased focus on energy-related issues worldwide. The threat of global warming is closely related to energy use. The world s largest growth in greenhouse gas (GHG) emissions originates from the use of energy (IPCC, 2007). The European Union has taken extensive action to reduce environment impact and the focus on energy efficient systems is becoming increasingly important in the European Union e.g. the European objectives. The objectives includes for example, a reduction of primary energy use by 20% by 2020, through energy efficiency and a reduction of at least 20% in greenhouse gases emissions by 2020, compared to 1990 levels (COM, 2008). Therefore, the efficient use of energy in general, and especially in industries is one of the most important means to reduce negative effects on the climate. The industry s energy use accounts for a key part of the world s annual energy use. Today, a significant amount of the industrial energy use originates from the use of fossil fuels. According to IEA (2011) industry accounts for about 77 percent of the world s annual coal consumption, 40 percent of the world s electricity use, 35 percent of the world s natural gas consumption, and nine percent of global oil consumption. In Sweden since 1970, the energy supply has increased by 35 percent from 457 TWh to 616 TWh and the final energy demand increased by 10 percent from 375 TWh to 411 TWh by 2010 (SEA, 2011). In 2010, the total industrial energy was about 149 TWh (see Table 1), which represents approximately 36 percent of final energy demand. Table 1 shows the amount of coal, biomass, natural gas, electricity and oil consumption in 2011 as well as how much they represent as a percentage value of the total Swedish energy use. For example, the Swedish energy-intensive industries (iron and steel, chemical industry and pulp and paper industries) account for more than 70 percent of the final industrial energy demand (SEA, 2011). Figure 1 shows the use of energy in Swedish industries from 1990 to 2010, in TWh. 1 Table 1. Swedish industrial energy use in 2010 (SEA, 2011). Type of energy Energy use (TWh) Percentage used in industry Biofuels Electricity Coal Oil products District Heating 7 7 Natural gas 5 63 Total Figure 1. Industrial energy use in Sweden distributed by industrial sector, , in TWh (SEA, 2011). The Swedish industries still consume more electricity than similar industries in other European countries. This is can be explained by the fact that Sweden historically has had lower electricity prices than elsewhere in Europe, which is due to the reason that a large proportion of the electricity production comes from hydropower and nuclear power [Johansson et al (2007), Klugman et al. (2007), Trygg et al. (2005) and Thollander et al. (2005)]. Due to the deregulation of the European electricity market, the electricity prices has been dramatically increased in the recent years in Sweden. As a consequence of the deregulation, it is also expected that the 2 electricity prices will fluctuate more than today, i.e. higher prices during the day when the electricity demand is high and lower prices during the evening and the night when the electricity demand is low. For the Swedish industry, and especially for the energy-intensive industries, rising electricity prices are considered to be one of the greatest threats to the long-term survival of the industries [Thollander et al. (2008) and SFA (2006)]. Furthermore, due to increased globalization, industries are facing greater competition, which is forcing them to decrease their costs in order to stay competitive and increase their profits. They also need, for example, to develop their production systems by improving quality, improving utilization of resources and increasing flexibility. In order to reduce both the negative effects on the climate and energy costs, industries must take action to fulfill their part in energy efficiency measures (Thollander et al., 2010). Apart from helping the environment, the industrial energy efficiency is an important factor that has a direct impact on the profits (Hirst and Brown, 1990) and productivity (Worrell et al., 2003). A company can lose a substantial amount of money if their processes and resources are not efficiently utilized. Therefore, the industrial energy efficiency is an essential task for the future and finding ways to decrease energy use is of great importance. The term energy efficiency is very loosely defined, however, in this thesis the term has been defined in accordance with EC (2006): Energy efficiency is a ratio between an output of performance, service, goods or energy, and an input of energy Energy efficiency improvement is an increase in energy end-use efficiency as a result of technological, behavioural and/or economic changes Energy efficiency improvement measures is all actions that normally lead to verifiable and measurable or estimable energy efficiency improvement The increase in industrial energy use can be slowed down through energy efficiency measures. According to Geller et al. (2006), the OECD countries would have used 49% more energy in 1998, if they didn't carry out the energy efficiency improvements during the last 30 years. There are different means to implement industrial energy efficiency improvement such as investments in energy efficient processes, changes in behavior through motivation and training of employees, and implementation of policy instruments such as the European Energy End-Use Efficiency and Energy Service Directive (ESD) as well as the Swedish programme for improving energy efficiency in energy-intensive industries (PFE). In The ESD each member state is obliged to design and formulate a national energy efficiency action plan in order to enhance cost effective improvements of energy end-use efficiency. This is contrasted against the Swedish PFE in which energy-intensive industries are offered a discount of electricity taxation during five years if the companies undertake an energy audit within the first two years, which results in a number of energy efficiency measures that could be implemented over the last three years that equal savings at least equivalent to the electricity tax discount. 3 The type of industrial energy efficiency measures may differ and depend on size of industry, production type, industry sector and relation degree of production and support processes, among other things,. According to Trygg and Karlsson (2005), equipment's energy use in an industry may be divided into two main categories: support processes and production processes. The support process is a process that supports production such as lighting, ventilation, space heating, hot tap water and compressed air, while the production process is related to the actual production methods such as melting, molding, coating, packing, cooling/freezing and heating. However,
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