Response Surface Methodology Approach Research Articles

Production and optimisation of biodegradable LDPE packaging films strengthened with inorganic filler through response surface methods using central composite design

ABSTRACT The current study emphasis on the optimisation of degradation of starch-blend LDPE films using nanosilica/modified nanosilica as a property-enhancing filler in order to establish an optimal composition used as a biodegradable packaging film. Weight loss and tensile and tear strength loss were used to determine the individual influence of the essential parameters defining degradability. The Central Composite Design (CCD) approach of Response Surface Methodology was used to optimise the process parameters. The obtained equation for film degradation is quadratic in nature and significant for the process, according to an ANOVA (analysis of variance) study. The addition of starch and modified nanosilica content in the matrix by 60% and above 0.6 g (1.5%) reduces the tensile and tear strength from 19.057 N/mm2 to 3.64 N/mm2 and 189.041 N/mm to 27.36 N/mm respectively, however increases the Young's modulus and stiffness to 582.76 MPa and 33654.62 N/m. The optimum composition of starch, LDPE and modified nanosilica was found to be 32, 8 and 0.6 g, respectively, showing an enhanced stiffness, tear strength and Young’s modulus of 29591.84 N/m, 155.5 N/mm and 464.25 N/mm2 along with induced biodegradability of 13.08% under garden soil and 12.07% under vegetable waste.

Optimization and utilization of waste fly ash and silica fume based eco-friendly geopolymer mortar using response surface methodology

Cement is the primary ingredient in concrete, which is one of the crucial building materials. The evolution of greenhouse gases, particularly carbon dioxide, which is produced during the hydration stage, has increased because of excessive use of concrete. Geopolymer, an alumina-silicate based bonding substance is made up of waste materials such as Fly ash (FA) or Silica Fume (SF) in the presence of alkaline solutions has been developed to reduce the effect of carbon emissions on the environment. This paper studied the optimization and utilization of FA and SF replacement as pozzolanic materials for the development of sustainable geopolymer mortar and to achieve optimal mechanical strength using response surface methodology (RSM). According to the study, with the addition of 90% FA and 10% SF content to the geopolymer design mix significantly improves their mechanical properties. When SF and sodium hydroxide are combined to create geopolymer mortar, they work as an alkaline catalyst, dramatically reducing carbon emission of the waste materials and providing it an edge over cement-based mortar. The construction of self-sustaining infrastructures ensuring human safety and eco-friendly practices will be encouraged by the multi-objective approach of RSM.

Response surface methodology approach of phenol removal study using high-quality activated carbon derived from H3PO4 activation of Acacia mangium wood

Response surface methodology approach of phenol removal study using high-quality activated carbon derived from H3PO4 activation of Acacia mangium wood

Performance characteristics optimization of CRDI engine fuelled with a blend of sesame oil methyl ester and diesel fuel using response surface methodology approach

The primary aim of this experiment was to use response surface methodology (RSM) to optimize engine operating parameters for optimal performance and emission characteristics of a common rail direct injection (CRDI) diesel engine fuelled with sesame oil methyl ester (SOME)/diesel blends. The experiments were carried out on a water-cooled common rail direct injection engine with a 4-stroke, single-cylinder connected to an eddy current dynamometer. As input variables, the SOME% (0%–20%), fuel injection pressure (FIP) (500–600 bar), EGR rates (0%–14%), and engine load (0–12 kg) were used. The optimization method is utilized to maximize brake thermal efficiency (BTE) while minimizing BSFC, CO, HC, and NOx emissions. Experimental research data were used to create the RSM model through DoE (Design of experiments). The most relevant factors impacting the responses were identified using an ANOVA analysis. According to the optimization findings, the engine’s optimum working parameters were found to be a 20% SOME ratio, 577.5 bar FIP, 5.26% EGR rates, and 5.12 kg engine load. Under these operating circumstances, the optimal responses were determined to be 18.92% BTE, 0.3705 kg/kWh BSFC, 0.03190% vol. CO, 13 ppm HC, and 447.5 ppm NOx emission. At the same time, R2 values were 96.35%, 87.54%, 91.57%, 95.87%, and 93.73% for BTE, BSFC, CO, HC, and NOx respectively.

Gas Porosity Defect – What It Means and How to Respond

The foundries are facing problem-related to the selection of the parameter’s value for minimum rejection and maximum productivity. The furan no-bake binders system guaranteed dimensional stability and a comparative good surface finish of the casting. Based on past data in the industry, it is found that gas porosity defect is one of the highest. The phenomenon of the formation of the bubble in the fissures of the mould-metal interface, and later on trapping during the solidification leads to gas porosity. The current research work is focused on the minimization of the defect by the selection of the optimum range of input variables. Based on rigorous literature survey and industrial expert’s opinion, it is found that the parameters like grain fineness number (GFN) of the sand, loss on ignition (LoI) of the used sand, the sand temperature at the mixing time, potential of hydrogen (pH) are important parameters for gas porosity defect in the casting.Design-Expert software and particularly response surface methodology (RSM) and sequential approach using the face-centered central composite design is used for the experiments. The results show that a quadratic model with the removal of some insignificant term is a comparatively best fit for gas porosity defects. After analysis, various favorable levels of different parameters are obtained. The research work is based on realistic problems of the foundries and based on the experimental work. Thus, the provided solution is very much useful for foundries to reduce the rejection, particularly for furan no-bake with furfuryl alcohol as resin and sulphonic acid as catalyst. The research problem addressed in the paper is a genuine problem of the foundries and the sole work is based on experimental evidence.

The Combination of Untargeted Metabolomics with Response Surface Methodology to Optimize the Functional Potential of Common Duckweed (Lemna minor L.).

The present study was designed to evaluate the functional potential of common duckweed (Lemna minor L.) as a source of bioactive compounds of nutraceutical interest. The untargeted profiling of the bioactive components of common duckweed was carried out through ultra-high-performance liquid chromatography coupled with high-resolution mass spectrometry (UHPLC-HRMS), in parallel with assessing in vitro antioxidant and enzymatic inhibition properties. The optimization of extraction parameters was determined using the response surface methodology (RSM) through a 3-factor central composite design. The process parameters included extraction temperature, % of ethanol, and ultrasound power, while the response variables were the phenolic content (considering each main phenolic class), total glucosinolates, total carotenoids, the antioxidant potential, and enzyme inhibition activities. The results revealed that common duckweed was a rich source of carotenoids and total flavonoids (mainly flavones and flavonols), followed by phenolic acids, low-molecular-weight phenolics, and glucosinolates. Interestingly, the total flavones, total flavonols and total carotenoid equivalents showed the highest and most positive correlation values with the bioactive properties measured. Finally, the combined RSM approach and unsupervised statistics allowed us to point out the pivotal impact of ethanol percentage in the extraction solvent to recover the highest amounts of bioactive compounds efficiently.

The assessment of response surface methodology (RSM) and artificial neural network (ANN) modeling in dry flue gas desulfurization at low temperatures

The performance of a flue gas desulfurization (FGD) system is characterized by SO2 removal efficiency () and reagent conversion (). Achieving a near-perfect reaction environment has been of concern in dry FGD (DFGD) due to the low reactivity compared to the wet and semi-dry units. This study will appraise output responses using modeling by response surface methodology (RSM) and artificial neural networks (ANN) approaches. The impacts of input parameters like hydration time, hydration temperature, diatomite to hydrated lime (Ca(OH)2), sulfation temperature and inlet gas concentration will be studied using a randomized central composite design (CCD). ANN fitting tool mapped the CCD metadata using the Levenberg-Marquardt (LM) algorithm activated by the hyperbolic tangent (tansig) function. The hidden cells ranged from 7 to 10 to ascertain the effect node architecture on modeling accuracy. Validation of each procedure was assessed using root mean square error (RMSE), mean square error (MSE) and R-Squared studies. The outcomes presented a more accurate 5-10-2 ANN model in the mapping of the DFGD from R2 data of = 0.993 and = 0.9986 with a mapping deviation from the RMSE values of = 0.48465; = 0.44971 and MSE results of = 0.23488; = 0.20229.

The Synthesis of Titania Nanoparticles (TiO2) and its Methylene Blue Dye removal efficiency through Photocatalysis based on Response Surface Methodology (RSM)

<p>Water pollution has been acknowledged as an issue of concern due to recalcitrant pollutants like textile dyes. Therefore, the eco-friendly synthesis of nano-particles (NPs) through green chemistry approach from sources and materials of organic origin has not only enhanced the properties of substance at nanoscale but has also reduced the toxicological impacts on human health and the environment. In this study a successful attempt was made to synthesize TiO2 NPs by Mangifera indica leaves extract as organic source. The structural morphology was determined by carrying out FTIR, SEM, EDX and XRD techniques. The removal of Methylene Blue (MB) dye was assessed by the application of the photocatalysis technique. The experiments were designed using the RSM (CCD) approach for selected parameters such as pH, TiO2 NPs, Dye concentration and Time. The maximal dye removal efficiency of 98% was achieved at optimum conditions of 4.7 pH, 0.04g of nano photocatalyst in 250ml of dye solution having 8ppm concentration, allowed to react for 45 min. The regression coefficient value of 79.37% was determined by ANOVA for MB dye. The results clearly indicate that the application of green nanoparticles for the removal of MB dye is a feasible option to overcome with the ever-increasing issue of water pollution and environmental degradation.</p>

Slurry erosion behaviour of HVOF sprayed VC+TiC based novel coatings: Characterization and optimization studies

The current experimental work is focused on development and characterization of HVOF sprayed VC-TiC+CuNi-Cr based coatings on hydro-machinery steel SS316. Empirical correlations for mass loss have been developed with the aid of response surface methodology (RSM) approach. Central composite design (CCD) matrix has been acquired with five different levels over four impingement parameters namely impingement angle, jet velocity, slurry concentration, and exposure time. When compared with bare SS316 steel, a significant increase in the slurry wear resistance of SS316 steel was noticed for different variants of developed coating. Moreover, ductile erosion mode was observed in coatings containing CuNi-Cr as the main component, which was reported to transform into the brittle mode with increasing VC-TiC percentage.

Application of plant-based coagulant with a novel MnO2.MoS2 nanocatalyst in coagulation/photocatalytic ozonation process for wastewater treatment

ABSTRACT In this work, Lallemantia royleana seed mucilage (LRSM) was applied as a plant based coagulant in the initial stage of an integrated method for dye degradation. Photocatalytic ozonation (PCO) process was used under visible-light irradiation in the post-treatment stage. MnO2.MoS2 heterostructure nanocomposite was fabricated by hydrothermal synthesis and applied for the first time in PCO to mineralize Acid Blue 113 dye. The synthesized nanocatalyst was characterized by FT-IR, XRD, FE-SEM, TEM, DRS and BET. Characterization of the nanocatalyst showed that the composite consisted MoS2 with a sheet-like structure and MnO2 in the form of nanoparticles with different sizes in the range of 15–30 nm. By using the response surface methodology (RSM) approach, the best operational condition in the coagulation-flocculation (CF) stage has been acquired at pH 5.0, LRSM dose of 220 mg/L within 27 min. By applying the PCO process, complete decolorization has been attained using 11 mg of the catalyst and 0.2 g/L.h ozone dosage within 9 min. The use of LRSM along with MnO2.MoS2 nanocatalyst in the CF/PCO combined process for treating real wastewater samples indicates that the proposed technique is highly efficient and can be effectively employed to treat dye-containing industrial effluents.

Effect of polymaleic acid and microwave radiations on reverse osmosis membrane's performance and properties: A response surface methodology approach

Polymaleic acid was tested for its ability to control inorganic fouling in reverse osmosis (RO) system for the first time as a membrane surface modifier. Using the design of experiments (DoE) based on the response surface methodological (RSM) approach, the effect of polymaleic acid concentration and microwave radiation time duration was studied on the membrane permeation properties. Considering the objective of achieving maximum water permeance and salt rejection properties, the conditions of polymerization were optimized using RSM and experimentally validated in the lab. At the optimum conditions of 10 mg·L−1 polymaleic acid, and 27 s radiation time, the pure water permeance and salt rejection were 3.05 L·m−2·h−1·bar−1, and 97.8 %, respectively. The optimized membrane was characterized through microscopic and spectroscopic techniques. When tested for its performance under inorganic fouling conditions, the modified membrane demonstrated a negligible decline in permeate flux for 6 h, and no formation of calcium sulfate precipitates on the membrane surface. This research aims to promote the development, optimization, and application of dual-functional RO membranes that can control different types of membrane fouling.

Fuzzy Modelling and Optimization of Yeast-MFC for Simultaneous Wastewater Treatment and Electrical Energy Production

Microbial fuel cells convert the chemical energy conserved in organic matter in wastewater directly to electrical energy through living microorganisms. These devices are environmentally friendly thanks to their ability to simultaneously produce electrical energy and wastewater treatment. The output power of the yeast microbial fuel cell (YMFC) depends mainly on glucose concentration and glucose/yeast ratio. Thus, the paper aims to boost the power of YMFC by identifying the best values of glucose concentration and glucose/yeast ratio. The suggested approach comprises fuzzy modelling and optimization. Fuzzy is used to build the model based on the measured data. In the optimization stage, the marine predators’ algorithm (MPA) is applied to identify the best glucose concentration values and glucose/yeast ratio corresponding to the maximum output power of YMFC. The results revealed the superiority of the combination of fuzzy and MPA compared with the response surface methodology (RSM) approach. Regarding the modelling accuracy, the coefficient of determination increased by 13.32% and 8.37%, respectively, for without methylene blue and with methylene blue compared with RSM. The integration between fuzzy and MPA succeeded in maximizing the output power from YMFC. Without MB, the power density increased by 25% and 29.3%, respectively, compared with measured data and RSM. In addition, with MB, the power density increased by 22.4% and 26%, compared with measured data and RSM.

Comparative investigation of artificial neural network and response surface approach in the optimization of indium recovery from discarded LCD screen with the presence of ionic liquids

This paper aimed to predict the solvent extraction conditions to maximize indium recovery from discarded LCD screens. Two approaches, including the response surface methodology (RSM) and the artificial neural network (ANN), were utilized to predict the efficiency of indium recovery. The main parameters, such as aqueous phase acidity (A), indium concentration (B), ionic liquid concentration (C), and aqueous to organic phase ratio (D), were investigated by using CyphosIL 101 diluted in kerosene as the organic phase. The experimental results were used to train a multilayer perceptron for creating an ANN model with the structure of one, eight, and three for input, hidden, and output layers, respectively. The optimum conditions by the RSM approach to provide the maximum efficiency of indium recovery were.4 mol/L A, 197.79 ppm of B, 0.009 mol/L of C, and 1.58 mol/L of D. By contrast, the ANN approaches illustrated the optimal A, B, C and D equal to 4.2, 194.32, 0.0085, and 1.52 %, respectively. Two statistical approaches described the satisfactory data, and the superior data was obtained with the ANN approaches. The use of two ionic liquids verified the indium recovery from the discarded LCD screen, and 99.7 % of indium ions were separated and extracted into the stripping solution.

Transesterification of waste cooking oil to biodiesel by walnut shell/sawdust as a novel, low-cost and green heterogeneous catalyst: Optimization via RSM and ANN

Walnut shell/sawdust catalyst was synthesized through a pyrolysis process using agricultural wastes and then utilized as a novel and cheap catalyst to generate biodiesel from waste cooking oils (WCO). XRD, FT-IR, TGA, SEM, and BET analyses were conducted to appraise the catalyst properties. Also, GC analysis was employed to find the fatty acid compounds in WCO. Besides, artificial neural network (ANN) and response surface methodology (RSM) were utilized to determine the optimal conditions. According to the outcomes, there is little difference between the laboratory data, RSM approach, and ANN approach. In optimized conditions, the utmost biodiesel yield was 92.25 ± 0.1%, which was attained at WCO to methanol molar ratio of 1:10.31, catalyst concentration of 9.76 ± 0.02 wt%, ultrasonic time of 30.14 ± 0.01 min, and temperature of 56.58 ± 1 °C. Also, the recyclability of the catalyst indicated that it can be used in 5 reuse steps without a significant decline in its yield. Moreover, the kinetics and thermodynamics of the transesterification process demonstrated that the reaction is endothermic and non-spontaneous. The produced biodiesel is characterized through HNMR and FTIR analyses. Furthermore, investigating the physical features of WCO-derived biodiesel indicated that they are in the ASTM D6751 standard range. The outcomes illustrated that utilizing WCO as feedstock and walnut shell/sawdust as a heterogeneous catalyst for biodiesel generation is a cost-effective and promising oil-recycling approach.

Use of response surface methodology approach for development of sustainable Jojoba biodiesel diesel blend with CuO nanoparticles for four stroke diesel engine

This study focuses primarily on the optimization of performance parameters, namely Brake thermal efficiency (BTE), Brake power (BP) and emission characteristics, namely Hydrocarbon (HC), carbon monoxide (CO) and oxides of nitrogen (NOX) emissions of a diesel engine fuelled with jojoba biodiesel-diesel blend with copper oxide (CuO) nanoparticles dispersed into it with different concentrations of 25, 50, 75 and 100 ppm. Variables such as blend (%), nanoparticles concentration (ppm), fuel injection pressure (bar) and Load (kg) are considered as input parameters. A series of experiments are designed under the central composite rotatable design (CCRD) approach considering 4 input factors each with 5 levels. A nonlinear RSM model is developed between the response and input variables based on the interaction plots. The model is validated by regression coefficient of R2, Adj. R2and Pred. R2which shows satisfactory results.Based on the desirability approach the model retrieve the best combination of input factors as 7.45 kg engine load, 16.76 % mixing of jojoba biodiesel with diesel, 209.89 bar fuel injection pressure, and CuO nanoparticles concentration of 76.76 ppm. After RSM optimization, target values of engine responses (BTE, BP, CO, HC, and NOx) are 27.18 %, 2.5582 kW, 0.1981 % volume, 24.5720 ppm, and 710.9470 ppm, respectively. When the results of the optimization process were compared to the experimental results, the deviations were found to be within the acceptable range of errors which establish the robustness of RSM model.

Application of Response Surface Methodology (RSM) for Optimizing Turbidity of Paper Recycling Wastewater Using Microwave Technology

Aims: The aim of the study was to use Response Surface Methodology (RSM) to find optimal experimental design for wastewater treatments from office paper recycling. In this way, interactive effects of treatment factors were evaluated, including microwave power (MW) and durations with centrifuge time while turbidity of wastewater was chosen as the dependent output variable or an optimal response.
 Methodology: The RSM approach was utilized for optimization of the process parameters and identifying the optimal conditions for the removal of turbidity in paper recycling wastewater. In this regard, a three-factor RSM were selected, using MW irradiation power (Watts), durations (seconds) and centrifuge time (min). Statistical analysis of variance (ANOVA) was carried out to identify the adequacy of the developed model. In this case, specially prepared office papers (one-side laser printed) were subjected to standard paper recycling procedure for obtaining wastewater at laboratory conditions. The experimentally derived RSM model was validated using range of statistical parameters.
 Conclusions: The study revealed that under the RSM optimized conditions, a marked reduction in the turbidity of wastewater was observed for both groups studied. The R2, R2(adj) and R2(pred) values were indicates, the developed model was significant which revealed a well agreement between the experimental data and proposed model. In this approach, the R2=99.710% and lack-of-fit value were found to be 0.111 (p>0.05), which shows the model and the data consisted to each other. The lowest turbidity value was found with 150.000 Watts and 60.000 seconds in MW conditions with 15.000 minutes of centrifugation time, experimentally. With employing these variables, the turbidity value of 6.65 NTU was determined. However, the highest turbidity value of 18.013 NTU was found with MW power of 200.00 Watts with 40.000 seconds of durations and 1.591 minute of centrifugation time. It is important to note that with using optimized parameters, the turbidity value of 1.43 NTU was calculated while 1.47 NTU was found with experimentally.

Determination of optimal operating conditions for AC-powered electrocoagulation process coupling green additive Tartaric Acid to remove Ni2+: Pyomo and RSM approach

A novel Python-based open-source optimization framework, namely Pyomo (Python optimization modeling objects), alongside a conventional optimization method, RSM (response surface methodology), was utilized to determine the optimal operating conditions of an alternating current-powered electrocoagulation (ACPE) process for nickel removal. In this regard, four mutable operating factors, current density (5–9 mA/cm2), initial nickel concentration (200–400 mg/L), initial pH of the solution (5–9), and electrolysis time (30–60 min), along with a fixed amount of an additional eco-friendly substance, Tartaric Acid (155 mg/L) were considered. Metal removal efficiency (OF1) and operating costs (OF2) were monitored and evaluated as objective functions with the aim of maximization and minimization, respectively. Experiments were conducted according to the central composite design (CCD), and validation outcomes established a reasonable agreement between the predicted models and the experimental data. The multi-objective optimization process yielded two sets of 30-optimal-solution obtained through Pyomo and RSM. Accordingly, the proposed solutions by the Pyomo were found to be more flexible and eclectic, supplying the local decision maker(s) with a diverse spectrum of optimal operating conditions. Adding TA was also effective in reducing electrical energy consumption by up to 46%.

Phenol contaminated municipal wastewater treatment using date palm frond biochar: Optimization using response surface methodology

Phenol is classified as an emerging contaminant which can be very toxic even at low concentrations and should be removed from wastewaters before reaching the environment. In this study date palm frond and leaf were pyrolyzed at different temperatures to identify the best adsorbent (feedstock) and pyrolysis temperature to remove phenol from aqueous solutions. Date palm frond pyrolyzed at 600 °C, termed DPF600, achieved the highest phenol removal rates of 64% and adsorption capacity of 15.93 mg/g. Response surface methodology approach using Box-Behnken design was implemented to obtain the optimal pH (6), contact time (20 h) and dosage (0.1 g) for the maximum phenol adsorption. A predicted adsorption capacity was found as 16.62 mg/g which was in close agreement with the experimental adsorption capacity of 17.38 mg/g. Isotherm and kinetic models in both linear and non-linear forms indicated that Freundlich model (R2 = 0.99, χ2 = 0.02, RMSE = 1.09) and pseudo-second order model (R2 = 0.99, χ2 = 0.85, RMSE = 5.41) fit best the obtained experimental data. Thermodynamics calculations affirmed that the adsorption of phenol onto DPF600 biochar was endothermic and spontaneous. The point of zero charge was found to be at 6.5 for DPF600 biochar. Scanning electron microscopy coupled with energy dispersive X-ray, Fourier transform infrared spectroscopy and X-ray diffraction confirmed adsorption of phenol onto DPF600 biochar. Application of DPF600 biochar to remove phenol from synthetic primary and secondary treated wastewater samples achieved 60 and 85% removal rates and 241 mg/g and 22.28 mg/g adsorption capacities, respectively. Regeneration studies showed promising adsorption capacities indicating the efficacy of DPF600 for the removal of phenol from wastewater.

BIOGAS DRY REFORMING: DEVELOPMENT OF Ni/Na ZEOLITE CATALYST USING RESPONSE SURFACE METHODOLOGY APPROACH WITH REACTION MECHANISM EVALUATION

Biogas with the composition of 60 % methane and 40 % carbon dioxide can be used as methane replacement in Dry Reforming of Methane (DRM) for high quality syngas. Endothermically nature of DRM requires an efficient catalyst to reduce the compound activation energy and change mechanism path. A nickel-based catalyst has advantages of comparable catalytic activity and abundantly available compared to a noble catalyst. However, the nickel-based catalyst has fast deactivation and sinters at higher temperature. Many factors and variables such as metals loading percentages, addition of secondary metal, support selection and promoter can affect the catalyst performance and properties. Conventionally, quest of ideal catalyst requires a lot of experiment which are time-consuming and cost-consuming. In this research, response surface methodology (RSM) approach was used to optimize the nickel-based catalyst formulation and DRM operating condition over prepared catalyst. For catalyst formulation, Box-Behken Design (BBD) was used to find the optimum ratio of nickel (1-10 %) with the use of cobalt as secondary metal (0-10 %) and magnesium as promoter (0-5 %). NaA-Zeolite synthesis from kaolin was used as support in this research. the kinetic study was carried out in order to understand the mechanism path of DRM reaction over prepared catalyst and the effect of prepared catalyst on activation energy (Ea). After conducting mathematical and statistical analysis, the result confirmed that Ni10Co9Mg1.6/NaA-Zeolite catalyst was an optimum combination that achieved 21.93 % CH4 conversion, 33.52 % CO2 conversion, 0.904 H2/CO ratio and 0.8440 stability score. The data analysis also indicated that there was an interaction effect between variables and responses. The characterization analysis was in line with the finding. Lastly, from the kinetic study, the reaction mechanism of DRM over Ni10Co9Mg1.6/NaA-Zeolite fit well with Eley-Rideal mechanism whereby CO2 was adsorbed on the catalyst surface and CH4 maintained in gas phase. The kinetic analysis also revealed that the catalyst has low activation energy for CH4 and CO2 consumptions (46.53 kJ/mol and 39.66 kJ/mol respectively). Low activation energy of CO2 indicated the involvement of RWGS reaction.

Enhanced l-Methioninase production by Methylobacterium sp. JUBTK33 through RSM and its anticancer potential

l-Methioninase (E.C.4.4.1.11) has gained greater significance due to its anticancer potential. In the present study, high l-Methioninase activity was seen in a newly isolated bacterium, Methylobacterium sp. JUBTK33. Enhancing enzyme production and activity is crucial for exploring the strain towards industrial applications. Hence, optimization of media components was carried out using one factor at a time method followed by statistical approach of Response Surface Methodology (RSM)– CCD matrix using Design-Expert software. After conducting triplicates of all thirty runs, Glucose 1%, Yeast 1%, and media of pH 7.0, and Tapioca peel at 10%, were found to be the optimal parameters for enhancing enzyme production from 1.26 U/mL/min to 2.10 U/mL/min (1.7 folds). The regression model analysis R2 value 95.8% indicated the best fitness model in explaining relationship between the responses and the variables. Further, the enzyme produced from the bacteria was analyzed for its cytotoxicity to Breast cancer (MCF-7), Hepatocellular carcinoma (Hep-G2), cervical cancer (HeLa), and Human embryonic Kidney (HEK-293) cell lines, with Doxorubicin as the positive control. This enzyme was effective on the Breast cancer cell line with 11% cell viability at 400 μg/mL, while Doxorubicin showed 4% viability. Toxicity of the enzyme on healthy cells seen as 44%, whereas Doxorubicin showed 83%. The study demonstrates the suitability of statistical methods in enhancing the enzyme productivity and promising anticancer activity of the l-Methioninase from Methylobacterium sp., with lesser toxicity towards healthy cells. l-Methioninase from the newly isolated Methylobacterium sp. has promising characteristics towards anticancer drug developmental studies.