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This paper presents the application of the Discrete Event Simulation (DES) in an industrial plant to analyze the behavior of the consumption and generation of electrical energy in conjunction with other variables of the process. In other words, the process modeling was conducted considering the energy consumption in equipment as well as the local photovoltaic (PV) energy generation. A medium-sized plant was the reference base used herein, and the main variables considered were the time of supply of tanks and processing of raw material, active power of equipment and energy tariffs. A simulation model with resolution in seconds was designed, enabling the simulation of consumption curves of each equipment of a given part of the production process. The equipment of variable active demand was modeled by a mathematical regression method, and the others were assumed with constant power. The same model was altered to unit in minutes for simulating the monthly electricity consumption, so as to obtain financial results. The contractual rules for electricity supply were included, and the use of a diesel-powered generator was taken into account. The application of a freeware was employed as a low cost alternative for small and medium-sized companies, even though the lack of graphic resources has increased the complexity of the system modeling process. The model with resolution in seconds proved to be efficient to simulate the consumption profile of a specific equipment that alters the power in a short-time period, such as the pumps. The model, on the other hand, in minutes allowed a more effective analysis in terms of finance, enabling the verification of alternative scenarios throughout a month. The monthly cost reduction perceived by considering self-generation in this model was about 36%. Detailed diagram of the main elements of modeling of a manufacturing process of an industry concentrating on the consumption and generation of electric energy. Image 1 • The electricity consumption in an industrial process can be approached in a simulation model. • PV energy can be simulated together with variables of industrial processes. • The discrete event simulation can be used in industrial processes to optimize the electricity management. [ABSTRACT FROM AUTHOR]

Environmental sustainability is an everlasting concern of our modern society and has been gaining importance in practice and academia over the last decades. For being a major contributor to global environmental pollution, stakeholder pressure on manufacturing companies is increasing to reduce their environmental impacts, while simultaneously delivering high value goods and services at competitive prices, understood as increasing their eco-efficiency. However, complex interrelations between the economic and environmental performance dimensions in production systems create a non-transparency to decision makers, hindering them to translate this strategic target into improvement measures on a shop-floor level. Discrete-event simulation has been established as a promising method to solve this problem, although current approaches are insufficient for focusing only on energy aspects and being too complex in their application. This paper proposes a decision support consisting of two elements: 1) A novel discrete-event simulation approach that seamlessly integrates LCA with common economic evaluations by embedding the Ecoinvent LCI database, while considering all relevant eco-efficiency drivers within a production system; and 2) a system analysis and modelling procedure supporting its application and the identification of appropriate improvement measures. [ABSTRACT FROM AUTHOR]

Abstract: Sustainable manufacturing is a new paradigm in which manufacturing industries produce products in a sustainable manner while maintaining global competitiveness and coping with recent challenges and problems. Sustainable manufacturing research is very broad in scope and has not yet been firmly established at the academic or practical levels. This paper introduces MAS2, which is an integrated modeling and simulation-based life cycle evaluation approach for sustainable manufacturing. Four requirements that are essential for evaluating the sustainability performance of manufacturing industries are suggested. The proposed MAS2 approach consists of the following main components: 1) a theoretical foundation comprising 20 principles and definitions of sustainable manufacturing and manufacturing sustainability, 2) indicators, namely the MAS2 indicator and Manufacturing Sustainability Index (MSI), 3) e-MAS2 as an evaluation method, and 4) i-MAS2 as an information management method. The MAS2 system is developed based on the proposed approach, and consists of two main modules: MS-Modeler and MS-Simulator. The MAS2 approach and system are validated by application to the product life cycle of an automotive stamping shop. [Copyright &y& Elsevier]

Abstract: There is increasing evidence that more and more companies throughout the world are seeking measures to reduce their production carbon emissions and product carbon footprint. Simulation tools can be used to characterize the carbon emission dynamics of manufacturing system to help companies clearly developing the potential reduction strategies of production carbon emissions. On the basis of the carbon emissions dynamics described by general state equations of manufacturing system as a hybrid system, a simulation approach based on hybrid Petri-nets (HPN) is proposed to display the carbon emission dynamics. In order to estimate the total carbon emissions of the whole process chain, the carbon emissions of production system are represented as segments of specific carbon emissions for each operation state of the individual production equipments in the approach. Furthermore, two function indicators deriving from general state equations, carbon emission rate and carbon efficiency, are defined to describe the dynamic characteristics. Production disturbance as one of the critical factors influencing the carbon emission performance of production system is also considered in this paper for demonstrating their impacts on the carbon emission dynamic characteristics. Finally, an experimental study is presented to illustrate validation of the proposed approach and shows its positional significance for carbon emission reduction. [Copyright &y& Elsevier]

Abstract: Machining tasks are performed on machine tools with enormous amount of energy consumption in manufacturing system. Understanding and characterizing task-oriented energy consumption is significantly essential to explore the potential on energy-saving in production management. To achieve it, this paper proposes a modeling method of task-oriented energy consumption for machining manufacturing system. The energy consumption characteristics driven by task flow in machining manufacturing system are analyzed, which describes that energy consumption dynamically depends on the flexibility and variability of task flow in production processes. Based on the task-oriented energy consumption characteristics, an event graph methodology has been exploited to model the energy consumption driven by tasks in production processes of machining manufacturing system. The proposed modeling method is solved in Simulink simulation environment, and applied to select the flexible processes of tasks for optimizing energy consumption. The results show a valuable insight of energy consumption in machining manufacturing system so as to make robust decisions on the potential for improving energy efficiency. [Copyright &y& Elsevier]

Abstract: To meet the increasing need for practical life-cycle thinking in manufacturing, this paper proposes a method that includes the life-cycle perspective in manufacturing decision making. This method combines discrete-event simulation (DES) – commonly used for the conceptual evaluation of manufacturing systems – with life-cycle assessment (LCA). This combination captures the dynamic interrelationships between manufacturing processes in order to analyse systemic responses to configuration changes, something static LCA modelling cannot do. The method evolved when a bearing production line at SKF was being examined to relate manufacturing decision making to environmental consequences. This was done using DES to investigate how parameters normally used to optimize traditional manufacturing system performance influence energy use and material losses in manufacturing systems. The environmental consequences of this material loss and energy use are further calculated using LCA methodology. Results indicate that while the combination of the two methods increases the data collection workload, it uncovers previously hidden environmental consequences of manufacturing decision making and introduces a way to assess an industrial actor’s manufacturing system using relevant LCA scenarios. [Copyright &y& Elsevier]

Additive manufacturing (AM) introduces a new domain for zero waste and cleaner production. Research for verification of materials in AM and effects of the process on the material behavior, however, demands a significant amount of materials, energy, and man-hours. The design of suitable physical simulation techniques that can duplicate complex AM thermal cycles without performing AM is therefore crucial for cleaner and more sustainable AM research. This paper aims at introducing a novel technique to reproduce AM thermal cycles in a controlled way on a small sample, thereby supporting sustainable alloy verification and cleaner research. In this technique, a stationary arc is applied to a disc-shaped sample mounted on a water-cooled chamber, where the arc and water provide rapid heating and cooling, respectively. In the present study, a super duplex stainless steel (SDSS) was used as the experimental alloy to simulate the evolution of microstructure and properties during wire-arc additive manufacturing. The experiment was performed using the stationary arc with the holding time of 5 s, applied 1, 5, or 15 times. The total processing time was only 450 s (7.5 min) for the 15 a.m. thermal cycles experiment. The SDSS showed a progressive increase in the austenite fraction at 600–1200 °C and the formation of detrimental sigma phase at 700–1000 °C, but a reduction of austenite fraction above 1300 °C. The results were in good agreement with the literature, verifying the applicability of the physical simulation technique for AM research. Calculations showed that using arc heat treatment as the initial step is 6–20 times more efficient in different respects (materials, energy, and man-hours) compared to wire arc additive manufacturing. Therefore, this methodology can be implemented to gain an understanding of materials in AM applications thereby eliminating the need for investments in additive manufacturing of a specific component. • A novel physical simulation was introduced for additive manufacturing (AM) research. • It uses arc as the power source and water as the cooling media. • It physically simulates material behavior subjected to AM without doing AM. • It significantly reduces materials, energy, and person-hours needed for AM research. • It contribute sustainable AM research with a fast, clean, and controllable approach. [ABSTRACT FROM AUTHOR]