Accurate Factors Research Articles

Kinetic Analysis of the Adsorption of Ethyl Violet onto Graphene Oxide Sheets Integrated with Gold Nanoparticles

An example of biosorption is when the sorbent is made from a biodegradable material. Biosorption is now being seen as a simple, cost-effective, and environmentally acceptable alternative to traditional pollution treatment methods. Bioremediation is one of the branches of bioremediation that is used to minimise pollution in the context of incorrect dye waste disposal. The sorption isotherm of Ethyl Violet onto graphene oxide were analyzed using three models—pseudo-1st, pseudo-2nd and Elovich, and fitted using non-linear regression. Statistical analysis based on root-mean-square error (RMSE), adjusted coefficient of determination (adjR2), bias factor (BF), accuracy factor (AF), corrected AICc (Akaike Information Criterion), Bayesian Information Criterion (BIC) and Hannan–Quinn information criterion (HQC) that showed that the pseudo-second-order model was the best which was the same finding from the original published work. The calculated evidence ratio was 11 with an AICc probability value of 0.91 indicating that the best model was at least 11 times better than the nearest best model, which was pseudo-1st. Further analysis is needed to provide proof for the mechanism usually tied to this kinetic. Nonlinear regression analysis using the pseudo-2nd order model for the highest concentration tested, which was 10 mM, gave values of equilibrium sorption capacity qe of 30.928 mg/g (95% confidence interval from 29.328 to 32.527) and a value of the pseudo-2nd-order rate constant, k2 of 0.020 (95% confidence interval from 0.011 to 0.028).

Isothermal Modelling of the Adsorption of Chromium onto Calcium Alginate Nanoparticles

Because of its extensive usage in industrial applications such as leather tanning, metallurgy, electroplating, and refractory materials, chromium is one of the most dangerous trace elements introduced into surface and ground waters. The sorption isotherm of chromium sorption onto calcium alginate nanoparticles were analyzed using ten models—Henry, Langmuir, Dubinin-Radushkevich, Freundlich, BET, Toth, Sips, Fritz-Schlunder IV, Baudu and Fritz-Schlunder V, and fitted using non-linear regression. Only the Toth and Fritz-Schlunder V models were unable to fit the data. Statistical analysis based on root-mean-square error (RMSE), adjusted coefficient of determination (adjR2), bias factor (BF), accuracy factor (AF), corrected AICc (Akaike Information Criterion), BIC and HQC showed that the Langmuir model was the best model in terms of overall best criteria. The calculated evidence ratio was 7 with an AICc probability value of 0.87 indicating that the best model was at least 7 times better than the nearest best model, which was Freundlich. The calculated Langmuir parameters qmL value of 79.174 mg/g (95% confidence interval from 50.702 to 107.646) and bL value of 0.332 L/mg (95% confidence interval from 0.294 to 0.371) is not much different from the linearized published work for the qmL value of 145 mg/g but lower than the bL value of 0.34 L/mg. The nonlinear regression method allows for the parameter values to be represented in the 95% confidence interval range which can better allow comparison with published results.

Kinetic Analysis of the Adsorption of Cibacron Blue onto Bean Peel

Biosorption is a kind of sorption technology in which the sorbent is derived from a biological source. At the moment, biosorption is seen as a simple, cost-effective, and environmentally friendly process that might be employed as a viable alternative to conventional techniques of pollution removal. When it comes to improper textile waste disposal, it falls under one of the branches of bioremediation that is used to reduce contamination in the setting of improper textile waste disposal. The sorption isotherm of Cibacron Blue onto bean peel were analyzed using three models—pseudo-1st, pseudo-2nd and Elovich, and fitted using non-linear regression. The Elovich model was the poorest in fitting the curve based on visual observation and the best was pseudo-2nd order based on statistical analysis such as root-mean-square error (RMSE), adjusted coefficient of determination (adjR2), bias factor (BF), accuracy factor (AF), corrected AICc (Akaike Information Criterion), Bayesian Information Criterion (BIC) and Hannan–Quinn information criterion (HQC). Nonlinear regression analysis using the pseudo-2nd order model gave values of equilibrium sorption capacity qe of 6.164 mg/g (95% confidence interval from 5.918 to 6.410 ) and a value of the pseudo-2nd-order rate constant, k2 of 0.034 (95% confidence interval from 0.024 to 0.045). Further analysis is needed to provide proof for the chemisorption mechanism usually tied to this kinetic.

Isothermal Modelling of the Adsorption of Cibacron Blue onto Bean Peel

Biosorption is a sort of sorption technology in which the sorbent is a substance that is biologically sourced. In today's world, biosorption is seen as a simple, inexpensive, and ecologically friendly way for removing pollutants from the environment. One of the branches of bioremediation that is utilised to decrease environmental pollution in the context of minimising improper textile waste disposal is this method. The sorption isotherm of Cibacron Blue onto bean peel were analyzed using ten models—Henry, Langmuir, Dubinin-Radushkevich, Freundlich, BET, Toth, Sips, Fritz-Schlunder IV, Baudu and Fritz-Schlunder V, and fitted using non-linear regression. Statistical analysis based on root-mean-square error (RMSE), adjusted coefficient of determination (adjR2), bias factor (BF), accuracy factor (AF), corrected AICc (Akaike Information Criterion), BIC and HQC showed that the Freundlich model was the best model in terms of overall best criteria. The calculated evidence ratio was 8 with an AICc probability value of 0.89 indicating that the best model was at least 8 times better than the nearest best model, which was Sips. The calculated Freundlich parameters KF (Freundlich isotherm constant) and nF (Freundlich exponent) were 5.369 (L/g) (95% confidence interval from 4.359 to 6.379) and 3.125 (95% confidence interval from 2.717 to 3.533). The Langmuir constant was utilized to calculate the maximum adsorption capacity QmL (mg/g) which gave a value of 27.83 mg/g (95% confidence interval from 23.69 to 31.98). The nonlinear regression method allows for the parameter values to be represented in the 95% confidence interval range which can better allow comparison with published results.

Kinetics Modeling on the Biosorption of Remazol Black B Dye by Aspergillus flavus

Azo dyes, such as Remazol Black B, are different from conventional dyes in that they establish covalent bonds with textile fibers like cotton. They are widely utilized in the textile industry because of their favorable properties of bright color, water resistance, simple application procedures, and low energy consumption. Their discharge into receiving streams has major environmental consequences, such as reducing photosynthesis in aquatic life due to lower light penetration. The biosorption isotherm data of Remazol Black B dye biosorption by Aspergillus flavus were investigated using two models—pseudo-1st order and pseudo-2nd order—and fitted using non-linear regression. The pseudo-1st order model was found to be the best by statistical analysis using root-mean-square error (RMSE), adjusted coefficient of determination (adjR2), bias factor (BF), accuracy factor (AF), corrected AICc (Akaike Information Criterion), Bayesian Information Criterion (BIC), and Hannan–Quinn information criterion (HQC). At 250 mg/L, kinetic analysis using the pseudo-1st order model yielded an equilibrium sorption capacity qe of 4.61 mg/g (95 % confidence interval from 4.54 to 4.68) and a pseudo-1st-order rate constant, k1 of 0.15 (95% C.I. from 0.128 to 0.164).

Kinetic Analysis of the Adsorption of Malachite Green onto Graphene Oxide Sheets Integrated with Gold Nanoparticles

Malachite green is extensively used in the textile dye industry and in agriculture as fish pests’ pesticide. Biosorption is a type of sorption technique that uses a biological sorbent. As of now, biosorption is viewed as a simple and cost-effective process that might be used as an alternative to traditional pollution treatment methods. Bioremediation is one of the branches of bioremediation that is used to minimise pollution in the context of incorrect textile waste disposal. The sorption isotherm of Malachite Green onto graphene oxide were analyzed using three models—pseudo-1st, pseudo-2nd and Elovich, and fitted using non-linear regression. The Elovich model was the poorest in fitting the curve based on visual observation and the best was pseudo-2nd order based on statistical analysis such as root-mean-square error (RMSE), adjusted coefficient of determination (adjR2), bias factor (BF), accuracy factor (AF), corrected AICc (Akaike Information Criterion), Bayesian Information Criterion (BIC) and Hannan–Quinn information criterion (HQC). Nonlinear regression analysis using the pseudo-2nd order model gave values of equilibrium sorption capacity qe of 6.164 mg/g (95% confidence interval from 5.918 to 6.410) and a value of the pseudo-2nd-order rate constant, k2 of 0.034 (95% confidence interval from 0.024 to 0.045). Further analysis is needed to provide proof for the chemisorption mechanism usually tied to this kinetic.

Kinetic Analysis of the Adsorption of Chromium onto Calcium Alginate Nanoparticles

The widespread use of chromium in industrial applications such as leather tanning, metallurgy, electroplating, and refractory materials has resulted in it being one of the most harmful trace elements to be introduced into surface and ground waters. The sorption isotherm of chromium sorption onto calcium alginate nanoparticles were analyzed using three models—pseudo-1st, pseudo-2nd and Elovich, and fitted using non-linear regression. The Elovich model was the poorest in fitting the curve based on visual observation followed by the pseudo-1st order. Statistical analysis based on root-mean-square error (RMSE), adjusted coefficient of determination (adjR2), bias factor (BF), accuracy factor (AF), corrected AICc (Akaike Information Criterion), Bayesian Information Criterion (BIC) and Hannan–Quinn information criterion (HQC) that showed that the pseudo-1ST order model is the best model. Kinetic analysis using the pseudo-1st order model at 400 mg/L 4-BDE gave a value of equilibrium sorption capacity qe of 31.89 mg g-1 (95% confidence interval from 30.37 to 33.42) and a value of the pseudo-1st-order rate constant, k1 of 0.22 (95% confidence interval from 0.019 to 0.025). Further analysis is needed to provide proof for the chemisorption mechanism usually tied to this kinetic.

Mathematical Modeling of Molybdenum-Blue Production from Bacillus sp. strain Neni-

Heavy metals can be remediated using microorganism by altering the redox function i.e. reduction from more toxic oxidation state to non-toxic one. Molybdenum reduction to molybdenum blue by bacteria is an emerging tool for remediation of the metal. Mathematical modelling via nonlinear regression of the heavy metal’s reduction can yield important reduction parameters such as theoretical maximum reduction, specific reduction rate, and the lag period of reduction. Nonlinear regression can be utilized using various models such as logistic, Richards, Gompertz, Baranyi-Roberts, Schnute, Buchanan 3-phase, Von Bertalanffy and Huang with the best model yielding an underlying mechanistic property for the reduction. We demonstrate that the Baranyi-Roberts model was the best model in modelling the Mo-blue production curve of the bacterium Bacillus sp. strain Neni-10 based on statistical tests such as root-mean-square error (RMSE), corrected AICc (Akaike Information Criterion), adjusted coefficient of determination (R2), accuracy factor (AF) and bias factor (BF). The model parameters or constants obtained were maximum lag time (λ), Mo-blue production rate (μm), and maximal Mo-blue production (Ymax). The construction of secondary models will benefit greatly from the use of bacterial growth models to acquire realistic Mo-blue production rates. According to a literature search, this technique is wholly unique for molybdenum reduction to Mo-blue in particular, and in the heavy metals’ detoxification process in general. The results of this study have demonstrated the usefulness of these models in simulating Mo-blue synthesis in bacteria.

Predictive Growth Model of Indigenous Green Microalgae (Scenedesmus sp. UKM9) in Palm Oil Mill Effluent (POME)

Microalgae cultivation in POME treatment to removed environmental pollutants synchronized with biomass production. Biomass production depends on the proliferation rate of microalgae in wastewater, which is rarely addressed. Thus, growth rate assessment was performed on a local strain of Scenedesmus sp. UKM9 by using four different mathematical models of logistic, modified logistic, modified Gompertz, and Baranyi-Roberts. Statistical analyses, including root-mean-square error (RMSE), R2, adjusted R2, bias factor (BF), accuracy factor (AF), and standard error prediction (%SEP) were applied to verify the accuracy of each model. The findings illustrated that the Baranyi-Roberts model had the highest AF of 1.000, the lowest RMSE and %SEP values of 0.1300 and 5.40%, respectively, and the predicted BF value was 1.0001. These results revealed that the model was relevant to complex environmental conditions, and its parameter was biologically interpretable. The Baranyi-Roberts model regards a suitable model for the growth of indigenous microalgae of Scenedesmus sp. UKM9 in POME.

Mathematical Modeling of the Molybdenum Blue Production from Serratia sp. strain DRY5

The reduction of molybdenum to molybdenum blue is a detoxification process and the development of Mo-blue is associated with growth. Significant parameters such as precise reduction rate, theoretical maximum reduction and whether reduction at high molybdenum concentration influenced the lag time of reduction can be discovered by mathematical modeling of the reduction phase. While common, the use of the linearization method by the use of natural logarithm transformation is inaccurate and can only provide an approximate value for the calculated single parameter, the real growth rate. In this work, a variety of models for such as logistic, Gompertz, Richards, Schnute, Baranyi-Roberts, Von Bertalanffy, Buchanan three-phase and more recently Huang were utilized to obtain values for the above parameters or constants. The Huang model was the best model in modelling the Mo-blue production curve of the Serratia sp. strain DRY5 based on statistical tests such as root-mean-square error (RMSE) (0.043), adjusted coefficient of determination (R2) (0.994), bias factor (BF) (1.00), accuracy factor (AF) (1.03) and corrected AICc (Akaike Information Criterion) (-67.02). Parameters obtained from the fitting exercise were maximum Mo-blue production rate (m), lag time () and maximal Mo-blue production (Ymax). We make use of primary growth models for modeling Mo-blue output in this work. This is an emerging method of identifying constants of parameters that control the reduction of molybdenum. For the creation of further secondary models, the constants calculated from this work will be utilized for such purpose. The use of these primary models can also be broadened to processes involving the detoxification of other heavy metals.

Kinetic Studies on the Biosorption of the Brominated Flame Retardant 4-Dibromodiphenyl ether (BDE-3) using Coconut Palm Leaf Powders

Methods such as membrane isolation, ion replacements, precipitation, transformation and biosorption are proven approaches to contaminant control. Biosorption has all of these technological features including low operating costs, very efficient detoxifying of toxicities at low volumes, minimal amounts of removal components and nutrient requirement, as well as bacterial remediation, which are limited to the presence of heavy metals and other toxicants. The biosorption of BDE-3 on coconut leaves powder on the biosorption of BDE-3 from coconut leaves powder were analyzed using three models—pseudo-1st, pseudo-2nd and Elovich, and fitted using non-linear regression. Statistical analysis based on root-mean-square error (RMSE), adjusted coefficient of determination (adjR2), bias factor (BF), accuracy factor (AF), corrected AICc (Akaike Information Criterion), Bayesian Information Criterion (BIC) and Hannan–Quinn information criterion (HQC) showed that the Pseudo-2nd order model is the best model. Kinetic analysis using the Pseudo-2nd order model gave a value of equilibrium sorption capacity qe for 0.01 g per L adsorbent of 488.16 mg g-1 (95% confidence interval from 463.68 to 512.64) and a value of the Pseudo-2nd-order rate constant, k2 of 0.00019 (95% confidence interval from 0.00010 to 0.00027) while the equilibrium sorption capacity qe for 0.002 g per L adsorbent of 2403.61 mg g-1 (95% confidence interval from 2313.99 to 2493.22) and a value of the Pseudo-2nd-order rate constant, k2 of 0.000043 (95% confidence interval from 0.000027 to 0.000059). These calculated values will be very useful in designing effective sorption experiment and understanding the limitations of the system developed.

Modeling of ethanol fermentation from carob extract–based medium by using Saccharomyces cerevisiae in the immobilized-cell stirred tank bioreactor

In this study, various mathematical functions were utilized to fit the observed values and the kinetic parameters of fermentation in immobilized-cell stirred tank reactor (ICSTR) with carob extract medium (CEM). The best model was selected based on the residual sum of squares (RSS), root-mean-square-error (RMSE), mean-absolute-error (MAE), determination coefficient (R2), slope (m), bias factor (BF), accuracy factor (AF), the objective function (Φ-factor), and f-testing. Therefore, the Stannard (ST) model was good agreement with the actual biomass production (X), ethanol production (P), and sugar consumption (S) data (RSS = 0.22, 6.23, and 45.70 g/L; RMSE = 0.17, 0.88, and 2.39 g/L; MAE = 0.14, 0.64, and 1.72 g/L; R2 = 0.9948, 0.9977, and 0.9972; m = 1.03, 1.02, and 1.02; BF = 1.35, 1.08, and 0.97; AF = 1.41, 1.14, and 1.04; and Φ-factor = 0.008, 0.004, and 0.005 (∑Φ-factor = 0.017) for X, P, and S, respectively). In the prediction of kinetic parameters belonging to the experimental fermentation, the ST model also gave better, satisfactory, and well-directed results than the other mathematical models. Besides, the ST model was also accomplishedly validated using two independent sets of the experimental data yielded from the different carbon sources in various concentrations. Consequently, the models proposed for X, P, and S can serve as universal functions to predict the real values and the kinetic parameters of the ethanol fermentation. Besides, these models can be applied to improve the ethanol fermentation in the ICSTR with CEM.

Temporal and spatial statistical analysis of ambient air quality of Assam (India)

ABSTRACT Present paper represents the spatio-temporal variation of air quality and performances of geostatistical tools for the identification of pollutants zone in various districts of Assam (India). Geographic Information System (GIS) and geostatistical analysis were utilized to estimate the spatio-temporal variations (2015–2017) of gaseous and particulate air pollutants. Data of 23 fixed monitoring stations were collected from the Central Pollution Control Board (CPCB). It was observed that SO2 and NOx concentrations are the major pollutants to the deterioration of air quality in Assam State. Exploratory data analysis was considered for the determination of spatial and temporal patterns of air pollutants. Air Quality index (AQI) was calculated based on the air pollutants and particulate matter. Radial Basis Function (RBF) interpolation techniques were used to analyze the spatial and temporal variation of air quality in Assam. Cross-validation is applied to evaluate the accuracy of interpolation methods in terms of Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Nash–Sutcliffe Equation (NSE) and Accuracy Factor (ACFT). In 2015, the high value of AQI portrayed in the central and northeast of the state. In 2016, the central and entire east of the study area was recorded the highest value of AQI. In 2017, it was observed that mostly the central part of the state recorded the high value of AQI. The spatio-temporal variation trend of air pollutants provides sound scientific basis for its management and control. This information of air pollution congregation would be valuable for urban planners and decision architects to efficiently administer air quality for health and environmental purposes. Implications Guwahati is one of the most polluted cities in India provided a novel evidence to find out the impact of air pollution. Present study has been suffered from several limitations, like (i) the daily or weekly concentration of air pollutants was not gained due to limited monitoring technique, (2) dearth of regular information of PM2.5 collection, which were not regularly connected. Present study is used to estimate the spatio-temporal variations (2015–2017) of gaseous and particulate air pollutants using GIS and spatial statistical approach. Probably, this is the first study to report the spatial and temporal variation of air quality distribution in Assam. Results showed there is a negative impact on the ambient air quality status of Assam. These industries and mining areas contribute significantly to the air pollution in this deltaic region. This district-wise information of air pollution congregation would be valuable for urban planners and decision architects to efficiently administer air quality for health and environmental purposes. The dissimilarity in geographical dissemination of the pollutant concentration has been more helpful in seasonal inevitability. Consequently, a continuous set of data and more parameters can be included to attain more reliable results.

Kinetic Modelling of Molybdenum-blue Production by Bacillus sp. strain Neni-10

Kinetic modelling of bacterial reduction process reveals vital parameters like specific reduction rate, hypothetical maximum reduction and deduce whether high substrate (molybdenum) concentration affects the lag phase of the reduction. The commonly used natural logarithmic transformation to linearize the reduction process seems inaccurate as it gives only an approximate value for the specific growth rate. This work for the first time utilized eight different primary models such as Gompertz, Baranyi-Roberts, Logistic, Von Bertalanffy, Richards, Schnute, Buchanan three-phase Huang to obtain values of the kinetic constants that could further be used for secondary modelling. Baranyi-Roberts model was the best model fitting Mo-blue production curve in Bacillus sp. strain Neni-10 based on statistical values for RMSE (root-mean-square error), R2 (adjusted coefficient of determination), AICc (corrected Akaike Information Criterion) BF (bias factor) and AF (accuracy factor). The fitting parameters obtained were lag time (l), maximal Mo-blue production (Ymax) and maximum Mo-blue production rate (mm). The use of microbial growth models to get accurate Mo-blue production rate is entirely new to molybdenum reduction (detoxification) process and the kinetic constants obtained could be very useful secondary modelling. This work has revealed the usefulness of these models in modelling bacterial Mo-blue production

Kinetics of the Removal of Polybrominated Diphenyl Ethers from Aqueous Solutions by Using pressure steam–washed Black Tea

Established approaches for the management of contaminants include things like membrane separation, ion exchange, precipitation, transformation and biosorption. Of all of these technologies, biosorption has lots of features that include low running expenditures, very effective detoxification of toxicants at low levels, low quantity of removal components as well as does not require nutrient needs as with bacterial-based remediation, theaforementioned being restricted to the existence of heavy metals and other toxicants. The biosorption of BDE-3 on spent black tea was successfully fitted to three models—pseudo-1st, pseudo-2nd and Elovich. Statistical analysis based on root-mean-square error (RMSE), adjusted coefficient of determination (adjR2), accuracy factor (AF), bias factor (BF), corrected AICc Bayesian Information Criterion (BIC), (Akaike Information Criterion) and Hannan–Quinn information criterion (HQC) showed that the pseudo-second order model is the best model giving valuable parameters such as the equilibrium sorption capacity qe and the pseudo-second-order rate constant, k2, which can be further utilized in isothermal modelling analysis. Further analysis is needed to provide proof for the chemisorption mechanism usually tied to this kinetic.

Isothermal Modelling of the Adsorption of Glyphosate onto Palm Oil Fronds Activated Carbon

Glyphosate is a heavily usage herbicide in Malaysia. It inhibits amino acids synthesis in plants and microorganisms with the latter inhibition makes it difficult to find biodegradation microorganism as a bioremediation agent for this pesticide. One candidate for bioremediation is biosorption that has several positive aspects which include low operating expenses, very efficient detoxification of toxicants at low concentrations, low amount of disposal materials and does not need nutrient requirements as in bacterial-based remediation, the latter of which is limited by the presence of heavy metals and other toxicants. The biosorption of glyphosate on palm oil fronds activated carbon can be an efficient and low-cost tool for remediation of glyphosate. The absorption kinetics data of biosorption isotherm on the biosorption of glyphosate on oil palm fronds activated carbon were analyzed using modelled according to various models ranging from one to five parameters models such as Henry, Langmuir, Dubinin-Radushkevich, Freundlich, BET, Toth, Sips, Fritz-Schlunder IV, Baudu and Fritz-Schlunder V, and fitted using non-linear regression. Only the Henry, Langmuir, Freundlich, BET and Toth models can fit the data. Statistical analysis based on root-mean-square error (RMSE), adjusted coefficient of determination (adjR2), bias factor (BF), accuracy factor (AF), corrected AICc (Akaike Information Criterion) showed that the Langmuir model is the best model. The calculated Langmuir parameters bL value of 0.002 L/mg (95% confidence interval from 0.001 to 0.004) and qmL value of 255.5 mg/g (95% confidence interval from 160.90 to 350.11).

Kinetic Analysis of the Adsorption of the Brominated Flame Retardant 4-bromodiphenyl Ether onto Biochar-immobilized Sphingomonas sp.

4-bromophenyl phenyl ether or 4-bromodiphenyl ether (4-BDE) is a less brominated PBDE, which are brominated flame retardants (BFRs), that has come into importance when it was detected as the main pollutant in Sungai Buah, a tributary to the Sungai Semenyih that have recently caused the pollution of drinking waters in the Semenyih Water Treatment Plant, Putrajaya Selangor that led to water shortage for many days to affecting many areas. 4-BDE is also a recalcitrant priority pollutant where it is barely degraded at all. Many of the PBDE can cause developmental neurotoxicity. The absorption kinetics data of biosorption isotherm on the biosorption of 4-BDE onto Biochar-immobilized Sphingomonas sp. were analyzed using three models—pseudo-1st, pseudo-2nd and Elovich, and fitted using non-linear regression. The Elovich model was the poorest in fitting the curve based on visual observation followed by the pseudo-1st order. Statistical analysis based on root-mean-square error (RMSE), adjusted coefficient of determination (adjR2), bias factor (BF), accuracy factor (AF), corrected AICc (Akaike Information Criterion), Bayesian Information Criterion (BIC) and Hannan–Quinn information criterion (HQC) that showed that the pseudo-1ST order model is the best model. Kinetic analysis using the pseudo-1st order model at 400 mg/L 4-BDE gave a value of equilibrium sorption capacity qe of 31.89 mg g-1 (95% confidence interval from 30.37 to 33.42) and a value of the pseudo-1st-order rate constant, k1 of 0.22 (95% confidence interval from 0.019 to 0.025). Further analysis is needed to provide proof for the chemisorption mechanism usually tied to this kinetic.

Isothermal Modelling on the Removal of Copper Using Durvillaea antarctica

Heavy metals pollution in the Antarctica is emerging as an important problem due to anthropogenic activities. Bioremediation of metal pollution especially copper in Antarctica in the future using biosorption is the most environmental-friendly method. Biosorption using Durvillaea antarctica, an alga that lives in the Antarctic region does not involve the introduction of new potentially foreign species. In this study, the isotherms of copper biosorption by the alga is modelled according to various models ranging from one to five parameters models such as Henry, Langmuir, Dubinin-Radushkevich, Freundlich, BET, Toth, Sips, Fritz-Schlunder IV, Baudu and Fritz-Schlunder V. All gave visually acceptable fitting with the exception of the Henry model. Statistical analysis based on root-mean-square error (RMSE), adjusted coefficient of determination (adjR2), bias factor (BF), accuracy factor (AF) and corrected AICc (Akaike Information Criterion) showed that the Sips model is the best model. The calculated Sips parameters kS value of 12.52 (95% confidence interval from 3.593 to 21.439), qmS value of 0.97 (95% confidence interval from 0.905 to 1.026) and nS value of 0.67 (95% confidence interval from 0.531 to 0.806).

Kinetic Analysis for the Removal of Copper Using Durvillaea antarctica

Existing techniques for the treatment of pollutants include membrane separation, ion exchange, precipitation, and transformation and biosorption. Of all this technology, biosorption has several positive aspects which include low operating expenses, very efficient detoxification of toxicants at low concentrations, low amount of disposal materials and does not need nutrient requirements as in bacterial-based remediation, the latter of which is limited by the presence of heavy metals and other toxicants. The reduction of copper by Durvillaea antarctica, an alga that lives as south as the Antarctic region can be an efficient and low-cost tool for remediation of copper. In this study, the kinetics of copper biosorption is modelled according to the pseudo-first order, pseudo-second order and Elovich models. Statistical analysis based on root-mean-square error (RMSE), adjusted coefficient of determination (adjR2), bias factor (BF), accuracy factor (AF) and corrected AICc (Akaike Information Criterion) showed that the pseudo-second order model is the best model. Kinetic analysis using the pseudo-second order model at 0.15 mM copper gave a value of equilibrium sorption capacity qe of 0.150 mmol g-1 (95% confidence interval from 0.149 to 0.151) and a value of the pseudo-second-order rate constant, k2 of 8.605 (95% confidence interval from 7.016 to 10.194).

Kinetic Analysis of the Adsorption of Glyphosate onto Palm Oil Fronds Activated Carbon

Existing techniques for the treatment of pollutants include membrane separation, ion exchange, precipitation, transformation and biosorption. Of all of this technology, biosorption has several positive aspects which include low operating expenses, very efficient detoxification of toxicants at low concentrations, low amount of disposal materials and does not need nutrient requirements as in bacterial-based remediation, the latter of which is limited by the presence of heavy metals and other toxicants. The biosorption of glyphosate on palm oil fronds activated carbon can be an efficient and low-cost tool for remediation of glyphosate. The absorption kinetics data of biosorption isotherm on the biosorption of glyphosate on palm oil fronds activated carbon were analyzed using three models—pseudo-1st, pseudo-2nd and Elovich, and fitted using non-linear regression. The Elovich model was the poorest in fitting the curve based on visual observation followed by the pseudo-1st order. Statistical analysis based on root-mean-square error (RMSE), adjusted coefficient of determination (adjR2), bias factor (BF), accuracy factor (AF), corrected AICc (Akaike Information Criterion), Bayesian Information Criterion (BIC) and Hannan–Quinn information criterion (HQC) that showed that the pseudo-second order model is the best model. Kinetic analysis using the pseudo-second order model at 250 mg/L glyphosate gave a value of equilibrium sorption capacity qe of 94.12 mg g-1 (95% confidence interval from 89.913 to 98.332) and a value of the pseudo-second-order rate constant, k2 of 0.02 (95% confidence interval from 0.012 to 0.023). Further analysis is needed to provide proof for the chemisorption mechanism usually tied to this kinetic.