scholarly journals Isothermal Modelling of the Adsorption of Chromium onto Calcium Alginate Nanoparticles

2021 ◽  
Vol 9 (2) ◽  
pp. 1-7
Author(s):  
Bilal Ibrahim Dan-Iya ◽  
Mohd Yunus Shukor
Keyword(s):  
Confidence Interval ◽  
Calcium Alginate ◽  
Information Criterion ◽  
Industrial Applications ◽  
Coefficient Of Determination ◽  
Refractory Materials ◽  
Bias Factor ◽  
Nonlinear Regression Method ◽  
Parameter Values ◽  
Better Than

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.

scholarly journals Isothermal Modelling of the Adsorption of Cibacron Blue onto Bean Peel

2021 ◽  
Vol 3 (2) ◽  
pp. 20-24
Author(s):  
Mohd Yunus Shukor
Keyword(s):  
Confidence Interval ◽  
Freundlich Isotherm ◽  
Information Criterion ◽  
Coefficient Of Determination ◽  
Maximum Adsorption Capacity ◽  
Cibacron Blue ◽  
A Value ◽  
Bias Factor ◽  
Nonlinear Regression Method ◽  
Parameter Values

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.

scholarly journals Kinetic Analysis of the Adsorption of Chromium onto Calcium Alginate Nanoparticles

2021 ◽  
Vol 5 (2) ◽  
pp. 8-13
Author(s):  
Bilal Ibrahim Dan-Iya ◽  
Salihu Yahuza ◽  
Ibrahim Alhaji Sabo
Keyword(s):  
Confidence Interval ◽  
Kinetic Analysis ◽  
Calcium Alginate ◽  
Information Criterion ◽  
Order Rate Constant ◽  
Industrial Applications ◽  
Visual Observation ◽  
Coefficient Of Determination ◽  
Order Model ◽  
A Value

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.

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

2021 ◽  
Vol 9 (2) ◽  
pp. 35-39
Author(s):  
Bilal Ibrahim Dan-Iya ◽  
Ain Aqilah Basirun ◽  
Mohd Yunus Shukor
Keyword(s):  
Graphene Oxide ◽  
Confidence Interval ◽  
Information Criterion ◽  
Cost Effective ◽  
Order Rate Constant ◽  
Coefficient Of Determination ◽  
Order Model ◽  
Ethyl Violet ◽  
Bias Factor ◽  
Pseudo Second Order

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).

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

2021 ◽  
Vol 9 (2) ◽  
pp. 48-52
Author(s):  
Ibrahim Alhaji Sabo ◽  
Salihu Yahuza ◽  
Bilal Ibrahim Dan-Iya ◽  
Abdussamad Abubakar
Keyword(s):  
Graphene Oxide ◽  
Confidence Interval ◽  
Malachite Green ◽  
Information Criterion ◽  
Cost Effective ◽  
Order Rate Constant ◽  
Visual Observation ◽  
Coefficient Of Determination ◽  
Textile Dye ◽  
Bias Factor

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.

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

2021 ◽  
Vol 9 (2) ◽  
pp. 21-24
Author(s):  
Ibrahim Alhaji Sabo ◽  
Salihu Yahuza ◽  
Mohd Yunus Shukor
Keyword(s):  
Confidence Interval ◽  
Degradation Rate ◽  
Growth Models ◽  
Coefficient Of Determination ◽  
Specific Substrate ◽  
The Novel ◽  
Monod Model ◽  
Molybdenum Blue ◽  
Kinetic Growth ◽  
Bias Factor

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.

scholarly journals Kinetic Analysis of the Adsorption of Cibacron Blue onto Bean Peel

2021 ◽  
Vol 5 (2) ◽  
pp. 14-18
Author(s):  
Ain Aqilah Basirun ◽  
Mohd Yunus Shukor
Keyword(s):  
Confidence Interval ◽  
Waste Disposal ◽  
Information Criterion ◽  
Cost Effective ◽  
Order Rate Constant ◽  
Visual Observation ◽  
Coefficient Of Determination ◽  
Textile Waste ◽  
Cibacron Blue ◽  
Biological Source

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.

Using Connectionist Modules for Decision Support

1990 ◽  
Vol 29 (03) ◽  
pp. 167-181 ◽  
Author(s):  
G. Hripcsak
Keyword(s):  
Decision Support ◽  
Standard Deviation ◽  
Confidence Interval ◽  
Posterior Probability ◽  
Back Propagation ◽  
Connectionist Model ◽  
Test Set ◽  
The Third ◽  
Independent Test ◽  
Better Than

AbstractA connectionist model for decision support was constructed out of several back-propagation modules. Manifestations serve as input to the model; they may be real-valued, and the confidence in their measurement may be specified. The model produces as its output the posterior probability of disease. The model was trained on 1,000 cases taken from a simulated underlying population with three conditionally independent manifestations. The first manifestation had a linear relationship between value and posterior probability of disease, the second had a stepped relationship, and the third was normally distributed. An independent test set of 30,000 cases showed that the model was better able to estimate the posterior probability of disease (the standard deviation of residuals was 0.046, with a 95% confidence interval of 0.046-0.047) than a model constructed using logistic regression (with a standard deviation of residuals of 0.062, with a 95% confidence interval of 0.062-0.063). The model fitted the normal and stepped manifestations better than the linear one. It accommodated intermediate levels of confidence well.

Recent Patents on Nanoporous Filtration Membranes with Complex Pores

2020 ◽  
Vol 14 (2) ◽  
pp. 229-233
Author(s):  
Yongbin Zhang
Keyword(s):  
Geometrical Parameter ◽  
Filtration Membranes ◽  
Analytical Results ◽  
Parameter Values ◽  
Mechanical Strengths ◽  
Better Than

Background:: The challenges to nanoporous filtration membranes are small fluxes and low membrane mechanical strengths. Objective:: To introduce newly invented nanoporous filtration membranes with complex pores, improved fluxes and mechanical strengths as registered in patents. Methods:: The analytical results are presented for the addressed membranes. Results:: The geometrical parameter values of the addressed membranes can be optimized for the highest fluxes. Conclusion:: The overall performances of nanoporous filtration membranes with complex cylindrical or/and conical pores can be significantly better than that of the conventional nanoporous filtration membranes with single cylindrical or conical pores.

scholarly journals Modeling of Lake Malombe Annual Fish Landings and Catch per Unit Effort (CPUE)

Forecasting ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 39-55
Author(s):  
Rodgers Makwinja ◽  
Seyoum Mengistou ◽  
Emmanuel Kaunda ◽  
Tena Alemiew ◽  
Titus Bandulo Phiri ◽  
...  
Keyword(s):  
Time Series ◽  
Information Criterion ◽  
Coefficient Of Determination ◽  
Series Data ◽  
Efficiency Coefficient ◽  
Arima Models ◽  
Catch Per Unit Effort ◽  
Annual Fish ◽  
Unit Effort ◽  
The Mean

Forecasting, using time series data, has become the most relevant and effective tool for fisheries stock assessment. Autoregressive integrated moving average (ARIMA) modeling has been commonly used to predict the general trend for fish landings with increased reliability and precision. In this paper, ARIMA models were applied to predict Lake Malombe annual fish landings and catch per unit effort (CPUE). The annual fish landings and CPUE trends were first observed and both were non-stationary. The first-order differencing was applied to transform the non-stationary data into stationary. Autocorrelation functions (AC), partial autocorrelation function (PAC), Akaike information criterion (AIC), Bayesian information criterion (BIC), square root of the mean square error (RMSE), the mean absolute error (MAE), percentage standard error of prediction (SEP), average relative variance (ARV), Gaussian maximum likelihood estimation (GMLE) algorithm, efficiency coefficient (E2), coefficient of determination (R2), and persistent index (PI) were estimated, which led to the identification and construction of ARIMA models, suitable in explaining the time series and forecasting. According to the measures of forecasting accuracy, the best forecasting models for fish landings and CPUE were ARIMA (0,1,1) and ARIMA (0,1,0). These models had the lowest values AIC, BIC, RMSE, MAE, SEP, ARV. The models further displayed the highest values of GMLE, PI, R2, and E2. The “auto. arima ()” command in R version 3.6.3 further displayed ARIMA (0,1,1) and ARIMA (0,1,0) as the best. The selected models satisfactorily forecasted the fish landings of 2725.243 metric tons and CPUE of 0.097 kg/h by 2024.

scholarly journals Research on Generation Method of Grasp Strategy Based on DeepLab V3+ for Three-Finger Gripper

Information ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 278
Author(s):  
Sanlong Jiang ◽  
Shaobo Li ◽  
Qiang Bai ◽  
Jing Yang ◽  
Yanming Miao ◽  
...  
Keyword(s):  
Field Of View ◽  
Convolution Kernel ◽  
Parallel Connection ◽  
Multi Scale ◽  
Spatial Pyramid Pooling ◽  
The Stability ◽  
Parameter Values ◽  
Filter Layer ◽  
Complex Dataset ◽  
Better Than

A reasonable grasping strategy is a prerequisite for the successful grasping of a target, and it is also a basic condition for the wide application of robots. Presently, mainstream grippers on the market are divided into two-finger grippers and three-finger grippers. According to human grasping experience, the stability of three-finger grippers is much better than that of two-finger grippers. Therefore, this paper’s focus is on the three-finger grasping strategy generation method based on the DeepLab V3+ algorithm. DeepLab V3+ uses the atrous convolution kernel and the atrous spatial pyramid pooling (ASPP) architecture based on atrous convolution. The atrous convolution kernel can adjust the field-of-view of the filter layer by changing the convolution rate. In addition, ASPP can effectively capture multi-scale information, based on the parallel connection of multiple convolution rates of atrous convolutional layers, so that the model performs better on multi-scale objects. The article innovatively uses the DeepLab V3+ algorithm to generate the grasp strategy of a target and optimizes the atrous convolution parameter values of ASPP. This study used the Cornell Grasp dataset to train and verify the model. At the same time, a smaller and more complex dataset of 60 was produced according to the actual situation. Upon testing, good experimental results were obtained.

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