Long-term performance validation of NH3 concentration prediction model for virtual sensor application in livestock facility.

Livestock facilities commonly generate NH3, a hazardous substance that may also harm livestock. Therefore, monitoring of NH3 concentrations in livestock facilities is necessary to ensure proper control. However, NH3 is alkaline and toxic, causing corrosion inside detection sensors and making monitoring difficult. This study proposes a virtual sensor concept to complement the durability of NH3 physical sensors. The study also conducts a long-term performance validation of a data-driven NH3 concentration prediction model. Results indicate that the model's prediction performance declines sharply when the data generation pattern inside the livestock facility changes due to changes in outdoor conditions and facility operation. Furthermore, the prediction performance of the model differed depending on the training data period settings when updating the model. Hence, the model needs versioning and update management to respond to the data generation pattern in the livestock facility when operating the NH3 concentration virtual sensor. The virtual sensor is expected to enhance monitoring and reduce sensor management costs in livestock facilities.

Applicability evaluation of a demand-controlled ventilation system in livestock.

The distribution of agricultural and livestock products has been limited owing to the recent rapid population growth and the COVID-19 pandemic; this has led to an increase in the demand for food security. The livestock industry is interested in increasing the growth performance of livestock that has resulted in the need for a mechanical ventilation system that can create a comfortable indoor environment. In this study, the applicability of demand-controlled ventilation (DCV) to energy-efficient mechanical ventilation control in a pigsty was analyzed. To this end, an indoor temperature and CO2 concentration prediction model was developed, and the indoor environment and energy consumption behavior based on the application of DCV control were analyzed. As a result, when DCV control was applied, the energy consumption was smaller than that of the existing control method; however, when it was controlled in an hourly time step, the increase in indoor temperature was large, and several sections exceeded the maximum temperature. In addition, when it was controlled in 15-min time steps, the increase in indoor temperature and energy consumption decreased; however, it was not energy efficient on days with high-outdoor temperature and pig heat.