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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]

Abstract: Qinghai–Tibet region is a unique geographic zone due to its average altitude of over 4000m, sparse human population and prevalent pastoral system. However, little is known about the energy consumption in the region. We reviewed current situation of energy consumption and its ecological impacts, outlined future energy prospects in the region. Per capita household energy consumption was about two and three times of the national average in Qinghai and Tibet. Per urban household consumed more energy than rural household in the region. Moreover, the urban households and rural households used different energy resources, natural gas and electricity accounted for 57.57% and 42.29%, respectively in household energy consumption in urban areas while biomass accounted for 79.13% and 94.19% in household energy consumption in rural areas in Qinghai and Tibet in 2007. This region was abundant in renewable energy resources and was among the richest in hydro-power and solar energy resources in China. Hydro-power accounted for 67.88% and 92.04% in total electricity in Qinghai and Tibet in 2007. Population density was rather low in this region (4.35 versus 137.63 persons per 100hm2 in Qinghai–Tibet region and China in 2007), thus small photovoltaic power stations and hydro-power plants were more cost-effective than the electricity grid. The overuse of biotic energy resources in rural areas caused decline in soil fertility and desertification. Whereas the low utilization of fossil energy in this region released less waste gas which was beneficial to the local, national and global carbon budgets. Accelerating economic growth and booming tourism have increased energy demand and posed a challenge to the unique ecosystem in the Qinghai–Tibet region. It was estimated that energy consumption of tourism accounted for 6.06% and 14.18% of the total energy consumption in Qinghai and Tibet in 2007. Thus, exploitation of renewable energy resources like solar energy, wind energy and biomass briquette is crucial in striving for a balance among economic development, booming tourism, and environmental protection in this region. [Copyright &y& Elsevier]

Abstract: Collection of fuelwood by local residents in or around nature reserves of China has caused problems such as disturbance of habitat for wildlife and deforestation. Thus, it is important to study the energy consumption patterns by those local residents live in or around nature reserves to find solutions to reduce the amount of fuelwood consumption. We chose four nature reserves: Longxi-Hongkou National Nature Reserve, Sichuan Province, Taohongling National Nature Reserve, Jiangxi Province, Qingmuchuan Nature Reserve, Shaanxi Province, and Laoxiancheng Nature Reserve, Shaanxi Province in the subtropical zone of China to study this problem. We found no significant difference of total energy consumption per household per year in the four nature reserves, which was about 2ton of standard coal per year. But the energy consumption patterns in different reserves were different. People who live in deep mountains more relied on collecting fuelwoods from surrounding forests whereas those live in areas with relatively developed economy and better transportation condition consumed more coals, liquidinized petroleum gas and electricity. Such a pattern also reflects the changing energy spectrum during the process of modernization in China. We concluded that economic status of household and the transportation condition are two important factors affecting the energy consumption patterns in households in the nature reserves in China, which reflect the general energy consumption in rural China. [Copyright &y& Elsevier]

Abstract: We chose five study sites, from a traditional village, the Laoxiancheng Village, in remote mountainous areas to towns, the Houzhenzi Xiang Township, the Mazhao Town and the Zhouzhi County Town, as well as in a modern city, Xi’an in the Shaanxi Province of China, to study differences in energy consumption between rural and urban households and to assess its conservation implications. This study confirmed the prediction of the ‘ladder of fuel preferences’ theory. Energies used in urban households are more convenient, cleaner, and more efficient than those used in rural areas, where biomass and coal are common fuel. The amount of energy used for entertainment and electric appliances is greater in urban areas, whereas the quantity used for cooking is larger in rural districts. People in Laoxiancheng Village completely depended on fuelwood for cooking and heating. In the Houzhenzi Xiang Township, 16.7% household use coal ball, and 23.3% use LPG. Electricity and fuelwood were used in each household. In the Mazhao Town, 96.7% households used crop residue, 90% used coal, about 30% use LPG, and every household used electricity. In the Zhouzhi County Town, of the entire sampled household, 92.7% used coal, 92.7% of household used LPG and 37.5% household used gasoline. In the Xi’an city, of the entire sampled household, 32.8% household used natural gas, 67.2% used LPG; about 34.9% household used gasoline, 48.6% household used electricity and 51.4% used natural gas for heating in winter. In the five study areas, the ratio of each energy source consumed per household was also different. In the Houzhenzi Xiang Township, coal took 6.2%, LPG 1.4%, electricity 1.4% and fuelwood 91%. In the Mazhao Town, crop residue took 31.4%, coal 62.2%, LPG 2.2%, and electricity 4.2%. In the Zhouzhi County Town, the importance of energy ranked as follows: coal 75.8%, LPG 12.8%, electricity 5%, and gasoline 6.4%. In the Xi’an city, LPG took 17.6%, natural gas 27.3%, gasoline 16.9%, and electricity 38.2%. Comparing total annual consumption, households in villages used more energy compared to the households in cities. The mean of total annual energy consumption was 79.57, 63.07, 37.75, 42.53, and 29.73GJ in the Laoxiancheng Village, Houzhenzi Xiang Township, Mazhao Town, Zhouzhi County Town, and Xi’an City, respectively. We should improve the efficiency of fuel burning and introduce cleaner energy resources, such as biogas and hydropower in villages. For the households in rural areas, it is necessary to substitute coal with other cleaner energy resources and it is more important and urgent for urban households to save energy, considering large population in cities. [Copyright &y& Elsevier]

To promote energy saving, high efficiency and quality production in the mechanical manufacturing industry, a large number of studies have been conducted on modelling and evaluation. However, most of the approaches have focused on single objective modelling, evaluation and conventional machining method like unidirectional turning (UDT). This paper presents a multi-objective modelling and evaluation method for energy, efficiency, economy and quality using a new process of forward-and-reverse multidirectional turning (MDT). Firstly, the multi-objective influential factors of cutting parameters for energy consumption, cutting time, production cost and surface roughness of MDT are analyzed to establish the corresponding models, respectively. Then taking the energy consumption, cutting time, production cost and surface roughness as the objectives, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is applied for the multi-objective evaluation through different processes. Finally, the case study shows the practicability of the MDT method and evaluation method for energy optimization and energy conservation. • Proposing a new method of energy saving and high efficiency production oriented multidirectional turning (MDT). • Illustrating the characteristics of MDT. • Establishing entropy-weight and fuzzy evaluation models. • Analyzing the practicability of the multi-objective evaluation and models. [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]

To promote energy saving, high efficiency and quality production in mechanical manufacturing industry, a large number of studies have been conducted on parameters optimisation, especially for turning. However, most of the approaches have focused on single objective model and unidirectional turning (UDT). This paper presents a multi-objective parameter optimisation method for energy consumption, cutting time and surface roughness using new process of forward-and-reverse multidirectional turning (MDT). Firstly, the composition characteristics of cutting parameters for energy consumption, cutting time and surface roughness of MDT are analyzed to establish the corresponding models through nonlinear regression fitting, respectively. Then taking the minimum energy consumption, cutting time and surface roughness as the optimisation objectives, the improved fireworks algorithm can generate nearly 100 groups of optimal solutions. The most suitable processing parameters solution needs to be selected according to different machining requirements. In application scenarios, most relative errors of the models are within 10%. Comparing the machining performance of MDT and UDT under the same cutting parameters, the processing efficiency is increased by 25.97% and energy consumption is reduced by 12.38%, using the MDT. This study provides a multi-objective optimisation approach and models for MDT to reduce energy consumption, improve production efficiency and quality. [Display omitted] • Proposing a multi-objective optimisation method for multidirectional turning. • Establishing multi-objective optimisation models. • Addressing solutions based on improved fireworks algorithm considering energy, efficiency and quality. • Analyzing the practicability of the optimisation models and method in the case. [ABSTRACT FROM AUTHOR]

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]

Highlights • Presenting an emergy-based evaluation and improvement for manufacturing systems. • Constructing an emergy-based sustainable measure model. • Establishing an evaluation index system. • Proposing an improvement benchmarking card for sustainable manufacturing systems. Abstract Sustainable manufacturing, regarded as resource efficient, environment-friendly and customer satisfaction production, is major driving force for sustainable development. With growing demand for decoupling between resources and world economic development, accelerating sustainable manufacturing has become an important strategy. Therefore, a novel method based on emergy theory is proposed to perform the comprehensive evaluation and improvement of manufacturing systems. Firstly, drivers and challenges are analyzed, and the boundary and connotation of manufacturing systems are defined. Then, an emergy-based calculation model of the energy, material, service and waste is presented considering the variety and dimension of the input and output of manufacturing systems. The model expresses the mechanism of the transfer, coupling and conversion of the emergy for manufacturing systems. Besides, some indicator systems are established including functional emergy indicators, structural emergy indicators, eco-efficiency emergy indicators and sustainability indicators of manufacturing systems. The inner link between the economic, environment and social benefits of manufacturing systems is revealed through these indicators. On this basis, an improvement benchmarking card is developed to achieve the excellent quality, high efficiency, energy reduction, resource saving and environmental protection of manufacturing systems. Finally, a case study illustrates the practicability of the proposed method, and results show that the proposed method provides theoretical support for evaluating and improving the sustainability of manufacturing systems to coordinate the resources and development of the manufacturing industry. [ABSTRACT FROM AUTHOR]

Improving energy performance has been recognized as an effective measure to promote the energy saving and emission reduction and to realize the sustainable development. Methods of improving energy performance towards sustainable manufacturing are numerous and scattered, resulting in insufficiency from the perspective of overall strategies and system integration. After the intensive selection of advanced literatures, about 166 research papers directly related to energy performance improvement are analyzed. A comprehensive review and analysis from multi-perspectives of energy monitoring, evaluation, optimization and benchmarking are performed, which is conducive to understand the energy consumption pattern and take effective energy-saving measures. This paper establishes a framework of energy performance integrated method, and four energy management methods and their energy models in energy monitoring, evaluation, optimization and benchmarking phase are analyzed and summarized. Besides, the proposed framework analyzes potential applications and key advantages, and some challenges and potential opportunities for energy performance improvement methods are discussed. This study will provide a significant foundation for energy-efficient production through various methods application. • Performing a review on methods of improving energy performance. • Constructing a framework of methods of improving energy performance. • Summarizing advances in energy monitoring, evaluation, optimization and benchmarking. • Discussing challenges and opportunities of improving energy performance. [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]