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Currently, practical optimisation models and intelligent solution algorithms for solving disassembly sequence planning are attracting more and more attention. Based on the importance of energy efficiency in product disassembly and the trend toward green remanufacturing, this paper proposes a new optimisation model for the energy-efficient disassembly sequence planning. The minimum energy consumption is used as the evaluation criterion for disassembly efficiency, so as to minimise the energy consumption during the dismantling process. As the proposed model is a complex optimization problem, called NP-hard, this study develops a new extension of the whale optimisation algorithm to allow it to solve discrete problems. The whale optimisation algorithm is a recently developed and successful meta-heuristic algorithm inspired by the behaviour of whales rounding up their prey. We have improved the whale optimisation algorithm for predation behaviour and added a local search strategy to improve its performance. The proposed algorithm is validated with a worm reducer example and compared with other state-of-the-art and recent metaheuristics. Finally, the results confirm the high solution quality and efficiency of the proposed improved whale algorithm. [ABSTRACT FROM AUTHOR]

Scheduling can have significant impacts on energy saving in manufacturing systems. The complex process constraints and dynamic manufacturing tasks in flexible manufacturing system make the scheduling a complicated nonlinear programming problem. To this end, this paper proposes a two-stage energy-saving optimization method for Flexible Job-Shop Scheduling Problems (FJSP). In this method, an operation-based integrated chart is firstly proposed to reveal the dynamic characteristics of the operations, enabling the energy-saving scheduling optimization. Then the optimization is conducted at two stages: the machine tool stage and the operation sequence stage. A Modified Genetic Algorithm (MGA) is applied at the first stage and a hybrid method that integrates Genetic Algorithm (GA) with Particle Swarm Optimization (PSO) is adopted at the second stage. Finally, a case study is employed to illustrate the applicability and validity of the proposed method. The results revealed that the proposed method can effectively optimize FJSP. This may provide a basis for decision makers to utilize a manufacturing scheduling that is optimized regarding its energy saving. [ABSTRACT FROM AUTHOR]

Dry or near dry machining is often regarded as an effective strategy for reducing ecological impacts of the cutting processes. However, due to the application limitations of dry or near dry machining, reduction of cutting fluid supply through machining parameter optimization offers a cost effective alternative. To this end, an optimization model of machining parameters considering minimum cutting fluid consumption and cost is proposed. Process cost and cutting fluid consumption are treated as the two objectives in the optimization model, which are affected by four variables, namely cutting depth, feed rate, cutting speed, and cutting fluid flow. In the model, process cost includes production operation cost and cutting tool cost, whilst cutting fluid consumption by a machining process, which consists of reusable cutting fluid and non-reusable cutting fluid, ie., the remaining cutting fluid deposited on the workpiece and chips as well as that diffused into the environment. The multi-objective optimization problem is solved by a hybrid genetic algorithm programmed in Matlab 7. An illustrative case study was implemented to verify the effectiveness of the multi-objective optimization model, and the simulation results showed 17% reduction of fluid consumption compared to that without optimization. This indicates that the proposed optimization is effective and has great potential to be adopted by industry. [ABSTRACT FROM AUTHOR]

Despite the increasing contribution of the automotive industry to China's national economy, CO2 emissions have become a challenge. However, the research about its energy consumption and carbon emissions is lacking. The significance of this study is to fill the research gap and provide suggestions for China's automotive industry to reduce its carbon emissions. In this paper, the extended logarithmic Division index (LMDI) method is adopted to decompose the factors affecting carbon emissions and determine the key driving forces. According to provincial statistical data in China in 2017, the annual emissions of six provinces exceeded five million tons, accounting for 55.44% of the total emissions in China. The largest source of emissions in China is in Jilin Province, followed by Jiangsu, Shandong, Shanghai, Hubei and Henan. The decomposition results show that investment intensity effect is the greatest factor for CO2 emissions, while R&D intensity and energy intensity are the two principal factors for emission reduction. After the identification of driving factors, mitigation measures are proposed considering the current state of affairs and real situation, including improving energy structure, accelerating product structure transformation, stimulating sound R&D investment activities, promoting energy conservation and new energy automobile industry development and boosting industrial cluster development. [ABSTRACT FROM AUTHOR]

Abstract: Despite the rapid development in fossil fuel, biomass is still the main energy resource in rural China. However, the research on household energy consumption on the Qinghai-Tibet Plateau is limited. We investigated the differences in household energy consumption pattern, the influencing factors of fuel type choice, and the willingness to use clean energy in agricultural, pastoral and agro-pastoral transitional zones in eastern part of Qinghai-Tibet Plateau. Information was collected through Participatory Rural Appraisal (PRA) and Physical Monitoring (PM). We found that biomass was the main energy resource in pastoral regions while fossil energy was the main fuel in agricultural regions. Energy consumption per capita in pastoral regions was higher than that in agricultural regions in our study area, and annual household energy consumption in pastoral regions was much higher than the provincial average. Altitude, livelihood and education level were main factors affecting domestic fuel type choice, while altitude and household size were two factors determining energy consumption per capita. The use of biomass as fuel could have negative influence on the material cycle in ecosystem and affect the carbon budget on the Qinghai-Tibet Plateau. Householders were willing to use clean energy and most interviewees chose electricity as their favorite fuel type. Therefore, the modern utilization of biomass and the exploration of renewable energy are promising in future energy development in eastern part of Qinghai-Tibet Plateau. However, energy transition might be constrained by poor local transportation and traditional consumption habit of indigenous community. [Copyright &y& Elsevier]

Process route planning is vital for implementing energy saving and low-cost production in mechanical processing as it can directly affect the energy consumption and the cost of mechanical product processing. Therefore, a multiobjective optimization approach of machining center process routes to realize energy saving and low-cost mechanical processing is proposed in this paper. To provide theoretical support for this study, process route optimization problems of a machining center are analyzed, the concept of workstep element is introduced to represent the features of machined parts, and a multiobjective optimization model is established. The optimization model is solved based on the combination of a workstep chain intelligent generation algorithm and a non-dominated sorting genetic algorithm II. Finally, the emulsion pump case process route is used as a case study to verify the feasibility and practicability of the proposed method. Comparison with actual data shows that with the single objective of energy consumption and processing cost, based on the multiobjectives of energy saving and low cost as the optimization goal, the energy consumption was 1.018 × 107 J, and the processing cost was CNY32.65. Compared with the other two experimental results, the energy consumption and the processing cost demonstrated the best comprehensive performances, consistent with energy saving, low cost and sustainable production, thereby validating the established model. Furthermore, the optimization analysis shows that the combinatorial optimization algorithm has a better solution speed and optimization precision than the general genetic algorithm. • Analyzed the underlying effects of the mechanism of process route optimization. • A multiobjective optimization model of the machining center process route was built. • The concept of workstep element was proposed. • An intelligent combinatorial optimization algorithm for the workstep chain was proposed. [ABSTRACT FROM AUTHOR]

Selecting a set of reasonable milling parameters of computerized numerical control (CNC) machines is of great importance in decreasing energy consumption and enhancing processing productivity. However, existing works pay little attention to the optimization of energy-conscious milling parameters. This work establishes a dual-objective optimization model for the selection of milling parameters such that power consumption and process time are minimized. With multiple constraints of milling processing conditions, an improved artificial bee colony (ABC) intelligent algorithm is used to handle the proposed dual-objective optimization model. Compared with the non-dominated sorting genetic algorithm (NSGA-II), our improved algorithm has good performance. [ABSTRACT FROM AUTHOR]

The additive manufacturing (AM) process is characterised as a high energy-consuming process, which has a significant impact on the environment and sustainability. The topic of AM energy consumption modelling, prediction, and optimisation has then become a research focus in both industry and academia. This issue involves many relevant features, such as material condition, process operation, part and process design, working environment, and so on. While existing studies reveal that AM energy consumption modelling largely depends on the design-relevant features in practice, it has not been given sufficient attention. Therefore, in this study, design-relevant features are firstly examined with respect to energy modelling. These features are typically determined by part designers and process operators before production. The AM energy consumption knowledge, hidden in the design-relevant features, is exploited for prediction modelling through a design-relevant data analytics approach. Based on the new modelling approach, a novel deep learning-driven particle swarm optimisation (DLD-PSO) method is proposed to optimise the energy utility. Deep learning is introduced to address several issues, in terms of increasing the search speed and enhancing the global best of PSO. Finally, using the design-relevant data collected from a real-world AM system in production, a case study is presented to validate the proposed modelling approach, and the results reveal its merits. Meanwhile, optimisation has also been carried out to guide part designers and process operators to revise their designs and decisions in order to reduce the energy consumption of the designated AM system under study. [ABSTRACT FROM AUTHOR]

The energy, economic and environmental performance of remanufacturing process are complex and diverse. In order to improve the comprehensive benefits, a sustainability evaluation method for multi-objective remanufacturing system synthesis based on energy, economy and environment is proposed in this paper. Starting from the remanufacturing process, the prediction model of each evaluation index is established and verified, and the technique for order performance by similarity to ideal solution (TOPSIS) based on entropy weight is used for analysis and calculation. The established quantitative prediction model is of high accuracy and innovative and practical application value. The example analysis shows that this method can be successfully applied to the remanufacturing of lathe spindle, and the optimal remanufacturing scheme with comprehensive energy, economic and environmental benefits is selected. This study is not only conducive to enterprises to obtain greater comprehensive benefits of remanufacturing, but also can provide a reference for the government to formulate more perfect laws and policies. Image 10109 • Evaluation of remanufacturing sustainability considering energy, economic and environmental benefits. • The analysis and calculation models of energy consumption, cost and carbon emissions are established. • Using TOPSIS based on entropy weight to make comprehensive decision, eliminating the interference of human factors. • It is of positive significance for the development of enterprises and the formulation of government policies. [ABSTRACT FROM AUTHOR]