Real-time modelling of indoor particulate matter concentration in poultry houses using broiler activity and ventilation rate

Measuring particulate matter concentration in poultry houses remains as a difficult task, primarily because aerosol analysers are expensive, require specialist knowledge to operate and are labour intensive to maintain. However, it is well known that high concentrations of particulate matter causes health and welfare problems with livestock, farm workers and people living in the vicinity of the farm premises. In this work, a data-based mechanistic model is developed to relate broiler activity and ventilation rate with indoor particulate matter concentration. For six complete growing cycles, in a U.K. commercial poultry farm, broiler activity was monitored using a camera-based flock monitoring system (eYeNamic®) and ventilation rate was measured. Indoor particulate matter concentration was continuously monitored by measuring size-segregated mass fraction concentrations with the aerosol analyser DustTrak™. A discrete-time multi-input single-output time-invariant parameters Transfer Function model was developed to determine the particulate dynamics within each day of the growing cycle in the poultry house using broiler activity and ventilation rate as inputs. This model monitored indoor particulate matter concentration with an average accuracy of R T 2 = ( 51 ± 26 ) % . A dynamic linear regression modelling with time-variant parameters improved average accuracy with R T 2 = ( 97.7 ± 1.3 ) % . It forecasted one sample-ahead the indoor particulate matter concentration level, using a time window of 14 samples, with a mean relative prediction error, M R P E = ( 4.6 ± 3.2 ) % . Thus, dynamic modelling with time-variant parameters has the potential to be part of a control system to manage in real-time indoor particulate matter concentration in broiler houses.