I-optimal Design Research Articles

Fabrication and Optimization of a Silodosin In Situ-Forming PLGA Implants for the Treatment of Benign Prostatic Hyperplasia: In Vitro and In Vivo Study

Objectives: Lower urinary tract symptoms (LUTSs) related to benign prostatic hyperplasia (BPH) are common in older men, and alpha-adrenoceptor blockers continue to be a key part of managing these symptoms. This study aimed to formulate injectable poly (lactic-co-glycolic acid) (PLGA) in situ-forming implants (ISFIs) loaded with silodosin (SLD) to address symptoms associated with BPH. This method, which ensures prolonged therapeutic effects of SLD, is intended to decrease dosing frequency and improve treatment outcomes, leading to better patient adherence. Methods: An appropriate solvent with favorable PLGA solubility, viscosity, and in vitro release profile was selected. Additionally, an I-optimal design was employed as an optimization technique. An in vivo study in albino male rats was conducted to investigate prostate-specific antigens (PSAs), prostate weight and prostatic index, histopathology, and SLD pharmacokinetics. Results: The optimized formulation showed experimental values of 29.25% for the initial burst after 2 h and 58.23% for the cumulative release of SLD after 10 days. Pharmacokinetic data revealed that the SLD–ISFI formulation had lower Cmax and higher AUC values than subcutaneous (SC) pure SLD and oral commercial SLD capsule, indicating the controlled-release impact and improved bioavailability of the ISFI systems. SLD–ISFI produced a marked drop in the prostatic index by 2.09-fold compared to the positive control. Serum PSA level decreased significantly from 0.345 ± 0.007 to 0.145 ± 0.015 ng/mL after SLD–ISFI injection compared to the positive control. Conclusions: This study indicated that the optimized SLD–ISFI formulation proved its efficacy in managing BPH.

Optimizing aggregate grading and residual mortar coefficient in low-cement concrete by using crushed coal mine waste rock as base layers for low-traffic roads

In the context of depleting natural resources and environmental challenges posed by large-scale extraction and use of natural aggregates, this study investigates the use of crushed coal mine waste rock (CCMWR) as a sustainable alternative for low-cement concrete in the base layers of low-traffic roads. The primary goal is to optimize the aggregate grading and the residual mortar coefficient (RMC) to enhance the performance and durability of low-cement concrete. Utilizing the I-optimal design methodology and curve of Fuller for particle size distribution, this research evaluates the mechanical properties, workability, and environmental impact of incorporating CCMWR in low-cement concrete. The findings demonstrate that with the appropriate optimization, CCMWR can effectively replace natural aggregates, significantly reducing the ecological footprint and providing an effective solution for sustainable road construction. This research holds substantial engineering application significance by offering an innovative approach to classify and reuse waste from the coal mining industry while addressing the urgent need for sustainable infrastructure development.

A chemometric approach using I-optimal design for optimising Pb(II) removal using bentonite-chitosan composites and beads

This paper reports adsorption studies of Pb(II) ions onto Bentonite-Chitosan (Bt-Ch) composites or beads when using an I-optimal design experiment approach. Three adsorption factors (pH, adsorbent dosage, and initial concentration) were optimised whilst simultaneously investigating multiple adsorbents. The Bt-Ch composites and beads (type A and B) adsorbents were made using weight ratios 90%/10% and differed characteristically due to their preparation methods of solution blending and precipitation, respectively. A batch procedure was used for adsorption experiments, and the amounts of Pb(II) ions (adsorbed onto Bt-Ch composites/beads) were analysed using inductively coupled plasma optical emission spectrometry (ICP-OES). Adsorption experimental parameters were analysed and optimised by using a response surface method (I-optimal design) generated from Design-Expert® 13.0 software. The main achievements of this study were to intensify the understanding and application of I-optimal experimental designs, which allow simultaneous determination of adsorption capacities and efficiencies across multiple adsorbents in an economical manner. A reduced quadratic model provided the best fit for the experimental data and exhibited minimal deviation between predicted and experimental values. This was evidenced by the very small covariance (CV) values of 1.81% and 1.33% observed for adsorption capacity and adsorption efficiency, respectively, also suggesting high reproducibility. It was observed that the adsorption factors studied (pH, adsorbent dose, and initial concentration) have a more pronounced effect on the adsorption capacity (F-value = 714.37) compared to adsorption efficiency (F-value = 140.62). Adsorbent dosage was found to have the greatest effect on adsorption capacity, while the initial pH of Pb(II) solution had the greatest effect on adsorption efficiency. Under optimal conditions, the adsorption capacities of beads-A (73.2 mg/g) and beads-B (77.6 mg/g) were found to be higher than that of the corresponding composite (51.7 mg/g). Whilst the optimum adsorption efficiency values for all three adsorbents were ∼100% (with ranges of pH 2–5, initial concentrations 50–200 mg/L, and adsorbent dosage 0.05–0.5 mg). The desirability indexes for the optimised conditions for these respective responses (and each adsorbent) were found to be within the ranges of 0.892–0.974 and 0.945–0.967 for adsorption capacity and adsorption efficiency, respectively. These high desirability index values for both responses indicate that the optimised conditions lead to very good performance for both measures. The information obtained in this study provides detailed understanding of the adsorption phenomena of the adsorbents studied. It gives confidence in the use of I-optimal designs to be applied as a chemometric tool for the specific adsorbents studied herein and others.

Prediction of the structure of polymer-coated cardboards produced by thermocompression using I-optimal design of experiment.

The production of contrasted polymer-coated cardboards is necessary to establish the structure/properties relationships and produce the « just necessary » packaging materials. For that purpose, the impact of the processing parameters on the resulting structure of polymer-coated cardboards was modeled using a statistical Design of Experiment (DOE) approach. Five independent factors were considered: three numeric continuous factors, the pressure, the temperature, and the duration of thermocompression, one numeric discrete factor, the initial polymer film thickness, and one categorical factor, the cardboard basis weight. Four responses were considered: the thicknesses of each of the three layers constituting polymer-coated cardboards (i.e., the free polymer, the impregnated, and the free cardboard) and the material’s curvature. The choice of the adequate DOE was first assessed using a Scoping Design and coupled with the characterization of the used polymer, i.e., poly(3-hydroxybutyrate-co-3-hydroxyvalerate (PHBV). The I-optimal DOE was found to be the most suitable and was therefore implemented. To validate the model, the DOE was then used to produce two targeted structures, one with no impregnation of the polymer and another one with a complete impregnation of the cardboard. The values of thicknesses and curvature were not significantly different from the model predictions, therefore verifying the model.

The Comparison A-Optimal and I-Optimal Design in Non-Linear Models to Increase Purity Levels Silicon Dioxide

One of the obstacles that arise in optimal design is the non-linear model. The relationship between temperature factors and the temperature increase rates with the purity of silicon dioxide (SiO2) forms a non-linear pattern. Determining the optimal design for a non-linear model is relatively more complex than a linear model because it requires additional information in its information matrix. Therefore, this issue necessitates further research on optimal design in non-linear models. This study uses the polynomial Taylor approach to approximate the non-linear equation through a linear equation using the appropriate optimal design methods, namely A-Optimal and I-Optimal criterion. The point search algorithm used was variable neighborhood search, this algorithm searches for design points by exploring several different neighborhood structures. These two methods were chosen to compare the characteristics and performance of the designs produced, aiming to obtain an optimal design to improve SiO2 purity (non-linear case) using the same algorithm, VNS. The research results showed that the design pattern produced by the A-Optimal design formed three temperature groups, namely the minimum temperature of 800°C - 820°C, the middle temperature of 850°C, 860°C, and the maximum temperature of 900°C, with varying temperature increase rates in the design area. The design pattern produced by the I-Optimal design formed a full quadratic pattern, namely the minimum temperature of 800°C and the maximum temperature of 900°C, with varying temperature increase rates in the design area. The I-Optimal design demonstrated the best performance (most optimal) in the aspect of prediction variance compared to the A-Optimal design across all alternative points in this study to improve SiO2 purity.

Optimisation of a Microwave Synthesis of Silver Nanoparticles by a Quality by Design Approach to Improve SERS Analytical Performances.

A major limitation preventing the use of surface-enhanced Raman scattering (SERS) in routine analyses is the signal variability due to the heterogeneity of metallic nanoparticles used as SERS substrates. This study aimed to robustly optimise a synthesis process of silver nanoparticles to improve the measured SERS signal repeatability and the protocol synthesis repeatability. The process is inspired by a chemical reduction method associated with microwave irradiation to guarantee better controlled and uniform heating. The innovative Quality by Design strategy was implemented to optimise the different parameters of the process. A preliminary investigation design was firstly carried out to evaluate the influence of four parameters selected by means of an Ishikawa diagram. The critical quality attributes were to maximise the intensity of the SERS response and minimise its variance. The reaction time, temperature and stirring speed are critical process parameters. These were optimised using an I-optimal design. A robust operating zone covering the optimal reaction conditions (3.36 min-130 °C-600 rpm) associated with a probability of success was modelled. Validation of this point confirmed the prediction with intra- and inter-batch variabilities of less than 15%. In conclusion, this study successfully optimised silver nanoparticles by a rapid, low cost and simple technique enhancing the quantitative perspectives of SERS.

Olaparib Process Development Employing Quality by Design (QbD) Principles.

This study focuses on multivariate experimental design and statistical analysis to optimize the process of Olaparib 1. Quality by design (QbD) methodology was adopted for optimization of the Olaparib process consisting of three reaction steps: (1) amidation, (2) deprotection, and (3) acylation. Every chemical conversion was studied in isolation, employing risk assessment to identify key material attributes and key process parameters that may have the potential to impact the reaction. Thereafter, the screening design of experiment (DoE) was employed to scrutinize the factors that significantly impacted yield. Moving forward, the scrutinized factors which were found to impact the responses, the set of critical material attributes (CMAs) and critical process parameters (CPPs), were considered for optimization by applying I-Optimal design to define design space arriving at a robust setting wherein the predefined targets were supposedly optimal. To our delight, we got 95, 91, and 75% yield with more than 99% purity in amidation, deprotection, and acetylation, respectively, which enabled us to systematically identify design space to meet the desired quality target of the product consistently. More importantly, to distinguish the CMAs and CPPs, these elements ought to be monitored to have control of the quality parameter throughout the active pharmaceutical ingredient (API) value chain until commercial manufacturing followed by marketing. Eventually, we have developed a greener process in comparison to precedented one for Olaparib 1.

Machine learning-driven optimization of mRNA-lipid nanoparticle vaccine quality with XGBoost/Bayesian method and ensemble model approaches

To enhance the efficiency of vaccine manufacturing, this study focuses on optimizing the microfluidic conditions and lipid mix ratios of messenger RNA-lipid nanoparticles (mRNA-LNP). Different mRNA-LNP formulations (n = 24) were developed using an I-optimal design, where machine learning tools (XGBoost/Bayesian optimization and self-validated ensemble (SVEM)) were used to optimize the process and predict lipid mix ratio. The investigation included material attributes, their respective ratios, and process attributes. The critical responses like particle size (PS), polydispersity index (PDI), Zeta potential, pKa, heat trend cycle, encapsulation efficiency (EE), recovery ratio, and encapsulated mRNA were evaluated. Overall prediction of SVEM (> 97%) was comparably better than that of XGBoost/Bayesian optimization (> 94%). Moreover, in actual experimental outcomes, SVEM prediction is close to the actual data as confirmed by the experimental PS (94∼96 nm) is close to the predicted one (95∼97 nm). The other parameters including PDI and EE were also close to the actual experimental data.

Effect of particle size versus surface charge on the brain targeting behavior of elastic nanovesicles: In-vitro characterization, comparison between I-optimal and D-optimal statistical optimization and in-vivo pharmacokinetic evaluation

Rasagiline mesylate (RM) is a potent monoamine oxidase-B inhibitor that suffers from extensive hepatic metabolism, low bioavailability, and low brain distribution. Thus, this study aimed to formulate an intranasal nanovesicular system with high RM encapsulation (EE%) and enhanced delivery to the brain tissues. In this perspective, negatively and positively charged RM-transfersomal vesicles were prepared, characterized and the formulation parameters were statistically optimized as per two different response surface methodologies namely, I-optimal and D-optimal design. Although both statistical designs acted almost similarly in terms of experimental response analysis; the I-optimal design might be functioning better in terms of formulations’ optimization and response prediction with higher desirability factors. Consequently, the optimized negative transfersomes (particle size: 213 nm, EE%:62.55 %) and positive transfersomes (particle size: 515 nm, EE%:70.9 %) suggested by the I-optimal design were furtherly formulated, characterized for their in-vitro release, elasticity, transmission electron microscope and pH. Finally, both systems were administered intranasally and compared to an intravenous RM solution where the optimized positive transfersomes has shown significantly higher brain targeting efficiency, positive direct transfer percentage values and longer brain mean residence time. These results suggested that the surface charge of the formulated vesicles could be more effective factor -than the particle size-in enhancing the brain bioavailability of the administered drugs.

A new methodology to robustify an experimental design: Application to the Baranyi model

A robust experimental design is a desired object for practitioners when there is uncertainty about any of the assumptions necessary to compute the optimal design. For instance, when they use non-linear models, which requires having nominal values of the parameters. Several alternatives have been developed in the literature to obtain robust experimental designs such as adaptive or Bayesian designs, among others. Here a new methodology is proposed to robustify the optimal experimental design. Based on the maximin idea, the method adds support points to the optimal design, to obtain designs that are robust. It is applied to the Baranyi model, one of the most used mathematical models in predictive microbiology to describe the behaviour of microorganisms in food products, an essential issue for human health. Previously, D-optimal designs are provided for the model, considering 4 and 6 parameters to be estimated. A sensitivity analysis is carried out regarding the deviations in the nominal values of the parameters, which shows a greater loss of efficiency for two of them. Given these results, the new methodology is applied to the D-optimal design, checking the robustness of the augmented designs through the efficiency achieved. Finally, c-, A- and I-optimal designs are calculated to provide accurate estimation of the model parameters and the predictions.

Statistical Applications in Pharmaceutical Product Development: A Comprehensive Review

The pharmaceutical industry often depends on statistics to improve product quality, optimize the compositions, streamline operations, and conform to regulatory requirements. In Bio Equivalency investigations, Statistics plays an integral role in decision-making while demonstrating the similarity between the test and reference products. In this review, we explore how statistics play a crucial role in pharmaceutical product development. We start with examining experiments, where statistics help scientists find the best combinations of ingredients and manufacturing conditions. We explored different types of experiments, like factorial designs, Plackett-Burman designs, Box-Behnken designs, and Taguchi designs. While optimizing the formulations and process parameters, these methodologies assist researchers in making meaningful conclusions supported by statistical inference. The next sections include Response Surface Methodology (RSM) and Mixture Designs, which help in the development of drug formulations and the comprehension of complex responses. The development of pharmaceuticals is made simpler by these approaches. Then, we examine optimal designs like A-optimal, I-optimal, and D-optimal. A-optimal designs save resources, I-optimal designs focus on precise estimates, and D-optimal designs are great for screening and getting accurate results. These designs help scientists make decisions based on data. The starting point for demonstrating dissolution similarity between generic and reference products is the statistical approach. This paper examines the use of model-dependent techniques, such as zero-order, first-order, Higuchi, and Weibull models, and model-independent techniques such as F1, F2, Bootstrap, MSD, and PCA techniques, it analyzes important parameters and bounds that are essential for determining the dissolution similarity. In the field of biostatistics, we discuss how statistics ensures that generic drugs are safe and work just as efficiently as brand-name drugs. Both regulatory approval and patient safety rely on this. We briefly discuss statistical process control (SPC) in closing. SPC checks that drugs/drug products are consistently of quality using graphs and figures. It facilitates the production of consistently effective pharmaceuticals by organizations. Advanced statistical techniques, including data mining, machine learning, and artificial intelligence, are revolutionizing the pharmaceutical industry. These tools enhance drug discovery, optimize manufacturing processes, and pave the way for personalized medicine. As real-time monitoring and predictive analytics become integral, the future holds exciting possibilities for improving patient outcomes. This review shows how statistics are like a guiding tool in the pharmaceutical industry. Whether it's in the lab or in manufacturing, statistics play a big role in making sure patients get the best medicines possible.

Development and Optimization of Andrographis paniculata Extract-Loaded Self-Microemulsifying Drug Delivery System Using Experimental Design Model.

The objectives of this study were to develop an optimized formulation for an Andrographis paniculata extract (AGPE)-loaded self-microemulsifying drug delivery system (SMEDDS) using an experimental design and evaluate the characteristics of the developed SMEDDS. The solubility of andrographolide (AGP) in various solvents was investigated. The pseudo-ternary phase was constructed to provide an optimal range for each component to form microemulsions (MEs). The formulation was optimized using an I-optimal design mixture type, where the physical stability, droplet size, polydispersity index, and zeta potential were examined. Soft capsules of the optimized AGPE-loaded SMEDDS were manufactured. The dissolution and ex vivo membrane permeation were studied. Oleic acid, Tween® 80, and PEG 400 were the best solubilizers for AGP. The promising surfactant to co-surfactant ratio to generate ME was 3:1. The optimized SMEDDS contained 68.998% Tween® 80, with 13.257% oleic acid and 17.745% PEG 400. The assayed content of AGP, uniformity of dosage unit, and stability complied with the expected specifications. The dissolution and membrane permeability of AGPE-loaded SMEDDS was significantly improved from the A. paniculata extract (p < 0.05). All in all, the developed optimized AGPE-loaded SMEDDS was proven to contain optimal composition and AGP content where a stable ME could spontaneously be formed with enhanced delivery efficacy.

Investigating the efficacy of mirtazapine-embedded invasomal gel nanocarriers via I-optimal design for management of atopic dermatitis

The objective of the present study was to optimize a novel invasomal gel (inva-gel) formulation enriched with terpene for upgrading the therapeutic action of anti-depressant mirtazapine on atopic dermatitis as an off-label use. Various factors were studied by I-optimal design to propose an optimized invasome with desirable characteristics. Its incorporation into different hydrogel bases was targeted to suggest a desirable inva-gel formulation, inspect its ex-vivo performance, and conduct in-vivo pre- and post-sacrifice assessments on atopic dermatitis-induced rats. Results revealed positive ethanol action on increasing entrapment efficiency (up to 77.88%) and decreasing size of invasomal vesicles (from 390.30 nm to 190.60 nm). Increased terpene amounts provided more space for mirtazapine incorporation with enhanced solubility and encapsulation inside invasomes. Terpene's molecular weight and lipophilicity significantly affected physicochemical characteristics of invasomes. More cross-linkages would occur by raising gel:liquid invasome ratio (from 0.5:1 to 2.5:1) and using carbopol other than hydroxylpropyl methyl cellulose (HPMC), therefore increasing rheological properties of inva-gels. Synergistic effects of phospholipid, ethanol, limonene, and carbopol to enhance mirtazapine permeation through skin were observed. Throughout the pre-sacrifice assessments, optimized inva-gel demonstrated a significant decrease in dermatitis severity score (from 8.33 to 3.00) and scratching behavior (from 184.32 s to 37.42 s) in disease-induced rats, hence showing favorable skin profile. Concerning the post-sacrifice assessments, optimized inva-gel significantly alleviated inflammation and pruritus symptoms by minimizing epidermal thickening (from 92.32 μm to 50.17 μm), down-regulating various inflammatory mediators, and decreasing infiltration of inflammatory mast cells. Besides, the ear swelling and spleen index values were markedly decreased (from 77.37 mg to 22.76 mg and from 0.56% to 0.35%, respectively). In conclusion, novel anti-depressant mirtazapine-embedded inva-gel displayed successful transdermal drug permeation offering valuable merits in atopic dermatitis management.

Bio-adsorption of Cationic-Cnionic Dyes from Synthetic Effluents using an Experimental Design Approach

In the current research, Cycas leaves powder (CL) as a low-cost agro-waste adsorbent was investigated for malachite green (MG) and Indigo Carmine (IC) dye removal from contaminated water. The Cycas leaves powder was characterized by Scanning electron microscopy (SEM) with energy dispersive X-ray spectroscopy (EDX) and Fourier transform infrared (FTIR) techniques. The adsorption parameters were investigated including contact time, dye concentrations, and adsorbent dosage to determine their effects on the adsorption efficiency. The I-optimal design approach was used to intimately detect the significance of adsorption parameters and their interactions by analyzing the overall statistical results. The adsorption isotherm and kinetics were analyzed using two models. The results showed that the data was well-fitted with the Freundlich isotherm model and the adsorption kinetics followed the pseudo-first-order model. The thermodynamics parameters including ΔHº, ΔSº, and ΔGº were determined, and the adsorption process was non-spontaneous, and exothermic for MG and endothermic IC dyes. The results demonstrated that Cycas leave can be exploited as an affordable inexpensive material for the treatment of industrial dyes from textile discharges.

Kinetic modeling of phenolic compounds extraction from nutshells: influence of particle size, temperature and solvent ratio

This paper studies the effects of particle size, temperature and ethanol–water solvent ratio on the extraction of total phenolic compounds (TPC) from peanut, coconut, and macadamia nutshells. Using an I-optimal design, the maximum TPC extraction obtained from the shells ranged from 63.5 ± 1.6 to 76.2 ± 3.1 mg gallic acid equivalent (GAE) per 100 g dry weight (dw) of nutshell. Next, a response surface model (RSM) was developed to describe the relationship between the process parameters and the extracted TPC concentration, in order to predict the optimal extraction conditions. For all of the examined biomasses, the optimal conditions for extraction were predicted at a particle size of 1 mm, temperature of 75 °C and ethanol/water mixture of 54, 53 and 65% ethanol, for peanut, coconut and macadamia nutshells respectively. Particle size seems to be the most important parameter, while temperature appears to be of lesser importance. Besides, the extraction kinetics were assessed by fitting kinetic models on the experimental data. The combined second-order diffusional model provided the best goodness of fit. This model revealed that, at the boundary layer, the effect of washing mechanism of extraction is more important than extraction due to diffusion kinetics. This study provides an understanding of the mass transfer mechanism involved in the TPC extraction process from nutshells, which yields valuable insights that could facilitate the industrial biorefinery of nutshells.Graphical

Applying I-Optimal Design for Tuning Internal Cavity of Hollow Mesoporous Silica Nanoparticles for 5-Fluorouracil Delivery: Investigating Drug-Loading/Releasing Behavior and Biocompatibility Properties

Applying I-Optimal Design for Tuning Internal Cavity of Hollow Mesoporous Silica Nanoparticles for 5-Fluorouracil Delivery: Investigating Drug-Loading/Releasing Behavior and Biocompatibility Properties

Improvement of tensile strength of fused deposition modelling (FDM) part using artificial neural network and genetic algorithm techniques

PurposeThis paper presents an experimental investigation in establishing the relationship between FDM process parameters and tensile strength of polycarbonate (PC) samples using the I-Optimal design.Design/methodology/approachI-optimal design methodology is used to plan the experiments by means of Minitab-17.1 software. Samples are manufactured using Stratsys FDM 400mc and tested as per ISO standards. Additionally, an artificial neural network model was developed and compared to the regression model in order to select an appropriate model for optimisation. Finally, the genetic algorithm (GA) solver is executed for improvement of tensile strength of FDM built PC components.FindingsThis study demonstrates that the selected process parameters (raster angle, raster to raster air gap, build orientation about Y axis and the number of contours) had significant effect on tensile strength with raster angle being the most influential factor. Increasing the build orientation about Y axis produced specimens with compact structures that resulted in improved fracture resistance.Research limitations/implicationsThe fitted regression model has a p-value less than 0.05 which suggests that the model terms significantly represent the tensile strength of PC samples. Further, from the normal probability plot it was found that the residuals follow a straight line, thus the developed model provides adequate predictions. Furthermore, from the validation runs, a close agreement between the predicted and actual values was seen along the reference line which further supports satisfactory model predictions.Practical implicationsThis study successfully investigated the effects of the selected process parameters - raster angle, raster to raster air gap, build orientation about Y axis and the number of contours - on tensile strength of PC samples utilising the I-optimal design and ANOVA. In addition, for prediction of the part strength, regression and ANN models were developed. The selected ANN model was optimised using the GA-solver for determination of optimal parameter settings.Originality/valueThe proposed ANN-GA approach is more appropriate to establish the non-linear relationship between the selected process parameters and tensile strength. Further, the proposed ANN-GA methodology can assist in manufacture of various industrial products with Nylon, polyethylene terephthalate glycol (PETG) and PET as new 3DP materials.

Development of an Ultrasound-Assisted Emulsification Microextraction Method for the Determination of Volatile Compounds in Wines

A fast and simple method based on ultrasound-assisted emulsification microextraction (USAEME) was developed for the analysis of volatile compounds in wines. A full factorial 24 screening design was built to investigate the main factors affecting the extraction of volatile components, namely the volume of extraction solvent, sonication time, salt content, and pH. Then, the factors with significant effects were optimized using an I-optimal design. The optimal value for all the variables studied was reached under the following experimental conditions: volume of extraction solvent 200 μL and salt content 5% m/v. The suitability of the optimized method was evaluated, resulting in very good linearity with coefficients of determination (R2) higher than 0.995 in all cases, while repeatability was lower than 8.4% except for d-limonene and p-cymene. Recoveries higher than 82% were observed for the groups of ethyl esters, acetate esters, alcohols, and terpenoid alcohols (linalool, α-terpineol). The recovery of acids ranged from 70.5% to 88.9%, whereas the three monoterpenes studied (d-limonene, γ-terpinene, p-cymene) were not extracted satisfactorily. The proposed method was effectively applied for the analysis of volatile compounds in laboratory-scale fermentations with selected strains of Saccharomyces cerevisiae.

Split injection strategy control map development through prediction-based calibration approach to improve the biodiesel-fuelled diesel engine characteristics.

Karanja oil blended with diesel acts as a good alternative for a CI engine due to its low emission and high oxygen content as compared to pure diesel which leads to the non-toxic and biodegradable nature of the fuel. The objective of this project is to enhance the performance and emission characteristics of a diesel engine that runs on biofuel by accurately calibrating its fuel injector control parameters. To achieve this goal, the project focuses on creating optimal split injector control maps using a novel global model-based calibration approach that considers the entire range of engine speed-load conditions. To develop the model, the experimentation was conducted using the I-optimal design of the experiment technique. To establish a relationship between the calibration parameters and engine performance parameters based on experimental data, the study employed response surface methodology (RSM). With the support of a developed model and multi-objective optimization approach under equivalent importance to performance and emissions, the optimum injector control points are derived. For the developed engine map for 20% KBD (Karanja bio-diesel)-blended fuel, the BTE (brake thermal efficiency) reaches up to 30% and lower BSFC (brake specific fuel consumption) of 0.37 kg/kW-hr. After optimization, the split injector control map showed significant improvements over the un-optimized map. At 19 Nm and 3000 rpm, the optimized map resulted in a 31.36% increase in BTE and a 29.31% decrease in BSFC. Moreover, the optimization successfully balanced the trade-off between reducing nitrogen oxide (NOx) emissions and smoke emissions. However, the optimized fuel map for 20% KBD-blended fuel shows slightly lower performance compared to diesel fuel. While the optimization process led to a decrease in smoke emissions about 22.3%, it also resulted in elevated NOx emissions about 9.83% when compared to diesel fuel. Furthermore, emissions of CO and HC are reduced by 12.8% and 19.2%, respectively, in an optimized control map of 20% KBD-blended fuel compared to un-optimized map.

Integrating experimental modeling techniques with the Pareto search algorithm for multiobjective optimization in the WEDM of Inconel 718

Inconel 718 is a superalloy with a high nickel content that is widely used in applications requiring solid mechanical behavior and resistance to oxidation and corrosion at high temperatures. This alloy has numerous applications in manufacturing steam turbine and jet aircraft interiors, aviation sector manifolds, and rotary spindles. It can be classified as a difficult-to-cut material unsuitable for traditional machining. The purpose of this paper is to develop prediction models for a wire electrical discharge machining (WEDM) process using response surface methodology (RSM), artificial neural networks (ANN), and adaptive neuro-fuzzy inference systems (ANFIS) and to determine which model is better at making accurate predictions. Pulse_ontime, pulse_offtime, servo voltage, flushing pressure, and wire feed were considered the main factors affecting volumetric material removal rate (VMRR) and arithmetic surface roughness (Ra), which were evaluated as WEDM performance characteristics. I-optimal design made with a computer algorithm was employed to develop experimental models. The results reveal that the wire feed and pulse_ontime were the most vital factors influencing VMRR, respectively, and the most significant factor influencing Ra is the pulse_ontime. The total percentage error of the three models demonstrated that the ANN and ANFIS models are more reliable and accurate than the RSM mathematical model. Finally, multiobjective optimization using the Pareto search algorithm was used to optimize mathematical, ANN, and ANFIS models to determine the optimum WEDM process parameters for machining Inconel 718 superalloy.