ABSTRACT:
Stimulated by the recent development of fog computing technology, in this paper, a fog computing aided process monitoring and control architecture is proposed for large-scale industrial processes, which enables reliable and efficient online performance optimization in each fog computing node without modifying pre-designed control subsystems. Moreover, a closed loop data-driven method is developed for the process monitoring system design and an adaptive configuration approach is proposed to deal with the problems caused by the changes of process parameters and operating points. The feasibility and effectiveness of the proposed design approaches are verified and demonstrated through the case study on the Tennessee Eastman (TE) benchmark system.
EXISTING SYSTEM:
The existing data-driven designs result in a central computing procedure where the global information is needed. For a large-scale industrial process, the whole design thus involves huge computational and communicational burden, especially when an online configuration of the designed monitoring and control systems is demanded. In order to release the central computational burden and reduce the communication efforts among isolated subsystems, the decentralized monitoring and control technologies, and the references therein, could be utilized.
PROPOSED SYSTEM:
In this paper, motivated by the advantages brought by fog computing technique, a fog computing aided process monitoring and control architecture is firstly proposed for large-scale industrial processes. Differing from the existing decentralized monitoring and control strategies, the proposed one avoids the modification of pre-designed control systems and enables online performance optimization in each fog computing node with stability guarantee. In addition, a data-driven design method is developed for the process monitoring system in which the effects of the local feedback system on the process data are considered. Moreover, an adaptive configuration approach is proposed for the designed data-driven process monitoring system in each fog computing node to deal with the problems caused by the changes of process parameters and operating points.
SYSTEM REQUIREMENTS
SOFTWARE REQUIREMENTS:
• Programming Language : Python
• Font End Technologies : TKInter/Web(HTML,CSS,JS)
• IDE : Jupyter/Spyder/VS Code
• Operating System : Windows 08/10
HARDWARE REQUIREMENTS:
Processor : Core I3
RAM Capacity : 2 GB
Hard Disk : 250 GB
Monitor : 15″ Color
Mouse : 2 or 3 Button Mouse
Key Board : Windows 08/10
