Control system design and control strategy of multiple environmental factors in confined swine building

Abstract: Swine building environment plays an important role on the pig production, and it is a nonlinear, time-varying and delay system with multiple factors coupling with each other. Air temperature, humidity, harmful gases, airflow, light, dust and other factors in such an environment can affect the growth, development, reproduction of pigs. In the confined swine building, the indoor environment quality can deteriorate due to the high breeding density. The indoor air quality can be improved by appropriate ventilation control that supplies fresh air from outdoor and at the same time dissipates heat and moisture, and decreases concentrations of harmful gases. Most of the swine building environmental control systems are only based on a single environmental variable (temperature) using traditional control method. New systems based on multiple environmental variables and using precision mathematical models are needed to improve swine building environment controls. A multi-factor environmental control system with two controllers, based on the fuzzy control theory was established for the confined swine building in this paper. In this control system, a fuzzy controller was the nucleus part. A temperature fuzzy controller and a ventilation fuzzy controller were established with two input and one output variables, respectively, to achieve environmental control. In the air temperature fuzzy controller, air temperature difference and its variation rate were selected as two input variables, and a ventilation mode and a heating mode were chosen as output variables. In the ventilation fuzzy controller, differences of relative humidity and ammonia concentration were selected as input variables, and fan operation mode was selected as an output variable. To meet the requirements in different seasons, the input variables of the two controllers were first processed through fuzzification and fuzzy logic reasoning based on different control strategies and rules. Then the output variables were obtained after defuzzification processing. To solve the coupling problems between temperature and ventilation controls and optimize the control system, a dynamic temperature compensation coefficient was added. The method developed in this paper was validated using the data collected from a swine building. Different ventilation modes were simulated to get the relationship between the changing rates of air temperature, relative humidity, ammonia concentrations with fan operation time. Results showed that the maximum relative error of temperature was 5% compared with the setting value; and the indoor temperature control was achieved. The maximum relative error of relative humidity deviation from the setting value was 6.3%, which met the required relatively humidity control. The NH3 concentrations ranged from 2.0 to 3.7 mg/m3, which were less than the setting value of 9.1 mg/m3. Therefore, the fuzzy control system and strategy with multi-factor in this paper could be used to improve the swine building environmental control.