Swine finishing room air infiltration quantification-modelling and use in ventilation system design
Modern day swine production buildings used in the Midwestern region (severe winters and moderately hot summers) of the US are typically ventilated using negative pressure mechanical ventilation systems (VSs). The design of VSs for livestock buildings is not a simple task due to complexities of air flow behavior, varying outside climate, and environmental requirements of animals. In addition, air infiltration (AI)-an integral part of negative pressure VSs, makes the design process increasingly complex. Due to AI, VSs perform in a non-optimum manner and are responsible for the unsatisfactory environment for animals.
Optimal performance of VSs for livestock rooms (LRs), such as swine finishing rooms (SFRs), can be achieved by maintaining proper pressure difference (PD) across the rooms. Furthermore, during cold weather periods, design ventilation rates (DVRs) are minimum for LRs and, in some cases, dominated by AI rates of the room. Low required DVRs along with high potential AI rates makes it impossible to maintain a desired pressure difference (minimum 10 Pa) across a LR/SFR. In this study, the AI rates of Midwestern style SFRs were quantified, modelled, and a procedure was developed for active use of AI in the design of the VSs. The AI rates were quantified for the whole room and room components. The power law equations and multiple linear equations (MLR) were used for modelling the AI data.
The standard (sea level) total air infiltration (It) of Midwestern style SFRs was 5.96 Ã Â±1.49 ACH (air changes per hour) at 20 Pa, and at the same 20 Pa PD, the standard curtain, fan, and other (excluding curtain and fan AI) AI rates were 1.49 Ã Â±1.00 ACH (about 25% of It), 1.52 Ã Â±1.38 ACH (about 26% of It), and 2.90 Ã Â±1.42 ACH (about 49% of It), respectively. The MLR models developed in this study found superior over the power law equations and can be used to predict AI rates of similar SFRs. Furthermore, the novel design procedure (NDP) introduced in this study found useful for designing ventilation systems (VSs) of LRs/SFRs with active use of AI data and can be used in digital control of LRs/SFRs.