Applicability evaluation of a demand-controlled ventilation system in livestock

Hakjong Shin; Younghoon Kwak; Seng-Kyoun Jo; Se-Han Kim; Jung-Ho Huh

doi:10.1016/j.compag.2022.106907

Applicability evaluation of a demand-controlled ventilation system in livestock

Comput Electron Agric. 2022 May:196:106907. doi: 10.1016/j.compag.2022.106907. Epub 2022 Mar 29.

Authors

Hakjong Shin¹, Younghoon Kwak², Seng-Kyoun Jo³, Se-Han Kim³, Jung-Ho Huh²

Affiliations

¹ Department of Architectural Engineering, University of Seoul, Seoul, South Korea.
² Department of Architecture, University of Seoul, Seoul, South Korea.
³ Industry and IoT Intelligence Research Department, Intelligent Convergence Research Laboratory, Electronics and Telecommunications Research Institute, Daejeon, South Korea.

Abstract

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.

Keywords: M ¯ , mean measured value [kWh]; Q design , design fan power [W]; Q tot , fan power [W]; f flow , flow fraction [0–1]; f pl , part-load-factor [0–1]; m design , design mass flow [m3/s]; ACH, Air changes per hour; ANFIS, Adaptive neuro fuzzy inference system; ASHRAE, American Society of Heating, Refrigerating and Air-Conditioning Engineers; BES, Building Energy Simulation; CFD, Computational fluid dynamics; CO2 concentration; CVRMSE, Coefficient of variance of the root mean square error; DCV, Demand controlled ventilation; Demand-controlled ventilation (DCV); EBE, Energy balance equation; HVAC, Heating, ventilation, and air conditioning; Indoor air temperature; Livestock facility; M, measured value [kWh]; MBE, Mean bias error; Mechanical ventilation; PLF, Part-load-factor; S, simulated value [kWh; SSE, Sum-of-squared error; VFD, Variable frequency drive; m, mass flow [m3/s]; n, number of data.