畜舍内循环除湿系统多目标优化调控算法研究

Multi-objective Optimal Control Algorithm of Internal Circulation Dehumidification System in Animal Houses

摘要:

针对寒区冬季密闭式畜舍内现有的温湿环境调控热量损失大、补温能耗高，以及仅依赖温度或湿度等单一指标调控而导致环境调控效果不佳等问题，基于课题组前期研发的寒区畜舍内循环除湿系统，提出一种改进的多目标优化算法（Improved non-dominated sorting genetic algorithm-Ⅱ，INSGA-Ⅱ）优化该系统保温、除湿和能耗的运行效果。INSGA-Ⅱ采用自适应差分变异算子和改进精英保留策略的方法，增加最优解的种群多样性，避免算法陷入局部收敛。通过Zitzler-Deb-Thiele（ZDT）系列测试函数验证，改进INSGA-Ⅱ算法的反向世代距离（Inverted generational distance，IGD）和世代距离（Generational distance，GD）指标都优于传统NSGA-Ⅱ算法，与真实解更为接近。内循环除湿系统优化调控性能数据表明，空间广泛性评价指标（Spacing，SP）从NSGA-Ⅱ的0.1118降低到INSGA-Ⅱ的0.0202，最优解分布域显著增加，可为系统运行的优化调控提供更为广泛的参考依据，同时求解速度提高106.42%。在除湿系统仿真调控效果方面，INSGA-Ⅱ算法调控平均降温比NSGA-Ⅱ算法减少1.43℃，降幅为23.06%，减少了由除湿带来的舍内降温，能够获得更好的保温节能效果。因此，改进INSGA-Ⅱ优化算法有助于提升畜舍内循环除湿系统工作性能，为畜舍温湿环境精准调控提供可行的技术支撑。

Abstract:

Currently, China’s livestock breeding industry is rapidly scaling towards intensification, making environmental control in livestock houses crucial. In cold northeastern winters, insulation used to prevent heat loss leads to high humidity levels, which harms livestock. Therefore, an efficient dehumidification system is essential to reduce heat loss and improve conditions. An internal air circulation dehumidification system was designed based on condensation and moisture separation, utilizing natural cold resources for effective dehumidification and energy savings. However, the system’s performance was influenced by several control factors, including the temperature difference between inside and outside, dehumidification airflow rate, and refrigerant flow rate, which affected multiple objectives such as indoor temperature decrease, dehumidification rate, and energy consumption. Thus, the system required a multi-objective control strategy. An improved non-dominated sorting genetic algorithm-Ⅱ (INSGA-Ⅱ) was proposed to optimize the balance between insulation,dehumidification, and energy preservation strategies, avoiding local convergence. Verified through the Zitzler-Deb-Thiele (ZDT) test functions, INSGA-Ⅱoutperformed the traditional NSGA-Ⅱ in both inverted generational distance (IGD) and generational distance (GD), reflecting a closer approximation to true solutions. In optimizing the dehumidification system, the INSGA-Ⅱ algorithm reduced the spacing (SP) value from 0.1118 in NSGA-Ⅱ to 0.0202,significantly expanding the optimal solution domain and preventing local optima. The operational efficiency was increased by 106.42%, and the solution speed was improved. In terms of temperature regulation, the average temperature drop using INSGA-Ⅱ was 1.43℃ lower than that of NSGA-Ⅱ, achieving a 23.06% reduction, which helped to reduce temperature fluctuations and energy consumption 〖JP3〗during dehumidification. Thus, the INSGA-Ⅱ algorithm effectively enhanced the multiobjective optimization and control performance of the internal circulation dehumidification system in livestock houses.