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Energy management effectively reduces energy consumption, improves material utilisation, minimises emissions and maximises product output during manufacturing. Thus, enterprises are implementing an energy management strategy and improving sustainable competitive advantage. With the rapid development of the Internet of Things, Cyber-Physical Systems and Big Data, sustainable smart manufacturing provides a new strategy for energy management by applying advanced information technologies. In practice, energy management is an enormous opportunity given the lack of such implementation. This paper presents a case-practice-theory-based (CPTB) method of implementing energy management in the manufacturing factory for energy saving and emission reduction. The closed-loop structure of the proposed CPTB method is designed and implemented. Then, an application of six hot-blast stoves in moulding workshops of a partner company is presented to demonstrate the proposed method. Results showed that after employing the method, the unit energy consumption of production reduced by approximately 3%, and saved energy costs reached 4%. Finally, a discussion analyses the managerial implications for ceramic and other energy-intensive manufacturing industries. [ABSTRACT FROM AUTHOR]

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. • An architecture of energy-CPS enabled management is proposed to save energy. • Energy-CPS enabled intelligent model is proposed for green manufacturing. • Qualitative and quantitative synergetic models are developed for cleaner production. • The energy consumption can be reduced through the synergistic evaluation models. [ABSTRACT FROM AUTHOR]

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. • A framework of data-driven sustainable intelligent manufacturing is proposed. • The framework is to achieve a circular economy for energy-intensive industries. • Multi-level demand response models based on PSO are developed to save energy costs. • The framework and models can benefit the environment, economy and society. [ABSTRACT FROM AUTHOR]