The temperature difference between day and night in a solar greenhouse is large. Heat in a greenhouse is typically in excess during the day while the temperature is low and the humidity is high at night. This study designs and tests an active heat storage and release air-source heat-pump system with a thermally insulated water tank as the energy storage body. By comparing air temperature and humidity in a test greenhouse with a control greenhouse in typical weather conditions, the power consumption and performance of the system are evaluated. The results show that compared with the control greenhouse, the average daytime temperature of the test greenhouse is lowered by about 3 °C during the operation of the system in typical weather conditions. At night, the average temperature is increased by about 4 °C, and the relative humidity is decreased by about 20%. When optimized, the maximum coefficient of performance (COP) of the system can reach 4.32 in heat storage mode. The nighttime heat release from the energy storage tank accounts for 26.9% to 51.2% of the nighttime energy consumption, and the energy utilization efficiency is 59.6% to 497.0%. This study provides a new way to control environmental parameters in solar greenhouses.

The progress of local environmental regulation in protected agriculture is sluggish, particularly concerning the local air supply, which poses a significant obstacle to greenhouse energy-saving research. This study establishes a test platform for local air supply in winter and summer by integrating design principles from human settlements’ supply air bag models with crop growth requirements. By utilizing a supply air bag to direct fresh air from the air conditioning system to specific areas within the greenhouse, non-uniform ventilation is created. Research has revealed that varying air supply levels in summer exerts a significant influence on environmental conditions, crop growth, and energy efficiency. Noticeable temperature stratification and cooling effects were observed within the conditioning greenhouse. The growth of lettuce was moderately enhanced, with mid-level local air supply demonstrating superior cooling effectiveness and range compared to the other two levels. Optimal control efficacy and energy conservation were achieved through mid-level local air supply. During daytime experiments in winter, this system did not have a significant impact on the greenhouse environment; however, during nighttime experiments, it consistently provided warming effects to maintain temperatures above the minimum requirement for lettuce growth. Therefore, utilizing air supply bags at secure specific positions and implementing targeted air supply methods within cultivation areas in greenhouses can facilitate the creation of suitable local environments for crop growth while achieving energy savings. Future research in this field could focus on further refining air supply bag models to enhance energy efficiency and local environmental control effects.

The thermophysical properties of a working-fluid play an important role in the process of stirring-heating. The heating process of stirring is accompanied by two processes: the friction between the solid mechanism and the working-fluid and the viscous dissipation of the working liquid. Traditionally, the sensible heat of water-based working-fluids is low, while that of oil-based working-fluids is higher, but the load capacity is relatively low. In order to find a balance between the two, an optimal stirring working-fluid should be selected. In this study, an experimental method was used to study the heating process of 30 kinds of working-fluids. The numerical evaluation model of the effects of thermophysical properties on the comprehensive evaluation index of heat (CEIH) was established by multiple linear regression methods, and a computational fluid dynamics (CFD) tool was used to analyze the heat generation and flow field of different working-fluids in the stirring-heating device. The results show that viscous dissipation is the most important way of stirring-heating. CFD can completely replace the experiment to study the heating effect of stirring. The thermophysical properties of the working-fluid affect the upper circulation and the overall velocity of the double circulation flow. The experimental results and regression model analysis show that specific heat capacity has the greatest effect on the heating effect, but density will also play a positive role in the stirring-heating. Water-based salt solutions such as KCl can achieve a better heating effect, and oil-based working-fluids are not always the best choice.

In the real world, there are many practical application problems that require simultaneously optimizing multiple conflicting objectives while satisfying certain constraints, which are called constrained multi-objective optimization problems (CMOPs). Accordingly, the key to solving CMOPs is to handle constraint satisfaction and objective optimization. Many scholars use the idea of divide and conquer in evolutionary multitasking to create auxiliary tasks to solve CMOPs, which is a very effective method. However, due to the different characteristics of different auxiliary tasks, it is obvious that they have different roles in solving CMOPs. Based on the above considerations, this paper proposes a constrained multi-objective evolutionary algorithm with an adaptive task transition framework to assist evolutionary multitasking (ATTCMO). In the adaptive task transition framework, the main population always performs a constrained multi-objective optimization task. The auxiliary population first performs an unconstrained multi-objective optimization task using genetic algorithm to help the population converge quickly and cross the infeasible regions. Moreover, it adaptively transitions the task into a multi-objective optimization task with constrained dynamic relaxation based on the stability of main population changes, and adopts differential evolution as the evolutionary operator, which helps to enrich the diversity of the population. ATTCMO is compared with nine most advanced constrained multi-objective evolutionary algorithms on 23 benchmark test problems and 6 real-world practical application problems, which validates the superior performance of ATTCMO.