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.

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).

Molybdenum Blue Production from Serratia sp. strain DRY5: Secondary Modeling

In this work, kinetic growth models such as Luong, Yano, Teissier-Edward, Aiba, Haldane, Monod, Han and Levenspiel were used to model molybdenum blue production from Serratia sp. strain DRY5. Based on statistical analyses such as root-mean-square error (RMSE), adjusted coefficient of determination (adjR2), bias factor (BF), and accuracy factor (AF), the Monod model was chosen as the best. The calculated values for the monod constants qmax (the maximum specific substrate degradation rate (h−1), and Ks (concentration of substrate at the half maximal degradation rate (mg/L)) were found to be 3.86 (95% confidence interval of 2.29 to 5.43), and 43.41 (95% confidence interval of 12.36 to 74.46) respectively. The novel constants discovered during the modelling exercise could be used in further secondary modelling.

Mathematical Modelling of the Growth of Bacillus cereus Strain wwcp1on Malachite Green Dye

In this paper, various growth models such as Von Bertalanffy, Huang, Baranyi-Roberts, Modified Gompertz, Buchnam-3-phase, Modified-Richards and Modified-Logistics, were presented in fitting and evaluating the growth of Bacillus cereus wwcp1 on Malachite green dye. The Von Bertalanffy model was found to be the best model with the lowest RMSE and highest R2 values. The Accuracy and Bias factor values were near unity (1.0). The von Bertalanffy parameters such as A (lower asymptote bacterial growth), μ (bacterial growth rate) and k (curve fitting parameter) were found to be 2.757 (95% confidence interval from 2.131 to 3.382 ), 0.287 (95% confidence interval from 0.244 to 0.329) and 4.323 (95% confidence interval from 4.285 to 4.361) respectively.

Kinetics Modelling of Pseudomonas stutzeri strain DN2 Growth Behaviour in Tributyltin Chloride

A predictive model was performed to describe Pseudomonas stutzeri strain DN2 growth behaviour in tributyltin chloride, using primary Modelling and a polynomial model as a secondary predictive model. In this investigation, data predicted using the modified Logistic (ML) was the most accurate. The Bias Factor (Bf) and Accuracy Factor (Af) values for the (ML) model were 1.39 and 1.51, indicating that the predictions were within a reliable range. The low RMSE value of 0.14, R2 and adj R2 (0.99) value closer to 1, showing that modified logistics is better than the other models at describing the growth behaviour of Pseudomonas stutzeri strain DN2 in toxic tributyltin chloride. Both the Aiba and Haldane models on the other hand, among the secondary model best fit the behaviours having low RMSE and MSE values and adjR2 value closer to 1. In this study, the primary and secondary kinetics of Pseudomonas stutzeri strain DN2 growth behaviour in tributyltin chloride was explored and it was shown in this study that the modified logistic and the Haldane models better suit the growth behavior of Pseudomonas stutzeri strain DN2 in tributyltin chloride. The parameters obtained from the modelling exercise will be very valuable in transferring the laboratory results to the field.

Mathematical Modeling of the growth of Acinetobacter baumannii YNWH 226 on Azo dye Congo red

Industrial effluents (Azo dyes) are brightly coloured, making their disposal into receiving waters undesirable not only because many Azo dyes and their breakdown products are toxic to aquatic life and mutagenic to humans, but also because many Azo dyes and their breakdown products are harmful to aquatic life due to the presence of aromatics and metals, chlorides, and other chemicals. Various kinetic models, including modified Gompertz, Baranyi-Roberts, modified Richards, Von Bertalanffy, modified Logistics, modified Schnute, Buchanan three-phase, and the most recently presented Huang, were used in this study. Based on statistical tests, the modified Schnute model provided the best fit, with the lowest values for RMSE and corrected Akaike Information Criteria (AICc), the greatest value for adjusted R2, and the closest to unity for both Accuracy and Bias Factor. The Modified Schnute parameters such as λ (lag time), µmax (maximum specific bacterial growth rate) and curve fitting parameters α and β (Constant), were found to be -4.39 (95% confidence interval of -77.58 to 68.79), 57.00 (95% confidence interval of -2854.30 to 2968.30), 0.78 (95% confidence interval of -0.34 to 1.89) and 0.96 (95% confidence interval of -0.85 to 2.78, respectively.

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 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.

AUTOMATIC GENERATION CONTROL OF A MULTI-AREA POWER SYSTEM BASED ON GREY WOLF OPTIMIZATION ALGORITHM

This paper presents an automatic generation control algorithm applicable for a multi-area power system. A more practical models is used to represent the main components of the power system. The frequency bias factor (B) is set optimally by using an artificial intelligence technique. The generation rate constraints (GRC) are taken into consideration with dead band characteristics of the governor. For the first time, a sequential optimization of the fractional order (PID) control parameters, the governor speed regulation (R) and the frequency bias factor (B) is proposed. The objective function to be minimized is the updated integral time absolute error (UITAE). The tuning of the control parameters is dependent on minimizing the objective function by using the Grey wolf optimization (GWO) algorithm. The proposed algorithm is tested on an interconnected three power pools different system (Reheat, Gas and Hydro) with varying degrees of load step change. The simulation results of the presented process are compared to those achieved by using the particle swarm optimization method. The results obtained reveal the robustness of the proposed algorithm in terms of settling time and peak overshoot.

Evaluation of Debris Flows for Flood Plain Estimation in a Small Ungauged Tropical Watershed for Hurricane Otto

The variability of climate, increase in population, and lack of territorial plans in Costa Rica have caused intense disasters with human and economic losses. In 2016, Hurricane Otto hit the country’s northern area, leaving substantial damages, including landslides, debris flows, and flooding. The present study evaluated different scenarios to estimate flooded areas for Newtonian (clean water), and non-Newtonian flows with volumetric sediment concentrations (Cv) of 0.3, 0.45, 0.55, and 0.65 using Hydro-Estimator (HE), rain gauge station, and the 100-year return period event. HEC–HMS modeled the rainfall products, and FLO-2D modeled the hydrographs and Cv combinations. The simulation results were evaluated with continuous statistics, contingency table, Nash Sutcliffe Efficiency, measure of fit (F), and mean absolute differences (E) in the floodplains. Flow depths, velocities, and hazard intensities were obtained in the floodplain. The debris flood was validated with field data and classified with a Cv of 0.45, presenting lower MAE and RMSE. Results indicated no significant differences in flood depths between hydrological scenarios with clean-water simulations with a difference of 8.38% in the peak flow. The flood plain generated with HE rainfall and clear-water condition presented similar results compared to the rain gauge input source. Additionally, hydraulic results with HE and Cv of 0.45 presented E and F values similar to the simulation of Cv of 0.3, demonstrating that the HE bias did not influence the determination of the floodplain depth and extent. A mean bias factor can be applied to a sub-daily temporal resolution to enhance HE rain rate quantifications and floodplain determination.