Bioremediation Condition Optimization of Hydrocarbon in Soil Using Response Surface Methodology by Microbial Consortium KL9-1

2012 ◽  
Vol 518-523 ◽  
pp. 2073-2078 ◽  
Author(s):  
Qi You Liu ◽  
Yun Bo Zhang ◽  
Dong Feng Zhao ◽  
Chao Cheng Zhao
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Microbial Consortium ◽  
Ph Value ◽  
Removal Rate ◽  
Desirability Function ◽  
Nitrogen And Phosphorus ◽  
Predictive Values ◽  
Independent Variables ◽  
Condition Optimization

A response surface methodology was applied to optimize the bioremediation condition of hydrocarbon in soil by microbial consortium KL9-1. A four-level Box-Behnken factorial design was employed to study the relationship of independent variables and dependent variable by applying pH value, inoculation amount of microbial consortium KL9-1, ratio of nitrogen and phosphorus (N/P ) and surfactant (SDBS) concentration as independent variables (factors) and crude oil removal rate as dependent variable (response). Then the statistically significant model was obtained and numerical optimization based on desirability function was carried out for pH 7.0, inoculation amount 50.0 mL, N/P 2: 1 and SDBS concentration 4.0 g, and the hydrocarbon removal rate reached as high as 52.58%. The predictive values showed good agreement with the experimental values under the optimization conditions, by standard variance <1.3%. It showed that the optimal result was reliable.

scholarly journals Optimization of Ammonia Oxidation Using Response Surface Methodology

Catalysts ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 249 ◽  
Author(s):  
Marek Inger ◽  
Agnieszka Dobrzyńska-Inger ◽  
Jakub Rajewski ◽  
Marcin Wilk
Keyword(s):  
Response Surface Methodology ◽  
Nitrous Oxide ◽  
Response Surface ◽  
Ammonia Oxidation ◽  
Oxidation Process ◽  
Desirability Function ◽  
Independent Variables ◽  
Dependent Variables ◽  
Oxidation Efficiency ◽  
Nitrous Gases

In this paper, the design of experiments and response surface methodology were proposed to study ammonia oxidation process. The following independent variables were selected: the reactor’s load, the temperature of reaction and the number of catalytic gauzes, whereas ammonia oxidation efficiency and N2O concentration in nitrous gases were assumed as dependent variables (response). Based on the achieved results, statistically significant mathematical models were developed which describe the effect of independent variables on the analysed responses. In case of ammonia oxidation efficiency, its achieved value depends on the reactor’s load and the number of catalytic gauzes, whereas the temperature in the studied range (870–910 °C) has no effect on this dependent variable. The concentration of nitrous oxide in nitrous gases depends on all three parameters. The developed models were used for the multi-criteria optimization with the application of desirability function. Sets of parameters were achieved for which optimization assumptions were met: maximization of ammonia oxidation efficiency and minimization of the N2O amount being formed in the reaction.

scholarly journals Optimization of Ammonia Stripping of Piggery Biogas Slurry by Response Surface Methodology

2019 ◽  
Vol 16 (20) ◽  
pp. 3819 ◽  
Author(s):  
Mengyuan Zou ◽  
Hongmin Dong ◽  
Zhiping Zhu ◽  
Yuanhang Zhan
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Flow Rate ◽  
Gas Flow ◽  
Ph Value ◽  
Removal Rate ◽  
Ammonia Nitrogen ◽  
Biogas Slurry ◽  
Gas Flow Rate ◽  
Ammonia Stripping

Ammonia stripping is a pretreatment method for piggery biogas slurry, and the effectiveness of the method is affected by many factors. Based on the results of single-factor experiments, response surface methodology is adopted to establish a quadratic polynomial mathematical model relating stripping time, pH value and gas flow rate to the average removal rate of ammonia nitrogen to explore the interactions among various influencing factors, obtain optimized combined parameters for ammonia stripping, and carry out experimental verification of the parameters. The results show that when hollow polyhedral packing is adopted under operating conditions including a stripping time of 90 min, pH value of 11, gas flow rate of 28 m3/h, gas–liquid ratio of 2000 and temperature of 30 °C, the average removal rate of ammonia nitrogen in biogas slurry can reach approximately 73%. The experimental value is only 4.2% different from the predicted value, which indicates that analysis on the interaction among factors influencing ammonia stripping of biogas slurry and parameter optimization of the regression model are accurate and effective.

scholarly journals Multi-response optimization of magnetic field assisted EDM through desirability function using response surface methodology

2020 ◽  
Vol 29 (1) ◽  
pp. 19-35
Author(s):  
Anish Kumar ◽  
Renu Sharma
Keyword(s):  
Magnetic Field ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Performance Measures ◽  
Process Parameters ◽  
Removal Rate ◽  
Desirability Function ◽  
Surface Model ◽  
Electrode Wear ◽  
En 31

AbstractMagnetic field assisted electrical discharge machining (MFAEDM) is the modification of in conventional EDM process by use of magnetic field on EN-31. This article explain the application of response surface methodology to analyzes the effect of various process parameters such as Ton, Toff and Ip on performance measures such as material removal rate (MRR), electrode wear rate (EWR) and overcut (OC). Analysis of variance was used to check the adequacy of response surface model and significance of process parameters on performance measures. Multi-objective desirability function has been applied to obtain the optimal process parameter settings. Thereafter, machined surface of EN-31 characterized through SEM and EDX. The novelty of this paper is to improve the strategies for flushing the debris which remain clogged in standard EDM in-between machining gap that will interrupts the regular discharge conditions and reduces cutting rate as well as deteriorate the surface characteristics.

scholarly journals Using response surface methodology (RSM) for optimizing turbidity removal by electrocoagulation/electro-flotation in an internal loop airlift reactor

2019 ◽  
Vol 19 (8) ◽  
pp. 2476-2484 ◽  
Author(s):  
T. Ntambwe Kambuyi ◽  
F. Eddaqaq ◽  
A. Driouich ◽  
B. Bejjany ◽  
B. Lekhlif ◽  
...  
Keyword(s):  
Response Surface Methodology ◽  
Energy Consumption ◽  
Response Surface ◽  
Removal Rate ◽  
Desirability Function ◽  
Airlift Reactor ◽  
Optimal Conditions ◽  
Turbidity Removal ◽  
Linear Interaction ◽  
Internal Loop

Abstract Response surface methodology (RSM) is used to optimize the electrocoagulation/electro-flotation process applied for the removal of turbidity from surface water in an internal loop airlift reactor. Two flat aluminium electrodes are used in monopolar arrangement for the production of coagulants. The central composite design is used as a second-order mathematical model. The model describes the change of the measured responses of turbidity removal efficiency and energy consumption according to the initial conductivity (X1), applied voltage (X2), treatment time (X3) and inter-electrode distance (X4). The evaluation of the model fit quality is done by analysis of variance (ANOVA). Fisher's F-test is used to provide information about the linear, interaction and quadratic effects of factors. Multicriteria methodology, mainly the desirability function (D), is used to determine optimal conditions. The results show that, for a maximal desirability function D = 0.79, optimal conditions estimated are X1 = 1,487 μS/cm, X2 = 5 V, X3 = 6.5 min, X4 = 14 mm. The corresponding turbidity removal rate and energy consumption are 84.15% and 0.215 kWh/m3 respectively. A confirmation study is then carried out at laboratory scale using the optimal conditions estimated. The results show a turbidity removal rate of 72.05% and an energy consumption of 0.210 kWh/m3.

Optimization of Kaolin Wastewater Treatment Processes Research via Response Surface Methodology

2011 ◽  
Vol 366 ◽  
pp. 361-365
Author(s):  
Li Ping Wu ◽  
Dian Mo Zheng ◽  
Sheng Gan Zhu
Keyword(s):  
Particle Size ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Ph Value ◽  
Removal Rate ◽  
Average Particle Size ◽  
Average Particle ◽  
Good Prediction ◽  
Turbidity Removal ◽  
Treatment Processes

The CCD mathematical model of response surface methodology (RSM) has been used to optimize the process parameters of treating kaolin wastewater, the interaction of the factors on removal rate of turbidity and average particle size of floc such as the flocculation temperature ,the pH value, the dosage of PAC, the dosage of St-PAM were studied. The optimum condition of the effluent disposal via the experiment was as follows: the flocculation temperature was 25°C,the pH value was 6.5, the dosage of PAC was 21.5/mg/L, the dosage of St-PAM was 5/mg/L.In these conditions,the turbidity removal rate of the kaolin wastewater was 97.56%,and the average particle size of flocculation body was 0.994mm. Under the optimum conditions, the removal rate of turbidity and the average particle size of flocculation body was respectively 98.05% and 1.09mm.The model had good prediction effect.

scholarly journals Comparative Prediction of Red Alga Biosorbent Performance in Dye Removal using Multivariate Models of Response Surface Methodology (RSM) and Artificial Neural Network (ANN)

2018 ◽  
Vol 7 (4.35) ◽  
pp. 551
Author(s):  
Nadiah Mokhtar ◽  
Edriyana A.Aziz ◽  
Azmi Aris ◽  
W.FW. Ishak ◽  
Anwar P.P. Abdul Majeed ◽  
...  
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Dye Removal ◽  
Removal Rate ◽  
Desirability Function ◽  
Quadratic Model ◽  
Ann Model ◽  
Biosorption Process ◽  
Rsm Model ◽  
High Removal Rate

Red algae species, Euchema Spinosum (ES) in Malaysia possesses excellent biosorbent properties in removing dyes from aqueous solutions. In the present study, the experimental design for the biosorption process was carried out via response surface methodology (RSM-CCD). A total of 20 runs were carried out to generate a quadratic model and further analysed for optimisation. Prior to the evaluation, the characterisation study of the ES was performed. It was observed that the maximum uptake capacity of 399 mg/g (>95%) is obtained at equilibrium time of 60 min, pH solution of 6.9-7.1, dosage of 0.72 g/L and initial dye concentration of 300 g/L through statistical optimisation (CCD-RSM) based on the desirability function. It is demonstrated in the present study that the ANN model (R2=0.9994, adj-R2=0.9916, MSE=0.19, RMSE=0.4391, MAPE=0.087 and AARE=0.001) is able to provide a slightly better prediction in comparison to the RSM model (R2= 0.9992, adj-R2= 0.9841, MSE=1.95, RMSE=1.395, MAPE=0.08 and AARE=0.001). Moreover, the SEM-EDX analysis indicates the development of a considerable number of pore size ranging between 132 to 175 mm. From the experimental observations, it is evident that the ES can achieve high removal rate (>95%), indeed become a promising eco-friendly biosorptive material for MB dye removal.

scholarly journals Determination of the Optimal Neural Network Transfer Function for Response Surface Methodology and Robust Design

2021 ◽  
Vol 11 (15) ◽  
pp. 6768
Author(s):  
Tuan-Ho Le ◽  
Hyeonae Jang ◽  
Sangmun Shin
Keyword(s):  
Response Surface Methodology ◽  
Transfer Function ◽  
Response Surface ◽  
Robust Design ◽  
Transfer Functions ◽  
Desirability Function ◽  
Likelihood Estimation ◽  
Statistical Simulation ◽  
Screening Procedure ◽  
Function Estimation

Response surface methodology (RSM) has been widely recognized as an essential estimation tool in many robust design studies investigating the second-order polynomial functional relationship between the responses of interest and their associated input variables. However, there is scope for improvement in the flexibility of estimation models and the accuracy of their results. Although many NN-based estimations and optimization approaches have been reported in the literature, a closed functional form is not readily available. To address this limitation, a maximum-likelihood estimation approach for an NN-based response function estimation (NRFE) is used to obtain the functional forms of the process mean and standard deviation. While the estimation results of most existing NN-based approaches depend primarily on their transfer functions, this approach often requires a screening procedure for various transfer functions. In this study, the proposed NRFE identifies a new screening procedure to obtain the best transfer function in an NN structure using a desirability function family while determining its associated weight parameters. A statistical simulation was performed to evaluate the efficiency of the proposed NRFE method. In this particular simulation, the proposed NRFE method provided significantly better results than conventional RSM. Finally, a numerical example is used for validating the proposed method.

scholarly journals Optimizing Adsorption of 17α-Ethinylestradiol from Water by Magnetic MXene Using Response Surface Methodology and Adsorption Kinetics, Isotherm, and Thermodynamics Studies

Molecules ◽  
2021 ◽  
Vol 26 (11) ◽  
pp. 3150
Author(s):  
Mengwei Xu ◽  
Chao Huang ◽  
Jing Lu ◽  
Zihan Wu ◽  
Xianxin Zhu ◽  
...  
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Quadratic Model ◽  
Adsorption Efficiency ◽  
Process Variables ◽  
Adsorption Time ◽  
Initial Concentration ◽  
Independent Variables ◽  
The Impact ◽  
Adsorbent Dose

Magnetic MXene composite Fe3O4@Ti3C2 was successfully prepared and employed as 17α-ethinylestradiol (EE2) adsorbent from water solution. The response surface methodology was employed to investigate the interactive effects of adsorption parameters (adsorption time, pH of the solution, initial concentration, and the adsorbent dose) and optimize these parameters for obtaining maximum adsorption efficiency of EE2. The significance of independent variables and their interactions were tested by the analysis of variance (ANOVA) and t-test statistics. Optimization of the process variables for maximum adsorption of EE2 by Fe3O4@Ti3C2 was performed using the quadratic model. The model predicted maximum adsorption of 97.08% under the optimum conditions of the independent variables (adsorption time 6.7 h, pH of the solution 6.4, initial EE2 concentration 0.98 mg L−1, and the adsorbent dose 88.9 mg L−1) was very close to the experimental value (95.34%). pH showed the highest level of significance with the percent contribution (63.86%) as compared to other factors. The interactive influences of pH and initial concentration on EE2 adsorption efficiency were significant (p < 0.05). The goodness of fit of the model was checked by the coefficient of determination (R2) between the experimental and predicted values of the response variable. The response surface methodology successfully reflects the impact of various factors and optimized the process variables for EE2 adsorption. The kinetic adsorption data for EE2 fitted well with a pseudo-second-order model, while the equilibrium data followed Langmuir isotherms. Thermodynamic analysis indicated that the adsorption was a spontaneous and endothermic process. Therefore, Fe3O4@Ti3C2 composite present the outstanding capacity to be employed in the remediation of EE2 contaminated wastewaters.

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