•Preparation and characterization of clay.•Detailed predictive modeling of the EBT adsorption process utilizing RSM, ANN and ANFIS models.•Critical comparative analysis of the three ...models.•Evaluation of mechanistic modeling of the adsorption process.•Optimization using genetic algorithm.
The application of artificial neural network (ANN), response surface methodology (RSM), and adaptive neuro-fuzzy inference system (ANFIS) in modeling the uptake of Eriochrome black-T (EBT) dye from aqueous solution using Nteje clay was the focus of this work. Acid activation with hydrochloric acid (HCl) was used to prepare the adsorbent while Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM) were utilized in the characterization of the adsorbent. The ANN, RSM, and ANFIS models were analyzed by considering the adsorbent dosage, contact time, solution temperature, and pH of the adsorption process. Sensitivity analyses involving six statistical error functions were further used to compare the acceptability of the models. Four mechanistic models (Weber and Morris, Film diffusion, Bangham, and Dummwald-Wagner models) were used to determine the mechanism of the EBT uptake. The result showed that the activation process enhanced the adsorption capacity of the clay. The ANFIS, ANN, and RSM models gave a high accuracy in predicting the adsorption of the EBT dye with correlation coefficients of 0.9920, 0.9910, and 0.9541, respectively. Further statistical indices lent credence to ANFIS as the best predictive model and RSM the least in adsorption of EBT dye. Process optimization using genetic algorithm gave optimum adsorption efficiency of 95.8%. Mechanistic modeling indicated film diffusion as the rate-limiting mechanism. The maximum amount of EBT adsorbed was 24.04 mg/g. The HCl-modified clay could be utilized as an efficient adsorbent in EBT uptake from wastewater.
The modeling and optimization of the substrate treatment process in biogas energy production from yam peel substrates via anaerobic digestion was the purpose of this work. Thermo-chemical ...pretreatment method was used in the substrate treatment. The instrumental analysis of the substrate was investigated using FTIR and SEM. Central composite design was used in the design of experiment and intelligent modeling of the substrate treatment in the biogas production via adaptive neuro-fuzzy inference systems (ANFIS), artificial neural network (ANN), and response surface methodology (RSM). The kinetics of the anaerobic digestion process was studied using five kinetic models. The result indicated that among the three process parameters investigated, temperature has the most significant effect on the substrate pretreatment. The instrumental analysis showed that the thermo-chemical modification of the substrate resulted in the transformation of its bond structure and the solubilization of the hemicelluloses molecules. ANFIS, ANN, and RSM models were efficient in modeling the anaerobic digestion process with correlation coefficient of 0.9997, 0.9997 and 0.9887, respectively showing good agreement between the experimental and predicted biogas yield. Further statistical error indices involving HYBRID (ANFIS=0.3180, ANN=0.3207, and RSM=11.157), RMSE (ANFIS=0.3359, ANN=0.8235, and RSM=4.969), and ARE (ANFIS=0.3068, ANN=0.3075, and RSM=1.5756) depicted the ANFIS as being marginally better than the ANN in simulating and modeling the anaerobic digestion. Optimization of the ANFIS model yielded a biogas volume of 356.24 ml at concentration, time, and temperature of 0.04 N, 60 s and 80 °C, respectively. The kinetics of the cumulative biogas production was described by the Modified Logistics, Transference and the Logistics models. The purification of the biogas by scrubbing gave about 92% methane gas. The sediments from the digestion with low C/N ratio could be used as manure and soil conditioner in agriculture.
•Activated carbon was produced from corn cob biomass•The adsorption of phenol onto corn cob activated carbon was studied in a packed bed column.•Thomas, Adams-Bohart, Wolborska, and Yoon-Nelson ...models fitted the experimental data.•Nonlinear regression is the most appropriate tool for the analysis of dynamic adsorption models.
In the present study, linear and nonlinear regression analysis for packed bed column adsorption of phenol onto corn cob activated carbon was investigated. The activation of the corn cob provided the activated carbon with enhanced surface area and micropore volume of 903.7 m2/g and 0.389 cm3/g respectively. The analysis of the physical properties of the corn cob activated carbon (CCAC) revealed that it contained 33.47% of fixed carbon, 5.82% of ash content, 18.01% of volatile matter, 0.63 g/mL of bulk density, 5.50% of moisture content, and a pH of 6.30. SEM images indicated the presence of interspatial pores within the matrix of the adsorbent, while the FTIR analysis revealed that the major functional groups in CCAC were alkanol, alkanes, alkyls, carboxylic acids, ethers, esters, and nitro compounds. The effect of the process parameters influencing the dynamic adsorption process was investigated at flow rates (9–18 mg/min), initial phenol concentration (100-300 mg/L), bed height (5–10 cm), and particle size (300-800 µm). The breakthrough time and adsorption capacity increased with an increase in bed height but decreased with an increase in flow rate, initial phenol concentration, and particle size. At optimum conditions of bed height 10 cm, initial phenol concentration, 100 mg/L, flow rate, 9 mL/min, and particle size 300 µm, the adsorption capacity at breakthrough (qb), adsorption capacity at saturation (qs), volume of effluent treated at saturation (Veff,s), length of mass transfer zone (MTZ), adsorbent exhaustion rate (AER), fractional bed utilization (FBU) factor, Reynolds number (Re), Sherwood number (Sh), and the percentage phenol removal (Ys) were 2.143 mg/g, 8.570 mg/g, 12.96 L, 7.50 cm, 30.86 g/L, 0.25, 17.93, 28.85, and 66.13% respectively. The linear and nonlinear regression analysis of the Thomas, Adams-Bohart, and Wolborska models fitted better with the experimental data than the Yoon–Nelson model. Generally, the nonlinear regression proved to be a better tool for dynamic adsorption model analysis as the model parameters generated by the technique have a better correlation with the experimental data when compared to those obtained via linear regression. Conclusively, this study has shown that CCAC can successfully be used for the removal of phenol from aqueous solutions. It also demonstrated that the modeling approach significantly affects the outcome of the analysis of dynamic adsorption systems.
The use of artificial intelligence models in predicting the moisture content reduction in the drying of potato (Ipomoea batata) slices was the focus of this work. The models used were adaptive neuro ...fuzzy inference systems (ANFIS), artificial neural network (ANN) and response surface methodology (RSM). The parameters considered were drying time, drying air speed and temperature. The capability and sensitivity analysis of the three models were evaluated using the correlation coefficient (R2) and some statistical error functions such as the average relative error (ARE), root mean square error (RMSE), Hybrid Fractional Error Function (HYBRID) and absolute average relative error (AARE). The result showed that the three models demonstrated significant predictive behaviour with R2 of 0.998, 0.997 and 0.998 for ANFIS, ANN and RSM respectively. The calculated error functions of ARE (RSM = 1.778, ANFIS = 1.665 and ANN = 4.282) and RMSE (RSM = 0.0273, ANFIS = 0.0282 and ANN = 0.1178) suggested good harmony between the experimental and predicted values. It was concluded that though the three models gave adequate predictions that were in good agreement with the experimental data, the RSM and ANFIS gave better model prediction than ANN.
•Investigation of moisture reduction of potato slices in a drying process.•RSM, ANN and ANFIS were used in the predictive modeling of the drying process.•Drying time and temperature were the most significant factors that affect the drying process.•RSM and ANFIS gave a better predictions when compared with the experimental values.•The optimal drying conditions were validated.
This research aimed to optimize and model the adsorption process of oil layer removal using activated plantain peels fiber (PPF), a biomass-based material. The adsorbent was activated by thermal and ...esterification methods using human and environmentally friendly organic acid. Effects of process parameters were examined by one factor at a time (OFAT) batch adsorption studies, revealing optimal conditions for oil removal. Also, RSM, ANN and ANFIS were used to adequately predict the oil removal with correlation coefficient > 0.98. RSM modelling revealed the best conditions as 90 °C, 0.2 mg/l, 1.5 g, 6 and 75 mins, for temperature, oil–water ratio, adsorbent dosage, pH and contact time respectively. Under these simulated conditions, the predicted oil removal was 96.88 %, which was experimentally validated as 97.44 %. Thermodynamic studies revealed the activation energy, change in enthalpy and change in entropy for irreversible pseudo-first order and pseudo-second order model as (15.82, 24.17, −0.614 KJ/mols) and (33.21,40.31, −0.106 KJ/mols) respectively, indicating non-spontaneous process; while modeling studies revealed that the adsorption process was highly matched to Langmuir’s isotherm, with maximum adsorption capacity of 50.34 mg/g. At the end of the overall statistical modelling, ANFIS performed marginally better than the ANN and RSM. It can be concluded from these results that our biomass-based material is an efficient, economically viable and sustainable adsorbent for oil removal, and has potentials for commercialization since the process of adsorption highly matched with standard models, and its capacity or percentage oil removal also compares favorably to that of commercially available adsorbents.
In this work, the predictive capabilities of response surface methodology (RSM) and adaptive neuro fuzzy inference systems (ANFIS) in modeling aluminum (Al) and mild steel corrosion inhibition by ...Aspilia Africana (A. Africana) were comparatively analyzed. Phytochemical and Fourier Transform Infrared Spectroscopy (FTIR) characterization of A. Africana leaf extract indicated that the inhibitor possessed high value flavonoids, Tannins and dominant functional groups necessary for promoting sustainable corrosion inhibition. While statistical parameters verified the applicability of RSM and ANFIS techniques in modeling the corrosion inhibition of Al and mild steel, error indices illustrated the dominance of ANFIS (R2 = 0.9917) and RSM (R2 = 0.9905) in predicting the inhibition efficiency of Al and mild steel, respectively. Analysis of variance (ANOVA) showed that acid concentration (F-value = 191.23) was the most influential process parameter in the modeling of Al corrosion inhibition process, while A. Africana inhibitor concentration presented an F-value of 160.5 to maintain its superior position among other factors in the modeling of mild steel corrosion inhibition. ANFIS coupled genetic algorithm optimization (ANFIS-GA) of Al corrosion inhibition was validated to be 80% at HCl conc. of 0.7 M, inhibitor conc. of 0.59 g/L and immersion time of 6.2 h. Similarly, mild steel corrosion inhibition process was optimized using RSM-GA and validated to be 77.3% efficiency (HCl conc. = 0.5 M, inhibitor conc. = 0.37 g/L and time of 4.8 h). Post optimization characterization using electrochemical studies demonstrated close agreement with inhibition efficiency obtained by gravimetric technique. Furthermore, polarization studies indicated that A. Africana leaf extract acted as a mixed type inhibitor in the corrosion process of Al and mild steel species.
The linear and nonlinear kinetics analysis and adsorption characteristics of phenol adsorption onto activated carbon synthesized from rice husk biomass were investigated in a packed bed column. ...Several analyses such as physical properties, BET surface area, pore size distribution, FTIR, and SEM were used to investigate the adsorption properties of the rice husk-activated carbon (RHAC). The column adsorption studies indicated that the adsorption of phenol onto RHAC is favored by an increase in bed height and a decrease in solution flow rate, influent phenol concentration, and particle size. Various dynamic adsorption parameters depicting the adsorption characteristics of phenol onto RHAC were estimated from the breakthrough analysis of the experimental data. The fitting of the experimental data to the Thomas, Adams–Bohart, Yoon–Nelson, and Wolborska models using linear and nonlinear regression techniques showed that the four models gave good fits to the experimental data. The R
2
values for the regressed lines ranged from 0.6827 to 0.9918, and 0.9958 to 1.0000 for the linear and nonlinear regression techniques, respectively. Experimentally, a maximum adsorption capacity value of 14.57 mg/g was obtained; at the same experimental conditions, 14.88 mg/g was predicted by the nonlinear regression, while 9.78 mg/g was predicted by the linear regression of the Thomas model. The results affirmed the potency of RHAC for the treatment of phenol-contaminated wastewater. It provided comprehensive data needed for the design of phenol adsorption columns using RHAC. It equally revealed that a better model analysis would be achieved with the application of nonlinear regression.
Banana peel fiber adsorbent (BPF) with well-arranged substructure of pores was fabricated via esterification reaction with organic acid and biomass. The emerged adsorbent (BPF) was employed in taking ...away crude oil from water surface. Three machine learning tools such as RSM, ANN and ANFIS was employed for the modelling and optimization of the process. From results, the optimal oil layer removal of 98.2% was achieved at oil water ratio of 0.2 g /100 cm3. For now, BPF displayed high adsorptive prospect at a very low pH of 4 with 96.8% oil removal. On the other hand, the activation energy, enthalpy change and entropy change of the system are (18.56, 25.44, −0.751 KJ/mols) and (25.77, 29.16, −0.813 KJ/mols) designating a non-spontaneous system. The process of removal by BPF really matched the Langmuir isotherm model as proved by statistical error analysis with highest adsorption capacity of 49.33 mg/g as shown through equilibrium modeling. RSM displayed the optimum conditions of the key variables such as temperature, oil concentration, adsorbent dosage, pH and time as 100 °C, 0.2 g/100 cm3, 1.5 g, 2 and 75 mins, respectively. Analysis of the three generic algorithm indicated significant oil removal prediction with quite remarkably similar coefficient of correlation of 0.999. Additional statistical analysis suggested that RSM was marginally better than ANN and ANFIS for the modelling of crude oil removal via esterified banana peels fiber.
•BSC was successfully extracted from crab shell waste via solubilization technique.•BSC was characterized and found to have consistent properties similar to those of α – chitin.•BSC is an effective ...natural coagulant for the removal of turbidity and color from FPE.•Equilibrium data were well expressed by Langmuir isotherm model.•Adsorptive de-colorization of FPE was spontaneous and endothermic.
High grade chitin extracted from Brachyura shell waste was successfully used for adsorptive de-colorization of highly turbid fishpond effluent. Brachyura shell chitin was characterized via Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), X-ray diffraction (XRD), thermo-gravimetric analysis (TGA) and differential scanning calorimetry (DSC). Results obtained from the characterization indicated that the extracted chitin possessed significant features required for surface phenomenon driven matrices. The best efficiency obtained for turbidity and color removal were 88% and 91%, respectively, at BSC dosage of 1.8 g/L, pH of 6.0 and temperature of 45°C. The maximum adsorption capacity of the de-colorization process onto BSC was found to be 265.59 mg/g. Results from kinetic analysis suggested that pseudo-second order model was most accurate (R2 > 0.99) in predicting the experimental data. Webber-Morris intraparticle diffusion rate constant (Kipd) demonstrated that the adsorptive process depended more on film diffusion mechanism. Equilibrium study was carried out using Langmuir, Freundlich, Temkin, Dubinin Radushkevich and Brunauer-Emmett-Teller isotherm model at 30, 40 and 50°C. Fitness appraisal analysis of the isotherm models pointed out that experimental data aligned significantly with Langmuir and Temkin isotherm (R2 > 0.94), suggesting the homogenous and monolayer mode of the adsorptive process. Values of Gibbs free energy (− ΔG0= 1.7816 – 2.6706 kJ /mol), enthalpy (ΔH0= 11.6867kJ/ mol), activation energy (EA= 13.2330kJ/ mol) and entropy (ΔS0= 44.4511J / molK) obtained from thermodynamic analysis confirmed the spontaneous, endothermic, favorable and physical nature of the process.
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The sorption mechanisms, point of zero charge, and isosteric heats involved in the adsorptive dephenolization of aqueous solutions using thermally modified corn cob (TMCC) were studied at different ...initial phenol concentrations (100–500 mg/l), TMCC dosage (0.4–2.0 g), contact time (5–60 min), pH (2–10), and temperature (30–60°C). Analysis of the adsorbent material showed that it possessed the properties typical of a good adsorbent. The adsorption experiments revealed that phenol uptake is favored by an increase in TMCC dosage and contact time and a decrease in temperature and concentration of phenol in the solution. The experimental data were well-fitted to the Sips, Langmuir, Toth, and Redlich–Peterson isotherm models. Thermodynamic studies suggested that the sorption of phenol onto TMCC is feasible, spontaneous, and endothermic. The isosteric heats of adsorption obtained are in the range 47.43-79.38 kJ/mol, confirming that the adsorption process is predominantly a physical process depicting the van der Waals interactions, and it is inversely proportional to surface loading. The analysis of the adsorption mechanisms showed that the intraparticle, film, and pore diffusion mechanisms were significantly involved in the phenol adsorption process. The involvement of electrostatic attraction, π‐π electron-donor interaction, and hydrogen bonding was also demonstrated. The point of zero charge (pHpzc) was obtained at a pH of 5.83; being slightly lower than the optimum pH of 6 indicates that the sorbent surface is obviously not negatively charged at pHpzc. The discoveries of this study have shown that the dephenolization process is feasible, spontaneous, endothermic, dominated by a physical process, and governed by intraparticle, film, and pore diffusion mechanisms.
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