Fuzzy modeling applied to the welfare of poultry farms workers

Abstract

The objective of this work was to develop a fuzzy model to classify the working environment in poultry farms. For this purpose, air temperature, relative humidity, noise level, and ammonia concentration were measured in a broiler house with lateral positive-pressure ventilation. Work days consisting of 8 hours were simulated and the results provide support for classifying the level of comfort under different thermal, noise and gas concentration conditions. Therefore, three input variables were used: temperature-humidity index (THI), noise level (dB) and ammonia concentration (ppm), and the output variable was the work environment classification (WEC). Sixty rules were defined based on combinations of THI, noise level and ammonia concentration and each result is a function of the combination of input data. Experimental data was used to test the application of the proposed model. The results indicate that the proposed methodology is promising for determining the worker well-being level, and as an aid in making decisions regarding the control of the work environment in order to reduce or eliminate sources considered stressful to humans.