Data-driven modeling and real-time distributed control for energy efficient manufacturing systems

Abstract

As manufacturers face the challenges of increasing global competition and energy saving requirements, it is imperative to seek out opportunities to reduce energy waste and overall cost. In this paper, a novel data-driven stochastic manufacturing system modeling method is proposed to identify and predict energy saving opportunities and their impact on production. A real-time distributed feedback production control policy, which integrates the current and predicted system performance, is established to improve the overall profit and energy efficiency. A case study is presented to demonstrate the effectiveness of the proposed control policy.

Event-Driven Online Machine State Decision for Energy-Efficient Manufacturing System Based on Digital Twin Using Max-Plus Algebra

“…Compared with time-based data sampling in the fuzzy logic method [26], our event-driven method greatly eases the burden of data collection and conforms to the stochastic of the system. The dynamic updating of the ESW of the target machine during one round decision is more reasonable compared with the constant opportunity windows [27,29,[55][56][57] and will be illustrated in Section 5.…”

“…Sun and Li [28] presented an analytical estimation of energy control opportunities considering buffer utilization. Zou et al [29] developed a stochastic analytical model which could predict shutdown time and recovery time of machines based on discrete-time Markov Chains in a stochastic production system. Li et al [30] developed an analytical approach to quantitatively predict the system level production loss resulting from an energy-saving control event.…”

“…A typical serial manufacturing system including six machines and five buffers (6M5B) in References [26,29] is taken to illustrate the effectiveness of our method. This system is a simplified version of a real automotive production line.…”

Energy efficiency in discrete-manufacturing systems: Insights, trends, and control strategies

“…Nonetheless, nowadays, it is considered that peripheral devices can be independently managed from machining devices in pro of energy efficiency and without to compromise the machining operation, since these devices could or could not have a periodic behavior. Based on this fact, application of MPC controllers for selective on/off switching of these devices, based on their own dynamic behavior and the total energy consumption of the machine have gained interest as a control strategy oriented to improve the energy efficiency of manufacturing systems [114,110,115].…”

“…This work shows a clear example of utility and application of the available technologies in Industry 4.0, from which it is possible to access the energy information and efficiently analyzing it to extract key performance indicators that can be used as helpful tools in the energy management/control. Following the same way towards the energy efficiency, the work proposed by Zou et al [114] present a novel strategy for controlling the energy consumption of manufacturing systems. In this work, a data-driven stochastic manufacturing systems modeling method is proposed to achieve a predicting system that will be used later to design control systems.…”