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Environmental Sustainability is one of the ‘Grand Challenges’ for manufacturing success in the 21st century. The present article mainly focuses on energy use during the manufacturing steps used for assembling products. In particular, industry still needs systematic and reliable ways to capture information for ultimately accounting for energy efficiency in manufacturing processes. For that purpose, an information model in the form of a formal ontology was created using information elements as building blocks, which emerge from manufacturing process models, supply chain operations, manufacturing process parameters, 3D form features and exergy-based thermodynamic analysis. These information elements were integrated by means of semantic relationships in an ontology. Once this information model was constructed, an application example of a welded hull panel assembly was used for verification of the model's representativeness and completeness. Results show that it is feasible to correlate all required data by means of semantic relationships for estimating energy efficiency indicators prior to physical prototyping, or ultimately comparing different alternatives for manufacturing processes regarding energy efficiency. However, manufacturing processes must be described in detail in order to allow energy efficiency analyses. In addition, geometric models need to be able to represent form features that can best relate to specific manufacturing process parameters. And finally, energy efficiency indicators, such as the degree-of-perfection can be derived from a comprehensive thermodynamic analysis, to be performed in each individual process step. Results indicate it is possible to provide an information structure for the calculation of the degree-of- perfection associated with an exergy-based thermodynamic analysis in assembly processes. Therefore, it has the potential to serve as a basis for a unifying framework for developing software applications that could allow the calculation of the degree of perfection in manufacturing operations. In addition, the resulting information model may facilitate data integration in the extended enterprise environment as well as the development of standards that can lead to seamless interoperability of manufacturing information systems. This paper presents the means by which energy efficiency can be accessed directly from an integrated semantic model, and opens the possibility to be extended for many different sorts of manufacturing processes. [ABSTRACT FROM AUTHOR]

Hybrid flowshop scheduling problem is a hot research topic, and is widely applied for production shop or line in chemical industry, metallurgical industry, semiconductor manufacturing and other industries. However, on the one hand, the uncertain events are inevitable in actual production, which will disrupt the production plan. On the other hand, nowadays the energy problem becomes more and more serious, and attracts much attention in the manufacturing industry. Therefore, an energy-efficient hybrid flowshop rescheduling problem under the machine breakdown is addressed in this paper. Firstly, the mathematical model for the problem is established, and an energy saving strategy based on problem model is designed, which can ensure the reduction of energy consumption without affecting the production efficiency. Then, an improved multi-objective firefly algorithm is proposed to optimize the production efficiency, energy consumption and production stability. To express the problem characteristics, a two-level encoding mechanism is used to describe the individual, and a corresponding decoding mechanism is presented to generate the scheduling scheme. By simulating the location updating law of the fireflies, the population updating rule is designed, in which the variable neighborhood search is employed to avoid the local optimal. To ensure the quality of the solution set, the fast non-dominated sorting method and elite individual reserving strategy are introduced to the population evolution. Finally, the numerical experimental results indicate that the designed energy saving strategy is effective, and the proposed algorithm obtains better Pareto frontier and performs the better convergence and diversity comparing with MOEA/D and NSGA-Ⅱ, the common algorithms to solve complex multi-objective optimization problem. • A multi-objective energy-efficient hybrid flowshop rescheduling problem is studied. • The problem characteristics of dynamic event and energy consumption are analyzed. • An improved multi-objective firefly algorithm is proposed and tested by experiments. • An energy saving strategy based on the processing speed selection is designed. • The total energy consumption is reduced without affecting the production efficiency. [ABSTRACT FROM AUTHOR]

Abstract: When performing life cycle assessment (LCA) assumptions regarding the energy use are often decisive for the outcome. In this paper, current approaches of identifying marginal electricity and heat technologies for consequential LCAs are challenged. The identification of marginal energy technologies is examined from three angles: The marginal electricity technology is identified in Danish historical and potential future energy systems. The methods of identifying and using marginal electricity and heat technologies in key LCA studies are analysed. Finally, the differences in applying energy system analysis and assuming one marginal technology are illustrated, using waste incineration with energy substitution as a case. The main problem with the current approach is the use of one single marginal technology. It is recommended to use fundamentally different affected technologies and identify these in several possible and fundamentally different future scenarios. If possible, the affected technologies should also be identified based on energy system analyses considering the technical characteristics of the technologies involved. Some results in this paper may be applicable to other affected technologies than energy. [Copyright &y& Elsevier]

Generally, industry includes various sectors like manufacturing, energy, materials & mining, and transportation. Industry consumes about one half of the world's total delivered energy, and manufacturing is one of the energy-intensive industrial sectors. With the rising energy price, the energy cost is becoming a controllable expenditure in manufacturing. In this paper, a generic method has been proposed to minimize the energy cost and improve the energy efficiency of manufacturing unit processes. Finite state machines have been used to build the transitional state-based energy model of a single machine. A mixed-integer linear programming mathematical model has been formulated for energy-cost-aware job order scheduling on a single machine. A generic algorithm has been implemented to search for an energy-cost-effective schedule at volatile energy prices with the constraint of due dates. As a result, plant managers can have an energy-cost-effective job order schedule which is associated with machine energy states along time, and can also get time-indexed energy simulation of the schedule. In comparison to most of the static scheduling approaches, stochasticity has been further handled through a cyclic interaction between the scheduler and the energy model, which facilitates to investigate how stochasticity on a shop floor affects the performance of energy-cost-aware scheduling. Empirical data have been used in the case study, including the power measured from a grinding machine, and the real-time pricing and time-of-use pricing tariffs. The proposed method has been demonstrated to be both energy-efficient and energy-cost-efficient even at the presence of stochasticity. As a joint effort of energy efficiency and demand response within demand side management, this method shows its effectiveness for contributing to the reduction of greenhouse gas emissions during peak periods, and for leading to energy-efficient, demand-responsive, and cost-effective manufacturing processes. [ABSTRACT FROM AUTHOR]

Abstract: In order to provide a reliable estimation of energy consumption under various machining conditions, an energy consumption model with high accuracy and well-defined coefficients is necessary to be developed. This paper presents an improved energy consumption model to characterize the relationship between process variables and energy consumption for material removal processes based on thermal equilibrium and empirical modelling. The improved model of milling process as a function of material removal rate and spindle speed has been tested and validated under various cutting parameters. Additionally, the values of the model coefficients are clearly defined, which will be easy to understand and explain each coefficient in the energy consumption model. The model coefficients were obtained using the statistic modelling based on experimental data. Experimental results show that the improved model is able to provide a reliable prediction of energy consumption for given process parameters with an accuracy of more than 96%. The good agreement between simulations and measured results led to the conclusion that the proposed model can be used effectively for the calculation of the total energy required by a machine tool system for face milling of a part. [Copyright &y& Elsevier]

Abstract: The aim of this research was to contribute towards the development of a new mathematical model and logic for predicting direct electrical energy requirements in machining toolpaths. This model will track the visibility and process dependence of energy and hence carbon footprint in machining process. This study includes a critical review of similar existing models and their limitations. The effect that machine modules, auxiliary units and machine codes have on power and energy consumption during machining was studied and the electrical current consumption measured. A mathematical model for electrical energy use in machining was developed addressing the limitations of existing models and validated on a milling tool path. The paper provides valuable information on the impact of machine modules, spindles, auxiliary units and motion states on the electrical energy demand budget for a machine tool resource. This knowledge is fundamentally important in evaluating toolpaths and re-designing machine tools to make them more energy efficient, to reduce electricity costs and associated carbon footprints. [Copyright &y& Elsevier]

Manufacturing industry accounts for about one-third of the world's total energy consumption (TEC). This study aims to develop a novel mixed-integer mathematical model to represent the direct energy consumption of machines and indirect energy consumption on a shop floor. In comparison with traditional modeling methods, this paper proposes an effective gene expression programming-based rule mining (GEP-RM) algorithm to generate dispatching rules automatically. This method consists of three attributes that have significant impacts on the TEC of a manufacturing process. In addition, diversified rule mining operators with self-learning are designed to ensure population diversity and convergence. Moreover, a perturbation trigger mechanism for reconstructing rules is introduced to avoid being trapped into a local optimum. An unsupervised learning algorithm is achieved by setting the evolution direction with global best and current worst in order to mine the value of the substantial historical data. Experimental results have shown that the proposed multi-attribute rule mining approach outperforms other dispatching rules in terms of energy saving and production efficiency. [ABSTRACT FROM AUTHOR]

Fuel consumption by agricultural vehicles presents a financial burden on operators, and an environmental burden on the community – so any reduction of fuel usage is of interest to all. Dividing fuel usage into functional components – working and non-working – allows prioritization of optimization efforts that also considers multiple vehicle types, capacities and fuel consumption rates. A general framework, including the mathematical model and solution approach, is proposed to optimize the fuel consumption for the non-working distance and is implemented by vehicles in intra- and inter-fields. A mixed integer model is formulated and combined with a search algorithm to find an optimal solution. Subsequently, a case study related to wheat harvesting is presented to verify the effectiveness and efficiency of the search. Compared with the operational schedule, fuel consumed by harvesters in non-working activities is reduced significantly by 19.5%. Furthermore, total fuel consumed by harvesters is reduced by 16.3%. This paper provides a general framework for reducing the fuel consumption of agricultural vehicles and makes these agricultural vehicles more environmentally friendly. Image 10640 • We contribute a schedule for intra- and inter-fields logistics to reduce fuel use. • A two stage algorithm is presented to find the schedule of agricultural vehicles. • A case study is proposed to reduce fuel consumption caused by harvesters. [ABSTRACT FROM AUTHOR]