Optimization of process factors for the efficient generation of biogas from raw vegetable wastes under the direct influence of plastic materials using Taguchi methodology

Mukhopadhyay, Debabrata; Sarkar, Jyoti Prakas; Dutta, Susmita

doi:10.1080/19443994.2012.749563

Cited by 12 publications

(10 citation statements)

References 18 publications

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“…However, a high R 2 value that was found emphasizes a high degree of similarity that was observed between the predicted and the experimental values. It can be concluded that the model has satisfied the assumptions of the analysis of variance and also reflected the accuracy and applicability of RSM to optimize the process factors for the efficient generation of biogas [40].…”

Section: Analysis Of Variance (Anova)mentioning

confidence: 78%

“…A coefficient of determination (R 2 ) is used to measure the variability in the actual response values that can be described by the corresponding independent factors [40]. R 2 was 0.9665; it implied that a sample variation of 96.65% of the total variation could be explained by the model, and only 3.35% could not be explained by the model for this work.…”

Section: Analysis Of Variance (Anova)mentioning

confidence: 99%

“…A second order mathematical model was used to fit the quadratic equation [39][40][41][42] as given below.…”

Section: Statistical Analysis and Modelingmentioning

confidence: 99%

“…Perturbation plot provides silhouette views of the response surface. For response surface designs, the perturbation plot shows how the response changes as each factor moves from the chosen reference point, with all other factors held constant at the reference value [54]. Design Expert sets the reference point default at the middle of the design space (the coded zero level of each factor).…”

Section: Perturbationmentioning

confidence: 99%

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Optimization of Biogas Production in a Batch Laboratory Digester Using Total Solids, Substrate Retention Time, and Mesophilic Temperature

Masinde¹,

Nyaanga²,

Njue³

et al. 2020

IJPER

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Optimization was done by investigating the interaction effects of total solids, mesophilic temperature, and substrate retention time on biogas production in a batch biodigester. The volume of the biodigester was 0.15m 3. Central composite design of Response Surface Methodology was used to design the experiment. Total solid levels were varied from 6.31% to 9.68%, temperature was from 26.59°C to 43.41°C, and substrate retention time was from 9.95 to 20.04 days. Analysis of results was done using Design Expert software statistical package (version 10.0.0.3). It gave a coefficient of determination of 0.9665 which indicated a high correlation between the variables. All the variables had a significant effect. The highest biogas production rate of 75.41litres/day (or 0.50 m 3 of biogas per m 3 of digester volume per day, m 3 /m 3 d) was achieved at a level of 8% total solids, a temperature of 43.41°C, and a substrate retention time of 15 days.

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Section: Analysis Of Variance (Anova)mentioning

confidence: 78%

Section: Analysis Of Variance (Anova)mentioning

confidence: 99%

“…A second order mathematical model was used to fit the quadratic equation [39][40][41][42] as given below.…”

Section: Statistical Analysis and Modelingmentioning

confidence: 99%

Section: Perturbationmentioning

confidence: 99%

See 2 more Smart Citations

Optimization of Biogas Production in a Batch Laboratory Digester Using Total Solids, Substrate Retention Time, and Mesophilic Temperature

Masinde¹,

Nyaanga²,

Njue³

et al. 2020

IJPER

View full text Add to dashboard Cite

show abstract

“…These selected factors and levels are provided in a Taguchi's orthogonal array (OA), in such a way that optimization should be in a minimum number of experiments/trials 49–51 . These figures of the testing are calculated as per Equation .

Minimum number of trials = [(F - 1) \times L] + 1 = [(3 - 1) \times 3] + 1 = 9 = {normalL}_{9},

where F, L is the number of factors and levels respectively selected for the study.…”

Section: Taguchi and Gra Technique For Optimizationmentioning

confidence: 99%

Optimization of multi‐response biodiesel fueled diesel engine parameters using Taguchi grey relational analysis and analytical hierarchy process

Warkhade

Katam

Babu

2023

Env Prog and Sustain Energy

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Compression ignition engines are currently optimized to run on petrodiesel fuel, but due to its increasing demand and depletion of fossil fuel reserves, alternative fuels must be utilized. In this article, an attempt was made to optimize the operating parameters of an unmodified compression ignition engine fueled with linseed methyl ester biodiesel. The input parameters were compression ratio, load, and fuel blend, while output responses included brake‐specific fuel consumption (BSFC), brake thermal efficiency (BTE), and exhaust gas temperature (EGT), peak cylinder pressure (PCP), the rate of pressure rise (RPR), and net heat release rate (NHR), hydrocarbon (HC), carbon monoxide (CO), NOx, and Smoke. The aim was to determine the optimal combination of input parameters for minimal emissions and better performance and combustion parameters, with a minimum number of experiments. The study used Taguchi's L9 orthogonal array with a grey relational analysis approach (GRA) to obtain the optimum combination of input parameters. The study found that fuel blend B10, CR 18, and load 100% were the optimal input parameter combinations using the TGRA method. The S/N ratio analysis of grey relational grade (GRG) showed the optimal input factor levels to be fuel B10, CR 16, and load 100%, which was confirmed by the AHP method. The analysis of variance (ANOVA) showed that the load was the most influential factor, followed by CR and fuel. The confirmatory results showed an improvement of 56.1%, indicating that biodiesel can be efficiently used in an unmodified compression ignition engine by varying these influencing factors.

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Effect of Physical Presence of Waste Plastics in the Degradation of Municipal Solid Waste in Landfill

Ghosh

Sarkar

Das

2019

Waste Valorisation and Recycling

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Optimization of process factors for the efficient generation of biogas from raw vegetable wastes under the direct influence of plastic materials using Taguchi methodology

Cited by 12 publications

References 18 publications

Optimization of Biogas Production in a Batch Laboratory Digester Using Total Solids, Substrate Retention Time, and Mesophilic Temperature

Optimization of Biogas Production in a Batch Laboratory Digester Using Total Solids, Substrate Retention Time, and Mesophilic Temperature

Optimization of multi‐response biodiesel fueled diesel engine parameters using Taguchi grey relational analysis and analytical hierarchy process

Effect of Physical Presence of Waste Plastics in the Degradation of Municipal Solid Waste in Landfill

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