Optimal Air Temperature Research Articles

The association of origin and environmental conditions with performance in professional IRONMAN triathletes

We have (i) little knowledge about where the fastest professional IRONMAN triathletes originate from and where the fastest races take place and (ii) we have no knowledge of the optimal weather conditions for an IRONMAN triathlon. The aims of the present study were, therefore, (i) to investigate the origin and the fastest IRONMAN race courses for professional triathletes and (ii) to evaluate the best environmental conditions (i.e. water and air temperatures and type of race course) for the fastest IRONMAN race times in professional IRONMAN triathletes. Data of all professional female and male IRONMAN triathletes competing between 2002 and 2022 in all IRONMAN races held worldwide were collected. A total of 6,943 finishers´ records (4,162 from men and 2,781 from women) from 58 different countries participating in 54 different event locations between 2002 and 2022 were considered. Data was analyzed using descriptive statistics and machine learning (ML) regression models. The models considered gender, country of origin, event location, water, and air temperature as independent variables to predict the final race time. Three different ML models were built and evaluated, based on three algorithms, in order of growing complexity and predictive power: Decision Tree Regressor, Random Forest Regressor, and XG Boost Regressor. Most of the athletes originated from the USA (1786), followed by athletes from Germany (674), Canada (426), Australia (396), United Kingdom (342), France (325), and Switzerland (276). Most of the athletes competed in IRONMAN Hawaii (925), IRONMAN Florida (563), IRONMAN Austria (452), IRONMAN France (354), IRONMAN Wisconsin (330), IRONMAN Lanzarote (322) and IRONMAN Texas (313). The Decision Tree and the XG Boost models were the best performing models (r2 = 0.48) and rated the relative feature importances in the order gender, country of origin, water temperature, air temperature and event location. Men were on average ~ 0.8 h faster than women. Switzerland had the fastest and Japan and Slovakia the slowest athletes. IRONMAN Brazil Florianopolis, IRONMAN Barcelona, and IRONMAN Louisville hold the fastest races. Optimal water temperature was over 22 °C and optimal air temperature between 19 and 26 °C. Between 2002 and 2022, most professional IRONMAN triathletes originated from the USA, and most professional IRONMAN triathletes competed in IRONMAN Hawaii. The fastest athletes originated from Switzerland, the fastest race courses were IRONMAN Brazil Florianopolis, IRONMAN Barcelona, and IRONMAN Louisville. The fastest race times were achieved in water temperature warmer than 22 °C and air temperature between 19 and 26 °C.

Modern Transformation in Agriculture for Onion Watering Automation with Solar Cell and IOT Technology

Shallots are a key agricultural commodity in Indonesia, requiring careful water management for optimal growth. Excessive water can cause rot in shallot seedlings, while insufficient water leads to wilting and yellowing leaves, affecting crop quality. This research designs an IoT-based automatic monitoring and watering system to address these challenges. The system includes a soil moisture sensor, a DHT22 sensor for air temperature and humidity, an ESP32 microcontroller, a water pump, and a solar panel as its energy source, controllable via an Android application. The system maintains ideal soil moisture for each growth stage: 80-90% during germination, 60-70% for the vegetative stage, and 50-60% during the generative stage, with optimal air temperatures of 25-30°C. Real-time data from sensors are processed by the ESP32, which activates the water pump via a DC relay when soil moisture falls below the threshold. Testing shows the system operates continuously, with solar panels charging a VRLA battery daily to sustain nighttime operation. Battery voltage fluctuates between 12.5 V and 15.0 V, maintaining a charge above 12.0 V after sunset. To ensure reliability in extreme weather, the system employs waterproof enclosures for all components and uses sensors calibrated to maintain accuracy in saturated soils. An MPPT charge controller optimizes solar energy usage during low sunlight, while selected components operate in a wide temperature range, ensuring extreme heat or cold performance. This system improves water efficiency, reduces manual labor, and offers an environmentally friendly solution for shallot cultivation, particularly in remote areas without conventional power access. Its resilience to environmental challenges enhances productivity and supports sustainable agricultural practices.

Optimization of Spray Drying Parameters and Wall Material Composition of Juniper (Juniperus drupacea L.) Extract Using Response Surface Methodology

The aim of this study was to produce a juniper extract powder rich in some bioactive and volatile components such as phenolics, α-pinene and d-limonene with a high yield. For this purpose, the juniper extract, which can be used in various food formulations, was spray-dried under optimized conditions. In this optimization, inlet air temperature (120°C - 180°C) in the spray drying process and the carrier composition were selected as independent variables, while dependent variables included drying efficiency and the total phenolic (TPC), and α-pinene contents of the extract powder. Response surface methodology was used to maximize product yield, TPC and volatile levels, especially α-pinene. The optimum inlet air temperature and carrier ratio were 180ºC and 15 g gum Arabic (GA) per 100 mL extract, respectively. The highest powder yield (37.92%), TPC (9.91 mg GAE/g dm powder) and α-pinene content (peak area 1.3×107) were obtained under the optimum conditions while the bulk and compressed bulk densities, TPC and antioxidant activity of the extract powder were 0.39±0.01 g/cm3 and 0.51±0.02 g/cm3, 9.89±0.27 g gallic acid equivalent (GAE)/100 g (dm) and 4.12±0.14 g Trolox® equivalent antioxidant activity (TEAC)/100 g dm, respectively. The particle size of the powder produced under optimum conditions ranged from 1.09 to 22.39 µm. Fifteen volatiles in both juniper extract and the reconstituted form of the extract powder were identified, and the major components of juniper extract were d-limonene, α-pinene and γ-muurolene.

A new personalized environment control system for hospital beds with design optimization by Taguchi-based grey relational analysis

The current ward environments consume excessive energy and fail to meet the personal comfort and health needs of patients. A promising solution is to create a personalized bed micro-environment and then extend the ward set-point temperature range. However, there is currently no suitable bed environment control system. This study proposes a novel bedside integrated system with three perforated panels that supplies conditioned air from different directions and prevents direct airflow towards the patient's head region. The system design was optimized using Taguchi-based grey relational analysis (GRA), with predicted mean vote (PMV), draft risk (DR), and personal exposure effectiveness (PEE) considered as response variables. The design variables included supply air temperature, airflow rate, and supply air angles. Taguchi's L16 (44) orthogonal array was employed for the experimental design. The results demonstrate that a low-velocity cold air lake can form above the bed, with the maximum velocity near the patient's head at only 0.2 m/s. In a 28 °C ward, the PMV, maximum DR, and PEE at the bed micro-environment are 0.13, 14.1 %, and 0.67, respectively. This implies that the proposed bed environment control system has the potential to provide both comfort and health benefits while reducing energy consumption. After optimization, the optimal supply air temperature, airflow rate, angles of top panel and side panels are 22 °C, 25 L/s, 0° and 45°, respectively, with an improvement of 5.8 % in the grey relational grade. This study provides a new solution for creating a comfortable and healthy ward environment in an energy-efficient manner.

Improving global gross primary productivity estimation using two-leaf light use efficiency model by considering various environmental factors via machine learning

Distinguishing gross primary productivity (GPP) into sunlit (GPPsu) and shaded (GPPsh) components is critical for understanding the carbon exchange between the atmosphere and terrestrial ecosystems under climate change. Recently, the two-leaf light use efficiency (TL-LUE) model has proven effective for simulating global GPPsu and GPPsh. However, no known physical method has focused on integrating the overall constraint of intricate environmental factors on photosynthetic capability, and seasonal differences in the foliage clumping index (CI), which most likely influences GPP estimation in LUE models. Here, we propose the TL-CRF model, which uses the random forest technique to integrate various environmental variables, particularly for terrestrial water storage (TWS), into the TL-LUE model. Moreover, we consider seasonal differences in CI at a global scale. Based on 267 global eddy covariance flux sites, we explored the functional response of vegetation photosynthesis to key environmental factors, and trained and evaluated the TL-CRF model. The TL-CRF model was then used to simulate global eight-day GPP, GPPsu, and GPPsh from 2002 to 2020. The results show that the relative prediction error of environmental stress factors on the maximum LUE is reduced by approximately 52 % when these factors are integrated via the RF model. Thus the accuracy of global GPP estimation (R2 = 0.87, RMSE = 0.94 g C m−2 d−1, MAE = 0.61 g C m−2 d−1) in the TL-CRF model is greater than that (R2 = 0.76, RMSE = 2.18 g C m−2 d−1, MAE = 1.50 g C m−2 d−1) in the TL-LUE model, although this accuracy awaits further investigation among the released GPP products. TWS exerts the greatest control over ecosystem photosynthesis intensity, making it a suitable water indicator. Furthermore, the results confirm an optimal minimum air temperature for photosynthesis. Overall, these findings indicate a promising method for producing a new global GPP dataset, advancing our understanding of the dynamics and interactions between photosynthesis and environmental factors.

Experimental and simulation study of the effect of temperature and air supply on energy performance of a temperature-controlled ventilated double-layer Trombe wall

To study the energy performance of the temperature-controlled ventilated double-layer Trombe wall system (TCVD-TW) during winter and summer seasons, experiments and simulations were conducted in an experimental room in Qingdao where the system was installed. The experimental results demonstrate that the rooms equipped with this system exhibit favourable temperature performance in both winter and summer, with a positive impact on indoor humidity during the summer. The simulation results show that the performance of TCVD-TW is mainly affected by the supply air temperature, air volume flow rate, and indoor cooling and heating setpoints. For winter operating mode, the optimal supply air temperature setting should be 1°C higher than the heating setpoint. When the supply air temperature was set to 19℃(with a heating setpoint of 18 °C) and 21 °C (with a heating setpoint of 20 °C), the energy efficiency increased by 35.96 % and 32.38 %, respectively. When the supply air volume flow rate was set to 234 m3/h, the energy efficiency increased by 35.09 % (with a heating setpoint of 18 °C) and 30.27 % (with a heating setpoint of 20 °C), respectively. For summer operating mode, the optimal supply air temperature setting should be 1 °C lower than the cooling setpoint. When the supply air temperature was set to 25 °C (with a cooling setpoint of 26 °C), the energy efficiency improved by 33.62 %. When the supply air volume flow rate was set to 156 m3/h, the energy efficiency increased by 31.18 %. The experimental and simulation results of this study provide data support for the application of TCVD-TW.

Large-scale energy cost optimization and performance analysis for dedicated outdoor air system: simulation results from ASHRAE RP-1865

This is the first journal article from the ASHRAE research project RP 1865, “Optimizing Supply Air Temperature Control for Dedicated Outdoor Air Systems,” focusing on the large-scale energy performance evaluation of the optimal control of dedicated outdoor air systems (DOAS). DOAS is a type of air handling system that is installed in buildings to ensure proper ventilation for the built environment. It is acknowledged that DOAS with proper operation strategies can provide sufficient ventilation and dehumidification while achieving energy efficiency. In this regard, there have been efforts to develop advanced control sequences (e.g., optimal control) for DOAS. Still, limited studies have demonstrated the effectiveness of such DOAS controls on a larger scale under varying climate zones with different DOAS configurations. To fill the research gap, this study aims to quantify the energy-saving potentials of applying an optimal supply air temperature (SAT) control strategy for DOAS considering diverse configurations of HVAC systems in various climate conditions across the United States. For this study, we first developed a total of 180 EnergyPlus models accommodating different types of buildings (i.e., detailed medium-sized office and primary school) and HVAC systems (i.e., DOAS types, DOAS sizes, and terminal units) under varying ASHRAE climate zones (i.e., 2A, 3B, 4A, 4C, 6A, and 6B). Next, genetic algorithm (GA)-based optimizations were conducted to determine the optimal DOAS SAT control that would minimize the energy cost of the entire HVAC system operation. Our study of comparing the optimal control to a typical rule-based control (i.e., outdoor air temperature-based reset control) revealed approximately 4–34% of energy cost saving potential by optimizing the DOAS SAT control sequence.

Site-specific apparent optimum air temperature for vegetation photosynthesis across the globe

The apparent optimum air temperature for vegetation photosynthesis (Topt) is a key temperature parameter in terrestrial ecosystem models estimating daily photosynthesis or gross primary production (GPP, g C/m2/day). To date, most models use biome-specific Topt (Topt-biome) parameter values. Given vegetation acclimation and adaptation to local climate, site-specific Topt (Topt-site) is needed to reduce uncertainties in estimating daily GPP across the scales from site to region and the globe. Previous studies have demonstrated using the Enhanced Vegetation Index (EVI) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) images and daytime air temperature data to estimate the Topt-site at the eddy covariance tower sites. This study used MODIS-derived EVI and ERA5 climate data to estimate and generate global Topt-site data products from 2000 to 2019. The Topt-site of individual pixels within a biome has large variation, which clearly cannot be represented accurately by the widely used Topt-biome. Therefore, using this global dataset of Topt-site estimates might significantly affect GPP simulation in current ecosystem models.

Multi-objective ventilation optimization for indoor air quality, thermal comfort, and energy conservation in the post-pandemic era: A case study for a moving elevator

Cases of respiratory disease transmission in enclosed elevators have been reported frequently. In the post-pandemic era, in order to mitigate the spread of respiratory diseases in moving elevators, a multi-objective genetic optimization method based on a response surface model is used to optimize the elevator ventilation. The ventilation parameters were optimized for three objectives: reducing carbon dioxide concentration, maintaining human thermal comfort, and achieving energy conservation. First, a response surface model is established using the computational fluid dynamics method and the Kriging model to correlate the design variables (air supply velocity in x, y, and z directions and air supply temperature) with the output function (CO2 concentration, average temperature, and average velocity). Subsequently, the Pareto optimal solution set of ventilation parameters was obtained by employing a multi-objective genetic algorithm. Finally, the optimal air supply velocity, angle, and temperature were obtained for both peak periods of elevator traffic (13 passengers) and other situations (4 passengers) when the elevator is moving up and down, which satisfy the objectives of health, comfort, and energy conservation.

Grey-box model-based demand side management for rooftop PV and air conditioning systems in public buildings using PSO algorithm

As the major power consumer in buildings, Heating, ventilation and air conditioning (HVAC) systems can significantly contribute to the economical operation of the power system during the peak periods in summer or winter, by flexibly responding to the grid demands. Indoor air temperature set-point resetting is a typical passive demand response (DR) control strategy that have potentials to be widely implemented by inverter air conditioners in residential buildings. However, in the existing small and medium-size public buildings, the compressor motor of HVAC systems mostly does not allow for stepless speed changes. This means that the indoor temperature set-point resetting strategy may results in frequent on/off switching of the chiller plant, leading to a compromised lifespan. Although rooftop photovoltaic systems (PVs) are expected to be widely adopted in new buildings, their potential for peak load shaving or valley load filling has received limited investigation. This study proposes a grey-box model-based demand side management (DSM) method for variable air volume systems (VAVs) and rooftop PVs in public buildings. The DSM method utilizes the particle swarm optimization (PSO) algorithm to determine optimal indoor air temperature and/or evaporator outlet water temperature set-point schedules for the VAVs, as well as optimal charging/discharging schedules for the storage battery of rooftop PVs. This approach aims to reduce the peak power demands of the integrated system without causing severe thermal discomfort, increasing chiller mechanical costs (refers to frequent on/off switching of the chiller plant in this paper), or overcharging/over-discharging of storage batteries. The results of case studies demonstrate that the strategy of co-resetting indoor air temperature – evaporator outlet water temperature set-points outperforms the indoor air temperature set-point resetting strategy in terms of both peak power demands reduction and mechanical cost saving when applied to the VAVs in low performance buildings. In comparison, for the VAVs installed in normal buildings, both the strategies yield significant decreases in peak power demands and mechanical cost by fully utilizing the thermal capacity of the building internal thermal mass. Furthermore, by implementing the DSM method for rooftop PVs, the integrated system can achieve zero or near-zero net power demands during the DR periods. The proposed DSM method can also be utilized in valley load filling programs to facilitate the accommodation of renewable energy.

The effect of tree shading on soil temperature at Arabica coffee plantation (Coffea arabica L.)

Coffee is a group of plants that require partial light (C3), whose cultivation combines the use of shade trees and cultivated plants with the aim of increasing production and maintaining the microclimate around the cultivated plants. The shade provided by shade trees will have a significant effect on soil temperature and water content. The growth and development of a cultivated plant is strongly influenced by water content, on the other hand, water content also affects soil temperature. The optimal air temperature needed by Arabica coffee plants is 18-21°C, while the soil temperature suitable for Arabica coffee plants is 21°C. The research was needed to determine the effect of shade tree type on the soil temperature of Arabica coffee plants. This research was at the Koperasi Ribang Gayo Musara, Pantan Musara Village, Pegasing Subdistrict, Central Aceh District, Aceh Province. This research used a non-factorial randomized block design with five treatments and four replications. Treatments consisted of shade tree type on coffee plants were control/no shade, small lamtoro shade (Leucaena leucocephala ssp. leucocephala), big lamtoro shade (Leucaena leucocephala subsp. glabrata), banana shade (Musa paradisiaca L.), avocado shade (Persea americana Mill.). The results showed that the shade tree type had a significant effect on soil temperature at a depth of 0, 10, and 20 cm. Coffee plants that were shaded by avocado trees have better temperatures for coffee plants and can reduce soil temperature until they reach optimal temperatures.

Optimal supply air temperature with respect to data center operational stability and energy efficiency in a row-based cooling system under fault conditions

This is the first study of data center row-based cooling strategies under fault conditions. An optimal supply air temperature (SAT) for row-based cooling is proposed to ensure energy efficiency and operational reliability. The appropriate SAT for emergency operation is determined by evaluating how long the allowable IT equipment's inlet air temperature (IAT) can be maintained by only running the fan of the computer room air handling (CRAH) unit when the chilled water supply is interrupted. Three cases with SAT values of 20 °C, 22 °C, and 24 °C are evaluated in a numerical analysis, and the maintenance potential of the IAT is assessed by analyzing 600 s of transients from the time of cooling failure. In all cases, the IAT is almost the same as the SAT under normal cooling, but in cooling failure, the time to reach downtime beyond the allowable IAT is 246s in Case 1, 133s in Case 2, and 85s in Case 3. Response strategies to cooling failure include increasing uninterruptible power supply (UPS) capacity and applying a thermal buffer tank. The study concludes that it is reasonable to set the SAT of row-based cooling to 22 °C or lower and to install a thermal buffer tank in the chilled water system for effective downtime delay. These results are based on a rack power density of 7 kW/rack.

A new method for the production of particles with heat-sensitive nutrients by combining low-temperature closed-loop spray drying and natural plant carriers

This work aimed to reduce the loss of thermally unstable nutrients during the powder production process using closed-loop spray drying using an innovative carrier, coconut husk powder. β-carotene was selected as the subject of this study because it decomposes easily, but it is essential for the human body. The use of coconut husk powder as the carrier achieved an excellent powder recovery rate (RR) and β-carotene concentration compared with whey protein isolate, maltodextrin, cellulose, pectin, casein, and other commonly used carriers. The optimal outlet air temperature for spray drying was also found to be 50-55 °C. Coconut husk powder showed an excellent powder recovery rate (84%) and concentration (0.32 mg/g), which is high compared with the other carriers, because of its multi-layered spherical structure. The coconut powder also increased the stability of β-carotene. After being exposed to a temperature of 85 °C for 6 h, coconut husk powder was more resistant to high temperatures than maltodextrin.

The Influence of Latitude on the Climatic Characteristics of Classroom in Campus 1 and Campus 2 of Khairun University in Achieving Adaptive Thermal Comfort

Microclimate variables are important factors that affect adaptive thermal comfort. These factors include air temperature, solar radiation, air humidity, and wind speed. This study focuses on discussing microclimate variables, specifically air temperature and air humidity. This is based on previous research indicating that the most influential climate variables are air temperature, solar radiation, and air humidity. This study is a field research aimed at comparing air temperature and air humidity between campus 1 and campus 2 of Khairun University, due to differences in geographical elevation. Measurements were taken in March. The variables of air temperature and air humidity were measured inside the building. The research results show significant differences between campus 1 and campus 2. These differences can be associated with the perceived adaptive thermal comfort by the users. The thermal sensation results direct that the majority of users in campus 2 feel cool, while the majority of users in campus 1 feel comfortable or neutral. These findings provide a strong basis for improving adaptive thermal comfort in both campuses by considering optimal air temperature and air humidity settings.

Optimization-informed Rule Extraction for HVAC system: A Case Study of Dedicated Outdoor Air System Control in a Mixed-Humid Climate Zone.

In the era of post-Coronavirus Disease 2019, the dedicated outdoor air system (DOAS), which provides 100% outdoor air for the building, is widely acknowledged as it can ensure acceptable indoor air quality by delivering fresh outdoor air to occupied space. The DOAS with a proper design and operation can provide sufficient ventilation and dehumidification while achieving energy efficiency. Nonetheless, there is limited guidance in determining the optimal control sequence of the DOAS for the designers and operators to implement in practice. Accordingly, in practice, a number of issues have been acknowledged in the design and control phases of DOAS, including insufficient ventilation and dehumidification, and increasing supply air dry-bulb temperature in fear of over-cooling, which might cause significant discomfort and energy waste. There have been efforts to develop high-performing DOAS controls for better energy efficiency. However, such controls are often complex, or difficult to interpret, for building designers and operators to consider in practice. In this regard, this paper explores a simulation-based framework for generating a supply air temperature control sequence of the DOAS not only to ensure improved energy-saving potential but also to guarantee the implement-ability of the control logic. The U.S Department of Energy prototype primary school with dynamic occupancy profiles was modeled with a whole building simulation program, EnergyPlus. The model consists of a DOAS with an exhaust air energy recovery system for ventilation and fan-coil units for space cooling and heating. Then, a Genetic Algorithm was adopted to find the true optimal supply air temperature control sequence in terms of minimizing the energy cost of the heating, ventilation, and air conditioning system operation. Lastly, Decision Tree was adopted to extract rules out of the optimums to derive an implementable sequence of operation for the DOAS supply air temperature. A total of 12 week-simulation including four weeks of heating, cooling, and shoulder seasons, separately, under the weather condition of New York City was conducted for the case study. This case study identified that the optimization-informed rule extraction-based control, when compared to conventional outdoor air temperature-based reset control, could save about 13% of energy cost and 25% of energy consumption throughout the heating, cooling, and shoulder seasons. It is notable that the energy-saving was mainly achieved by reducing the heating energy consumption. Importantly, it nearly corresponds to the true optimal control result, which reduces approximately 14% of energy cost and 27% of energy consumption. From the results, it can be highlighted that the optimization-informed rule extraction can be as energy effective as the optimal control, while significantly reducing the complexity of the control.

Effect of operating parameters on the performance of rotary desiccant wheel energized by PV/T collectors

The main energy input of a desiccant air conditioning system is the low-quality thermal ener-gy required for regeneration, which can be obtained from waste heat, geothermal resources or solar energy. Regeneration thermal energy can be produced as well as energizing components such as fans, pumps, auxiliary air heaters, and control elements of the system by using pho-tovoltaic-thermal solar collectors (PV/T). In this study, parametric analyzes were performed to investigate the effect of regeneration temperature and air frontal velocity on the tempera-ture and dehumidification performance of a solid silica-gel desiccant wheel and on the wa-ter-cooled PV/T collectors used to provide the regeneration thermal energy. The regeneration temperature was varied between 50 and 70°C, and air frontal velocity between 1.3 and 4.1 m/s. The analyzes show that the dehumidification efficiency increases from 13.94% to 33.04% as regeneration temperature increased from 50°C to 70°C at 1.3 m/s air frontal velocity at which dehumidification efficiency is maximum. At 4.1 m/s air frontal velocity, the required regener-ation thermal energy is maximum and increases from 49.64 kW to 132.48 kW at the same re-generation temperature change. The low regeneration temperature resulted in desirable latent performance and undesirable sensible heat transfer performance in DEW. Finally, considering the whole system, it was concluded that the optimum regeneration air temperature for the performance parameters is 60°C.

CO<sub>2</sub> Exchange Of Seedlings Of <i>Rhizophora Apiculata Bl.</i> In Artificial And Natural Mangrove Forests Of Southern Vietnam

Mangrove forests are an important part of tropical coastal ecosystems. Until recently, these forests were intensively exterminated. Currently, the issue of mangrove conservation is being discussed at a number of symposiums due to their significant role in reducing the effects of greenhouse gas emissions. However, there has recently been uncertainty in estimation of CO2 fluxes in mangrove forests due to a lack of field research. The results of studies of photosynthesis at the leaf level in-situ in seedlings of Rhizophora apiculata Blume, 1827 of both natural and artificial origin are presented. The studies were carried out on a mangrove plantation growing in Can Gio Mangrove Biosphere Reserve, which is 50 kilometres from Ho Chi Minh City (South Vietnam). CO2 gas exchange during photosynthesis was measured using a gas analysing system called the LI-6800 (USA). Photosynthetically active radiation (PAR) is the main factor affecting the photosynthesis of the studied seedlings. Artificial seedlings that were grown in open areas had higher productivity and greater photosynthetic rates. It has been determined that the measured photosynthesis are scattered over three clearly marked zones, which correspond to the measurements of photosynthesis made in the pre-noon, noon and afternoon hours. The water reserves used up before noon were not fully replenished in the afternoon by the seedlings. Based on the results obtained, it has been suggested that the main inhibitory factor affecting the photosynthesis of R. apiculata (if PAR is not taken into account) is a violation of the water balance of the leaves.The optimum air temperature for photosynthesis processes in seedlings is (35 ± 2) °C. The intensity of photosynthesis also increases with an increase in the concentration of CO2 in the air. The increases of photosynthesis continue until the concentration of CO2 reaches ~1000 µmol·mol-1 and then do not increase. We associate this circumstance with the maximum possibilities of the photosynthetic apparatus of the leaf of the studied plant. The obtained research results will contribute to a better theoretical understanding of the productivity of plants of this species in the respective ecosystems, and will also allow us to move from photosynthesis at the leaf level to photosynthesis at the planting level. The work’s mathematical models can be used to model changes in R. apiculata photosynthesis from the point of view of climate change.

Qualitative indicators of spring barley varieties (Hordeum vulgare L.) under different growing conditions

Purpose. To evaluate the quality indicators of spring barley seeds grown under different soil and climatic conditions. Methods. Laboratory, calculation and statistical methods were used during the research, analysis and synthesis methods were used to draw conclusions. Results. As a result of research, the dependence of spring barley quality indicators, namely: grain uniformity and protein content, on soil and climatic zones and growing conditions was revealed. Thus, grain uniformity ranged from 86.7% (high, Polissia) to 95.1% (very high, Forest Steppe) in the different test years. In 2021, thanks to favorable meteorological conditions during the growing season, its indicators increased by 4.3% in the Steppe, 4.6% in the Forest Steppe and almost 6.0% in Polissia compared to 2020. On average, the uniformity of grain grown in different bran­ches of UIPVE increased by 5.6%. The maximum values were obtained for the varieties ‘Avus’ – 93.9% (Steppe), ‘Novyi Svitanok’ – 94.5–96.6% (Polissia, Forest Steppe). Depending on the soil and climate zone and the year of the experiment, the protein content in the grain varied from 10.9 (low, Steppe) to 13.4% (medium, Forest Steppe). On ave­rage, this indicator decreased by 2.5% in 2021 compared to 2020, and only in the Polissia zone it increased by 2.6%. The highest protein content was found in the varieties ‘Istr’ – 13.5–13.9% (Steppe and Forest Steppe) and ‘Hercules’ – 13.4% (Polissia). The varieties ‘Amadei’, ‘Istr’ and ‘Novyi Svitanok’ combined both economic and valuable traits. Optimum air temperature and high grain uniformity cont­ribute to increasing the yield of spring barley. At the same time, during the formation of a high yield in the culture, the protein content in the grain decreases. Conclusions. It was revealed that, on average, in 2020–2021, the uniformity of spring barley grain was 92.3% (Steppe), 95.4% (Forest Steppe) and 93.2% (Polissia); the protein content in the grains was 11.8% (Steppe), 12.4% (Forest Steppe) and 11.6% (Polissia). The formation of quality indicators of spring barley seeds was influenced by the growing conditions in the relevant soil and climatic zone.

Energy Efficiency and Optimization Strategies in a Building to Minimize Airborne Infection Risks

Heating, ventilation, and air conditioning (HVAC) systems play a crucial role in either increasing or decreasing the risk of airborne disease transmission. High ventilation, for instance, is a common method used to control and reduce the infection risk of airborne diseases such as COVID-19. On the other hand, high ventilation will increase energy consumption and cost. This paper proposes an optimal HVAC controller to assess the trade-off between energy consumption and indoor infection risk of COVID-19. To achieve this goal, a nonlinear model predictive controller (NMPC) is designed to control the HVAC systems of a university building to minimize the risk of COVID-19 transmission while reducing building energy consumption. The NMPC controller uses dynamic models to predict future outputs while meeting system constraints. To this end, a set of dynamic physics-based models are created to capture heat transfer and conservation of mass, which are used in the NMPC controller. Then, the developed models are experimentally validated by conducting experiments in the ETLC building at the University of Alberta, Canada. A classroom in the building is equipped with a number of sensors to measure indoor and outdoor environmental parameters such as temperature, relative humidity, and CO2 concentration. The validation results show that the model can predict room temperature and CO2 concentration by 0.8%, and 2.4% mean absolute average errors, respectively. Based on the validated models, the NMPC controller is designed to calculate the optimal airflow and supply air temperature for every 15 min. The results for real case studies show that the NMPC controller can reduce the infection risk of COVID-19 transmission below 1% while reducing energy consumption by 55% when compared to the existing building controller.

Optimisation of spray drying parameters using mixed flow in whey powder production using response surface methodology (RSM)

AbstractThis work explored the impact of mixed flow spray drying on the physical and functional properties of whey powder without any subsequently added drying agent to increase whey utilisation. Spray drying was performed on a pilot scale using a mixed flow spray dryer. The effects of the inlet air temperature (150–210 °C) and feed flow rate (2–7 L h−1) on several responses such as moisture content, yield, dispersibility, bulk density, and outlet air temperature were investigated using response surface methodology. In addition, with the optimised parameters, Carr index, Hausner ratio, solubility, wettability, hygroscopicity, degree of caking, crystallinity, and morphology of the obtained whey powder were determined. The investigation revealed that feed flow rate is the main parameter influencing all responses. The inlet air temperature significantly affected the bulk, tapped density, and outlet air temperature. The optimal inlet air temperature and feed flow rate for the production of whey powder were 182 °C and 3.2 L h−1, respectively. Under these parameters the moisture content, yield, bulk density, hygroscopicity, and degree of caking of the obtained product were 28.6, 1.80%, 0.24 g cm−3, 16.10 g H2O/g powder, and 85.56%, respectively.