Statistical Experimental Design Research Articles

Scalable Process Development of Ceritinib: Application of Statistical Design of Experiments

Scalable Process Development of Ceritinib: Application of Statistical Design of Experiments

Osseoconductive CaTi4-zZrz(PO4)6 Ceramics: Solutions Towards Nonunion, Osteoporosis, and Osteoarthrosis Conditions?

Transition (Ti, Zr) metal-substituted calcium hexaorthophosphate CaTi4-zZrz(PO4)6 coatings with an NaSICon structure were deposited by atmospheric plasma spraying (APS) onto Ti6Al4Veli substrates using a statistical design of experiments (SDE) methodology. Several coating properties were determined, including chemical composition, porosity, surface roughness, tensile adhesion strength, shear strength, and solubility in protein-free simulated body fluid (pf-SBF) and TRIS-HCl buffer solution. The biological performance evaluation involved cell proliferation and vitality studies and osseointegration tests of coated Ti6Al4Veli rods intramedullary implanted in sheep femora. After a 6 months observation time, a satisfactory gap-bridging potential was apparent as shown by a continuous, well-adhering layer of newly formed cortical bone. These tests suggest that the coatings possess a suitable osseoconductive potential and present an enhanced expression of bone growth-supporting non-collagenous proteins and cytokines, a high cell proliferation, spreading and vitality, and substantial osseointegration by strong bone apposition. The moderate intrinsic ionic conductivity of CaTi4-zZrz(PO4)6 compounds can be augmented by doping with highly mobile Na+ or Li+ ions to levels that suggest their use in electric bone growth stimulation (EBGS) devices, able to treat nonunion (pseudoarthrosis) and osteoporosis, and that may also support spinal stabilisation by vertebral fusion.

A supervised machine learning statistical design of experiment approach to modeling the barriers to effective snakebite treatment in Ghana.

Snakebite envenoming is a serious condition that affects 2.5 million people and causes 81,000-138,000 deaths every year, particularly in tropical and subtropical regions. The World Health Organization has set a goal to halve the deaths and disabilities related to snakebite envenoming by 2030. However, significant challenges in achieving this goal include a lack of robust research evidence related to snakebite incidence and treatment, particularly in sub-Saharan Africa. This study aimed to combine established methodologies with the latest tools in Artificial Intelligence to assess the barriers to effective snakebite treatment in Ghana. We used a MaxDiff statistical experiment design to collect data, and six supervised machine learning models were applied to predict responses whose performance showed an advantage over the other through 6921 data points partitioned using the hold-back validation method, with 70% training and 30% validation. The results were compared using key metrics: Akaike Information Criterion corrected, Bayesian Information Criterion, Root Average Squared Error, and Fit Time in milliseconds. Considering all the responses, none of the six machine learning algorithms proved superior, but the Generalized Regression Model (Ridge) performed consistently better among the candidate models. The model consistently predicted several key significant barriers to effective snakebite treatment, such as the high cost of antivenoms, increased use of unorthodox, harmful practices, lack of access to effective antivenoms in remote areas when needed, and resorting to unorthodox and harmful practices in addition to hospital treatment. The combination of a MaxDiff statistical experiment design to collect data and six machine learning models allowed the identification of barriers to accessing effective therapies for snakebite envenoming in Ghana. Addressing these barriers through targeted policy interventions, including intensified advocacy, continuous education, community engagement, healthcare worker training, and strategic investments, can enhance the effectiveness of snakebite treatment, ultimately benefiting snakebite victims and reducing the burden of snakebite envenoming. There is a need for robust regulatory frameworks and increased antivenom production to address these barriers.

Epigenetic training of human bronchial epithelium cells by repeated rhinovirus infections.

Humans are subjected to various environmental stressors (bacteria, viruses, pollution) throughout life. As such, an inherent relationship exists between the effect of these exposures with age. The impact of these environmental stressors can manifest through DNA methylation (DNAm). However, whether these epigenetic effects selectively target genes, pathways, and biological regulatory mechanisms remains unclear. Due to the frequency of human rhinovirus (HRV) infections throughout life (particularly in early development), we propose the use of HRV under controlled conditions can model the effect of multiple exposures to environmental stressors. We generated a prediction model by combining transcriptome and DNAm datasets from human epithelial cells after repeated HRV infections. We applied a novel experimental statistical design and method to systematically explore the multifaceted experimental space (number of infections, multiplicity of infections and duration). Our model included 35 samples, each characterized by the three parameters defining their infection status. Trainable genes were defined by a consistent linear directionality in DNAm and gene expression changes with successive infections. We identified 77 trainable genes which could be further explored in future studies. The identified methylation sites were tracked within a pediatric cohort to determine the relative changes in candidate-trained sites with disease status and age. Repeated viral infections induce an immune training response in bronchial epithelial cells. Training-sensitive DNAm sites indicate alternate divergent associations in asthma compared to healthy individuals. Our novel model presents a robust tool for identifying trainable genes, providing a foundation for future studies.

Competitive adsorption of two phenolic pollutants compounds using a novel biosorbent: Analytics (HPLC), Statistical (experimental design), and theoretical studies (DFT)

In this study, we focused on a novel agricultural waste material, specifically a plant known as Nicotiana glauca Graham (NgG), which is highly abundant in Morocco. The investigation involved testing four activating agents H3PO4, H2SO4, NaOH, and ZnCl2 on Nicotiana glauca Graham (NgG) to assess their effects. By employing an experimental design, we successfully determined the optimal conditions for this activation process. The resulting activated carbon was then evaluated for its effectiveness in removing two phenolic pollutants, Bisphenol A (BPA) and β-naphthol (BNL). Analysis using FTIR revealed various functional groups on the activated carbon surface, including P = O, P-O-C aromatics, and O-H groups, which played a crucial role in the adsorption of BPA and BNL. XRD analysis indicated that the optimal adsorbent was amorphous, while Zeta potential measurements showed a significant decrease in pollutant removal rates after reaching a pH level of nearly 10. The activated carbon produced from H3PO4 exhibited a surface area of 1078 m2/g. Experimental adsorption results at the highest removal rate showed qBPAs = 125.82 mg/g for BPA and qBNLs = 62.82 mg/g for BNL in individual mode, while in the mixed mode, qBPAm = 16.22 mg/g for BPA and qBNLm = 16.04 mg/g for BNL were observed. To enhance our study, we utilized Density Functional Theory (DFT) calculations to identify the most electrophilic and nucleophilic regions on BPA and BNL. The analysis highlighted the hydroxyl groups (–OH) of BNL and BPA as the most significant negative zones, crucial for understanding the underlying mechanisms and explaining the experimental observations. In the isotherm analysis, we identified the Temkin model in a singular mode and the Langmuir model in a mixture mode, showcasing a notable distinction between the two modes.

The optimal formulation of a readily compostable horticultural growing substrate for vertical farming was determined using design of experiments

A novel, optimized, polysaccharide and biochar-based, compostable hydrogel horticultural growing substrate for use in hydroponics and vertical farming was created based upon empirical methods and statistical design of experiments. A 15-run D-optimal mixture design of experiments was completed that increased the 14-day plant growing ability of a five-component hydrogel nearly ten-fold from 4.3695 g to 41.2623 g per 100 plants. The data were analyzed using a standard least squares method with an effect screening emphasis, and a model was created that maximized the signal to noise ratio. There was a good correlation between the measured and predicted values of the model, with an r-squared value of 0.90. The predictions of efficacy and compostability were confirmed with subsequent experiments that showed the hydrogel was composted in less than 84 days and that the plant growth predicted by the model differed from the experimental growth by 0.65%. The resulting optimized formulation had a high fertilizer content for a growth medium. We therefore suggest that an empirical approach to formulation research can produce superior outcomes with a statistically designed study.

Investigating Crude Glycerol Electrooxidation

Decarbonization of chemical manufacturing requires a range of strategies, including the utilization of process waste. This waste can then be used in processes powered by renewable electricity, such as electrocatalysis, to synthesize other useful chemicals. In doing so, process waste is reduced and industrially-relevant chemicals are produced without the use of fossil fuel-based feeds.One intriguing candidate feedstock for electrocatalytic conversion to relevant chemicals is glycerol, produced as a byproduct of biodiesel production at ~10% by mass. Glycerol electrooxidation can produce up to 12 different compounds, all with relevant industrial uses. Prior work has studied glycerol electrooxidation extensively to understand the mechanisms by which these compounds are produced on a variety of different catalysts.1,2 However, most of these studies utilize 99+% pure glycerol to investigate these mechanisms. There is very limited information on the electrooxidation of impure, or “crude,” glycerol – the glycerol that will exit the biodiesel production process in an industrial setting. This crude glycerol is made up of a mixture of glycerol, methanol, sodium hydroxide, and water, and its composition varies dramatically depending on the manufacturer. It is critical to understand the electrooxidation of crude glycerol to effectively scale the reaction.In this work, we investigate the electrooxidation of crude glycerol in scalable flow electrolyzers on precious3 and nonprecious metal catalysts. Using a multifaceted approach that includes system design, reaction engineering, and technoeconomic analysis, we explore the electrochemical performance of relevant compositions of crude glycerol and identify approaches to improve and augment that performance in terms of chemical production and cost efficiency.(1) Fleischmann, M.; Korinek, K.; Pletcher, D. The Oxidation of Organic Compounds at a Nickel Anode in Alkaline Solution. Journal of Electroanalytical Chemistry and Interfacial Electrochemistry, 1971.(2) Houache, M. S. E.; Hughes, K.; Baranova, E. A. Study on Catalyst Selection for Electrochemical Valorization of Glycerol. Sustainable Energy and Fuels, 2019.(3) Gaines, R. N.; Kleimenhagen, B. A.; Griebler, J. J.; Harris, L. C.; Gewirth, A. A.; Rogers, S. A.; Kenis, P. J. A. Optimizing the Flow Electrooxidation of Glycerol Using Statistical Design of Experiments. Journal of the Electrochemical Society, 2024.

Multi-parameter-based Box–Behnken design for optimizing energy transfer rate of Darcy–Forchheimer drag and mixed convective nanofluid flow over a permeable vertical surface with activation energy

The optimization of energy transfer rate in Darcy–Forchheimer inertial drag and mixed convective nanofluid motion over a vertical permeable surface with Arrhenius kinetics is obtained by utilizing a multi-parameter-based Box–Behnken design. The proposed investigation aims to enhance thermal management systems, with a particular focus on advanced cooling technologies and geothermal energy extraction. Precise control of heat transfer is essential in these applications. The integration of Brownian motion and thermophoresis effects elucidate the study for the energy transfer characteristics of the nanofluid flow. The employment of the Darcy–Forchheimer drag effects in the porous medium and the mixed convection is considered for the combined influence of buoyancy and forced convection. Activation energy is incorporated for the simulation of chemical reaction that is useful in various industrial purpose. Numerical technique such as shooting-based Runge–Kutta is adopted for the solution of transmuted dimensionless equations obtained by using adequate similarity rules. A critical parametric analysis is projected for the contributing factor with a strong validation comparing with earlier study. The robust Box–Behnken design, a statistical experimental design is utilized for the exploration of the influence of multi parameters involving radiating heat, Eckert number, Brownian motion, thermophoresis, and thermal Biot number. Further, the important outcomes are; the flow through permeable surface give rise to the impact of suction enhances the fluid velocity and the stronger thermal convection with increasing thermal Biot number also favors in enhancing the heat transport phenomenon.

Statistical design of experiments for efficient performance characterization of protonic-ceramic electrolysis cells

Statistical design of experiments for efficient performance characterization of protonic-ceramic electrolysis cells

Hamming distances of unsaturated orthogonal arrays

Orthogonal arrays (OAs) are applied in the statistical design of experiments, coding theory, cryptography, various types of software testing and quality control and have many connections to other combinatorial designs. The Hamming distances of OAs play a critical role in many areas such as algebraic combinatorics, quantum information theory, and wireless sensor networks. However, the calculation method of Hamming distances is difficult, especially for mixed orthogonal arrays (MOAs), since they rely on constructions or structures of OAs in many cases. In this article, by using the number of coincidences, we find some OAs with high strength and MOAs with strength 2 and 3, whose Hamming distances do not depend on their structures. We also calculate Hamming distances of a series of unsaturated asymmetrical OAs via their construction methods such as deleting columns, the expansive replacement method, and difference schemes.

Remote Sensing of Nitric Acid and Temperature via Design of Experiments, Chemometrics, and Raman Spectroscopy.

This study presents an effective method for the quantification of nitric acid (0.1-9 M) and the temperature (20-60 °C) through optimal experimental design, chemometrics, and Raman spectroscopy. Raman spectroscopy can be deployed using fiber-optic cables in hot cell environments to support processing operations in the nuclear field and industry. Chemical operations frequently use nitric acid and operate at nonambient temperatures either by design or by circumstance. Examples of Raman spectroscopy for the quantification of nitric acid with applications in the industrial field are profuse. However, the effect of temperature on quantification is often ignored and should be considered in real-world scenarios. Statistical design of experiments was used to build training sets for partial least-squares regression and support vector regression (SVR) models. The SVR model with a nonlinear kernel outperformed the top partial least-squares models with respect to temperature and resulted in percent root-mean-square error of prediction of 1.8% and 2.3% for nitric acid and temperature, respectively. The D-optimal design strategy decreased the sampling time by 75% compared to a more traditional seven-level full factorial option. The new method advances chemometric applications within and beyond the nuclear field and industry.

Kinetic modelling of carbothermal reduction of nickel oxide (NiO): Design of Experiments (DOE) approach

ABSTRACT This study presents a mathematical model to evaluate the kinetics of the carbothermal reduction of NiO using graphite. Based on the shrinking core theory, the model was validated against experimental data obtained under both isothermal and non-isothermal conditions. The shrinking core model for isothermal and non-isothermal conditions was created using MATLAB software for the kinetic modelling of the reduction process to determine the extent of reduction and the reaction rate. The kinetics were mathematically modelled from independently measured physical and thermodynamic properties of the reaction system and experimentally measured properties. The carbothermal reduction method was effective in producing Ni metal from NiO and was considered an established basis for evaluating the modelling aspect of the solid–gas system. The experimental investigation of carbothermal reduction was conducted using a statistical design of experiments (DOE), employing a two-level factorial design (23). The experiments were performed for a range of different factors, namely reduction temperature (800-1000°C), reduction time (1-2 h), and the molar ratio of graphite to NiO (0.5-1.5) on the extent of reduction. The extent of reduction increases with increasing reduction temperature, reduction time, and molar ratio of C to NiO. The highest extent of reduction (77.9%) was achieved at a reduction temperature of 1000°C, 2 h of reduction time, and the molar ratio of C to NiO as 1.5. The X-ray diffraction (XRD) studies confirmed the formation of Ni at a lower reduction time, but the intensity of the Ni phase was higher for a longer duration of reduction. SEM-EDS analysis of the reduced products confirmed the formation of Ni in the form of elongated and larger grains, along with traces of residual or unreacted carbon. The predicted extent of reduction is then compared with the experimentally measured results. The kinetic modelling results proved that both isothermal and non-isothermal shrinking core models showed similarity and significant closeness to the experimental data. The deviation between the experimental and model results could be due to the varying geometry and porosity of the reactants and intermediate products.

NANO TITANIUM DIOXIDE AS AN EFFICIENT COMPLEXING AGENT FOR THE SYNTHESIS OF CURCUMIN

Curcumin is a natural polyphenol known to exhibit multiple therapeutic effects. Methods for the synthesis of curcumin employ complexing of acetylacetone with boron compounds, which protects the active methylene group. As the reactions are not optimal with boron-based complexes, alternative complexing agents for greater efficiency were explored. Amongst these, titanium dioxide and its nanoparticles offered superior complexing activity. This complex was prepared in situ and was characterized to be titanyl acetylacetonate. The reaction conditions were optimized with different solvents, catalysts, water scavenging agents and temperatures using the OVAT (one variable at a time) approach. We then utilized the statistical design of experiments (DOE) approach to optimize critical parameters that influence the yield and purity of curcumin. The optimized reaction conditions yielded curcumin exceeding 90 %, with a reaction time of 8 h, suggesting that nano TiO2 functions very effectively as a complexing agent.

Implementing the Design of Experiments (DoE) Concept into the Development of Mucoadhesive Tablets Containing Orange Peel Extract as a Potential Concept for the Treatment of Oral Infections.

This study explores for the first time the impact of chitosan (CS) with varying molecular weights (MW), orange peel extract concentration, and hydroxypropyl methylcellulose (HPMC) content on the formulation of buccal tablets for treating oral infections. Utilizing a statistical design of experiments (DoE), nine different formulations were evaluated for mechanical properties, dissolution behavior, mucoadhesion, and biological activity. A formulation with high CS MW, 60% orange peel extract, and 8% HPMC, emerged as the optimal formulation, demonstrating superior tabletability, compressibility, and compactibility. Dissolution studies indicated that hesperidin release followed the Higuchi model, with higher extract content enhancing this phenomenon. Mucoadhesion improved with increased HPMC and CS concentrations, although higher extract content reduced bioadhesion. Biological assays showed that higher extract levels boosted antioxidant activity, while CS primarily contributed to anti-inflammatory effects. The optimized formulation exhibited broad antimicrobial activity against key oral pathogens, surpassing the effectiveness of the individual components. Principal component analysis (PCA) further confirmed the significant influence of extract content on tablet properties. These findings suggest that the optimized tablet formulation holds promise for effective buccal delivery in the treatment of oral infections, warranting further investigation in clinical settings.

Drinking water treatment through Optimising Phosphate Coagulation collected from four villages in Srikakulam, India: a Response Surface Methodology approach

ABSTRACT The drinking water quality is highly affected by industrialisation and by natural geographical features of the region. This work highlights on the quality survey and treatment of drinking water collected from four villages (Kalyanipeta, Kesavadasupuram, Arinam Akkivalasa, and Chilakapalem) near Nagarjuna Agrichem Limited in Srikakulam district, Andhra Pradesh. Physicochemical parameters of the samples were analysed. The presence of phosphates in the samples (3.15 mg/L P) were found to be above the permissible limits (1 mg/L P) which required instant treatment by coagulation process. Various influencing factors like temperature, dosage, mixing speed, and mixing time were considered for optimization of the process. The statistical experimental design was done using central composite approach of response surface methodology to optimise phosphate removal via coagulation and flocculation technique. An optimised removal of phosphates was determined to be 79.5% and 76.06% at a temperature of 27°C and 28°C, dosage of 13.412 and 14.906 mg/L, mixing speed of 91 and 78 rpm, and mixing time of 14.25 and 11.62 min for coagulants ferric chloride and aluminium sulphate respectively. Ferric chloride was found to be a better coagulant with a removal efficiency of 79.5% and the treated water contained 0.645 mg/L of phosphate.

The Utilization of Central Composite Design for the Production of Hydrogel Blends for 3D Printing

Central composite design (CCD) is a statistical experimental design technique that utilizes a combination of factorial and axial points to study the effects of multiple variables on a response. This study focused on optimizing hydrogel formulations for 3D printing using CCD. Three biopolymers were selected: sodium alginate (SA), gelatin (GEL), and carboxymethyl cellulose (CMC). The maximum and minimum concentrations of each polymer were established using a Google Scholar search, and CCD was employed to generate various combinations for hydrogel preparation. The hydrogels were characterized in accordance with their swelling degree (SD) in phosphate-buffered saline (PBS) and Dulbecco’s Modified Eagle Medium (DMEM), as well as their printability in 2D and 3D assays. The formulation consisting of 7.5% SA, 7.5% GEL, and 2.5% CMC exhibited the best swelling properties and exceptional printability, surpassing all other tested formulations. This study highlights the effectiveness of design of experiment methodologies in accelerating the development of optimized hydrogel formulations for various applications in 3D printing and suggests avenues for future research to explore their performance in specific biological contexts.

The importance of factorial design on the optimization of biosensor performance: immobilization of glucose oxidase as a case study.

Conventionally, the optimization of glucose biosensors is achieved by varying the concentrations of the individual reagents used to immobilize the enzyme. In this work, the effect and interaction between glucose oxidase enzyme (GOx), ferrocene methanol (Fc), and multi-walled carbon nanotubes (MWCNTs) at different concentrations were investigated by a design of experiments (DoE). For this analysis, a factorial design with three factors and two levels each was used with the software RStudio for statistical analysis. The data were obtained by electrochemical experiments on the immobilization of GOx-Fc/MWCNT at different concentrations. The results showed that the factorial DoE method was confirmed by the non-normality of the residuals and the outliers of the experiment. When examining the effects of the variables, analyzing the half-normal distribution and the effects and contrasts for GOx-Fc/MWCNT, the factors that showed the greatest influence on the electrochemical response were GOx, MWCNT, Fc, and MWCNT:Fc, and there is a high correlation between the factors GOx, MWCNT, Fc, and MWCNT:Fc, as shown by the analysis of homoscedasticity and multicollinearity. With these statistical analyses and experimental designs, it was possible to find the optimal conditions for different factors: 10mMmL-1 GOx, 2mgmL-1 Fc, and 15mgmL-1 MWCNT show a greater amperometric response in the glucose oxidation. This work contributes to advancing enzyme immobilization strategies for glucose biosensor applications. Systematic investigation of DoE leads to optimized immobilization for GOx, enables better performance as a glucose biosensor, and allows the prediction of some outcomes.

The optimization of low volume-SPME method for volatilomics analysis of exhaled breath condensate

The analysis of breath can provide insight into the metabolic state of the body. However, the collection, transport, and storage of breath samples present certain challenges. The collection of exhaled breath condensate (EBC) emerges as a promising method for breath analysis. Despite this, the volume of EBC is relatively low, necessitating a sensitive method capable of extracting analytes from a small sample volumes. Therefore, this study aimed to develop and optimize a robust and green solid-phase extraction method for the analysing volatile organic compounds (VOCs) of various polarities in a low-volume EBC using a statistical design of experiment (DoE).Based on the screening experiments and DoE, the optimal conditions were determined to be 15 min equilibration time, and 20 min of extraction at 44 °C using DVB/CAR/PDMS fibre in direct immersion (DI) mode. The method was validated for linearity, precision and accuracy, yielding satisfactory results. The optimized method was applied to quantify the concentration of selected VOCs in real samples collected from a healthy subject, as well as after consumption of coffee and smoking of an electronic cigarette. Many of the selected compounds are associated with diseases and are challenging to detect in specimens from a healthy subject, but this method can measure them as potential disease markers.The proposed method enables the analysis of VOCs is very low-volume samples (200 µL), which is particularly important for studies involving body fluids. Notably, the applied approach demonstrated that different VOCs, representing various chemical classes and polarities, can be detected and quantified.

Optimization of Mesoporous Silica Nanoparticles of Silymarin through Statistical Design Experiment and Surface Modification by APTES

ABSTRACT: The major goal of this study was to create surface-modified mesoporous silica nanoparticles (MSN) of Silymarin, which has a short half-life and requires regular dosing due to first-pass metabolism. MSN was prepared by cost effective Sol-Gel method, functionalized by (3-Aminoprpyl) triethoxysilane (3-APTES). Evaluated and compared the Silymarin-loaded MSN and APTES functionalized MSN for drug loading, in-vitro dissolution, particle size, surface morphology, surface area, pore size and volume. The percent drug loading and encapsulation efficiency of APTES-functionalized MSN were found to be 92.50±0.72 % percent and 93.20±0.13 %, respectively, which showed a higher value in APTES-functionalized MSN as compared to Silymarin-loaded MSN. Drug release of APTES-functionalized MSN was 92.15 0.03%, which was more as compared to silymarin-loaded MSN. XRD showed that after functionalization silymarin changed from crystalline to amorphous form. SEM indicates that MSN was formed in a spherical shape with particle size of 196.7nm. The BET analysis proved that the nanoparticles had a larger surface area, which was reduced after drug loading and functionalization. Also, the pore size was increased and the pore volume was reduced after functionalization, indicating the incorporation of silymarin. The results suggested that the functionalization might successfully increase adsorption capacity, larger surface area, and increased encapsulation. Mesoporous silica nanoparticles also showed a greater impact on dissolution.

Lignocellulose Treatment Using a Flow-Through Variant of OrganoCat Process.

This study adapts the biphasic OrganoCat system into a flow-through (FT) reactor, using a heated tubular setup where a mixture of oxalic acid and 2-methyltetrahydrofuran (2-MTHF) is pumped through beech wood biomass. This method minimizes solvent-biomass contact time, facilitating rapid product removal and reducing the risk of secondary reactions. A comparative analysis with traditional batch processes reveals that the FT system, especially under severe conditions, significantly enhances extraction efficiency, yielding higher amounts of lignin and sugars with reduced solid residue. Notably, the FT system shows partial hydrolysis of the cellulose, which increases with temperature while not producing significant amounts of furfural or 5-HMF, indicating more efficient depolymerization of polysaccharides without substantial sugar degradation. A statistical design of experiments (DOE) using a Box-Behnken design elucidates the influence of process variables (time, solvent flow rate, temperature) on the yield. Key findings highlight reactor temperature as the dominant factor affecting yields, with process time showing a significant but less pronounced impact. This study demonstrates the potential of the FT OrganoCat system for efficient lignocellulosic biomass fractionation and represents an advancement towards continuous lignocellulose processing, contributing to our knowledge of process optimization for improved biorefinery applications.