Modeling and Optimal Control of Thermal Environment in Pig Houses

TL;DR

This paper develops a novel enthalpy-based thermal ventilation model for pig houses and employs model predictive control to optimize energy use while maintaining thermal comfort, validated with real-world data and simulations.

Contribution

It introduces a new thermal ventilation model integrating temperature and humidity control, and applies MPC for dynamic ventilation adjustment in pig housing environments.

Findings

The TVM accurately predicts temperature with low RMSE across seasons.

MPC improves environmental comfort zones significantly over traditional control.

Energy consumption is higher with MPC in summer due to comfort-energy trade-offs.

Abstract

The management of thermal environments in pig farming is crucial for optimizing animal health, productivity, and operational energy efficiency. This study introduces a novel thermal ventilation model (TVM) based on enthalpy balance, which integrates both temperature and humidity control to address the specific thermal regulation requirements of pig housing in regions characterized by high temperatures and humidity, such as Guangdong, China. These challenging environmental conditions can lead to heat stress in pigs, adversely affecting their health and productivity. The TVM provides a precise representation of thermal comfort by accounting for the combined effects of temperature and humidity. Building on the TVM, we formulate an optimization problem using Model Predictive Control (MPC), which dynamically adjusts ventilation rates in real-time by modifying weight factors to minimize energy consumption while keeping the temperature and humidity within the comfort zone of the pigs. The accuracy of the TVM is validated against real-world environmental data from pig housing facilities in Guangdong. The root mean square error of temperature in winter, spring and summer were 1.23, 0.81, and 0.60, demonstrating its reliability and robustness across diverse climatic conditions. Furthermore, simulation results show that the proposed MPC strategy significantly improves energy efficiency and environmental comfort, achieving a 100% comfort temperature zone in spring and 83% in summer, compared to 91% and 43% with traditional rule-based control, respectively. However, the model's energy consumption in summer (91.2 kWh) was higher than that of rule-based control (80.8 kWh), reflecting the trade-off between maintaining optimal comfort and energy efficiency under extreme conditions.