Optimization of energy spending with accelerated ventilation

Abstract

Currently the cost of electric power to maintain indoor microclimate of a production facility is one of the highest. This issue is especially acute for livestock farming, where chambers and placements are not sealed from outside temperature and other factors. One of the most difficult issues of energy systems efficiency improvement is the development of adaptive climate controls for production areas and placements. A large number of factors, some of which are caused by the influence of random variables, makes the development of an algorithm for controlling ventilation systems a multi-faceted task. Classical methods of calculating microclimate parameters and their management do not allow to effectively use the adaptive control capabilities of energy efficiency of the processes under consideration. In this case the ventilation systems operation requires an excessive consumption of electric power since their design is calculated according to the maximum temperature indicators of the facility under study. One way to optimize the operation could be the introduction of an automated control system using strict algorithm or fuzzy logic algorithm. In this case, the economization of power consumption can reach up to 50% per a ventilation unit output. The authors propose using a concept of wind-cold index as a basis for constructing adaptive control of an accelerated ventilation system. Its assessment was made for the fixed indoor air movement speed. The introduction of a wind-cold index concept allows using a coefficient of correction due to the combined influence of an air movement speed in the room and air temperature, which, in turn allows to increase the minimum adjustment temperature from 16 degrees Centigrade to 18 degrees Centigrade (0C) while maintaining comfortable conditions for animals. The introduction of correction coefficients allows reducing energy expenditure by 32% in accelerated ventilation system use, that is, by the 5th month. The calculations made by authors show that to improve the adaptive control process, it is necessary to include air humidity into the algorithm as another parameter. To determine the impact of this parameter on the adaptive control process will be the goal of further research.