PhD Thesis

Plant-wide management/control oriented to the energy efficiency of cyber-physical systems with time-variant constraints

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Information

  • Started: 01/04/2017
  • Thesis project read: 13/02/2018

Description

Based on the nature, the size (large scale), the complexity of the current manufacturing industry, and the increasing necessity of reducing both the energy consumption and the waste production, the design of management/control strategies at plant level is a complex task that can be addressed based on PWC approaches and optimization-based control techniques. From these techniques, the control problem can be expressed as a cost function in which the total energy consumption can be penalized, while all constraints of both the system and its environment can be included into the problem formulation. Therefore, the inclusion of the advances in sensing technology could help the optimization-based control techniques in transforming acquired information into a predictive behavior, in order to reduce the total energy consumption as well as smoothing load proles, avoiding peak-load penalties. From this fact, Model Predictive Control (MPC) is a kind of strategy that is not only an optimization-based controller but also is a predictive control strategy. Thus, MPC together with IoT and available energy consumption data will allow designing management/control strategies for reducing the energy consumption, which can be scaled to dierent levels in the manufacturing industry.

Then, aware of the necessity of improving the energy eciency of manufacturing industry (plant level, production line, machine level), the motivation of this doctoral thesis proposal is to integrate the fundamental concepts of the Industry 4.0 (Smart manufacturing, CPS, IoT, among others) and the PWC approach based on optimization techniques for designing energy management/control strategies oriented to reduce the energy consumption and improving the energy eciency of manufacturing systems towards becoming them into smart manufacturing systems (SMS). Therefore, these strategies must process information in real time to predict the energy consumption of an SMS taking into account its possible time-varying constraints from both the process and the environment and, based on this, propose a decision structure compromises neither the quality and quantity of the production nor the energy consumption of the plant. In this scenario, two possible cases will be considered: rst, to reduce the energy consumption and, second, to smooth the energy consumption prole to avoid the consumption peaks of the load. Finally, this work proposes to develop a methodology of adaptive plant-wide management/control for minimizing the energy consumption of an SMS, incorporating the connectivity features of the Industry 4.0 for handling in real time the process and the environment uncertainty.

The work is under the scope of the following projects:

  • IKERCON: Control avanzado de procesos complejos de manufactura (web)