Greenhouse climate models serve as a basis for optimal management of greenhouse climate. However, a thoroughly tested mechanistic model of the Chinese solar greenhouse (CSG) climate that integratively simulates shortwave radiation, air temperature, humidity, and CO2 concentration is still lacking. This study developed such a full-scale CSG climate model for the standard CSG that describes the effects of outdoor weather, greenhouse structure, crop states, and greenhouse controls on the indoor climates. The model is structured into eleven submodules, incorporating new insights and expanded descriptions, such as the switch of condensation to deposition on the south roof, and shading effects of the north roof on the north wall. It depicts crop activities specifically targeting lettuce, and has been validated in CSG lettuce production scenarios involving two greenhouse structures and across both warm and cold seasons. The model demonstrated acceptable performance. Simulated CSG climates closely mirrored the measurements throughout the crop growth cycles, with the RRMSE being 12.6-23.0 % for shortwave radiation, 14.5-21.1 % for air temperature, 17.7-30.8 % for relative humidity, and 3.0-12.4 % for CO2 concentration predictions. Contributions of energy, water vapour, and CO2 fluxes to indoor climates were further explored. Additionally, layering was found necessary to describe temperature dynamics of the north wall and indoor soil with heat storage capacities. Ice layer formation and seasonal shading variations indicated that incorporating related processes could improve model completeness. The developed model can be used for model-based CSG climate control considering its acceptable accuracy, high computational efficiency, and robust generalisation.

Efficient year-round production of the Chinese solar greenhouse (CSG) requires integrated thermal management. Existing active heat storage-release systems perform well in greenhouse heating but lack cooling capability, limiting climate regulation and cost-effectiveness. This study proposes a system integrating greenhouse surplus heat storage-release and positive pressure ventilation for heating and cooling (SHVS). Centered around a combined air conditioning unit, the system collects surplus CSG air heat for nighttime heating during cold seasons via surface coolers with internal air circulation, and achieves cooling in warm seasons using evaporative wet pads under positive pressure ventilation. Field tests showed the system maintained a daily average heat collection rate of 36.3 W m- 2, peaking at 58.1 W m- 2, and delivered a daily heat release of 0.41 MJ m- 2. During operation, the average nighttime air temperature inside the CSG was 18.5 degrees C higher than outdoors, 4.4 degrees C above the unheated scenario. For greenhouse heating, the system achieved a coefficient of performance (COP) of 6.4 for heat collection and 4.8 across the entire heating process. In summer, the system provided 0.0158 m s-1 greenhouse ventilation and decreased the average CSG air temperature by 3.0 degrees C compared to natural ventilation, at a power consumption of 14.7 W m- 2. Its energy efficiency ratio (EER) for evaporative cooling reached 11.8. The system achieved an average cooling efficiency of 90.6%, with water consumption ranging from 0.040 to 0.047 g m- 2 s- 1. The proposed integrated system, which demonstrated good performance in greenhouse heating, cooling, and energy savings, facilitates sustainable CSG cultivation.

Accurate prediction of air temperature inside Chinese solar greenhouses (CSGs) is crucial for model-based optimization of greenhouse thermal parameters. However, existing data models primarily use environmental data as inputs and neglect effects of structural parameters, exhibiting limitations in model generalization and supporting greenhouse design. This study developed a model that captures the effects of both structural parameters (related to thermal insulation, passive and active energy storage, and light interception) and outdoor environment (including air temperature, solar radiation, solar altitude, and soil temperature) on the dynamics of CSG air temperature, using machine learning techniques. Specifically, fifteen algorithms, including Back Propagation, Convolutional Neural Network, Long Short-Term Memory (LSTM), Random Forest, eXtreme Gradient Boosting (XGBoost), and their hybrid models (ten in total), were explored. A detailed dataset was collected from ten different CSGs in Beijing during winter for model training and testing. All models exhibited robust predictive performance (R-2 >= 0.88, MAE <= 1.8 degrees C), in which the LSTM-XGBoost model performed best (R-2 >= 0.94, MAE <= 1.0 degrees C) and reduced the prediction error by up to 41% compared to the LSTM model. Then, a particle swarm optimization algorithm was employed to perform inverse predictions of the required heat storage capacity of the north wall or the water circulation system, to maintain target indoor temperatures under different thermal insulation parameters of the envelope. Based on the results generated, regression equations were formulated to quantify the relationship among the three variables, followed by an optimization trail of the thermal parameters considering investments. The developed model can be applied to different CSG structures and provide a basis for thermal parameter design.

Efficient regulation of supplementary lighting in greenhouse tomato production remains a challenge owing to the lack of quantitative decision-making methods and the strong coupling among environmental factors. To address this issue, this study proposes a yield-oriented LED supplementary lighting decision framework for greenhouse tomato production, which directly links the target yield to dynamic lighting regulation under natural light variability. Within this framework, the required annual light integral (ALI) corresponding to a target yield is first determined from the yield-light response relationship via data-driven estimation and marginal effect analysis. This ALI is then allocated to the daily light integral (DLI) at the tomato growth week scale based on dynamic light allocation (Dx), which simultaneously accounts for the stage-specific characteristics of tomato growth (Wy,x) and the phenological variations in natural light availability (Px). In the validation experiment, a target yield of 22 kg center dot m-2 center dot year-1 for cherry tomatoes was set according to actual production. The framework determined the required ALI of 6.92 kmol center dot m-2 center dot year-1 (toplighting) and 3.74 kmol center dot m-2 center dot year-1 (interlighting), which was then allocated to the weekly DLI ranging from 0 to 32 mol center dot m-2 center dot day-1 throughout the growing period. The results demonstrated that compared with the empirically derived supplementary lighting strategy, the strategy based on the proposed decision-making framework increased the yield target achievement rate of cherry tomatoes by 17.3%, enhanced light use efficiency (LUE) by 28.6%, and improved electrical energy use efficiency (EUE) by 40.7%. In addition, the framework incorporates uncertainty handling through error propagation and sensitivity analysis, ensuring stable decision performance under varying environmental conditions. Overall, this study provides a practical and robust decision-support approach for yield-oriented and energy-efficient supplementary lighting regulation in greenhouse production systems.

Multi-span greenhouses have been widely used in recent years.Conventional negative pressure fan-pad cooling has suffered from the low uniformity of indoor air temperature and the integrity of the cultivation area.Existing positive pressure ventilation with the air-handling corridor is also limited to low integration and difficult to control.In this study,a packaged positive pressure ventilation and cooling system was designed for highly efficient and quality crop production in the multi-span greenhouses during warm seasons.This system consisted of an equipment room,a combined air conditioning unit with three-sided air intake,ventilation ducts,and a control module.It can be further extended with heat sources and a water recirculating system,enabling integrated greenhouse climate conditioning based on positive pressure ventilation.The procedure of the air flows during cooling was as follows:The outdoor air entered the equipment room via outer vents,evaporative cooling in the air conditioning unit,and the air was conveyed into the greenhouse via underground ducts,while warm air was exhausted under roof vents.Field tests were conducted in Shouguang,Shandong Province,China.The results showed that the cooling system with the external shading screen maintained the daily mean air temperature between 28.4 and 32.5 ℃,which was 0.8 to 3.8 ℃lower than the outdoor temperature during the peak temperature hours(10:00-16:00)in summer.The vapor pressure deficit of the indoor air averaged 0.87-1.33 kPa during operation.The daily mean relative humidity ranged from 62％to 80％,17-29 percentage points higher than outdoors.The air was uniformly distributed over the cultivation area at the terminal air outlets.The airflow was also delivered at the velocities of 7.7-13.3 m/s,with the uniformity(standard deviation)of 1.9 m/s.In horizontal,the uniformity of air temperature reached 0.4 ℃ inside the greenhouse under the supply air condition.Vertically,the air temperature increased with height,with a temperature gradient of 0.76 ℃/m,and a 3.1 ℃ difference between the tomato canopy and the greenhouse roof.The high-pressure fogging system was installed inside the greenhouse.A three-dimensional cooling performance was achieved to reduce the vertical temperature gradient to 0.5 ℃/m.The designed specific ventilation rate of the greenhouse was 0.028 m/s for cooling purposes.The actual ventilation rate and system power consumption were 0.014 m/s and 15.2 W/m2,respectively,during the test.The good performance was achieved in the average cooling capacity of 144.2 W/m2 in the greenhouse,an energy efficiency ratio of 9.5,and an average indoor-outdoor air temperature difference of 2.1 ℃(08:30-17:30).The overall cooling efficiency reached 95.9％.The daily average water consumption rate ranged from 0.033 to 0.065 g/(m2·s)for evaporative cooling.Compared with the negative pressure fan-pad cooling system,the proposed packaged positive pressure ventilation and cooling system requires a lower specific ventilation rate to achieve the same greenhouse cooling amplitude,while exhibiting superior cooling uniformity and efficiency.In comparison with the air-handling corridor based positive pressure ventilation system,the proposed system offers a longer air supply distance,though with relatively higher energy consumption.This finding can provide an efficient mechanical ventilation solution for cooling multi-span greenhouses and support the design of semi-closed greenhouses.

Supplemental lighting is essential for overcoming low-light stress and enabling overwintering tomato production in greenhouses. This study investigated the effects of LED supplemental lighting with different spectral qualities in the upper and lower canopy on the fruit weight and quality of tomatoes. Six treatments were established: upper-red/lower-blue (RUBL), full red (R), full blue (B), upper-blue/lower-red (BURL), red-blue mixture (RB), and a non-lit control (CK). The results demonstrated that: (1) All supplemental lighting treatments increased tomato fruit weight. During the early overwintering stage (October-December), the highest fruit weight was observed under the RB treatment, representing an increase of 22.62-24.02% compared to CK at the same truss positions. The light gain coefficient (LGC) under RB treatment reached up to 4.41 times that of other treatments. During the later phase (January-February), the BURL treatment achieved the highest LGC, reaching 1.28 to 5.30 times that of other treatments, and it increased the fruit weight by 48.2-72.88% compared to CK. (2) Regarding fruit quality, R and BURL promoted lycopene accumulation the most, followed by RB treatment. Additionally, lycopene was found positively correlated with key color parameters (a, a*/b*, CCI, and C). (3) Compared to CK, all supplemental lighting treatments increased the soluble sugar content in tomato fruits (ranging 5.36 similar to 95.35%), with the highest sugar-acid ratios typically observed under R or BURL treatments. The RB treatment yielded the highest VC levels during the later overwintering stage, exceeding the control by 29.97-39.65%. In summary, for overwintering greenhouse tomato production, application of the RB treatment during the early phase (October to December) and transition to the BURL treatment in the late phase (January to February) could be considered. This phased strategy may help achieve synergistic improvements in yield, fruit coloration, and quality.

High energy consumption challenges the multi-span greenhouse industry in China. To address this, a greenhouse heating system utilizing energy transfer between greenhouses based on the dual source heat pump (ETGHP) was designed in our previous research. However, its performance in practical application remains largely unexplored. This study conducted a field test to comprehensively assess this system. Results showed stable heating effects, and the heat collection of the system in Chinese solar greenhouse (CSG) air source heating mode accounted for 2.1% to 28.2% of the total, validating the feasibility of energy transfer between greenhouses. The use of CSG air source increased heating capacity by 27% and coefficient of performance (COP) by 23% for air source heat pumps. Then the dual source configuration achieved a 10.8% increase in heat collection and a 7.9% improvement in COP compared with the single air source. During the test, the COP of the system achieved 2.8 during heat collection and 2.5 for heating the multi-span greenhouse. Outdoor weather, greenhouse structures and management were found to influence system operation. This study also conducted performance comparation and explored the economic and environmental benefits for the system, proving it to be an efficient solution for multi-span greenhouse heating.

Crop models serve as a basis for optimal management of greenhouse climate, while the current simulations for lettuce growth are incomplete. This study presents a lettuce growth model that describes the effects of a broad range of greenhouse climates, including air temperature with extreme conditions, humidity, CO2 concentration, and shortwave radiation on dynamics of the single state variable, structural crop dry weight. The proposed model framework performs two parallel sets of mass flows: dry matter accumulation and buffer evolution. The buffer carbohydrates flow to growth conversion based on the temperature-dependent sink strength. The inhibition of canopy assimilation occurs when the carbohydrate storage approaches the buffer capacity. The humidity effects are incorporated by describing stomatal resistance and specific leaf area of new leaves. The model was first calibrated at both sub-model and model levels and then validated against data collected in three experiments, covering a broad range of greenhouse climates. Results demonstrated that the model performance was good and acceptable; the simulated crop dry weights closely mirrored the measured values, with the RRMSE of 10.5-24.9% and the RMSE of 0.0070-0.0131 kg m- 2. The model predicted the leaf area index with an RRMSE of 12.1-54.7% and performed well for the vegetative growth stage concerned by commercial production. The photosynthesis inhibition time accounted for 27-41% of the total photosynthesis time, indicating that the model framework and underlying hypothesis worked in simulations. The developed model, simulating instantaneous lettuce dynamics for the potential situation, can be applied to low-tech greenhouses and enables optimal control of all four climate factors.

Predicting environmental factors within the indoor agriculture environment, namely greenhouses, is important as they play essential roles in improving yields and reducing energy consumption. In particular, predicting temperatures because plants are sensitive to extreme cold and overheating could lead to potential losses of the crops. Specifically, the Chinese Solar Greenhouses (CSG) are completely passive, meaning that it is only heated by the sun and has no controllable heating system. Therefore, it is important to create an accurate prediction model for predicting the temperate in the CSG several hours before. By predicting the CSG temperature, suitable actions could be taken, such as rolling up/down the covering material and opening/closing the top and the bottom side for adjusting the ventilation to avoid losses of the crops. In this paper, a Multilayer Perceptron (MLP) neural network with the Levenberg-Marquardt (LM) algorithm to optimise the structural parameters of MLP further improves the prediction accuracy. The proposed LM-MLP model is tested and evaluated based on two real datasets collected from CSG during warm and cold seasons. The obtained results illustrated the ability of the proposed LM-MLP to predict the maximum and minimum temperature during two different seasons (warm and cold) with more accuracy compared to some of the existing prediction models. The performance of the proposed method is compared with the standard NNs and SVM techniques.

To address the issues of excessive heat loss from the roofs of multi-span greenhouses and high energy consumption for heating during winter production, we propose an approach for the external insulation of the roof of multi-span glass greenhouses and have developed an external insulation system (EIS) to practice this approach. The system achieved full coverage of the greenhouse roof through mechanized unfurling and furling of external thermal blankets, thereby achieving energy-saving insulation. This paper describes the overall design and working method of the EIS, providing detailed design and structural parameters for critical components such as the traction rope transmission mechanism and the rail-type sealing structure. Through a system verification experiment, the specifications of the traction rope were determined and the rationality of the EIS’s thermal blanket unfurling and furling time was confirmed. An insulation performance experiment indicated that the average heat flux of the greenhouse roof covered with the external thermal blanket over 14 continuous nights was 54.2 W/m2, compared with 198.6 W/m2 for a single-layer glass roof. Covering the roof with the external thermal blanket reduced heat loss from the glass roof by 72.7%. The average heat flux of the roof of the Venlo-type multi-span greenhouse with double-layer internal insulation was 99.9 W/m2 during the same period, indicating that the heat loss from the roof using external insulation was only 50.3%. This study provides a novel thermal insulation approach and an energy-saving system for multi-span greenhouses.

为推动江苏响水地区露地青花菜高效、丰产和可持续发展,综合考虑青花菜种植规模、地理地形、种植面积和水源概况等,通过计算灌溉管网系统各项参数,优化设计了基于沟渠水源水肥一体化管网布局,并根据当地灌溉分区规划和灌溉施肥制度原则,构建了1套合理的水肥综合管理系统,可有效地提高劳动生产效率及水肥利用效率.相比传统农田灌溉施肥节水30%以上,节肥25%以上,增产15%左右,减少劳动投入80%,降低了人工管理费用,大大减少了灌溉不均匀的现象.

Abstract Understanding how plants respond to environmental conditions such as temperature, CO2, humidity, and light radiation is essential for plant growth. This paper proposes an Artificial Neural Network (ANN) model to predict plant response to environmental conditions to enhance crop production systems that improve plant performance and resource use efficiency (e.g. light, fertiliser and water) in a Chinese Solar Greenhouse. Comprehensive data collection has been conducted in a greenhouse environment to validate the proposed prediction model. Specifically, the data has been collected from the CSG in warm and cold weather. This paper confirms that CSG’s passive insulation and heating system was effective in providing adequate protection during the winter. In particular, the CSG average indoor temperature was 18 $$^{\circ }$$ ∘ C higher than the outdoor temperature. The difference in environmental conditions led to a yield of 320.8g per head in the winter after 60 growing days compared to 258.9g in the spring experiment after just 35 days. Three different architectures of Bayesian Neural Networks (BNN) models have been evaluated to predict plant response to environmental conditions. The results show that the BNN network is accurate in modelling and predicting crop performance.

Large-scale greenhouse can be expected to serve as the future direction in the horticulture industry. However,the multi-span greenhouses can consume a large amount of energy for heating in winter in northern China, resulting in low profitability and sustainability. In this study, a multi-span greenhouse was designed with large roofs and external insulation, in order to reduce the heat loss of the greenhouse roof. The external insulation system was innovatively applied to the multi-span greenhouse. The greenhouse design was expected to improve thermal insulation performance and reduce heating energy consumption. A field test was carried out in Shouguang, Shandong Province, China. Taking the Venlo-type multi-span greenhouse in the same area as a reference, a systematic investigation was made on the light and thermal environment, thermal insulation performance of the multi-span greenhouse with external insulation. The experimental data were analyzed from continuous 40 winter days. The results show that: 1) The average solar radiation was 152 W/m~2 above the crop canopy inside the tested greenhouse during the day（10: 00-16: 00）, and the total light transmittance was 40%, which was 7 percentage points higher than that of Venlo type multi-span greenhouse. The best daylighting was found in the middle of the greenhouse span, due to the influence of the gutter. The solar radiation intensity at the east and west of the greenhouse span and under the gutter was reduced by 17%, 29%, and 46%,respectively, compared with the middle. 2) There was the folded in turn for the external thermal blankets covering the east and west greenhouse roofs after the sun rose. Specifically, the indoor air temperature rose at 1.9 ℃/h from 09:30 to 12:00, which was 0.3 ℃/h slower than that of the Venlo-type one. However, the sudden drop in the air temperature of the multi-span greenhouse with the external insulation was reduced by 0.3℃ within 10 min after folding insulation devices. The tested greenhouse was heated by the internal air circulation, with the air coming out from the ground and then returning to the equipment room through the inner side windows. During the heating period（20:00-07:00）, the average temperature difference of indoor air in the horizontal direction did not exceed 1.2 ℃, without exceeding 1.0 ℃in the vertical direction. The uniform distribution was observed in the horizontal temperature of the multi-span greenhouse with the external insulation. The vertical temperature difference was smaller than that of the Venlo-type one.3) The average air temperature at nighttime inside the multi-span greenhouse with external insulation ranged from 13.1to 16.1 ℃, and the average temperature difference between indoor and outdoor air was 12.8-21.0℃. The average heat flux of the glass roof that was covered with the external thermal blanket was 50.0-97.7 W/m~2, while the single-layer glass roof was 217.6-367.9 W/m~2. The greenhouse covering with the external thermal blanket was reduced by 75% in the heat loss of the glass greenhouse roof. At the same time, the average heat flux was 141.1-232.2 W/m~2 in the Venlo-type one with double-layer indoor thermal screens in use. The roof heat loss of the multi-span greenhouse with the external insulation was reduced by 36%, indicating a better insulation performance. The mean heat energy input of the multi-span greenhouse with external insulation was measured to be 74.5 W/m~2 during the heating period, maintaining an average temperature difference between indoor and outdoor air of 17.4 ℃. Thus, the energy consumption of heating the multi-span greenhouse with the external insulation was low. Finally, the fitted influence of indoor and outdoor air temperature differences on the heat fluxes of greenhouse roofs was presented, and the tested greenhouse showed better goodness of fitting. This finding can provide a new type of greenhouse structure for the low-carbon and energy-saving production of multi-span greenhouses. A data basis can also be offered for the optimal design and engineering application of the multi-span greenhouse with external insulation.

Despite the steadily increasing area under protected agriculture there is a current lack of knowledge about the effects of the insect-proof screen (IPS) on microclimate and crop water requirements in arid and semi-arid regions. Field experiments were conducted in two crop cycles in Ningxia of Northwest China to study the impact of IPS on microclimate, reference evapotranspiration (ET0) and growth of Chinese Flowering Cabbage (CFC). The results showed that IPS could appreciably improve the microclimate of the CFC field in the two crop cycles. During the first crop cycle (C1), compared with no insect-proof screen (NIPS) treatment, the total solar radiation and daily wind speed under the IPS treatment were reduced by 5.73% and 88.73%. IPS increased the daily average air humidity, air, and soil temperature during C1 by 11.84%, 15.11% and 10.37%, respectively. Furthermore, the total solar radiation and daily wind speed under the IPS treatment during the second crop cycle (C2) were markedly decreased by 20.45% and 95.73%, respectively. During C2, the daily average air temperature and air humidity under the IPS treatment were increased slightly, whereas the daily average soil temperature was decreased by 4.84%. Compared with NIPS treatment, the ET0 under the IPS treatment during C1 and C2 was decreased by 6.52% and 21.20%, respectively, suggesting it had great water-saving potential when using IPS. The plant height, leaf number and leaf circumference of CFC under the IPS treatment were higher than those under the NIPS treatment. The yield under the IPS treatment was significantly increased by 36.00% and 108.92% in C1 and C2, respectively. Moreover, irrigation water use efficiency (IWUE) was significantly improved under the IPS treatment in the two crop cycles. Therefore, it is concluded that IPS can improve microclimate, reduce ET0, and increase crop yield and IWUE in arid and semi-arid areas of Northwest China.

Multi-span greenhouses consume enormous amounts of energy for heating in northern China, resulting in poor profitability and unsustainability. A greenhouse heating system, utilizing energy transfer between greenhouses based on a dual source heat pump, was designed to remedy this issue. The system collects surplus air heat inside Chinese solar greenhouses (CSGs) for heating multi-span greenhouses. Through enabling a greenhouse energy transfer in time and space, improved utilization efficiency of surplus air heat in CSGs is achievable, resulting in an overall reduction of heating costs. This study defines the heating approach and describes the overall system design. The dual source heat pump acts as the core component, with two separate evaporators placed in the CSG and ambient air. Calculations for system sizing are then presented, including a heating load model of multi-span greenhouses, a surplus air heat model of CSGs, the selection of required equipment (dual source heat pump, heat storage tank, and surface air cooler of the combined air conditioning unit), and the area matching. Finally, a case study illustrates the implementation processes of the heating system. The available CSG surplus air heat ranged 100.8-112.6 W m(-2) for system sizing, and the minimum area of CSGs was suggested to be twice the multi-span greenhouse area. The pilot test showed that the running status and heating effect of the system was stable. The coefficient of performance (COP) of the heat pump reached 4.3-4.8 when using CSG surplus air heat as the heat source, performing 23-26 % higher than when using ambient air over the same periods. Throughout the entire course of heat collection, dual source heat pumps, switching sources based on their setting, achieved a total COP of 3.4-4.2, increased by 6-11 % compared with air source heat pumps. This study provides a novel heating approach and an energy-saving system for multi-span greenhouses.

为降低北方地区连栋温室冬季生产能耗、提高温室保温性能,设计了大斜面外保温连栋玻璃温室,即寿光型智能玻璃温室.该温室采用大天沟设计,安装了外保温被及传动机构,因此形成了较宽的遮阴带,影响了栽培区的太阳辐射及温室透光率.为分析天沟尺寸对室内光环境的影响,构建了连栋温室天沟对温室栽培区内不同位置辐射强度影响的动态模型,并基于该模型对室内光环境进行了均匀性与敏感性分析.结果 表明:天沟结构对栽培区内日累积辐射平均值的影响程度从大到小依次为天沟间距、天沟宽度、天沟垂直厚度和天沟高度;寿光型智能玻璃温室的天沟设计为相邻两天沟间距12.00m、天沟水平宽度1.60m、垂直厚度0.86m、天沟下沿离地面高度6.30m,可以保证栽培区内最佳的光照均匀性.不同情景下的模型模拟结果表明,为确保栽培区内光照均匀性,在栽培区内辐射强度变异系数最小的情况下,山东省寿光地区温室天沟高度、天沟垂直厚度之和与天沟间距、天沟宽度之和的比值在0.49 ～0.54之间.本研究可为寿光型智能玻璃温室在不同地区的设计应用提供理论依据.

Solar greenhouses have been used for producing vegetables in northern China during early spring, late autumn or over-winter. To improve the thermal performance of solar greenhouses, a traditional type and a retrofitted design were comparatively evaluated. In the retrofitted design, three adjustments were incorporated: the material and structure of the walls, south-facing roof angle, and structure of the north-facing back-roof. The results indicated that the thermal and light performance of the retrofitted greenhouse was much better than that of the traditional greenhouse. Specifically, the daily mean temperature, minimum air temperature, and soil temperature inside the greenhouses after retrofit ting were increased by 1.3, 2.4, and 1.9°C, respectively, meanwhile, the daily total solar radiation and PAR were increased by 28.2% and 9.2%, respectively. The wall temperature and its daily variation range were reduced with increasing depth and height. The characteristic analysis of heat storage and release indicated that higher locations have longer heat storage, and shorter heat release time in vertical direction, as well as a lower ratio of heat release to storage. In horizontal direction, the western wall has the shortest heat storage time but the highest heat release flux density. Altogether, the heat storage time of the wall is 1.5 h less than that of the soil. The heat storage flux density of the wall is 1.5 times of that of the soil, but the heat release flux is only 61% of the soil’s value. The total wall heat storage is half of that of the soil in the greenhouse; the total wall heat release amount is only a quarter of that of the soil. Therefore, the thermal environment of solar greenhouses can be further improved by improving the thermal insulation properties of the wall. Keywords: structural change, heat storage performance, heat flux, back wall, thermal environment DOI: 10.25165/j.ijabe.20191201.3829 Citation: Xu F, Shang C, Li H L, Xue X Z, Sun W T, Chen H, et al. Comparison of thermal and light performance in two typical Chinese solar greenhouse in Beijing. Int J Agric & Biol Eng, 2019; 12(1): 24–32.

Year-round and efficient production for crop products of high yield, quality and cleanliness is the development trend of the Chinese solar greenhouse (CSG). However, this is limited by unfavorable climate conditions inside the CSG, such as high air temperature in warm seasons. The fan-pad cooling system, normally adopting negative pressure ventilation, has been widely used for greenhouse cultivation. But it generates a large air temperature gradient in greenhouse, limits the greenhouse dimensions. Above deficiencies are more serious in the CSG. Because CSG always has a long distance between the sidewalls, fans and gaskets are installed separately on the sidewalls. In order to overcome the limitations of negative fan-pad cooling system and improve ability of the CSG in coping with high temperature, a positive pressure fan-pad cooling system (PPFPCS) was designed in this study. By using this system, the cold and humid air enters the CSG from bottom of south roof, and then hot air leaves the CSG through roof vents. Performance of the PPFPCS was tested in a CSG without crops in Beijing area during summer. Results showed that in typical summer hot days, the PPFPCS cooperating with external shading net could decrease mean air temperature of the CSG experimental area to 30.7-33.4 ℃, which was lower than that in the CSG contrast area using natural ventilation combination with external shading net by 5.4-11.1 ℃. Air temperature of the CSG experimental area was also lower than that outside the CSG with a temperature difference of 2.4-5.4 ℃. Nevertheless, both natural and mechanical ventilations were tested to have limited cooling capacity to meet climate requirement for CSG cultivation. The PPFPCS could also decrease the CSG air temperature at night, but had a poorer performance in comparison with daytime cooling due to the smaller vapor pressure deficit (VPD). The contrast area of CSG encountered an extreme low air humidity state with mean VPD of 3.4-6.1 kPa. PPFPCS could effectively alleviate low humidity stress: the average relative humidity in CSG experimental area was between 49.8% and 62.3%, which was 13.6% - 21.2% higher than that in CSG control area and 13.6%-24.6% higher than that in outdoor area. Wind velocity inside the CSG experimental area ranged from 0.35 to 1 m/s, which indicated a relative uniform air flow distribution. Cooling efficiency of the PPFPCS was about 91%, which was over 10 percentage points higher than that of the traditional negative pressure fan-pad cooling system. Low temperature of the PPFPCS circling water contributed to the high cooling efficiency. Average water consumption rate of the PPFPCS used for CSG cooling was 0.035-0.079 g/(m2·s) during the test. It had a positive linear correlation with VPD of outdoor air, that is drier outdoor air anticipates larger water consumption and better cooling performance. Both cooling load model of the CSG and selection method for fan-pad cooling system were derived. Cooling load model is the basis for capacity calculation of cooling equipment to be installed. Cooling load of the CSG in summer was 299.1 W/m2. Contribution ratios of convective heat transfer between north wall and indoor air, convective heat transfer between greenhouse floor with indoor air, hot air infiltration, as well as heat transfer between indoor and outdoor air though south roof, north roof and side walls were 11.0%, 73.3%, 1.3% and 14.4%, respectively. The maximum specific ventilation rate of the PPFPCS used for CSG cooling was recommended to be 0.067 m/s. This study can provide technical support for the application of PPFPCS in CSG cultivation and provide theoretical basis for the climate control of CSG production in summer.

利用北京市西城区某地下停车场,通过配备环境调控系统、LED人工光源系统、营养液循环系统、叶菜立体栽培系统、食用菌与芽苗菜栽培系统、计算机智能控制系统等,建立了混合型植物工厂,具有低碳节能的优势,可为城市地下空间植物工厂的设计、施工及商业化发展提供参考.