A big data driven analytical framework for energy-intensive manufacturing industries

Energy-intensive industries account for almost 51% of energy consumption in China. A continuous improvement in energy efficiency is important for energy-intensive industries. Cleaner production has proven itself as an effective way to improve energy efficiency and reduce energy consumption. However, there is a lack of manufacturing data due to the difficult implementation of sensors in harsh production environment, such as high temperature, high pressure, high acid, high alkali, and smoky environment which hinders the implementation of the cleaner production strategy. Thanks to the rapid development of the Internet of Things, many data can be sensed and collected in the manufacturing processes. In this paper, a big data driven analytical framework is proposed to reduce the energy consumption and emission for energy-intensive manufacturing industries. Then, two key technologies of the proposed framework, namely energy big data acquisition and energy big data mining, are utilized to implement energy big data analytics. Finally, an application scenario of ball mills in a pulp workshop of a partner company is presented to demonstrate the proposed framework. The results show that the energy consumption and energy costs are reduced by 3% and 4% respectively. These improvements can promote the implementation of cleaner production strategy and contribute to the sustainable development of energy intensive manufacturing industries.Funding Agencies|National Natural Science Foundation of China [51675441, 51475096, U1501248]; Fundamental Research Funds for the Central Universities [3102017jc04001]; Circularis (Circular Economy through Innovating Design) project - Vinnova - Swedens Innovation Agency [2016-03267]; Simon (New Application of Al for Services in Maintenance towards a Circular Economy) project - Vinnova - Swedens Innovation Agency [2017-01649]</p

Digital twin and big data-driven sustainable smart manufacturing based on information management systems for energy-intensive industries

Internet of Things (IoT) technology, which has made manufacturing processes more smart, efficient and sustainable, has received increasing attention from the industry and academia. As one of the most important applications for IoT, sustainable smart manufacturing enables lower cost, higher productivity and flexibility, better quality and sustainability during the product lifecycle management. Over the years, numerous enterprises have promoted the implementation of both sustainable and smart manufacturing. In the Industry 4.0 context, a digital twin is widely used to achieve smart manufacturing, although this approach often ignores sustainability. This study aims to simultaneously consider digital twin and big data technologies to propose a sustainable smart manufacturing strategy based on information management systems for energy-intensive industries (EIIs) from the product lifecycle perspective. The integration of digital twin and big data provides key technologies for data acquisition in energy-intensive production environments, prediction and mining in uncertain environments as well as real-time control in complex working conditions. Moreover, a digital twin-driven operation mechanism and an overall framework of big data cleansing and integration are designed to explain and illustrate sustainable smart manufacturing. Two case studies from Southern and Northern China demonstrate the efficacy of the strategy, with the results showing that Companies A and B achieved the goals of energy saving and cost reduction after implementing the proposed strategy. By applying an energy management system, the unit energy consumption and energy cost of production in Company A decreased by at least 3%. In addition, the cradle-to-gate lifecycle big data analysis indicates that the costs of environmental protection in Company B decrease significantly. Finally, the effectiveness of the proposed strategy and some managerial insights for EIIs in China are analysed and discussed.Funding Agencies|Special Construction Fund for Key Disciplines of Shaanxi Provincial Higher Education and the Youth Innovation Team of Shaanxi Universities ?; Natural Science Basic Research Plan in Shaanxi Province of China [2022JQ-37]; National Natural Science Foundation of China [52005408]</p

Edge computing-based real-time scheduling for digital twin flexible job shop with variable time window

Production scheduling is the central link between enterprise production and operation management and is also the key to realising efficient, high-quality and sustainable production. However, in real-world manufacturing, the frequent occurrence of abnormal disturbance leads to the deviation of scheduling, which affects the accuracy and reliability of scheduling execution. The traditional dynamic scheduling methods (TDSMs) cannot solve this problem effectively. This paper presents a real-time digital twin flexible job shop scheduling (R-DTFJSS) method with edge computing to address the issue. Firstly, an overall framework of R-DTFJSS is proposed to realise real-time scheduling (RS) through real-time interaction between physical workshop (PW) and virtual workshop (VW). Secondly, the implementation process of R-DTFJSS is designed to realise real-time operation allocation. Then, to obtain the optimal RS result, an improved Hungarian algorithm (IHA) is adopted. Finally, a case simulation from an industrial case of a cooperative enterprise is described and analysed to verify the effectiveness of the proposed R-DTFJSS method. The results show that compared with the TDSMs, the R-DTFJSS method can effectively deal with unexpected and frequent abnormal disturbances in the production process.Funding Agencies|Key Research and Development Program of Shaanxi [2021GY-069]; National Natural Science Foundation of China [52005408]</p

Energy-cyber-physical system enabled management for energy-intensive manufacturing industries

Cleaner production is a green production way to minimize emissions and waste as well as maximize product output. Cleaner production has proven itself as an effective way to reduce energy consumption and improve material utilization during the whole manufacturing process. However, the implementation of cleaner production strategy is facing barriers due to the lack of applying advanced technologies such as cloud manufacturing, Internet of Things, and cyber-physical system. Based on these advanced technologies, this paper presents an architecture of energy-cyber-physical system enabled management for energy-intensive manufacturing industries to promote the implementation of cleaner production strategy. An energy-cyber-physical system enabled green manufacturing model for the future smart factory is proposed. Then the qualitative and quantitative synergetic models based on energy-cyber-physical system are developed for cleaner manufacturing. Finally, an application of a partner company is presented to demonstrate the proposed framework and synergistic models. The results show that energy consumption can be greatly reduced using synergistic evaluation models. Further managerial implications are summarized to increase the level of synergy of energy-cyber-physical system, which can achieve cleaner production strategy by making energy-efficient decisions from different departments

Edge-cloud cooperation-driven smart and sustainable production for energy-intensive manufacturing industries

Energy-intensive manufacturing industries are characterised by high pollution and heavy energy consumption, severely challenging the ecological environment. Fortunately, environmental, social, and governance (ESG) can promote energy-intensive manufacturing enterprises to achieve smart and sustainable production. In Industry 4.0, various advanced technologies are used to achieve smart manufacturing, but the sustainability of production is often ignored without considering ESG performance. This study proposes a strategy of edge-cloud cooperation -driven smart and sustainable production to realise data collection, preprocessing, storage and analysis. In detail, kernel principal component analysis (KPCA) is used to decrease the interference of abnormal data in the eval-uation results. Subsequently, an improved technique for order preference by similarity to ideal solution (TOPSIS) based on the adversarial interpretative structural model (AISM) is proposed to evaluate the production efficiency of the manufacturing workshop and make the analysis results more intuitive. Then, the architecture and models are verified using real production data from a partner company. Finally, sustainable analysis is discussed from the perspective of energy consumption, economic impact, greenhouse gas emissions and pollution prevention.Funding Agencies|Youth Innovation Team of Shaanxi Universities ?; Special ConstructionFund for Key Disciplines of Shaanxi Provincial Higher Education; Natural Science Basic Research Plan in Shaanxi Province of China [2022JQ-37]; Shaanxi Provincial Education Department [22JK0567]; Project of National Natural Science Foundation of China [62271390, 51905399]; Postgraduate Innovation Fund of Xian University of Posts and Telecommunications [CXJJDL2022012]</p

Data-driven sustainable intelligent manufacturing based on demand response for energy-intensive industries

The circular economy plays an important role in energy-intensive industries, aiming to contribute to ethical sustainable societal development. Energy demand response is a key actor for cleaner production and circular economy strategy. In the Industry 4.0 context, the advanced technologies (e.g. cloud computing, Internet of things, cyber-physical system, digital twin and big data analytics) provide numerous opportunities for the implementation of a cleaner production strategy and the development of intelligent manufacturing. This paper presented a framework of data-driven sustainable intelligent/smart manufacturing based on demand response for energy-intensive industries. The technological architecture was designed to implement the proposed framework, and multi-level demand response models were developed based on machine, shop-floor and factory to save energy cost. Finally, an application of ball mills in a slurry shop-floor of a partner company was presented to demonstrate the proposed framework and models. Results showed that the energy efficiency of ball mills can be greatly improved. The energy cost of the slurry shop-floor saved approximately 19.33% by considering electricity demand response using particle swarm optimisation. This study provides a practical approach to make effective and energy-efficient decisions for energy-intensive manufacturing enterprises. (C) 2020 The Author(s). Published by Elsevier Ltd.Funding Agencies|National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [51675441, 51475096, U1501248, 51705428]; Fundamental Research Funds for the Central UniversitiesFundamental Research Funds for the Central Universities [31020190505001]; 111 Project Grant of Northwestern Polytechnical University [B13044]; FlexSUS: Flexibility for Smart Urban Energy Systems [91352]; European UnionEuropean Union (EU) [775970]</p

Big data-driven correlation analysis based on clustering for energy-intensive manufacturing industries

In Industry 4.0, the production data obtained from the Internet of Things has reached the magnitude of big data with the emergence of advanced information and communication technologies. The massive and low-value density of big data challenges traditional clustering and correlation analysis. To solve this problem, a big data-driven correlation analysis based on clustering is proposed to improve energy and resource utilisation efficiency in this paper. In detail, the production units with abnormal and energy-intensive consumption can be classified by using clustering analysis. Additionally, feature extraction is carried out based on clustering analysis and the same cluster data is migrated to the training data set to improve correlation analysis accuracy. Then, correlation analysis can balance the relationship between energy supply and demand, which can reduce carbon emission and enhance sustainable competitiveness. The sensitivity analysis results show that the feature extraction method can improve the correlation analysis accuracy compared to the original analysis model. In conclusion, the big data-driven correlation analysis based on clustering can uncover the potential relationship between energy consumption and product yield, thus improving the efficiency of energy and resources.Funding Agencies|Youth Innovation Team of Shaanxi Universities "Industrial Big Data Analysis and Intelli- gent Processing "; Special Construction Fund for Key Disciplines of Shaanxi Provincial Higher Education; Fundamental Research Funds for the Central Universities [XJSJ23095]; Natural Science Basic Research Plan in Shaanxi Province of China [2022JQ-37]; Shaanxi Provincial Education Department [22JK0567]; Postgraduate Innovation Fund of Xi an University of Posts and Telecommunications [CXJJDL2022012]</p