Inlet Air Temperature Research Articles

Machine learning-based process design of a novel sustainable cooling system

An exponential rise in cooling requirements occurred in the last few years because of rising global surface temperatures, population growth, faster urbanization, and income growth. Developing countries are facing major issues because of the larger impact of these cooling drivers. Conventional vapor compression systems are energy-demanding and involve dangerous chemicals. The current paper proposed an innovative indirect evaporative cooling system with high energy performance, less emissions, and chemical-free operation. To map the full-scale performance, a prototype was developed,and tested in a variety of outside air conditions. Then artificial neural network (ANN)-based machine learning model was developed incorporating important input parameters including outdoor air temperature, air flow rate ratio, working air temperature, and working air wet bulb temperature to predict the supply air temperature. The ANN model having nine neurons in the hidden layer exhibits excellent modeling performance with a coefficient of determination (R2) value of ∼ 1 and root mean square error of 0.046 °C, 0.06 °C, and 0.06 °C in the training, testing, and validation phases respectively. The variable significance analysis carried out by one factor at a time (OFAT) technique reveals that working air inlet temperature is the most important parameter to predict supply temperature with a significance factor of 33 %. According to the combined experimental and ML model, the proposed system generated 130 W of cooling capacity and dropped the temperature by more than 20 °C at 48 °C of outdoor air. The corresponding coefficient of performance achieved (just for cooling) was 32. It is also shown that the enhanced IEC operates steadily in ambient temperatures ranging from 30 to 48 °C and maintains supply air temperatures within the comfort zone of ASHRAE-55 and ISO7730.

Effective design of sustainable energy productivity based on the experimental investigation of the humidification-dehumidification-desalination system using hybrid optimization

Reliable and cost-effective industrial-based sustainable desalination technologies are crucial for efficient and effective desalination of saline water. Seawater desalination emerges as a vital treatment, with humidification-dehumidification identified as a promising technology due to its simple, low-maintenance design and compatibility with renewable energy sources. The present work was developed using a large-scale experimental test rig based on the performance of a humidification-dehumidification system operated by the heat pump. As such, the study presents an advanced approach for optimizing humidification-dehumidification desalination systems using hybrid machine learning optimization techniques. The effectiveness of a neuro-fuzzy model, a decision algorithm based on a boosted tree, and a simple averaging ensemble in enhancing the performance of humidification-dehumidification systems were investigated. Experimental data includes cold water flowrate (L/min), hot water flowrate (L/min), inlet air temperature (°C), inlet cold water temperature (°C), inlet hot water temperature (°C), and inlet air relative humidity (%) as input variables combination and freshwater productivity (L/h), recovery ratio (%) as target variables. Several statistical indicators were used to evaluate the models’ prediction skills; similarly, a 2-dimensional Taylor diagram and error-radial plot were also utilized. In the calibration phase, boosted tree models slightly outperform neuro-fuzzy models, particularly for recovery ratio, demonstrating high accuracy and low bias. However, all models exhibit robust predictive accuracy in the verification phase, especially for recovery ratio, emphasizing their potential in generalizing to unseen data. The result indicated that the boosted tree outperformed others with Nash–Sutcliffe Efficiency = 94 % and 88 % for recovery ratio and freshwater productivity, respectively. The simple averaging ensemble shows marginal improvement with 95 % accuracy for the recovery ratio. Integrating hybrid artificial intelligence optimization with humidification-dehumidification desalination systems marks a transformative step in addressing freshwater shortages. By utilizing cutting-edge machine learning techniques, we achieve a more efficient, accurate, and reliable prediction of desalination performance.

Analysis of energy and exergy of lavender extract powder in spray dryer

AbstractIn recent years, the research in the field of medicinal plants as therapeutic supplements has increased significantly. Lavender extract is widely used among medicinal plant products due to its unique therapeutic properties. Since drying processes require high energy, this study was conducted to study the performance of the spray dryer in the production of lavender extract powder at three levels of air temperature 150°C, 180°C, and 210°C, three levels of compressed air flow rate 6, 8, and 10 L/min and ratios of maltodextrin‐drying aid to the mass of dry matter of the extract 0%, 25%, and 50% by response surface method. For this purpose, the energy efficiency and exergy of the powder production process were examined. According to the obtained results, increasing the inlet air temperature and the inlet air flow rate increased the energy efficiency and exergy and decreased the energy efficiency and exergy, respectively. The energy efficiency of the drying process varied in the range of 6.55%–15.87%, and the exergy efficiency () of the drying process in the range of 2.87%–5.84%. Also, the of the spray dryer was calculated in the range of 20.05%–38.78%. Based on the energy efficiency and exergy, the optimal production of lavender plant extract powder was obtained at the air temperature of 210°C, the compressed air flow rate of 6 L/min, and the amount of drying aid of 11.9 g/L.

Protective role of wood hemicelluloses: Enhancing yeast probiotics survival in spray drying and storage

Wood hemicelluloses, specifically galactoglucomannans (GGM) and glucuronoxylans (GX) are investigated for their efficacy in spray-dried microencapsulation of Saccharomyces cerevisiae subsp. boulardii (SB). This study demonstrated that GGM and GX effectively protected SB during spray drying at feed concentrations of 15 and 20 % and inlet air temperatures of 105 and 140 °C, ensuring survival rates over 90 %, comparable to gum Arabic, with all achieving over 108 cfu/g in the microcapsules. However, GGM and GX were unable to sustain SB viability when the microcapsules were stored at 33 and 75 % RH (22 °C), beyond 21 and 7 days, respectively. When stored at 4 °C, GX demonstrated a greater ability to protect SB than GGM, with log-cycle reductions of 3 and 6, respectively, after two months. Microstructure analyses showed almost all SB were entrapped by wall materials, with many microcapsules having a wall thickness of 1 − 2 µm. Overall, GX is effective for stable yeast probiotic powders, enabling the development of new probiotic enhanced formulations with prolonged viability and stability.

CFD modeling of spray drying of fresh whey: Influence of inlet air temperature on drying, fluid dynamics, and performance indicators

Whey is a very perishable food and a potent source of protein. It might be more commercialized through the process of spray drying, which would enhance its shelf life. No CFD computational models applied to the drying of fresh sweet whey have been reported in the literature to investigate transport phenomena in spray dryers and to predict crucial design parameters such as deposition, powder recovery, and drying efficiency. Using an Eulerian-Langragean technique, the behavior of multiphase flow as well as heat and mass transfer were investigated. The influence of the inlet air temperature was evaluated in relation to the velocity profiles of the continuous and discrete phases, the residence time distribution, the particle diameter formed, the air temperature near the injection zone, the particle temperature, and the mass fraction of evaporated water. Furthermore, performance parameters such as powder recovery, particle deposition, and drying efficiency were projected for varied air inlet temperatures. The velocity, residence time, and particle size distribution patterns were similar for the different air inlet temperatures. Greater variations might be noticed in the injection zone, especially in the temperature and mass fraction profiles. The lowest drying temperature resulted in the lowest particle deposition and the best thermal efficiency, making it the optimal process condition. This investigation can serve as a benchmark for the design of optimized spray dryers with greater thermal efficiency and yield to produce powdered whey.

Effects of spray drying on the content of crystal proteins of Bacillus thuringiensis and the protection by organic and inorganic auxiliaries.

Spray drying is an important industrial method for the preparation of B. thuringiensis powder from fermentation liquor. The effect of spray drying on the crystal proteins, however, has not been reported in the literature so far. The present study systematically investigated the effect of inlet air temperature, outlet air temperature, atomizing air pressure and additives (including organic and inorganic auxiliaries) on the thermal destruction of crystal proteins of B. thuringiensis. The results indicated that the content of crystal proteins of B. thuringiensis powder decreased with increased inlet air temperature, outlet air temperature and atomising air pressure. The pseudo-z values for inlet air temperature, outlet air temperature and atomizing air pressure were 826.4℃, 204.0℃ and 4.74MPa, respectively. Among them, the outlet air temperature was a major parameter influencing the thermal destruction of crystal proteins, therefore, the decrease of the outlet air temperature was beneficial to increase the protein content in powder. Although the spray drying had an adverse effect on crystal proteins, the crystal protein content in spray-dried powder approached that in freeze-dried powder when the inlet air temperature of 165℃, outlet air temperature of 70℃ and atomizing air pressure of 0.15MPa were employed. The addition of some organic and inorganic auxiliaries to fermentation liquor can protect the crystal proteins from heat damage.

Energy and exergy analysis for a laboratory-scale closed-loop spray drying system: Experiments and simulations

Improving exergy efficiency is crucial to the drying process, especially in reducing costs and carbon dioxide emissions. A closed-loop drying system with a condenser-reheater system was built and analyzed using a numerical simulation to explore the optimal drying conditions, such as inlet air temperature and the air flow rate. Heat-loss coefficients in the simulation were fitted to experimental measurements. The study found that increasing the inlet temperature led to an increase in energy inflow and exergy loss in the system. With an increase in air flow rate from 0.005 to 0.01 kg/s, the exergy loss of the chamber decreased (0.07–0.04 kW), and the exergy efficiency increased (69–92%). The exergy loss was mainly concentrated in the drying chamber, that is, the drying process, and the newly connected condenser and reheater had good exergy efficiencies, exceeding 60%.

Experimental investigation of earth-air heat exchanger using porous clay vessels for eco-friendly buildings

This study introduces an experimental investigation of a novel direct trend evaporative cooler based on a ground-air heat exchanger (GAHE) using porous clay vessels as an evaporation media under a variety of operational conditions, including air flow rate, inlet air temperature, temperature of inlet water, and in air humidity. The evaluation of the GAHE performance was based on the air-cooling effect, wet-bulb and dew-point efficiencies, energy efficiency ratio, water evaporation rate, specific water evaporation, specific cooling capacity, specific total cost, and CO2 emission rate. The influences of dry-bulb temperature, the incoming air's relative humidity (RH), and six air flow rates ranging from 11 to 25 L/s on the performance are investigated and discussed. Results indicated that increasing the air flow rate leads to an increase in the cooling capacity. Energy efficiency ratio (EER) reaches the highest value of about 25.5 recorded at 3:00 PM with air flow rate = 11 L/s. The lowest EER value is approximately 7.2 when the measured inlet and outlet temperatures are the closest at 7:00 PM, with a flow rate of 25 L/s. Increasing the air flow rate from 11 to 17 L/s increased the wet bulb efficiency, and the airflow rate was inversely proportional to wet-bulb efficiency. The maximum and minimum average dew-point efficiencies are 64% and 58% at 17 L/s and 22 L/s respectively. The water evaporation rate increases by 182.1%, increasing the air flow rate from 11 to 25 L/s.

Performance analysis of a planar shaped strut injector based supersonic combustion chamber

Abstract This current work presents the performance analysis of a supersonic combustor interface and flow construction through a scramjet engine with a planar shaped strut injector (PSSI) at the supersonic Mach. An important aspect of this study is discovering the fuel mixing mechanism inside the combustion chamber with PSSI. The novel PSSI configuration can enhance mixing and combustion performance. The scramjet configuration is incorporated with a supersonic inlet air temperature of 1250 K, where the vitiated air follows at Mach 3, and this technique is based on a high accelerating effect of the scramjet propulsion mechanism. Scramjet engines can maintain naturalistic high enthalpy conditions in minimum durations. It is observed that the maximum temperature of 3510 K is attained at the recirculating zones produced because of undulation enlargement and therefore the fuel jet losses concentration whereas the maximum combustion efficiency of 86 % is investigated from the current research work.

A fast and high-fidelity multi-parameter thermal-field prediction system based on CFD and POD coupling: Application to the RPV insulation structure

The thermal performance of the reactor pressure vessel (RPV) insulation structure plays a crucial role in ensuring the operational safety of the reactor. In this study, a fast and high-fidelity prediction system is proposed, which is based on the snapshot ensemble generated by the computational fluid dynamics (CFD) method and incorporates proper orthogonal decomposition (POD) as its core component to predict the heat transfer performance of the RPV insulation structure. The proposed thermal-field prediction system is employed to examine the impacts of air inlet temperature, mass flow rate, and RPV outer wall temperature on the thermal performance parameters: average heat flux qout and temperature Tout of the RPV insulation outer wall, as well as the maximum local temperature Tmax of the concrete pit inner wall. The POD predictive results showcase that the maximum relative deviations of qout, Tout and Tmax for eight sets of off-design cases are 2.25 %, 3.04 %, and 2.37 %, respectively. Meanwhile, the maximum values of qout, Tout and Tmax are 90.13 W·m−2, 44.69 ℃, and 87.51 ℃, respectively. They are all less than their prescribed limits. The field distributions of heat flux and temperature in off-design cases exhibit consistency between the CFD and POD methods. Notably, in comparison to the CFD simulation method, the POD method capturing more than 99.9 % energy demonstrates significant computational time savings for 99.87 %. Moreover, a multivariate linear regression (MLR) model for thermal performance parameters is established based on the POD prediction datasets. The results indicate that the maximum relative deviations between the POD and MLR methods in predicting qout, Tout and Tmax for the 72 sets of test cases are 5.1 %, 7.9 % and 7.1 %, respectively. Meanwhile, 99.99 % CPU time is reduced for MLR model compared with CFD method. Obviously, the predictive system showcases excellent accuracy and efficiency in forecasting.

Enhancing Air Conditioning System Performance via Dual Phase Change Materials Integration: Seasonal Efficiency and Capsulation Structure Impact

Enhancement of the cooling and heating capabilities of an air conditioning unit (ACU) coupled with a thermal energy storage system of dual phase change materials (PCM) is investigated. The dual PCM, namely SP24E and SP11_gel, are coupled with the ACU outdoor device (condenser/evaporator) during the summer/winter seasons, respectively. Moreover, ACU performance assisted with dual-PCM heat exchanger is compared with a single heat exchanger of SP24E in summer and single heat exchanger of SP11_gel in winter at different PCM capsulation structures (aligned and staggered cylinders). The system dynamic mathematical model is computationally solved using ANSYS software and experimentally validated. Results affirm that charging/discharging periods are minimal for the dual-PCM system and slower for PCM inline cylinder layouts than staggered ones. Inline design yields greater ACU average power savings. In summer, higher inlet air temperature to the PCM system reduces PCM discharging time and ACU power savings, with the opposite effect during winter. ACU COP with PCMs is improved by around 80 % in summer and 40 % in winter, respectively, compared to ACU without PCMs. The maximum average power saving over 4 h of ACU working in summer by single and dual-PCM systems is 21.4 % and 11.8 %, respectively, whereas the results in winter are 18.5 % and 12.8 %, respectively.

Numerical study of an energy storage unit based on zeolite-water adsorption for mobilized thermal energy storage

Mobile energy storage heating trucks present a promising solution to address current challenges associated with low energy utilization and reliance on a singular heat supply method. This technology seamlessly integrates waste heat recovery and thermal energy utilization, offering potential advantages such as streamlining construction cycles and providing timely energy supply in scenarios with diverse energy demands. To enhance the energy delivery efficiency of mobile heating, this paper employs computational fluid dynamics (CFD) simulation to thoroughly examine the heat accumulator at the core of an adsorption-based mobilized thermal energy storage. Furthermore, the paper conducts simulations and analyses of heat and mass transfer within a zeolite-water accumulator, closely scrutinizing variations in temperature, vapor content, and water content within the zeolite field. The results indicate that, during the heat storage process in the 3 m/s to 5 m/s system, the zeolite sensible heat storage capacity increases by 6.48 %, accompanied by a reduction in latent heat. Specifically, when the inlet steam velocity is 5 m/s compared to 3 m/s, the equilibrium heat transfer time shortens by 24.6 %. Furthermore, for an inlet steam temperature ranging from 180 °C to 220 °C, the average temperature can be elevated by 28.8 °C, resulting in a 10.6 % increase in sensible heat storage capacity. However, the heat storage time experiences a 3.4 % increase. In the exothermic process, with an inlet air velocity of 5 m/s compared to 3 m/s, there is a 22.2 % reduction in latent heat exothermic and a 16.7 % reduction in exothermic time. The inlet air temperature during the exothermic process is 8.9 °C lower than the final temperature of 20 °C, leading to a 3.2 % increase in exothermic quantity and a 20 % improvement in exothermic efficiency.

Optimization of CRDI engine operating parameters using response surface methodology utilizing lemon peel oil biofuel enriched with hydroxy gas

The current study aims to optimize the operational variables of a CRDI engine in Dual-Fuel Mode (DFM). Hydroxy gas (HHO) was generated through water electrolysis using a concentric four-cylinder dry cell electrolyzer with NaOH catalyst, and it was introduced into the inlet manifold as the primary fuel. The pilot fuel consisted of Lemon Peel Oil (LPO) and pure diesel directly injected into the cylinder. An experimental matrix comprising 17 distinct runs was designed using Design of Experiments (DoE), utilizing Box-Behnken Design (BBD) based on Response Surface Methodology (RSM). The RSM model was created depending on the experimental data, and then operating parameters were optimized utilizing the desirability technique. The optimal engine operational variables were identified for the B25 LPO mixture with a constant HHO gas flow rate of 0.5 LPM, a Compression Ratio (CR) of 21.5, an Injection Pressure (IT) of 450 bars, and an inlet air temperature of 87℃. The optimized results were 30.18 % for Brake Thermal Efficiency (BTE), 9.532 g/kW-hr for Carbon Monoxide (CO) emission, 0.253 g/kW-hr for Unburned Hydrocarbon (UHC) emission, and 23.510 g/kW-hr and Nitrogen Oxides (NOx) emission, yielding a total desirability of 0.7641. Empirical validation of the optimized responses at these input conditions demonstrated their conformity to acceptable error ranges.

Thermal analysis of batteries and prediction with artificial neural networks

PurposeIn this study, it is aimed to develop cooling models for the efficient use of batteries and to show how important the busbar material is. Batteries, which are indispensable energy sources of electric aircraft, automobiles and portable devices, may eventually run out. Battery life decreases over time; the most critical factor is temperature. The temperature of batteries should not exceed the safe operating temperature of 313 K and it is recommended to have a balanced temperature distribution through the battery.Design/methodology/approachIn this study, the effect on the battery temperature caused by using different busbar materials to connect batteries together was investigated. Gold, copper and titanium were chosen as the different busbar material. The Air velocities used were 1 m/s and 2 m/s, the air inlet temperatures were 295 and 300 K and the discharge rates 1.0–1.5–2.0–2.5C were chosen for cooling the batteries.FindingsThe best busbar material was identified as copper. Because these studies are long-term studies, it is also suggested to estimate the data obtained with ANN (Artificial Neural Networks). The purpose of ANN is to enable the solution of many different complex problems by creating systems that do not require human intelligence. Four different program (BR-LM-CGP-SCG) were used to estimate the data obtained with ANN. It was found that the most reliable algorithm was BR18. The R2 size of the BR18 algorithm in the test phase was 0.999552, the CoV size was 0.007697 and the RMSE size was 0.005076.Originality/valueWhen the literature is considered, the cooling part of the battery modules has been taken into consideration during the temperature observation of the battery modules, but busbar materials connecting the batteries have always been ignored. In this study, various busbar materials were used and it was noticed how the temperature of the battery model changed under the same working conditions. These studies are very time-consuming and costly studies. Therefore, an estimation of the data obtained with artificial neural networks (ANN) was also evaluated.

Mucus, airway and plume temperature effects on pMDI-drug delivery in a mouth-throat airway: Experimental and numerical studies

Effective pulmonary drug delivery through pressurized-metered dose inhalers (pMDIs) depends on accurately targeting pharmaceutical aerosols to specific lung areas. Achieving this necessitates a comprehensive understanding of airflow dynamics in the airway and particle transport mechanisms.In this study, a replica of the realistic geometry of the VCU medium-sized mouth-throat (MT) airway was fabricated by rapid prototyping (3D printing) to connect to a next-generation impactor (NGI) setup. The drug concentration deposited in the replica was measured at a constant flow rate of 30 L/min and room temperature using a high-performance liquid chromatography (HPLC) assay. This measurement validated our computational fluid dynamics (CFD) model for simulating particle transport under the same conditions. Large eddy simulation (LES) and discrete phase model (DPM) were employed to model the MT's airflow and particle transport. Using our CFD modeling, we focused on the effects of the temperature distribution of aerosol injection (plume), the influence of inlet air temperature, and the presence of the mucus layer on particle transport and deposition.Our findings revealed that decreasing the plume temperature from 10 °C to −54 °C reduced deposition by approximately 15%, although increasing the average deposited particle sizes within the MT by about 34.5%. The airflow pattern, affected by different plume temperatures, was the prevalent parameter in particle MT deposition. In contrast, the effect of different air inlet temperatures on deposition was negligible. Additionally, incorporating mucus layer features in CFD modelling could further modify the inhaler's efficiency by up to 11%, depending on the specific conditions like diverse plume temperature (−54 °C–10 °C) and airflow temperature conditions (−15 °C–45 °C).

Reduction of Heating Energy Demand by Combining Infrared Heaters and Infrared Reflective Walls

We study the potential of infrared (IR) heaters in combination with IR reflective walls to reduce heating energy demand in buildings. Using IR heaters increases radiant temperature. Combined with IR reflective walls, less radiant heat is absorbed by the surrounding walls, and more is reflected to and absorbed by the occupants. This allows for lower air temperatures while maintaining constant thermal comfort. Lower air temperatures result in heating energy savings. In simulations, we examine the impact of four parameters on the thermal comfort indicator Predicted Mean Vote (PMV): wall temperature, inlet air temperature, IR heater power, and IR emissivity of the walls. To reduce the number of data points needed, we use a Central Composite Design for the layout of the simulation plan. The results show that the PMV can be changed from 0.15 to 1.16 only by lowering the emissivity of the surrounding walls from 0.9 to 0.1. At high IR heater power and at low wall temperature the impact of the emissivity on the PMV becomes larger. From the simulation data, we derive a response surface function to determine the required IR heating power for any given room conditions, which could be used for automated IR heater control.

Development and Testing of Small-Scale Flash Dryer for Maize Bran

Sun drying is the most common technique used to dry various food products due to its economic convenience. However, it is not effective owing to its unreliable nature leading to microbial deterioration and aflatoxin contamination of food. In Tanzania, about 1.3 million tonnes of maize bran are at risk of deterioration annually due to improper drying. The aim of this study was to design, fabricate and test an efficient small-scale flash dryer for maize bran. Using principles of material and energy balance, a small-scale flash dryer with a throughput of 500 kg of dry product per hour was designed resulting in a nominal diameter of 387 mm and a drying length of 20 m. Design calculations and drawings were done using MS Excel 2021 and SolidWorks 2018 respectively. Empirical procedures were used to fabricate and test the dryer. The material of construction used in the flash duct was food grade (stainless steel, SS 201). A fuel blend of Waste Engine Oil (WEO) and diesel (85%-15%) was used with a novel heavy oil burner. It was observed that at an inlet air temperature of 150 °C and air velocity of 12 m/s, moist maize bran was dried from 37% to 10% w.b. The fabricated flash dryer's energy efficiency, specific energy consumption, and specific energy utilization are 74%, 3.4 MJ/kg water evaporated, and 1.71 MJ/kg dry maize bran respectively. It was concluded that the fabricated small-scale flash dryer displayed improved energy performance compared to others presented in literature. Better instrumentation was recommended for feedback control on burner operation and process monitoring which further enhances the performance of the fabricated small-scale flash dryer.

A novel simplified simulation method for the precooler based on the virtual source term: modeling, validation and application

Precooling technology is one of the promising approaches for expanding the flight envelope of conventional turbine engines and enhancing flight performance at high Mach numbers. Within this domain, a crucial challenge is to rapidly and accurately obtain the flow and heat transfer characteristics of the precooler. Currently, full-size simulation of the precooler is impractical, while simplified and fast simulation methods employed in previous research have exhibited certain deficiencies, including excessive simplification and unconvincing validation. In this study, a novel simplified simulation method for the annular precooler based on the virtual source term model was proposed. This method comprehensively considered the axial and radial non-uniformity of airflow caused by the coupling effect between the precooler and the inlet channel in the actual flow field. Subsequently, systematic validations of the reliability and superiority of this method were conducted, along with its application in the overall performance calculation of the precooled engine. The results demonstrate that the simplified method proposed in this paper showcases higher accuracy than the methods employed in previous research. Compared with the real model, the relative errors of the overall parameters are within ± 1 %, and the relative errors of the distortion indices for total temperature and total pressure are 5.1 % and −1.5 %, respectively. Meanwhile, the number of computational mesh cells is reduced by more than 95 %. Moreover, the first application of the simplified method in the overall performance calculation of the precooled engine reveals that the traditional low-dimensional model underestimates both the total pressure loss of the precooler and the fuel consumption for precooling, resulting in larger calculation deviations in the thrust and equivalence ratio, respectively. The values of thrust calculated with the high-dimensional virtual model show an average decrease of 2.01 % compared to those derived from the low-dimensional model. In addition, a higher Mach number results in lower effectiveness, higher air inlet temperature, and reduced resistance of the precooler to heat exchange imbalance caused by flow non-uniformity. These factors lead to an escalating relative deviation of the equivalence ratio between the two models, reaching 26.45 % at Mach 5.

Impact of Spray Drying on the Properties of Grape Pomace Extract Powder

Incorporating anthocyanins, valuable natural pigments, into a powder can improve their stability, but exposure to high temperatures during processing can cause them to degrade. The purpose of this study was to investigate how the inlet air temperature during spray drying affects the physical and chemical characteristics as well as the flowability of a grape pomace anthocyanin powder obtained through ultrasound-assisted extraction using acidified water as the solvent. An anthocyanin solution containing 13% (w/v) maltodextrin was subjected to spray drying at temperatures ranging from 120 to 170 °C. Tukey’s test was applied to compare the means of the samples. The samples dried at temperatures between 130 and 170 °C were adequate, with a moisture content < 5% and a water activity < 0.3, indicating that the powder was stable. The highest anthocyanin retention (91.94 ± 1.59%) and process yield (50.00 ± 3.06%) were achieved at 140 °C, while higher temperatures resulted in anthocyanin degradation. Furthermore, the powder exhibited poor flowability, indicating cohesive behavior (Hausner ratio > 42.29% and Carr index > 1.73), which is an industrial parameter rarely considered in spray-drying studies. The acidification process was found to promote high anthocyanin retention following high-temperature processing. However, powders obtained from food matrices with low pH and high sugar content may exhibit increased cohesion due to interaction forces. These findings highlight the potential of utilizing grape pomace and green solvents to produce bioactive-rich powders for industrial applications.

Yacon powder mix: Effects of the composition and the process of microencapsulation by spray drying

Yacon is a tuber known as a healthy food due to its effects as an antidiabetic, anti-inflammatory, anticancer, and prebiotic agent; it is rich in fructooligosaccharides (FOS) and antioxidants, and due to its sweet taste and low-calorie content, it is used as a substitute for ordinary sugar. This research aimed to evaluate the influence of the composition of the feed and the microencapsulation process by spray drying (SD) on the properties of a yacon powder mixture (YP). Response surface methodology with a central composite design with a face-centered composition (α = 1) was used, considering the independent variables: inulin (IN) (3–5% w/w), maltodextrin (MD) (3–5% w/w), air inlet temperature (AIT) (140–160 °C), air outlet temperature (AOT) (75–85 °C) and atomizer disc speed (ADS) (18000–22000 rpm), and the dependent variables: moisture (Xw), water activity (aw), hygroscopicity (Hy), solubility (S), particle size (percentile D10, D50, and D90), total phenols (TP), antioxidant capacity (ABTS and DPPH), color (CIE-Lab*) and yield (Yi). The suspension formulation contained xanthan gum (0.167 %) and a mixture of ascorbic and citric acids (0.3 %). The aw and Xw values of the YP guarantee its microbiological stability; however, the process formulation produces a complex matrix (FOS- sugars- MD - IN) with high affinity for water, which favors adsorption phenomena (hygroscopic material) and high reconstitution (high solubility). The independent variables that best fit the experimental optimization criteria were: IN = 3.0 %, MD = 5.0 %, AIT = 143.7 °C, AOT = 80.1 °C, ADS = 22000 rpm, where Yi = 84.2 %, and the quality of the YP: Xw = 2.4 %, aw = 0.220, Hy = 23.0 %, S = 96.9 %, D10 = 10.6 μm, D50 = 23.4 μm and D90 = 169.3 μm, TP = 1228.2 mg gallic acid equivalent/100 g, ABTS = 2295.9 mg Trolox equivalent (TE)/100 g, DPPH = 5192.3 mg TE/100 g, L* = 80.5, a* = 5.1 and b* = 17.4. SD is an effective technology that positively impacts the development of new food products. In addition, the YP could have multipurpose applications for the industry, generating value in this agri-chain.