Efficient use of resources and utility is the key to reduce the price of the commodities produced in any industry. This in turn would lead to reduced price of the commodity which is the key to success. Sustainability involves integration of all the three dimensions: environmental, economic and social. Sustainable manufacturing involves the use of sustainable processes and systems to produce better sustainable products. These products will be more attractive, and the industry will know more about the climate impact from their production. Manufacturing companies use a considerable amount of energy in their production processes. One important area to understand the sustainability level at these types of industries is to study this energy use. The present work studies energy use in a large-scale tool manufacturing company in Sweden. Value Stream Mapping method is implemented for the purpose of mapping the energy use in the different operations. To complement this, an energy audit has been conducted, which is a method that include a study and analysis of a facility, indicating possible areas of improvements by reducing energy use and saving energy costs. This presents an opportunity for the company to implement energy efficiency measures, thus generating positive impacts through budget savings. Less energy use is also good for the environment resulting in less greenhouse gas emissions level. This also helps in long-term strategic planning and initiatives to assess the required needs and stabilize energy use for the long run. Social sustainability completes the triad along with environmental and economic sustainability. In this study, the social sustainability is reflected with the company’s relationship with its working professionals by conducting a survey. The sustainable manufacturing potential found in the case study indicates that significant progress can be made in the three sustainability dimensions. Although, the scope of the thesis is limited to a tool manufacturing company, several of the findings could be implemented in other tool companies as well as industries belonging to other sectors. / <p>The thesis is a joint report between Linköping and Uppsala University. My thesis teammate has published it before at UU Diva Portal. The URL is: https://uu.diva-portal.org/smash/record.jsf?dswid=8179&amp;pid=diva2%3A1449223&amp;c=1&amp;searchType=SIMPLE&amp;language=en&amp;query=sustainable+manufacturing&amp;af=%5B%22dateIssued%3A2020%22%5D&amp;aq=%5B%5B%5D%5D&amp;aq2=%5B%5B%5D%5D&amp;aqe=%5B%5D&amp;noOfRows=50&amp;sortOrder=author_sort_asc&amp;sortOrder2=title_sort_asc&amp;onlyFullText=false&amp;sf=undergraduate</p><p> </p> / Green Factory project, AB Sandvik Coromant

Indiana University-Purdue University Indianapolis (IUPUI) / Air handling unit systems are the series of mechanical systems that regulate and circulate the air through the ducts inside the buildings. In a commercial setting, air handling units accounted for more than 50% of the total energy cost of the building in 2013. To make the system more energy efficient and reduce amount of CO₂ gases and energy waste, it is very important for building energy management systems to have an accurate model to help predict and optimize the energy usage and eliminate the energy waste. In this work, two models are described to focus on the energy usage for heating/cooling coils as well as fans for the air handling unit. Enthalpy based effectiveness and Dry Wet coil methods were identified and compared for the system performance. Two different types of control systems were modeled for this research, and the results are shown based on occupancy reflected by the collected CO₂ data. Discrete On/O and fuzzy logic controller techniques were simulated using Simulink MATLAB software and compared based on energy reduction and system performance. Air handling unit located in the basement of one campus building is used for the test case of this study. The data for model inputs is collected wirelessly from the building using fully function device (FFD) and pan coordinator to send/receive the data wirelessly. The air handling unit modeling also is done using Engineering Equation Solver EES Software for the coils and AHU subsystems. Current building management system Metasys software was used to get additional data as model inputs. Moving Average technique was utilized to make the model results more readable and less noisy. Simulation results show that in humid regions where there is more than 45% of relative humidity, the dry wet coil method is the effective way to provide more accurate details of the heat transfer and energy usage of the air handling unit comparing to the other method enthalpy-based effectiveness. Also, fuzzy logic controller results show that 62% of the current return fan energy can be reduced weekly using this method without sacrificing the occupant comfort level comparing to the ON/OFF method. Air quality can be optimized inside the building using fuzzy logic controller. At the same time, system performance can be increased by taking the appropriate steps to prevent the loss of static pressure in the ducts. The implementation of the method developed in this study will improve the energy efficiency of the AHU.

Indiana University-Purdue University Indianapolis (IUPUI) / The purpose of this research is to analyze the overall energy consumption of an industrial compressed air system, and identify the impact of various energy saving of individual subsystem on the overall system. Two parameters are introduced for energy consumption evaluation and potential energy saving: energy efficiency (e) and process effectiveness (n). An analytical energy model for air compression of the overall system was created taking into consideration the modeling of individual sub-system components: air compressor, after-cooler, filter, dryer and receiver. The analytical energy model for each subsystem included energy consumption evolution using the theoretical thermodynamic approach. Furthermore, pressure loss models of individual components along with pipe friction loss were included in the system overall efficiency calculation. The efficiency analysis methods and effectiveness approach discussed in this study were used to optimize energy consumption and quantify energy savings. The method was tested through a case study on a plant of a die-casting manufacturing company. The experimental system efficiency was 76.2% vs. 89.3% theoretical efficiency. This showed model uncertainty at ~15%. The effectiveness of reducing the set pressure increases as the difference in pressure increase. The effectiveness of using outside air for compressors intake is close to the compressors work reduction percentage. However, it becomes more effective when the temperature difference increase. This is mainly due to extra heat loss. There is potential room of improvement of the various component using the efficiency and effectiveness methods. These components include compressor, intercooler and dryer. Temperature is a crucial parameter that determines the energy consumption applied by these components. If optimum temperature can be determined, plenty of energy savings will be realized.

This master thesis work is aimed to investigate the possibilities of achieving more efficient industrial lighting. The study is divided in four parts: 1) Industrial lighting energy efficiency measures, 2) Added value of lighting, 3) Drivers and barriers for adopting lighting efficiency measures, and 4) Applications of AI in industrial lighting.The first part of the study explores various energy efficiency measures that could be applied in industrial lighting. The results show that using energy-efficient lighting fixtures, optimizing lighting controls, and adopting smart lighting solutions that integrate daylight in the illumination strategy and design are the most effective measures for reducing energy consumption and increasing efficiency.In the second part, the study examines the added values or non-energy benefitsof efficient industrial lighting. The findings indicate that apart from cost savings, efficient lighting leads to improvements on the quality of work environments, enhances workers health and safety conditions and improves environmental performance. Moreover, the study suggests that in many cases, the added values of lighting are not given the importance they should have and are not considered when an energy efficiency investment is planned to be done.The third part of the study identifies the drivers and barriers for adopting lighting efficiency measures in industrial settings. The study found that factors such as cost and energy savings, energy efficiency regulations are the main drivers for implementing efficient lighting solutions. However, barriers such as lack of awareness, perceived high initial costs, technology adoption and insufficient government incentives are the main obstacles to adoption.Finally, the study investigates the potential of artificial intelligence (AI) in industrial lighting. The results show that AI-based solutions, such as predictive maintenance and intelligent lighting control could significantly improve energy efficiency and reduce maintenance costs. Moreover, AI can bring the work environment to another level by the application of human centred and personalized lighting.Overall, this master thesis work provides valuable insights into achieving more efficient industrial lighting by highlighting effective energy efficiency measures, identifying the added value of efficient lighting, and examining the drivers and barriers to adoption. Moreover, the study sheds light on the potential of AI in industrial lighting and its potential benefits and future challenges.

Energy efficiency and renewable energy become more attractive in smart grid. In order to efficiently reduce global energy usage in building energy systems and to improve local environmental sustainability, it is essential to optimize the operation and the performance of combined heat and power (CHP) systems. In addition, intermittent renewable energy and imprecisely predicted customer loads have introduced great challenges in energy-efficient buildings' optimal operation. In the deterministic optimal operation, we study the modeling of components in energy-efficient building systems, including the power grid interface, CHP and boiler units, energy storage devices, and building appliances. The mixed energy resources are applied to collaboratively supply both electric and thermal loads. The results show that CHP can effectively improve overall energy efficiency by coordinating electric and thermal power supplies. Through the coordinated operation of all power sources, the daily operation cost of building energy system for generating energy can be significantly reduced. In order to address the risk from energy consumption forecast errors and renewable energy production volatility, we utilize the approach of stochastic programming and robust optimizations to operate energy-efficient building systems under uncertainty. The multi-stage stochastic programming model is introduced so that the reliable operation of building energy systems would be probabilistically guaranteed with stochastic decisions. The simulation results show that the stochastic operation of building energy systems is a promising strategy to account for the impact of the uncertainty on power dispatch decisions of energy-efficient building systems. In order to provide absolute guarantee for the reliable operation of building energy systems, a robust energy supply to electric and thermal loads is studied by exploring the effectiveness of energy storage on energy supply against the uncertainty. The robustness can be adjusted to control the conservativeness of the proposed robust operation model. For the purpose of achieving adaptability in the robust optimal operation and attaining robustness in the stochastic optimal operation of building energy systems, we also develop an innovative robust stochastic optimization (RSO) model. The proposed RSO model not only overcomes the conservativeness in the robust operation model, but also circumvents the curse of dimensionality in the stochastic operation model.

The increasing world energy demand has become the need of achieving energy efficiency and sustainable practices a key element in order to ensure the future of the societies. In Sweden, buildings accounts for 40% of total energy consumption. Within a sector that consumes almost half of the energy in the country it is possible to identify a group of buildings which generally presents a higher consumption and bigger problems, the historic and protected buildings. There is big room for improvement in the field of achieving sustainability in historic building although most of this improvements are constrained by limitations and protections due to their condition. Studying this limitations and needs for improvement of these buildings it is possible to come up with energy efficient solutions according with the regulations. The aim of this report will be achieving sustainability in Swedish historic/protected buildings, while respecting the applicable law to ensure that heritage and cultural value is respected, by proposing energy efficiency improvement measures that requires a feasible investment with a reasonable pay-back time.

Chilled water systems constitute a major portion of energy consumption in air conditioning systems of commercial buildings and process cooling of manufacturing plants. These systems do not operate optimally in most of the cases because of the operating parameters set and/or the components used. A Chilled water system analysis tool software (CWSAT) is developed as a primary screening tool for energy evaluation. This tool quantifies the energy usage of the various chilled water systems and typical measures that can be applied to these systems to conserve energy. The tool requires minimum number of inputs to analyze the component-wise energy consumption and incurred overall cost. This thesis also examines various energy conservation measures that are available for chilled water systems. The components, arrangements, and the common energy conservation opportunities for chilled water systems are presented. The new version of the tool is developed in Object Oriented Programming Language Microsoft Visual Basic.Net© to maintain the tool latest with current technology, add and expand capabilities and avoid obsolescence. Many Improvements to the previous tool are made to improve quality and the types of the systems the tool can handle. The development of the new routines and interfaces is also accommodated in the new version to make the tool universal. In order to determine the accuracy of the new version of the tool, a comparison is made between the results from the previous and new version of the tool. The results of the comparisons are presented.

Portable electric spas are typically the largest electrical load in homes that have one. In recent years, the California Energy Commission’s California code of regulations, Title 20 has been revised to regulate the maximum allowable standby power for a portable electric spa based on the spa volume. The goal of this regulation is to require improvements to the lowest performing models, for which simple and cost effective improvements are readily available, without eliminating average or better performing products. In this research, the standby power of twenty seven portable electric spas was measured and compared to the Title 20 requirement. These tests resulted in nine of the twenty seven spas not meeting the allowable standby power. Analysis demonstrates that simple and inexpensive improvements to these nine spas would likely cause the spas to pass the Title 20 requirement. Additionally, temperature normalization developed in this research have been adopted by the California Energy Commission and included in revisions to Title 20.

No / A dynamic system with memory is a system for which knowledge of the equations of motion, together with the state at a given time instant t0 is insufficient to predict the evolution of the state at time instants t>t0. To calculate the response of systems with memory starting from an initial time instant t0, complete knowledge of the history of the system for t<t0 is needed. This is because the state vector does not contain all the information necessary to fully characterize the state of the system, i.e., the state vector of the system is not complete. In this paper, a state space formulation of viscoelastic systems with memory is proposed, which overcomes the concept of memory by enlarging the state vector with a number of internal variables that bear the information about the previous history of the system. The number of these additional internal variables is in some cases finite, in other cases, it would need to be infinite, and an approximated model has to be used with a finite number of internal variables. First a state space representation of the generalized Maxwell model is shown, then a new state space model is presented in which the relaxation function is approximated with Laguerre polynomials. The accuracy of the two models is shown through numerical examples.

Yes / Next-generation cellular networks such as fifth-generation (5G) will experience tremendous growth in traffic. To accommodate such traffic demand, there is a necessity to increase the network capacity that eventually requires the deployment of more base stations (BSs). Nevertheless, BSs are very expensive and consume a significant amount of energy. Meanwhile, cloud radio access networks (C-RAN) has been proposed as an energy-efficient architecture that leverages cloud computing technology where baseband processing is performed in the cloud, i.e., the computing servers or baseband processing units (BBUs) are located in the cloud. With such an arrangement, more energy saving gains can be achieved by reducing the number of BBUs used. This paper proposes a bin packing scheme with three variants such as First-fit (FT), First-fit decreasing (FFD) and Next-fit (NF) for minimizing energy consumption in 5G C-RAN. The number of BBUs are reduced by matching the right amount of baseband computing load with traffic load. In the proposed scheme, BS traffic items that are mapped into processing requirements, are to be packed into computing servers, called bins, such that the number of bins used are minimized and idle servers can then be switched off to save energy. Simulation results demonstrate that the proposed bin packing scheme achieves an enhanced energy performance compared to the existing distributed BS architecture.