Statistical analysis for orange G adsorption using kola nut shell activated carbon

Chinonye, Okpe Emmanuel; Oluchukwu, Asadu Christian; Elijah, Onu Chijioke

doi:10.1080/22243682.2018.1534607

Cited by 17 publications

(9 citation statements)

References 14 publications

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“…Analysis of variance (ANOVA) can be employed in the analysis and validation of experimental result of many processes (Amadi et al, 2020). The significance and adequacy of the RSM model and input parameters were determined using the ANOVA, as shown in (Oguanobi et al, 2019;Emmanuel Chinonye et al, 2018). It is used to compare the range of the predicted responses at the design points to the mean prediction error (Ezedinma et al, 2021).…”

Section: Anova Resultsmentioning

confidence: 99%

ANFIS, ANN, and RSM modeling of moisture content reduction of cocoyam slices

Onu

Igbokwe

Nwabanne

et al. 2021

Food Processing Preservation

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The capability of response surface methodology (RSM), artificial neural network (ANN), and adaptive neuro-fuzzy inference systems (ANFIS) in modeling and predicting moisture content reduction in the drying of cocoyam (Colocasia taro) slices was the purpose of this study. Design of experiment was utilized. Modified Fick's second law of diffusion was used to determine effective moisture diffusivity (D eff ). Results indicated that drying time, air velocity, and temperature significantly affected the drying process. D e ff values obtained ranged from 2.97 × 10 −10 to 7.30 × 10 −10 m/s. Page model with R 2 = 0.994, SSE = 0.0053, RMSE = 0.0611, best described the kinetics of the drying process. ANN, RSM, and ANFIS all showed significant modeling and predicting ability with R 2 of 0.9583, 0.9519, and 0.9971, respectively. Genetic algorithm (GA) optimization showed minimum moisture content of 14.362%, 11.919%, and 11.293% for RSM-GA, ANN-GA, and ANFIS-GA, respectively. Additional statistical analysis lent credence to ANFIS as the best in modeling and predicting the moisture content reduction of cocoyam slices.

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Section: Anova Resultsmentioning

confidence: 99%

ANFIS, ANN, and RSM modeling of moisture content reduction of cocoyam slices

Onu

Igbokwe

Nwabanne

et al. 2021

Food Processing Preservation

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show abstract

“…The signal to noise ratio which is given as the value of the adequacy precision indicates that an adequate relationship of signal to noise ratio exists. An adequate precision ratio value greater than 4 indicates adequate model efficacy [38]. The results showed that the adequate precision ratio was 19.23.…”

Section: Anova Analysis and Model Fittingmentioning

confidence: 94%

Characterization and Optimization of Biodiesel Produced from Palm Oil Using Acidified Clay Heterogeneous Catalyst

Ijeoma

Jospeh

Chijioke

2021

AJACR

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The objective of the present work is to synthesize and characterizethe production of biodiesel from palm oil. Acid activated heterogeneous catalyst was used in the synthesis process. The clay was characterized using SEM. The effect of process variables such as reaction temperature, catalyst weight and oil to alcohol ratio were investigated. Response surface methodology (RSM) was utilized in the design of experiment and modeling of the biodiesel production process. The biodiesel was characterized and compared to standard properties of biodiesel. The result showed that H2SO4 activation increased the surface area of the clay. In the RSM modeling, the maximum biodiesel yield obtained was 82.99% at reaction time of 5 hours, temperature of 140oC, methanol to oil ratio of 10:1, and catalyst weight of 3.6 wt%. Quadratic model with correlation coefficient of 0.9657, best described the experimental data. The physio-chemical properties of the biodiesel synthesized were comparable to the properties of standard biodiesel. This study has shown that clay heterogeneous catalyst can be used to catalyst the synthesis of biodiesel from palm oil. The finding of this study is recommended for solving some of the challenges posed by the use of fossil fuel.

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“…From figure also, the optimum adsorption/removal of oil with unmodified groundnut shell adsorbents occurred at 100 mins with 71.72% removal. The difference in percentage removal was occasioned by the formation of monolayer on the surface of the adsorbents, thus the extent of removal of oil layer from the solution was then controlled by the rate of transport of the adsorbate species from the outer layer (pore sites) to the inner layer sites of the adsorbents, hence adsorbent with more microporous pores removes greater percentage of oil layer (adsorbate) according to Okpe et al [20]; Asadu et al [16]; Nwabanne et al [21]; Nwabanne et al [22]. It can be seen that the two adsorbents have demonstrated a good potential for the adsorption of oil from solution with alkaline activated groundnut shell showing a greater potential as shown in Fig.…”

Section: Effect Of Timementioning

confidence: 99%

Comparative Analysis and Regenerative Studies of Alkaline Modified Groundnut Shell as Adsorbent for the Removal of Oil Layer from Polluted Water

Ezenwa

Ogochukwu

2021

JERR

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This work focuses on the comparative analysis and regenerative studies of alkaline modified groundnut shell as adsorbent for the removal of oil layer from water surface. The groundnut shell was carbonized at 800°C before chemical activation. Scanning Electron Microscope (SEM) and UV/VIS Spectrophotometer were used in characterizing the adsorbents. Batch adsorption studies were carried out to determine the effect of process conditions on the percentage removal. Regeneration studies was applied to investigate the reusability of the adsorbent after each batch of experiments. SEM characterization revealed that micro porous space on the surface of the adsorbent (groundnut shell) increased significantly after activation. The surface area of the composite increased from 28.5cm2 to 87.9cm2 after activation. pH of point zero charge ranging from to revealed negative charge predominance on the surface of the adsorbents and adsorption was found to be very fast at low pH due to strong electrostatic force between oil layer and ion diffusion on the adsorbent surface. The optimum adsorption time was found to be 100 minutes with 79.4% oil removal.at pH of 6.9. Regeneration of the adsorbent after each batch of experiments shows that the surface area remained intact at 77.8cm2 which is almost equal to the modified (unused) adsorbent. Experiments with the regenerated adsorbent shows that 84.89% of oil was removed at oil water ratio of 0.2g/100cm3 against 88.56% of oil removed using the modified adsorbent at the same oil water ratio. Thus, the sorption process was feasible, spontaneous, rapid and showed high performance with a proven ability of the adsorbent to be regenerated after use. However, the present approach has the advantage of simplicity, less time consuming and most importantly, low cost considering the availability of groundnut shell at no cost.

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Statistical analysis for orange G adsorption using kola nut shell activated carbon

Cited by 17 publications

References 14 publications

ANFIS, ANN, and RSM modeling of moisture content reduction of cocoyam slices

ANFIS, ANN, and RSM modeling of moisture content reduction of cocoyam slices

Characterization and Optimization of Biodiesel Produced from Palm Oil Using Acidified Clay Heterogeneous Catalyst

Comparative Analysis and Regenerative Studies of Alkaline Modified Groundnut Shell as Adsorbent for the Removal of Oil Layer from Polluted Water

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