Predictive Modeling Coupled with Multiple Optimization Techniques for Assessing the Effect of Various Process Parameters on Oil and Pectin Extraction from Watermelon Rind

Olatunji, Ololade; Itam, Daniel Hogan; Akpan, Godwin Edem; Horsfall, I.T.

doi:10.1007/s41660-022-00248-0

Cited by 4 publications

(6 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The fit summary findings demonstrate that quadratic models may effectively represent the examined responses (TPH and pH). This result is based on the sequential p-value of the quadratic model, which is 0.05 and validates the findings of Olatunji et al [20]. The RSM optimization procedure was finished by maximizing PL-500, RS-400, and TPH.…”

Section: Response Surface Numerical Optimization Of Tph Degradationsupporting

confidence: 86%

“…Analysis of variance (ANOVA) was performed as described in Table 5, and the pvalue was used to determine the significance of each regression coefficient (0.0260). The optimal parameters were obtained by repeating the experiment under perfect conditions, which is congruent with the conclusions of Olatunji et al [20]. Based on this result, a fit summary table was made to evaluate the ideal model for the optimization project.…”

Section: Response Surface Numerical Optimization Of Tph Degradationsupporting

confidence: 77%

“…With the aim of maximizing the response, RSM is a common mathematical and statistical approach for analyzing and modeling a process in which the response of interest is impacted by a number of factors [19]. It is a useful technique because it makes it possible to assess how various factors and their interactions affect one or more response variables [20]. The process parameters (PL-500, RS-400, and PW-500) are known as the independent variables, while the response (TPH) is referred to as the dependent variable.…”

Section: Design Of Experiments For Bioremediationmentioning

confidence: 99%

“…The independent variables are the factors (PL-500, RS-400, and PW-500) influencing the remediation of total petroleum hydrocarbons (TPH). Equation ( 4) thus depicts the empirical expression [19,20,[22][23][24] as:…”

Section: Design Of Experiments For Bioremediationmentioning

confidence: 99%

See 3 more Smart Citations

Modelling and Optimizing the Effect of pH on Remediation of Crude Oil Polluted Soil with Biochar Blend: RSM Approach

Itam

Udeh

Ugwoha

2023

AIR

View full text Add to dashboard Cite

This research modelled the effect of pH on the remediation of crude oil-polluted soil using biochar blend. The biochar blends, PL-500, pW-500, and RS-400, were made by pyrolyzing poultry litter, pine wood, and rice straw at varied temperatures and times. The pH of the crude oil polluted soil was 4.72. Response surface experimental design mixed biochar to remediate total petroleum hydrocarbons (TPH). Following 30 days of bioremediation, 15g PL-500, 3g PW-500 and 6g RS-400, removed a maximum of 46% TPH. The experimental data were statistically modelled and optimized using design expert software and response surface methods. Analysis of variance (ANOVA) was used to determine the significance of each regression coefficient. Biochar blend improved soil pH to 6.9 following remediation. ANOVA indicated that PL-500 was significant for predicting TPH % degradation at p =0.0290, suggesting that its high pH, nutrient, and soil water conservation values made it more effective in remediating TPH. The quadratic model predicts with R2 =0.8567. A model fit statistics were used to examine soil pH influence on TPH remediation. RSM study indicated a good positive association between statistical model and experiment with R2 = 0.7612. The model fits experimental data and predicts that . Remediation requires soil pH and biochar's alkalinity raised soil pH to 6.9, which promoted hydrocarbon-utilizing bacteria.

show abstract

Section: Response Surface Numerical Optimization Of Tph Degradationsupporting

confidence: 86%

Section: Response Surface Numerical Optimization Of Tph Degradationsupporting

confidence: 77%

Section: Design Of Experiments For Bioremediationmentioning

confidence: 99%

Section: Design Of Experiments For Bioremediationmentioning

confidence: 99%

See 2 more Smart Citations

Modelling and Optimizing the Effect of pH on Remediation of Crude Oil Polluted Soil with Biochar Blend: RSM Approach

Itam

Udeh

Ugwoha

2023

AIR

View full text Add to dashboard Cite

show abstract

“…In addition, to evaluate the numerical model the accuracy and performance of the COMSOL Multiphysics prediction, the root means square error (RMSE) and the mean square error (MSE) were utilized as a function of error 43 , 44 via Eqs. ( 30 ) and ( 31 ): …”

Section: Methodsmentioning

confidence: 99%

Mathematical modeling and numerical simulation technique for selected heavy metal transport in MSW dumpsite

Usoh

Ahaneku

Ugwu

et al. 2023

Sci Rep

View full text Add to dashboard Cite

The study focused on development of mathematical modeling and numerical simulation technique for selected heavy metal transport in Uyo municipal solid waste dumpsite in Akwa Ibom State to investigate the level in depth to which leachate from the dumpsite extends and the quantity of leachate at various depth of the dumpsite soil. Uyo waste dumpsite is operating open dumping system where provisions are not made for preservation and conservation of soil and water quality, hence, the need for this study. Three monitoring pits within Uyo waste dumpsite were constructed and infiltration runs were measured, and soil samples were collected beside infiltration points from nine designated depths ranging from 0 to 0.9 m for modeling heavy metal transport in the soil. Data collected were subjected to descriptive and inferential statistics while the COMSOL Multiphysics software 6.0 was used to simulate the movement of pollutants in the soil. It was observed that heavy metal contaminant transport in soil of the study area is in the power functional form. The transport of heavy metals in the dumpsite can be described by a power model from linear regression and a numerical model based on finite element. Their validation equations showed that the predicted and the observed concentrations yielded a very high R2 value of over 95%. The power model and the COMSOL finite element model show very strong correlation for all selected heavy metals. Findings from the study has identified level in depth to which leachate from the dumpsite extends and the quantity of leachate at various depth of the dumpsite soil which can be accurately predicted using leachate transport model of this study.

show abstract

Nature-inspired grasshopper optimization algorithm (GOA) for predictive modeling and machinability evaluation of laminated polymer nanocomposites

Kumar

Verma

2022

Multiscale and Multidiscip. Model. Exp. and Des.

View full text Add to dashboard Cite

Predictive Modeling Coupled with Multiple Optimization Techniques for Assessing the Effect of Various Process Parameters on Oil and Pectin Extraction from Watermelon Rind

Cited by 4 publications

References 29 publications

Modelling and Optimizing the Effect of pH on Remediation of Crude Oil Polluted Soil with Biochar Blend: RSM Approach

Modelling and Optimizing the Effect of pH on Remediation of Crude Oil Polluted Soil with Biochar Blend: RSM Approach

Mathematical modeling and numerical simulation technique for selected heavy metal transport in MSW dumpsite

Nature-inspired grasshopper optimization algorithm (GOA) for predictive modeling and machinability evaluation of laminated polymer nanocomposites

Contact Info

Product

Resources

About