Energy Center Structure Optimization by using Smart Technologies in Process Control System

4th International Conference on Advanced Engineering and Technology

Shilkina, Svetlana V.^1

Moscow State University of Civil Engineering, 26, Yaroslavskoye Shosse, Moscow

129337, Russia^1

关键词: Agro-industrial complex;    Automated process control systems;    Fuzzy logic controllers;    Fuzzy logic method;    Generating equipments;    In-process control;    Power generating equipments;    Structure optimization;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/317/1/012012/pdf
DOI  :  10.1088/1757-899X/317/1/012012

来源: IOP

【 摘 要 】

The article deals with practical application of fuzzy logic methods in process control systems. A control object - agroindustrial greenhouse complex, which includes its own energy center - is considered. The paper analyzes object power supply options taking into account connection to external power grids and/or installation of own power generating equipment with various layouts. The main problem of a greenhouse facility basic process is extremely uneven power consumption, which forces to purchase redundant generating equipment idling most of the time, which quite negatively affects project profitability. Energy center structure optimization is largely based on solving the object process control system construction issue. To cut investor's costs it was proposed to optimize power consumption by building an energy-saving production control system based on a fuzzy logic controller. The developed algorithm of automated process control system functioning ensured more even electric and thermal energy consumption, allowed to propose construction of the object energy center with a smaller number of units due to their more even utilization. As a result, it is shown how practical use of microclimate parameters fuzzy control system during object functioning leads to optimization of agroindustrial complex energy facility structure, which contributes to a significant reduction in object construction and operation costs.