Models for the development of an intelligent control system for the process of fattening broiler chickens

To ensure maximum efficiency of poultry enterprises specializing in meat production, the necessary conditions are improving the quality of manufactured products, reducing production costs, and minimizing stressful situations that arise when birds come into contact with service personnel. Compliance with these conditions is ensured through the use of automated process control systems that generate control actions according to the standard data of process indicators at a certain time interval of the production cycle. The disadvantage of these systems is the lack of consideration of the physiological needs of birds, which change depending on their condition. To solve this problem, it is necessary to develop control systems that provide for the adjustment of the production process in real time based on the information received about the conditions of keeping birds, their productivity, weight, etc. The development of these systems is based on the dependence of the parameters of feed consumption, water, manure output, air exchange, temperature, illumination on the indicator that determines the physiological state of birds. In this paper, the weight of birds determined every day was considered as such an indicator. The aim of the work was to model the dependencies of changes in feed consumption, water, manure output, required air exchange, temperature and illumination on the increase in live weight of broilers throughout the production cycle. Modeling was carried out using the linear interpolation method based on regulatory, statistical and experimental data. The obtained models will allow developing a process control system at a poultry farm, providing for tracking changes in the physiological state of birds with the subsequent formation of control actions that ensure the adjustment of the production process in real time, due to which an increase in production efficiency will be ensured, as well as a reduction in production costs.