Statistical Experimental Design Research Articles

Design of Low-Cost Hydroponic Technology for Cultivation of Leafy Vegetables for Human Food Security in a Cold Desert Area of the Trans-Himalayan Range

For many decades, food security has been a worldwide concern. The present research aimed to examine an efficient technique for planting system i.e. hydroponic (a soil less system of farming) and statistical experimental design approach was used to compare the growth of different Leafy Vegetables (Spinach and Lettuce Varieties) in the Cold Desert Region of Trans Himalaya under the designed hydroponic system. The results showed that the leafy vegetable variety that were grown in the NFT system, exhibited significant differences in above attributes. The BC ratio of nutrient solution was found to be four times higher.

Magnetic Hydrogel Beads as a Reusable Adsorbent for Highly Efficient and Rapid Removal of Aluminum: Characterization, Response Surface Methodology Optimization, and Evaluation of Isotherms, Kinetics, and Thermodynamic Studies.

Biopolymers such as alginate and gelatin have attracted much attention because of their exceptional adsorption properties and biocompatibility. The magnetic hydrogel beads produced and used in this study had a core structure composed of magnetite nanoparticles and gelatin and a shell structure composed of alginate. The combination of the metal-ion binding ability of alginate and the mechanical strength of gelatin in magnetic hydrogel beads presents a new approach for the removal of metal from water sources. The beads were designed for aluminum removal and fully characterized using various methods, including Fourier transform infrared spectroscopy, X-ray photoelectron spectroscopy, scanning electron microscopy-energy-dispersive X-ray spectroscopy, vibrating sample magnetometry, microcomputed tomography, and dynamic mechanical analysis. Statistical experimental designs were employed to optimize the parameters of the adsorption and recovery processes. Plackett-Burman Design, Box-Behnken Design, and Central Composite Design were used for identifying the significant factors and optimizing the parameters of the adsorption and recovery processes, respectively. The optimum parameters determined for adsorption are as follows: pH: 4, contact time: 30 min, adsorbent amount: 600 mg; recovery time: reagent 1 M HNO3; and contact time: 40 min. The adsorption process was described by using the Langmuir isotherm model. It reveals a homogeneous bead surface and monolayer adsorption with an adsorption capacity of 5.25 mg g-1. Limit of detection and limit of quantification values were calculated as 4.3 and 14 μg L-1, respectively. The adsorption process was described by a pseudo-second-order kinetic model, which assumes that chemisorption is the rate-controlling mechanism. Thermodynamic studies indicate that adsorption is spontaneous and endothermic. The adsorbent was reusable for 10 successive adsorption-desorption cycles with a quantitative adsorption of 98.2% ± 0.3% and a recovery of 99.4% ± 2.6%. The minimum adsorbent dose was determined as 30 g L-1 to achieve quantitative adsorption of aluminum. The effects of the inorganic ions were also investigated. The proposed method was applied to tap water and carboy water samples, and the results indicate that magnetic hydrogel beads can be an effective and reusable bioadsorbent for the detection and removal of aluminum in water samples. The recovery values obtained by using the developed method were quantitative and consistent with the results obtained from the inductively coupled plasma optical emission spectrometer.

Optimization of the Hydrolysis Process of Microalgae Porphyridium cruentum Biomass with Variations of Hydrochloric Acid Concentration, Temperature, and Time using Response Surface Methodology (RSM)

Microalgae Porphyridium cruentum has potential as a raw material for bioethanol production because it has a high carbohydrate content. These carbohydrates can be broken down into reducing sugars through the hydrolysis process. The reducing sugar obtained will be used as a substrate in the production of bioethanol. This study aimed to produce a substrate with the best-reducing sugar indicator and to determine the optimum conditions for hydrolysis of P. cruentum microalgae biomass following the Box-Behnken statistical experimental design, using Response Surface Methodology (RSM). The parameters of the optimized hydrolysis process were HCl concentration (2 - 0.2 N), temperature (60 -120 ˚C), and hydrolysis time (30-180 min). The optimum conditions that have been achieved using RSM are an HCl concentration of 1.91 N, a temperature of 60 °C, and a hydrolysis time of 180 min were predicted a maximum total reducing sugar production of 810 mg/L. The experimental result of total reducing sugar obtained at optimum conditions was 895 mg/L, which was well close to the predicted value, verifying the appropriateness of the model.Abstract is informed about the statements of the problem, methods, scientific finding results and conclusion concisely

Evaluation of Critical Parameters for the Fabrication of Metal Hollow Spheres Using an Alginate‐Based Sol–Gel Process

This study focuses on the fabrication of metal hollow spheres using an alginate‐based sol–gel technology in combination with a freeze‐drying process. The mixing routine for the metal slurry is investigated. Critical processing parameters, influencing the yield of intact spheres such as the aging time in the reaction solution and in water as well as the freezing time and the rotation of the spheres during freezing are evaluated with statistical design of experiments. A customized debinding regime is developed with a heating rate of 0.1 K min−1 in critical temperature ranges and a maximum temperature of 350 °C. For the fabrication of hollow metal spheres with additions of 1.0 wt% potassium alginate, the optimum aging time in Ca(OH)2‐reaction solution is 4 min (4C) and the spheres are only to be rinsed with purified water (0 W). Furthermore, the rotation of the spheres (+R) during freezing significantly contributes to the increased yield of 80.2%, which is significantly higher as compared to the previous studies. Sintered spheres have a compressive strength of 159 MPa with a corresponding strain of 42%.

Process Development of Screen-Printed Magnetic Sheets for Electric Machines via Statistical Design of Experiments

Electrical machines play a major role in achieving a more sustainable economy by driving the further electrification of industry and transportation. To enhance the electric motor productivity during production and the efficiency during operation, it is essential to maximize the material yield rate during the production of stator and rotor sheet stacks and reduce iron losses during operation. Screen-printing technology can help to achieve these goals by producing thin magnetic sheet laminations in near-net-shape geometry, resulting in minimum material waste during production. Moreover, reducing the sheet thickness decreases the eddy current losses and avoids mechanical stress during manufacturing, leading to improved motor efficiency. Additionally, screen printing facilitates the production of multi-material components and variable alloy compositions. This study aims to identify the relevant factors and printing parameters so that screen-printed magnetic sheets can achieve product specifications. A method involving the statistical design of experiments is performed in several iterations to investigate the target parameters of the print cycle time, green part weight, shape integrity and layer thickness, and to analyze the main interdependencies. The results of this study provide valuable insights into optimizing the screen-printing process for soft magnetic sheets, enabling the production of efficient electric motors while reducing material scrap.

Optimization of low-grade coal and refuse-derived fuel blends for improved co-combustion behavior in coal-fired power plants.

This study is aimed at utilizing three waste materials, i.e., solid refuse fuel (SRF), tire derived fuel (TDF), and sludge derived fuel (SDF), as eco-friendly alternatives to coal-only combustion in co-firing power plants. The contribution of waste materials is limited to ≤5% in the composition of the mixed fuel (coal + waste materials). Statistical experimental design and response surface methodology are employed to investigate the effect of mixed fuel composition (SRF, TDF, and SDF) on gross calorific value (GCV) and ash fusion temperature (AFT). A quadratic model is developed and statistically verified to apprehend mixed fuel constituents' individual and combined effects on GCV and AFT. Constrained optimization of fuel blend, i.e., GCV >1,250 kcal/kg and AFT >1,200 °C, using the polynomial models projected the fuel-blend containing 95% coal with 3.84% SRF, 0.35% TDF, and 0.81% SDF. The observed GCV of 5,307 kcal/kg and AFT of 1225 °C for the optimized blend were within 1% of the model predicted values, thereby establishing the robustness of the models. The findings from this study can foster sustainable economic development and zero CO2 emission objectives by optimizing the utilization of waste materials without compromising the GCV and AFT of the mixed fuels in coal-fired power plants.

Experimental statistical modeling of tensile properties and flexural stiffness of recycled high-density polyethylene (rHDPE) thermoplastic using response surface methodology (RSM)

The widespread use of thermoplastic polymer products, particularly high-density polyethylene (HDPE), in daily consumer goods has led to the generation and accumulation of large amounts of plastic waste. This waste poses a significant threat to the environment due to the challenges and limitations in managing plastic waste effectively. Establishing new large volume applications of recycled HDPE (rHDPE) would incentivize recycling by creating new revenue streams. Before rHDPE (specifically rHDPE-talc blends) can be used more heavily in lieu of or in addition to virgin HDPE (vHDPE), its material properties in relation to application temperatures must be better understood. This study focused on characterizing tensile and flexural properties of rHDPE-talc blends across broad talc filler content and temperature ranges. Experimentally, tensile strength, elastic modulus, storage modulus, and nominal yield strain data were collected. Tensile strength and elastic modulus were observed to increase with an increase in talc content and decrease with an increase in temperature. Conversely, yield strain was observed to decrease with an increase in talc content and increase with an increase in temperature. Response Surface Methodology (RSM) was applied using the Central Composite Design (CCD) statistical experimental design approach to study the stiffness and strength of rHDPE and the effects that temperature and talc filler content have on these mechanical properties. The surfaces generated in this analysis fit the data well, with coefficients of determination greater than 0.94 for all but one property, storage modulus, which was 0.80. The evaluation of model performance revealed that the surfaces yielded good correlation and statistical significance. The results provide useful relationships that can be considered for use in practical applications involving rHDPE-talc blends, including engineering applications.

Optimization techniques for mortars with self-compacting characteristics through Central Composite Design

The demand for concrete production for civil construction promotes an investment by researchers and builders to find methodologies that improve their performance, the costs and less environmental degradation. Self-compacting concrete works it possible to offer a mixture with high flowability and compaction and good mechanical properties. For a better optimization of the mixtures, the statistical tool Design of Experiments (DoE) was used. Therefore, the objective of this research was to optimize an experimental dataset of high strength self-compacting mortars, through statistical analysis, using the Response Surface Methodology (RSM) for a Central Composite Design (CCD). The results showed a strong correlation between the D-Flow and T-funnel response factors, when compared with CS14h and CS28d models that showed moderate significance. The input variable w/c was the most significant model. Numerical optimization solutions showed good accuracy and high compliance for low cost and environmental impact, maintaining high performance in fresh and hardened properties.

Tailored synthesis of iron oxide nanoparticles for specific applications using a statistical experimental design

For this work, iron oxide nanoparticles are synthesized by the co-precipitation method with stoichiometric amounts of Fe2+ and Fe3+ salts in a 1:2 ratio in distilled water and the pH is raised by adding an aqueous ammonia solution by controlled dripping. Nanoparticles precipitating after the reaction time are magnetically filtered and stored in ethanol for further analysis. Superparamagnetic Fe3O4 nanoparticles with a slight deviation from the stoichiometry are obtained, with sizes between 7.4 and 12.8 nm and saturation magnetization between 40 and 78 emu/gr. At pH 6, rod-shaped nanoparticles are obtained in addition to spherical ones. With a statistical design, it is shown how the morphological, structural and magnetic properties of the resulting nanoparticles can be manipulated by the synthesis parameters, offering many possibilities to tailor the materials to a wide range of applications.

Pharmaceutical wastewater treatment in ceramic separator MFC: Optimisation of operating parameters to improve organic removal and power generation

Microbial fuel cell (MFC) can be used to simultaneously treat wastewater and generate energy using microorganisms as biocatalysts. To improve its performance, optimization of the various process variables and determination of effects of their interactions needs to be explored. In the present study, a full factorial statistical experimental design study of 11 runs was conducted to analyse the effect of influent COD (1500–3500 mg/L), pH (5–9) and recirculation ratio (0–1:1) on power density and COD removal efficiency while treating pharmaceutical wastewater. Statistical models were developed using full factorial design (α = 0.05, p < 0.05). The main effects of the all the three factors were found to be significant on COD removal efficiency; however, the interaction effects were found to be insignificant. On the other hand, the main effects as well as the interaction effect of COD and pH were found to be significant on power density. It was predicted that the optimum COD removal efficiency of 68 % and power density of 219.4 mW/m3 can be achieved in an MFC with an influent COD of 1960 mg/L and pH of 8.9 at a recirculation ratio of 0.99.

Experimental Investigation to Improve Heat Transfer Rate of Transformer Oil Using Cuo Nanoparticles

In this research, we employed a statistical experimental design approach, specifically the Taguchi method using an L9 orthogonal array robust design, to optimize experimental conditions. Our aim was to maximize the Heat Transfer coefficient of CuO-Transformer oil Nano fluid. We carefully selected three controllable factors with different levels: Concentration of Nanoparticles (0.1, 0.2, and 0.3g/lit), Size of nanoparticles (10, 15, and 20 Nm), and Ratio of Surfactant weight to Nano particles weight (0.1, 0.5, and 1).Our analysis of the results highlighted the significant role played by the Size of nanoparticles in influencing the heat transfer coefficient of the Nano fluid, accounting for approximately 88.852% of the variation. We determined the optimal settings for these three factors to maximize the heat transfer coefficient: Concentration of Nano particlesof0.2g/lit, Size of Nano particles of 10 Nm, and Ratio of Surfactant weight to Nano particles weight of 1. The Heat transfer coefficient predicted by ANOVA under these optimized conditions was 559.443W/m2 K and by regression model was 549.45 W/m2 K.To validate our findings, a confirmation test was conducted at these optimal conditions, and the results demonstrated a high degree of consistency between the experimental and predicted values.

Semen Quality and Reproductive Morphometry of Two Breeds of Cocks Fed Ficus Exasperate Leaf Meal and Vitamin C Supplemented Diets

The assessment of semen quality and reproductive morphometry of two breeds of cocks is based on the evaluation of sperm, the testicular and epididymal morphometry from the breeds of cocks. Two breeds of cocks (Noiler and Rhode Island Red) fed Ficus exasperata leaf meal and vitamin C supplemented diets were evaluated for the semen quality and reproductive morphometry. One hundred and forty-four birds were used for the experiment. The cocks were weighed randomly and assigned to four dietary treatments of six replicates with three cocks per replicate. The birds were fed their respective experimental diet for 12weeks, while feed and water were provided ad-libitum. The experiment was carried out using Factorial Design or a Statistical Experimental Design. The diets were divided into four treatments which the diet 1 was the control (basal diet), the diet 2 consists of 200mg/kg and vitamin C supplement, the diet 3 consists of Ficus exasperata and 1kg of basal diet and the diet 4 consists of 200mg/kg vitamin C with Ficus exasperata and 1kg of basal diet. The result showed that there is no significant (p&gt;0.05) different in the semen characteristics except the total sperm cell. It was thus concluded that Ficus exasperata leaf meal and vitamin C could be used in the diet without adverse effect on the quality of semen and reproductive morphology and function.

Using Data-Driven Learning to Predict and Control the Outcomes of Inorganic Materials Synthesis.

The design of inorganic materials for various applications critically depends on our ability to manipulate their synthesis in a rational, robust, and controllable fashion. Different from the conventional trial-and-error approach, data-driven techniques such as the design of experiments (DoE) and machine learning are an effective and more efficient way to predictably control materials synthesis. Here, we present a Viewpoint on recent progress in leveraging such techniques for predicting and controlling the outcomes of inorganic materials synthesis. We first compare how the design choice (statistical DoE vs machine learning) affects the type of control it can offer over the resulting product attributes, information elucidated, and experimental cost. These attributes are supported by discussing select case studies from the recent literature that highlight the power of these techniques for materials synthesis. The influence of experimental bias is next discussed, followed finally by our perspectives on the major challenges in the widespread implementation of predictable and controllable materials synthesis using data-driven techniques.

Production of perillic acid from orange essential oil by Yarrowia lipolytica using a top-aerated bioreactor.

R-(+)-Perillic acid, a promising anticancer and immunomodulatory agent, is the major product from the biotransformation of R-(+)-limonene-rich orange essential oil by the yeast Yarrowia lipolytica. Due to the abundance and low cost of orange essential oil, which is a byproduct of the citrus industry, we attempted to improve the biotransformation process by optimizing yeast cell mass production. Then, the whole process was transposed and adapted to a 2-L instrumented bioreactor. Cell mass production was optimized in shaker flasks using a statistical experimental design. The optimized medium (g·L-1: 22.9 glucose, 7.7 peptone, 4.1 yeast extract and 1.0 malt extract) resulted in a 13.0g·L-1 final cell concentration and 0.18g cell·L-1·h-1 productivity. A further increase to 18.0g·L-1 was achieved in a 2-L bioreactor upon fed-batch culture. High-purity limonene bioconversion was performed in the same bioreactor utilizing top aeration to diminish terpene volatilization; as a result, 839.6mg·L-1 perillic acid accumulated after 48h. Under the same conditions, industrial orange essential oil afforded 806.4mg·L-1 perillic acid. The yeast growthmedium optimization resulted in a twofold increase in biomass accumulation and a reduction in growth medium nitrogen sources, which lowered the catalytic biomass production cost. Compared with conventional bottom aeration, the bioreactor top aeration strategy resulted in higher bioconversion rates. The conditions developed for high-purity limonene bioconversion were successfully applied to low-cost orange essential oil, showing the robustness ofY. lipolyticayeast.

The Impact of Pulp Temperature on the Flotation Performance of Copper-Lead-Zinc Sulphide Ore

Flotation performance can significantly be affected by seasonal variations due to the changes in water temperature and pulp temperature, type and concentration of dissolved ions in process water. Extreme temperature conditions could be the major factor affecting flotation performance and mask the influence of water chemistry. Therefore, the interactive effects of the temperature and water chemistry should be taken into consideration, particularly for mine sites experiencing extreme temperature conditions. In this paper, effects of temperature, sulphate (SO42−), thiosulphate (S2O32−) and calcium (Ca2+) ions on the flotation performance of a Cu-Pb-Zn complex sulfide ore were investigated using a statistical experimental design and modelling approach. The results were evaluated using ANOVA and regression analysis to determine the significant parameters and derive individual regression models for each flotation response using Design Expert software version 6.0.8. Individual regression models were developed for mass pull, water recovery, grade and recovery of the sulfide minerals using the statistically significant main effects and their interactions. The models were used to determine the concentration of the dissolved ions and pulp temperature required to achieve the maximum zinc recovery, maximum zinc grade or the optimum zinc grade and recovery. The results showed that the water chemistry (i.e., the concentrations of Ca2+, SO42− and S2O32−) affected the flotation performance significantly at low temperature (25 °C). At high pulp temperature (60 °C), however, the temperature was the dominant parameter and masked the effects of water chemistry. Details of the statistical experimental design, discussions of the effects of experimental factors and their interactions on flotation performance, and the development of regression models are presented in this paper.

An all-encompassing study on the joint effect of powder feedstock characteristics and manufacturing process parameters on the densification and mechanical properties of additively manufactured nickel alloy 718

Laser-powder bed fusion (L-PBF) is a commonly employed additive manufacturing (AM) technique in manufacturing 3D metallic parts with complex geometries from powder materials. It is a complex process with more than 100 process parameters affecting the properties of fabricated parts. Studies on AM of nickel alloy 718 are currently focusing on investigating the effect of the process parameters and heat treatment on final part properties. Nevertheless, the effect of different powder characteristics such as particle size distribution (PSD), the presence of fine particles, powder reuse, and moisture content has not been studied comprehensively in the literature. In this study, two types of powder: virgin and recycled, as well as four parameters: PSD, which is represented by the median particle size and the particle size range, moisture content, and volumetric energy density (VED), were studied. To reduce experimental cost and burden, statistical design of experiment (DOE) techniques were utilized. Then, the measured results were analyzed using analysis of variance (ANOVA) to ascertain the effect of the different powder characteristics on multiple quantities of interest (QoIs). Different densification and mechanical tests were performed, and their outputs were studied as the QoIs. This study revealed that VED, powder type, PSD range, and median are the most common significant factors for most QoIs. Meanwhile, moisture content had a significant effect only on the Charpy impact energy. Besides, the study showed that the presence of fine particles <20μm improved the densification of the fabricated parts but reduced the mechanical properties.

Current approaches toward the removal of methylene blue dye from synthetic textile effluent using bacterial treated agricultural waste absorbent through statistical design

Massive amounts of wastewater are produced by the textile industry, and this waste needs to be appropriately managed. Agricultural waste wheat straw (WS), a biosorbent that is both economically available and environmentally acceptable, was used in this work to treat textile effluent. Microbial treated modification approaches were utilized for WS to study the dye removal from textile wastewater. Total 15 different isolates were screened for the dye degradation ability from Surat textile industrial effluent. The most significant deterioration was seen in PPSUHB3 when compared to other isolates. The amount of methylene blue dye removal was examined using the isolate PPSUHB3 due to its high efficiency. Based on 16s rDNA sequencing, it was predicted that the isolate PPSUHB3 was Bacillus licheniformis, having great capacity to degrade dye & wheat straw by producing efficient enzyme. The isolate showed the highest decolorization % of MB dye during optimization with WS absorbent which was verified using FTIR and SEM. The dye removal process parameters were statistically optimized using a central composite design (CCD). Wheat straw with particle sizes of 180–250 mm was discovered to be a possible adsorbent for the removal of colour. The maximum removal of MB (55.89%) was obtained using a statistical experimental design at pH 6.36, Temperature 44.6 °C, and Bacteria Concentration 3.04%. The created model is highly significant, according to the ANOVA, which found an R2 value of 0.9812 for it. The validation experiment revealed that the experimental and projected results were strikingly similar. The study found that using bacterial treated wheat straw as an adsorbent may remove wastewater that contains colours at a low cost.

Phytoconstituents Loaded Liposomes Fabricated Based on Box Behnken Design for Metabolic Syndrome: &lt;i&gt;In Vitro&lt;/i&gt; and &lt;i&gt;In Vivo&lt;/i&gt; Characterization

The global use of secondary metabolites like flavonoids, plant sterols, and alkaloids has been increasing due to their therapeutic benefits and fewer side effects compared to modern medicines. However, despite promising in vitro results, many herbal drugs and extracts demonstrate limited effectiveness in vivo due to their low lipid solubility and poor bioavailability. To address this issue, novel formulation strategies, particularly lipid-based delivery systems, are being proposed as carriers to enhance their bioavailability. This study focuses on the pharmaceutical development of liposomes that encapsulate three phytoconstituents, namely quercetin, berberine, and phytosterol, using the Quality by Design (QbD) concept. The Ishikawa diagram was utilized to identify the key factors affecting formulation quality, and the statistical experiment design concept was employed to optimize these factors. The liposomes were designed using the screening with the Placket-Burman approach and further optimized using the Box-Behnken method. The optimized liposomes exhibited an ideal size and achieved high entrapment efficiencies of 80.6%, 81.3%, and 80.35% for quercetin, berberine, and phytosterol, respectively. These liposomes were prepared using Phospholipon 90 G and cholesterol through the thin film hydration method. The resulting liposomes were thoroughly characterized and evaluated for morphology, % drug release, pharmacodynamic investigation, and stability studies.

Hamdard Institute of Pharmaceutical Sciences, Hamdard University, Pakistan

Tablet production is a very intricate process influenced by numerous process variables or parameters. This study aimed to identify the critical processing variables that affect Critical Quality Attributes (CQAs) of vitamin C film-coated caplets utilizing a statistical experimental design. A two-level complete factorial design with two central points was used to examine the process parameters that posed the greatest risk to CQAs. The process variables investigated included mesh size and duration for the lubrication process, as well as speed and main thickness for compression. Statistical results showed that mesh number, lubrication time, and their interaction significantly affect flow rate, Hausner ratio, and compressibility index. Higher mesh number and longer duration improved flow properties; lower main thickness significantly increased core caplet hardness; and lower dissolution rates were observed at higher compression speeds. Based on this study, it can be concluded that mesh number and lubrication time only significantly affected the bulk quality attributes but did not have a significant impact on the quality attributes of vitamin C caplets. On the other hand, the parameters of the compression process, such as speed and main thickness, greatly impacted the quality attributes of vitamin C caplets. In this study, the use of mesh number 20 with 7 minutes of lubrication, and a speed of 17 rpm with a main thickness scale of 2.00 were determined as the optimal process parameters. The optimal process parameters for the lubrication and compression processes were obtained from statistical analysis of the response data.

Optimization and reactor-scale production of plant growth regulators by Pleurotus eryngii.

The aims of the this study are to select the best cultivation type for plant growth regulator (PGR) production, to optimize PGR production with statistical experimental design, and to calculate bioprocess parameters and yield factors during PGR production by P. eryngii in flask and reactor scales. Submerged fermentation was the best cultivation type with 4438.67 ± 37.14, 436.95 ± 27.31, and 54.32 ± 3.21mg/L of GA3, ABA, and IAA production values, respectively. The Plackett-Burman and Box-Behnken designs were used to determine effective culture parameters and interactive effects of the selected culture parameters on PGR production by Pleurotus eryngii under submerged fermentation. The statistical model is valid for predicting PGR production by P. eryngii. After these studies, maximum PGR production (7926.17 ± 334.09, 634.92 ± 12.15, and 55.41 ± 4.38mg/L for GA3, ABA, and IAA, respectively) was reached on the 18th day of fermentation under optimized conditions. The optimum formula was 50g/L fructose, 3g/L NaNO3, and 1.5g/L KH2PO4, 1mg/L thiamine, incubation temperature 25°C, initial medium pH 7.0, and an agitation speed of 150rpm. The kinetics of PGR production was investigated in batch cultivation under 3-L stirred tank reactor conditions. Concentrations of GA3, ABA, and IAA of 10,545.00 ± 527.25, 872.32 ± 21.81, and 60.48 ± 3.48mg/L were obtained at the reactor scale which were 4.1, 3.4, and 2.3 times higher than the initial screening values. The specific growth rate (µ), the volumetric (rp) and specific (Qp) PGR production rates, 486.11mg/L/day and 107.43mg/g biomass/day for GA3, confirmed the successful transfer of optimized conditions to the reactor scale. In the presented study, PGR production of P. eryngii is reported for the first time.