Optimization Of Process Variables Research Articles

Cu-Based Praseodymium-Modified γ-Al2O3 Oxygen Carrier for Chemical Looping Combustion Process Optimization

Chemical looping combustion (CLC) emerges as a cost-effective CO2 capture technology, demonstrating high competitiveness for both industrial and energy applications. This study explores the synthesis of a Cu-based, praseodymium (Pr)-modified gamma-alumina-supported (20CuPA) oxygen carrier (OC) through the wet impregnation method and investigates its performance in CLC. The characteristics of the synthesized OC were investigated using field emission scanning electron microscopy, energy-dispersive X-ray spectroscopy, temperature-programmed reduction analysis, and X-ray diffraction analysis. The CLC of methane gas was performed in a thermogravimetric analyzer (TA-Q50). The oxygen transport capacity (OTC) of the 20CuPA-based OC was investigated for 10 redox cycles. The impact of temperature and time as process variables in determining the OTC of OCs was studied. The TGA results indicated that the most important factor influencing the optimization of the OTC of OCs was time. In comparison to time, temperature had less of an impact on the OTC of 20CuPA-OC. The maximum OTC of 20CuPA-OC, which was 0.0546 mg of O2/mg of OC, was reached using optimized process variables, including a temperature of 800 °C and a time of 3 min.

Taguchi optimization of Wire EDM process parameters for machining LM5 aluminium alloy.

LM5 alloy is suitable for metal castings for marine and aesthetic uses due to its admirable resistance to corrosion. In order to make intricate shapes in the LM5 alloy, this study intends to assess the impact of Wire Electric Discharge Machining process variables, like Pulse on Time (Ton), Pulse off Time (Toff), Gap Voltage (GV) and Wire Feed (WF) on responses like Material Removal Rate (MRR), Surface Roughness (SR), and Kerf Width (Kw). The LM5 aluminium alloy plate was produced through stir casting process. SEM, EDAX and XRD images confirm the LM5 Al alloy's microstructure and crystal structure. WEDM studies were conducted using design of experiments approach based on L9 orthogonal array and analysed using Taguchi's Signal to Noise Ratio (S/N) analysis. Pulse on Time has the greatest statistical effects on MRR (68.25%), SR (79.46%) and kerf (81.97%). In order to assess the surface integrity of the WEDM machined surfaces, the SEM study on the topography was conducted using the optimum surface roughness process variables: Ton 110 μs, Toff 50 μs, GV 40 V, and WF 9 m/min. SEM images show the recast layer and its thickness. The average absolute error for MRR is 1.69%, SR is 3.89% and kerf is 0.88%, based on mathematical (linear regression) models. The Taguchi's Signal to Noise ratio analysis is the most appropriate for single objective optimization of responses.

Design and Optimization of Solid Lipid Nanoparticles for Enhanced Therapeutic Activity of Linagliptin.

To augment oral bioavailability, linagliptin (LGP) loaded solid lipid nanoparticles (SLNs) have been formed using the solvent evaporation technique of emulsification. Utilizing the Box Behken design (BBD), examining the relation between independent and dependent variables was possible. Particle size (PS), entrapment efficiency (EE), and in-vitro drug release (DR) studies were considered response factors, whereas the various concentrations of lipid carrier, surfactant, and co-surfactant were considered process variables for optimization. The design expert program was used to obtain the optimized batch. At 24 hours, it was discovered that the optimized batch expected responses for PS, %EE, and %DR were 105.83 nm, 81.19, and 96.69%, respectively. The observed PS, %EE, and %DR responses of the optimized batch at 24 hours were determined to be 101.36 nm, 80.56, and 96.31%, respectively. There is no interaction seen in the basic physical mixing of drug and polymer, according to the FTIR spectra of pure drugs, polymers, and drug and polymer mixtures. The optimized formulation’s average size was found to be 138 nm by the particle size analysis. Similarly, a value of 0.255 was found by the polydispersity index (PDI). This suggests that the SLN in the formulation has a homogeneous size dispersion. With a zeta value of -32.8 mV, stability is indicated. Linagliptin was effectively developed as a drug with a 24-hour sustained release. Wistar rats were used in the in-vivo evaluation of linagliptin SLN formulations. The drug bioavailability increased five times (a highly significant fivefold increase) in LGP-loaded SLNs (AUC0-t ≈ 134.5 μg*h/mL) compared to raw LGP (AUC0-t = 27.23 μg*h/mL).

Wire and arc-based additive manufacturing of 316L SS: predicting and optimizing process variables using BRFFNN, NSGA-GP and TOPSIS approach

Wire and arc-based additive manufacturing of 316L SS: predicting and optimizing process variables using BRFFNN, NSGA-GP and TOPSIS approach

Green biorefinery of walnut husk: Phenolic extraction and ethanol production

Microwave and ultrasound techniques were utilized on walnut green husk (WGH) to extract valuable phenolic compounds and enhance the residual biomass's susceptibility to hydrolysis for subsequent ethanol production. The aim was to optimize process variables in order to achieve an integrated biorefinery with the zero-waste-base standpoint. For microwave-assisted extraction (MAE), four-time levels (0.5, 1.0, 2.0, and 5 min) and power settings (300, 450, 600, and 800 W) were tested. Ultrasound-assisted extraction (UAE) was conducted at a frequency of 28 kHz and power of 100 W over varying time intervals (2, 6, 10, 14, 18, and 22 min). Generally, higher power settings and longer extraction times produced higher contents. Specifically, the maximum total phenolic content (TPC) (220.13 mg GAE/g-DW), total flavonoid content (TFC) (63.21 mg catechin/g-DW), and DPPH scavenging activity (86.13 %) were achieved using the highest power and longest extraction time for MAE. Similarly, the longest UAE time resulted in the highest TPC (214.32 mg GAE/g-DW), highest TFC (58.44 mg catechin/g-DW), and DPPH scavenging activity (76.15 %). Moreover, increased microwave power and longer extraction times favored glucose and ethanol yields, with maximum yields of 55.30 % and 36.67 %, respectively, obtained at 800 W for 5 min. For UAE, the highest glucose and ethanol yields (52.33 % and 34.16 %, respectively) were obtained at 22 min. Conversely, lower extraction times and power settings for microwave, as well as shorter ultrasound times, were beneficial for lignin removal. The application of MAE and UAE in the WGH biorefinery process enhanced the extraction of phenolics and improved biomass hydrolysis, leading to more efficient, sustainable, and economically viable production of biofuels and bioproducts.

Utilization of opium poppy seed oil for biodiesel production: A parametric characterization and statistical optimization

Consuming traditional petroleum-derived diesel fuel has long been associated with issues such as the depletion of natural energy resources. To solve these challenges, an alternate source like as biodiesel is an appealing option. Seed oils have long been recognized as an abundant and diverse source of biodiesel. In this study, poppy seed oil from the poppy (Papaver somniferum) was investigated for biodiesel production. Poppy seed biodiesel was generated and refined using acid-pretreated esterification with sulphuric acid prior to transesterification, as well as single-step alkaline catalyzed transesterification with methanol and potassium hydroxide. Finally, the percentage yield was compared. Using Statistica, the Box-Behnken design was applied to optimize process variables like time, temperature, catalyst concentration, and methanol-oil ratio to produce maximum yield. The relationship of process variables was also shown with the help of the Response Surface Methodology. A maximum yield of 94.87 % was obtained at optimized conditions, i.e., 90min reaction time, 60 °C of temperature, 0.25 mg of catalyst concentration, and 3v/v% alcohol-oil ratio. The fuel properties of biodiesel produced, such as acid value, moisture content, saponification value, iodine value, specific gravity, percentage of free fatty acids, refractive index, viscosity, boiling point, and peroxide value, were measured and compared with the American Society for Testing and Materials (ASTM) D6751 and European Standards (EN) 14214. Further results were studied and discussed using Fourier Transfer Infrared (FTIR) analysis, which showed maximum similarity of raw material to formed biodiesel. Gas Chromatography-Mass Spectrometry (GC-MS) analysis was performed to identify and quantify various fatty acid methyl esters. The results obtained were in accordance with various international standards for biodiesel fuel. Thus, poppy seeds can be used to obtain biodiesel.

Multi-scale reactor designs extend the physical limits of fixation.

valorization is a promising strategy for climate adaptation and transitioning towards a circular carbon economy. Here, we present a multi-scale, integrated systems approach for designing biomanufacturing systems that can utilize as a feedstock, focusing on the Wood-Ljungdahl and reductive glycine pathways. This approach relies on first principles, coupling the optimization of pathway and process variables. We examine the -fixation capacity of both pathways in single- and multi-compartment reactor systems, demonstrating that the reductive glycine pathway has the potential to fix at significantly higher rates than photosynthetic organisms. We show that small differences in the energy-dissipative and stoichiometric structures of carbon-fixation pathways could significantly impact optimal designs and feasible design spaces. Our first-principle, systems-level approach quantifies these differences and uncovers strategies to expand the design space and extend the physical limits of carbon fixation, offering insights into pathway selection and process configurations for efficient biomanufacturing.

Production of 5-fluorouracil-loaded PLGA nanoparticles with toroidal microfluidic system and optimization of process variables by design of experiments

In recent decades, microfluidics has presented new opportunities for the production of nanoparticles (NPs). However, to achieve rapid clinical translation, the production of PLGA NPs in a single microfluidic channel for both the pharmaceutical research and industry without the need for scaling is still limited. The aim of this study was to accomplish the production of reproducible and stable 5-FU loaded Poly(lactic-co-glycolic acid) (PLGA) NPs, using an innovative toroidal microfluidic system, for cancer therapy. The toroidal microfluidic system enabled the production of spherical NPs ranging from 100 to 150 nm by adjusting both the TFR within the range of 5–15 mL/min and FRR between 1:3 and 1:7. A systematic assessment of critical process variables (total flow rate; TFR, flow rate ratio; FRR) for the production of PLGA NPs was conducted using Design of Experiment (DoE). The NPs, which exhibit a uniform size distribution, remained stable even after centrifugation and storage for 3 months at 4 °C. The encapsulation efficiency of drug and the concentration of NPs were not affected by changing process parameters. The effective 5-FU encapsulation into NPs resulted in a controlled in vitro drug release. Due to the controlled release profile of the 5-FU loaded PLGA NPs, the formulation was a promising candidate for mitigating the toxic side effects of free 5-FU and improving cancer treatment. In conclusion, toroidal microfluidic system enables high-volume production of stable PLGA NPs, both with and without 5-FU.

Optimization and quality evaluation of functional pasta made from germinated finger millet, buckwheat, and black gram.

The investigation was conducted to optimize process variables to manufacture functional pasta from composite flour. The selected grains were steeped, germinated, dried, and milled to produce flour. The flours were mixed at optimized proportions (57.31% buckwheat flour, 12.68% finger millet flour, and 30% paheli dal flour) to produce composite flour. The full factorial experimental design opted for optimization of process variables namely, moisture content (mc) (28, 30, 32, and 34%) and mixing speed (60, 80, 100, and 120 rpm). The optimized multi-grain pasta showed shorter processing time, in-range cooking loss, and higher cooking weight and water absorption capacity (WAC). The highest overall acceptability was recorded for multi-grain pasta processed at 60 rpm with an initial mc of 32%. Proximate analysis of optimized multi-grain pasta showed that pasta contained protein (13.95%), crude fiber (5.05%), ash (2.05%), a lower amount of fat (0.74%), and carbohydrates (71.71%).

Computer‐Aided Design of Large‐Scale Nanomaterials Synthesis Processes: A Detailed Review

AbstractEfforts from the scientific community and the private sector are required to lower the costs of large‐scale production of nanomaterials (NMs) to enhance their commercialization. In this work, the computer‐aided process design of large‐scale NM synthesis procedures is comprehensively reviewed. Moreover, a generalized process flow diagram for all large‐scale production processes was constructed by surveying numerous scalable experimental procedures reported in the literature. Previous studies reporting simulation cases of large‐scale production processes of NMs and nanocomposites (NCs) are also reviewed based on the type of material produced, e.g., oxides, sulfides, carbonaceous materials, organic materials, metals, and other types of NMs. Finally, technical insights from classical chemical engineering specializations, such as altering process configurations, optimizing process variables, integrating chemical processes, utilizing renewable energy sources, conducting computational calculations, employing machine learning techniques, and studying the process's environmental impact, are reviewed for large‐scale NMs and NCs synthesis.

Process variable optimization on hydrogen production from sawdust mill of Paraserianthes falcataria wood with one stage gasification process using promoted calcium hydroxide absorption

Process variable optimization on hydrogen production from sawdust mill of Paraserianthes falcataria wood with one stage gasification process using promoted calcium hydroxide absorption

Sustainable green cutting fluid for interpreting optimization of process variables while machining on various CNC manufacturing systems—an experimental approach for exploring

Sustainable green cutting fluid for interpreting optimization of process variables while machining on various CNC manufacturing systems—an experimental approach for exploring

Optimization of Slugging and Compression Process for Bicalutamide Tablets

The critical nature of pharmaceutical dosage forms necessitates the optimization of manufacturing process variables to achieve the desired product quality. In this study, slugging (Dry granulation) was selected as an appropriate granulation method to mitigate poor flow properties in the final bicalutamide blend. The chosen process variables include slug hardness, mill screen size, and mill speed. A 23-factorial design was employed to investigate these variables. Process parameters were reproducible, with minimal impact on tablet properties, even when roller speeds varied. Response surface models effectively examine the relationship between response variables and quantitative parameters. The input and process variables for the compression process were predetermined based on the desired quality attributes of bicalutamide tablets. Notably, the p-value for the slugging process was not more than 0.05, indicating their insignificance in tablet content uniformity. Product quality attributes affected by the compression process step include content uniformity, disintegration time, and dissolution. The optimal hardness range was found to be 2.0 to 7.0 kp.

Enhancing waste-derived biodiesel yield using recyclable zinc sulfide nanocatalyst: Synthesis, characterization, and process optimization

With the increasing demand for sustainable and renewable energy sources, biodiesel has emerged as a promising alternative to conventional fossil fuels. This study presents a novel approach to enhancing Thevetia peruviana biodiesel (TPB) yield from plant waste by optimizing process variables using Zinc sulfide (ZnS) as a catalyst. Co-precipitation was utilized to synthesize the ZnS nanoparticles, followed by comprehensive characterization using SEM, XRD, EDAX, and FTIR. Response surface methodology was employed to determine optimal values for process parameters, specifically catalyst concentration (CC), oil-to-alcohol ratio (OAR), and process temperature (PT). The results demonstrate that the nanoparticle can be effectively reused over five successive cycles without significant loss of catalytic activity. The optimal condition identified was a CC of 107.7 ppm, OAR of 8.9:1, and PT of 94.6 °C, yielding a TPB of 92.5 %, which was validated through confirmation experiments. The yield of TPB using the nanocatalyst exhibited a 3.6 % increase compared to a typical chemical catalyst. ANOVA results revealed that CC had a 43.9 % influence on biodiesel yield, while OAR and PT had influences of 33.2 % and 22.9 %, respectively. Elemental analysis indicated that TPB biodiesel has a 10.9 times higher oxygen concentration compared to diesel fuel, with GC-MS analysis identifying linoleic acid as the predominant component. The elemental, FTIR, GC-MS, and physicochemical analysis of TPB were found to be comparable to the biodiesel standard. This comprehensive assessment suggests that TPB has the potential to serve as a viable alternative to conventional diesel fuel.

Determination of selective laser melting process parameters of tungsten carbide powders coated with nickel via electroless plating for improved interface properties

Tungsten carbide powders are commonly used at various industrial applications due to their high hardness. However, these powders may exhibit low binding and processing challenges without a binder. Therefore, various binding methods such as electroless Ni coating are used to hold WC powders together, increase their processability and improve the mechanical properties of the final product. Electroless Ni plating plays an important role in the use of WC powders, especially in processes such as selective laser melting. The structural, tribological, and mechanical properties of parts manufactured by SLM method are significantly influenced by the parameters employed in the process. Hence, optimizing process variables is essential to unlock the full potential of this advanced manufacturing technique. The main objective of this study was to identify the optimum process parameters for the SLM method of WC-Ni composite powders, which were prepared by coating WC powder materials with electroless Ni. Experimental investigations focused on analyzing the laser power, scanning speed, and hatch spacing parameters. The effects of these parameters on the mechanical and tribological properties of WC-Ni composite material were investigated using microhardness tester, tribometer device, optical microscope, 3D optical profilometer and scanning electron microscope. Eventually, it was determined that the manufacturing parameters employed in the SLM method had a significant impact on relative density, microstructure, surface roughness, hardness, and tribological properties. Samples exhibited a relative density of nearly 99%, and the highest observed hardness value was 1962 HV. This article supports a specialized SLM manufacturing process developed specifically to enhance durability and performance across a broad spectrum of industrial applications through interface improvement and the optimization of manufacturing parameters.

Bio2Py: An API for integrating Python with BioWin for enhanced data acquisition in wastewater treatment simulations

Commercial simulators for modeling wastewater treatment processes (WWTPs), such as BioWin, play a crucial role for optimizing the design, operation, and control of treatment facilities. These simulators offer fast and user-friendly interfaces and can be employed to enhance the efficiency of wastewater treatment plants. However, a notable limitation of BioWin and similar platforms is the lack of direct integration with Python®, a programming language renowned for its versatility and extensive data analysis and machine learning libraries. Bridging this gap constitutes an opportunity to significantly improve the simulation process by leveraging Python's capabilities. The Python package Bio2Py (BioWin to Python) is presented as a compelling solution, reconciling BioWin's specialized functionality and Python's automation and data processing strengths. By utilizing Python's PyAutoGUI package, Bio2Py automates various aspects of the simulation process, such as loading influent data, running simulations, and saving simulation results without manual intervention. The integration of BioWin with Python not only speeds up the workflow by automating repetitive tasks but also opens new possibilities, such as parameter calibration for specific wastewaters and using BioWin as a data generator for machine learning applications. This brief communication presents the Bio2Py API, illustrating its use and applicability through a simple implementation example. Bio2Py's ability to automate and optimize various aspects of wastewater treatment process simulation, including data acquisition, and its potential use for process variable optimization and parameter tuning, positions it as a valuable tool for engineers and researchers who work in this field.

A physical simulation-machine learning model for optimal process schemes in laser-based directed energy deposition process

The major challenge faced is the definition of optimal process variables for rich quality of fabricated parts in laser-based directed energy deposition (DED-LB) processes. However, predicting the optimal process scheme using machine learning models is still challenging owing to the need for a large amount of training experimental data with high costs in DED-LB. In view of this, a physical simulation (PS)-machine learning (ML) model using a relatively small accurate data set of 31 simulation data for optimal process schemes is proposed in DED-LB process in this study. Firstly, a powder-scale high precision phenomenon model incorporating the mass transfer, phase transformations and heat transfer and using Lagrangian particle model to add mass is developed to depict the DED-LB process. Then, a Gaussian process regression (GPR) agent model is established to rapidly and accurately predict the geometry and dilution rate of deposition tracks under different manufacturing parameters based on the high precision simulation results. Finally, a PS-ML model for process parameter optimization is developed using the particle swarm algorithm (PSO), and the optimized parameters are experimentally validated. The results show that the developed powder-scale model and GPR model results are consistent with the Inconel 718 alloy experimental results. The proposed PS-ML model can increase the accuracy of the ML models even with a small simulation data set. None of PS-ML model optimization results has a relative error of more than 3% to the experimental results, and the dilution rate is reduced by up to 61.66% compared to the experimental design parameters without optimization. The proposed physical simulation-machine learning model in this study enables inexpensive and accurate optimization of DED-LB process parameters.

QbD Based Formulation Development and Optimisation of Ozenoxacin Topical Nano-Emulgel and Efficacy Evaluation Using Impetigo Mice Model.

To formulate and optimize Ozenoxacin nano-emulsion using Quality by Design (QbD) concept by means of Box-Behnken Design (BBD) and converting it to a gel to form Ozenoxacin nano-emulgel followed by physico-chemical, in-vitro, ex-vivo and in-vivo evaluation. This study demonstrates the application of QbD methodology for the development and optimization of an effective topical nanoemulgel formulation for the treatment of Impetigo focusing on the selection of appropriate excipients, optimization of formulation and process variables, and characterization of critical quality attributes. BBD was used to study the effect of "% of oil, % of Smix and homogenization speed" on critical quality attributes "globule size and % entrapment efficiency" for the optimisation of Ozenoxacin Nano-emulsion. Ozenoxacin loaded nano-emulgel was characterized for "description, identification, pH, specific gravity, amplitude sweep, viscosity, assay, organic impurities, antimicrobial effectiveness testing, in-vitro release testing, ex-vivo permeation testing, skin retention and in-vivo anti-bacterial activity". In-vitro release and ex-vivo permeation, skin retention and in-vivo anti-bacterial activity were found to be significantly (p < 0.01) higher for the nano-emulgel formulation compared to the innovator formulation (OZANEX™). Antimicrobial effectiveness testing was performed and found that even at 70% label claim of benzoic acid is effective to inhibit microbial growth in the drug product. The systematic application of QbD principles facilitated the successful development and optimization of a Ozenoxacin Nano-Emulsion. Optimised Ozenoxacin Nano-Emulgel can be considered as an effective alternative and found to be stable at least for 6months at 40°C / 75% RH and 30°C / 75% RH.

Ultrasonication-Assisted Aqueous Extraction of Waste Orange Peel Polyphenols: Optimization of Process Variables and Effect on Extract Composition

The citrus processing industry is responsible for the generation of large volumes of waste side streams, represented principally by fruit peels. These tissues are exceptionally rich in polyphenolic bioactive phytochemicals, and there has been a great industrial interest for their valorization. The examination presented herein targeted at developing a fast and straight-forward aqueous extraction process, based on ultrasonication, for the efficacious recovery of polyphenolic compounds from waste orange peels. After an initial single-factor examination, the response surface optimization showed that a maximum total polyphenol yield of 12.81 mg chlorogenic acid equivalents (GAE) per g−1 dry mass could be achieved by setting sonicator amplitude at 80%, for 15 min, using a duty cycle of 2/2 (2 s on/2 s off). Comparison of this methodology with a stirred-tank extraction demonstrated that the ultrasonication technique was equally effective, requiring ambient temperature and considerably shorter resident time. The combination of both techniques using the ultrasonication process as a pretreatment step did not boost extraction yield, and the extracts produced had similar polyphenolic composition and antioxidant activity. However, a slight enhancement of the recovery of individual constituents was noted. It is proposed that efficient extraction of polyphenolic substances from waste orange peels may be accomplished using the present methodology, which is a low-cost (ambient temperature, short time) and sustainable (water as solvent) process.

Multi-stage batch adsorption of acephate onto cauliflower like Fe3O4-MMT: Characterization and statistical optimization using response surface methodology

Globally acephate (an organophosphate pesticide) contaminates water bodies, and detriment to the biota is cancer-causing and neurotoxic which needs to be safely removed. This study presents the synthesis and characterization of magnetic montmorillonite (Fe3O4-MMT) as an adsorbent for the adsorption of acephate. The features and characteristics of the nanocomposite were traced by XRD, SEM-EDX, gas sorption analysis, FTIR, and XRF. RSM techniques were used to identify the optimal process variables that result in the highest removal. The numerical optimization of optimum variables corresponds to an initial acephate concentration of 2 mg/L, pH 6 and material adsorbent dose of 0.5 g/L. The uptake of acephate achieved 83.18 % under optimum environs. Dual factors i.e., concentration and dosage remarked as vital parameters that affected the response from ANOVA. Results revealed that equilibrium adsorption data were best fitted with Langmuir and kinetic data were well described by pseudo-first order kinetic model. Thermodynamic parameters such as enthalpy, entropy and Gibb’s energy were evaluated and the effect of temperature on acephate adsorption was studied. Greater acephate adsorption onto on Fe3O4 serves as an excellent material for pesticide mitigation.