scholarly journals Optimization of Chlorella Culture Conditions with Response Surface Methodology to Increase Biomass

2021 ◽  
Vol 20 (5) ◽  
Author(s):  
R. Kanimozhi ◽  
D. Arvind Prasath ◽  
R. Dhandapani ◽  
Santhosh Sigamani
Keyword(s):  
Response Surface Methodology ◽  
Light Intensity ◽  
Response Surface ◽  
Biomass Yield ◽  
Culture Conditions ◽  
Optimal Conditions ◽  
Optimum Conditions ◽  
Accession Number ◽  
Microalgal Biomass ◽  
Chlorella Sp

Microalgae is gaining popularity as a major ingredient in nutrition supplements. To mass cultivate, it is imperative to improve the biomass yield hence optimization of cultures conditions becomes paramount. In this work, an attempt has been made to optimize the microalgal production using response surface methodology (RSM) and validate further the optimized parameters. The optimum conditions for the cultivation of Chlorella sp. KPU016 under optimized nutrient conditions were pH 8.2, the light intensity of 3100 lx, glycerol 1.44 g.L-1 (under pre-set conditions of 12 h lighting, the temperature at 27±1°C. With these RSM-driven optimum conditions, the yield of microalgal biomass achieved was 282.50 mg.L-1. For larger-scale microalgal harvesting, the validated optimal conditions can be inferred as the best for enhanced microalgal production. The isolate was partially sequenced and submitted to the NCBI database and the GenBank accession number is MZ348364.

scholarly journals Experimental study and parameters optimization of microalgae based heavy metals removal process using a hybrid response surface methodology-crow search algorithm

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
N. Sultana ◽  
S. M. Zakir Hossain ◽  
M. Ezzudin Mohammed ◽  
M. F. Irfan ◽  
B. Haq ◽  
...  
Keyword(s):  
Heavy Metals ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Removal Efficiency ◽  
Search Algorithm ◽  
Synthetic Wastewater ◽  
Order Kinetic ◽  
Optimum Conditions ◽  
Microalgal Biomass ◽  
Multiple Heavy Metals

Abstract This study investigates the use of microalgae as a biosorbent to eliminate heavy metals ions from wastewater. The Chlorella kessleri microalgae species was employed to biosorb heavy metals from synthetic wastewater specimens. FTIR, and SEM/XRD analyses were utilized to characterize the microalgal biomass (the adsorbent). The experiments were conducted with several process parameters, including initial solution pH, temperature, and microalgae biomass dose. In order to secure the best experimental conditions, the optimum parameters were estimated using an integrated response surface methodology (RSM), desirability function (DF), and crow search algorithm (CSA) modeling approach. A maximum lead(II) removal efficiency of 99.54% was identified by the RSM–DF platform with the following optimal set of parameters: pH of 6.34, temperature of 27.71 °C, and biomass dosage of 1.5 g L−1. The hybrid RSM–CSA approach provided a globally optimal solution that was similar to the results obtained by the RSM–DF approach. The consistency of the model-predicted optimum conditions was confirmed by conducting experiments under those conditions. It was found that the experimental removal efficiency (97.1%) under optimum conditions was very close (less than a 5% error) to the model-predicted value. The lead(II) biosorption process was better demonstrated by the pseudo-second order kinetic model. Finally, simultaneous removal of metals from wastewater samples containing a mixture of multiple heavy metals was investigated. The removal efficiency of each heavy metal was found to be in the following order: Pb(II) > Co(II) > Cu(II) > Cd(II) > Cr(II).

scholarly journals Application of Response Surface Methodology (RSM) for optimizing removal of Cr(VI) wastewater using Cr(VI)-reducing biofilm systems

2014 ◽  
Vol 6 (1) ◽  
Author(s):  
Nurfadilah Mohammed ◽  
Wan Azlina Ahmad
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Operating Conditions ◽  
Optimal Conditions ◽  
Optimum Conditions ◽  
Nutrient Supplementation ◽  
Dependent Variables ◽  
Reduction Efficiency ◽  
Central Composite ◽  
Experimental Runs

Response surface methodology (RSM) involving central composite design (CCD) was employed to obtain optimal conditions for Cr(VI) wastewater treatment by Cr (VI) reducing biofilm systems. On the basis of a CCD, RSM was used to determine the effect of initialmetal concentrations (40-100 mgL-1), nutrient supplementations (10-20%) and flowrate (3-6 mLmin-1) on the levels of response, i.e. Cr(VI) reduction efficiency. A set of 20 experimental runs were needed for optimizing of the operating conditions. Quadratic regressionmodels with estimated coefficients were developed to describe the Cr (VI) reduction. Analysis of variance (ANOVA) showed a highcoefficient of determination (R2) value of 0.9941, thus ensuring a satisfactory adjustment of the second-order regression model with theexperimental data. Cr (VI) reduction had significant effect on all the three dependent variables. The experimental results show that Cr(VI)-reducing biofilm systems could effectively reduce Cr (VI), 100% at the optimum conditions of initial metal concentration of 100mgL-1, nutrient supplementation of 20% and flowrate of 3 mLmin-1. The experimental observations were in reasonable agreement withthe modelled values.

scholarly journals Medium optimization for biomass production of three peat moss (Sphagnum L.) species using fractional factorial design and response surface methodology

2021 ◽  
Author(s):  
Melanie A Heck ◽  
Ingrida Melkova ◽  
Clemens Posten ◽  
Eva L. Decker ◽  
Ralf Reski
Keyword(s):  
Response Surface Methodology ◽  
Factorial Design ◽  
Response Surface ◽  
Large Scale ◽  
Biomass Yield ◽  
Fractional Factorial Design ◽  
Sucrose Concentration ◽  
Culture Conditions ◽  
Fractional Factorial ◽  
Peat Moss

Peat moss (Sphagnum) biomass is a promising bioresource to substitute peat in growing media with a renewable material. For sustainable production on a large scale, the productivity of Sphagnum mosses has to be increased by optimizing culture conditions. Optimization was achieved using fractional factorial design and response surface methodology based on central composite design to determine concentrations of eight factors leading to highest biomass yield. We improved a standard Sphagnum medium by reducing the concentrations of NH4NO3, KH2PO4, KCl, MgSO4, Ca(NO3)2, FeSO4 and a microelement solution up to 50 %. Together with a reduced sucrose concentration for Sphagnum fuscum, while it remained unchanged for Sphagnum palustre and Sphagnum squarrosum, moss productivities were enhanced for all tested species in shake flasks. Further upscaling to 5 L photobioreactors increased the biomass yield up to nearly 50-fold for S. fuscum, 40-fold for S. palustre and 25-fold for S. squarrosum in 24 days.

scholarly journals Optimizing of Microalgae Scenedesmus sp. Biomass Production in Wet Market Wastewater Using Response Surface Methodology

2021 ◽  
Vol 13 (4) ◽  
pp. 2216
Author(s):  
Najeeha Mohd Apandi ◽  
Mimi Suliza Muhamad ◽  
Radin Maya Saphira Radin Mohamed ◽  
Norshuhaila Mohamed Sunar ◽  
Adel Al-Gheethi ◽  
...  
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Biomass Yield ◽  
Biomass Productivity ◽  
Maximum Growth ◽  
Maximum Growth Rate ◽  
Aeration Rate ◽  
Composition Analysis ◽  
Wet Market ◽  
Central Composite

The present study aimed to optimize the production of Scenedesmus sp. biomass during the phycoremediation process. The biomass productivity was optimized using face centred central composite design (FCCCD) in response surface methodology (RSM) as a function of two independent variables that included wet market wastewater concentrations (A) with a range of 10% to 75% and aeration rate (B) with a range of 0.02 to 4.0 L/min. The results revealed that the highest biomass productivity (73 mg/L/d) and maximum growth rate (1.19 day−1) was achieved with the 64.26% of (A) and 3.08 L/min of (B). The GC-MS composition analysis of the biomass yield extract revealed that the major compounds are hexadecane (25%), glaucine (16.2%), and phytol (8.33%). The presence of these compounds suggests that WMW has the potential to be used as a production medium for Scenedesmus sp. Biomass, which has several applications in the pharmaceutical and chemical industry.

Optimization of the Ultrasonic Extraction of Gypenoside III from Gynostemma pentaphyllum by Response Surface Methodology

2012 ◽  
Vol 550-553 ◽  
pp. 1866-1870
Author(s):  
Xiao Dan Tang ◽  
Hai Yang Hang ◽  
Shao Yan Wang ◽  
Jing Xiang Cong
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Extraction Yield ◽  
Raw Material ◽  
Extraction Temperature ◽  
Optimal Conditions ◽  
Gynostemma Pentaphyllum ◽  
Bioactive Component ◽  
Yield Value ◽  
Ultrasonic Time

Gypenosides III is a major bioactive component which is rich in Gynostemma pentaphyllum. For better utilization of the native resource, response surface methodology was used to optimize the extraction conditions of gypenosides III from G. pentaphyllum. The effects of three independent variables on the extraction yield of gypenosides III were investigated and the optimal conditions were evaluated by means of Box-Behnken design. The optimal conditions are as follows: ratio of ethanol to raw material 25, extraction temperature 58°C and ultrasonic time 25min. Under these conditions, the yield of gypenoside III is 1.216±0.05%, which is agreed closely with the predicted yield value.

scholarly journals Enzyme optimization to reduce the viscosity of pitanga (Eugenia uniflora L.) juice

2016 ◽  
Vol 19 (0) ◽  
Author(s):  
Ricardo Schmitz Ongaratto ◽  
Luiz Antonio Viotto
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Incubation Time ◽  
Regression Models ◽  
Incubation Temperature ◽  
Correlation Coefficients ◽  
Enzyme Concentration ◽  
Optimum Conditions ◽  
Activity Time ◽  
Independent Variables

Summary The aim of this work was to separately evaluate the effects of pectinase and cellulase on the viscosity of pitanga juice, and determine the optimum conditions for their use employing response surface methodology. The independent variables were pectinase concentration (0-2.0 mg.g–1) and cellulase concentration (0-1.0 mg.g–1), activity time (10-110 min) and incubation temperature (23.2-56.8 °C). The use of pectinase and cellulase reduced the viscosity by about 15% and 25%, respectively. The results showed that enzyme concentration was the most important factor followed by activity time, and for the application of cellulase the incubation temperature had a significant effect too. The regression models showed correlation coefficients (R2) near to 0.90. The pectinase application conditions that led to the lowest viscosity were: concentration of 1.7 mg.g–1, incubation temperature of 37.6 °C and incubation time of 80 minutes, while for cellulase the values were: concentration of 1.0 mg.g-1, temperature range of 25 °C to 35 °C and incubation time of 110 minutes.

scholarly journals Response surface methodology-artificial neural network based optimization and strain improvement of cellulase production by Streptomyces sp.

2020 ◽  
Vol 36 (4) ◽  
Author(s):  
Ega Soujanya Lakshmi ◽  
Manda Rama Narasinga Rao ◽  
Muddada Sudhamani
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Wild Strain ◽  
Cellulase Activity ◽  
Cellulase Production ◽  
Optimal Conditions ◽  
Zone Formation ◽  
Artificial Neural

ABSTRACT Thirty seven different colonies were isolated from decomposing logs of textile industries. From among these, a thermotolerant, grampositive, filamentous soil bacteria Streptomyces durhamensis vs15 was selected and screened for cellulase production. The strain showed clear zone formation on CMC agar plate after Gram’s iodine staining.  Streptomyces durhamensis vs15 was further confirmed for cellulase production by estimating the reducing sugars through dinitrosalicylic acid (DNS) method. The activity was enhanced by sequential mutagenesis using three mutagens of ultraviolet irradiation (UV), N methyl-N’-nitro-N-nitrosoguanidine (NTG) and Ethyl methane sulphonate (EMS). After mutagenesis, the cellulase activity of GC23 (mutant) was improved to 1.86 fold compared to the wild strain (vs15). Optimal conditions for the production of cellulase by the GC 23 strain were evaluated using Response Surface Methodology (RSM) and Artificial Neural Network (ANN). Effect of pH, temperature, duration of incubation, , and substrate concentration on cellulase production were evaluated. Optimal conditions for the production of cellulase enzyme using Carboxy Methyl Cellulase as a substrate are 55 oC of temperature, pH of 5.0 and incubation for 40 h. The cellulase activity of the mutant Streptomyces durhamensis GC23 was further optimised to 2 fold of the activity of the wild type by RSM and ANN.  

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