Multicomponent adsorption modeling: isotherms for ABE model solutions using activated carbon F-400

Biobutanol has attracted significant interest in recent decades and is seriously considered as a potential biofuel to partly replace gasoline. However, some production challenges must be addressed to make butanol economically viable such as the low product concentration and product toxicity inhibiting the microorganism. To alleviate these limitations, several in situ or ex situ separation techniques have been investigated in view of their integration to the biobutanol production process to enhance its economic viability. One of these techniques is adsorption which is one of the most energy-efficient techniques used for biobutanol separation. Considering the number of chemical species present in the ABE fermentation broth, it is essential to develop multicomponent adsorption isotherms for all components as a first step to design a high performance adsorption process. Few multicomponent isotherm models have been proposed such as multicomponent Langmuir and Freundlich. In this study, these two models as well as artificial neural networks were used to model the isotherms of each component in an ABE fermentation broth as a function of the equilibrium concentrations of all components for activated carbon F-400. Results showed that the multicomponent Langmuir model was not accurate due to the many simplifying assumptions. The multicomponent Freundlich and feedforward neural network (FFNN) isotherm models were able to predict the behavior of multicomponent systems very well. Indeed, the predictive model of the experimental data had a coefficient of determination (R²) of 0.97 and 0.99, for multicomponent Freundlich and FFNN isotherm models, respectively.     

Full-range optical pH sensor array based on neural networks

A neural network multivariate calibration is used to predict the pH of a solution in the full-range (0–14) from hue (H) values coming from imaging an optical pH sensor array based on 11 sensing elements with immobilized pH indicators. Different colorimetric acid-base indicators were tested for membrane preparation fulfilling the following conditions: 1) no leaching; 2) change in tonal coordinate by reaction and 3) covering the full pH range with overlapping between their pH responses. The sensor array was imaged after equilibration with a solution using a scanner working in transmission mode. Using software developed by us, the H coordinate of the colour space HSV was calculated from the RGB coordinates of each element.The neural network was trained with the calibration data set using the Levenberg–Marquardt training method. The network structure has 11 input neurons (each one matching the hue of a single element in the sensor array), 1 output (the pH approximation value) and 1 hidden layer with 10 hidden neurons. The network provides an MSE = 0.0098 in the training data, MSE = 0.0183 in the validation data and MSE = 0.0426 in the test data coming from a set of real water samples. The resulting correlation coefficient R obtained in the Pearson correlation test is R = 0.999.     

Application of artificial neural networks for formulation and modeling of dye adsorption onto multiwalled carbon nanotubes

In this study, an artificial neural network (ANN) has been developed to predict the adsorption amount of dye (methylene blue) onto multiwalled carbon nanotubes. Batch experiments have been carried out to obtain experimental data. Important parameters in the adsorption system such as initial dye concentration, adsorbent dosage, temperature, pH and contact time have been used as the inputs of the network, while the output is the final concentration of dye in aqueous solution after adsorption. The neural network structure has been optimized by testing various training algorithms and different number of neurons in a hidden layer. An empirical equation for determination of final dye concentration in aqueous solutions after adsorption has been developed by using the weights of the optimized network. The results of the optimized ANN have been compared with conventional models in equilibrium and kinetic fields. According to error analysis and determination coefficient, the ANN was found to be the most appropriate model to describe this adsorption process. Sensitivity analysis showed that initial dye concentration, pH and contact time are the most effective parameters in this process. The influence percentages of these parameters on the output were 28, 24 and 24 %, respectively.     

Adsorption behaviour of sugars versus their activity in single and multicomponent liquid solutions

In this work the activity of three carbohydrates (sucrose, glucose and fructose) in highly concentrated aqueous solutions was studied along with its effect on the adsorption behaviour of the investigated compounds. Activities of individual sugars in aqueous solutions of single solute as well as in binary mixtures were quantified on the basis of solubility properties. Solid–liquid equilibria of sugars were correlated with the NRTL (nonrandom, two liquid) model of activity coefficient formulation. Activities of individual sugars were incorporated into the single component adsorption isotherm model, which reproduced accurately the course of the adsorption equilibria of sugars in aqueous solutions obtained experimentally in previous work using an ion-exchange resin. Activities of sugars determined in binary solute systems along with the single component isotherms were used to predict competitive adsorption equilibria. To calculate adsorbed phase concentrations of individual sugars in binary mixtures the adsorbed solution theory was adopted. The isotherm shapes calculated were compared to the data of competitive adsorption from the former study and found to be able to describe these experimental results.     

Neural Network Modeling of Adsorption of Binary Vapour Mixtures

Three neural network models were used for prediction of adsorption equilibria of binary vapour mixtures on an activated carbon. The predictions were compared both with published experimental data and calculated values from the Ideal Adsorption Solution (IAS) model. The neural network was trained using both binary and single component experimental adsorption data. Even for a limited number of data points (about 60) the network models were capable of approximating experimental data very precisely.     

High-throughput isotherm determination and thermodynamic modeling of protein adsorption on mixed mode adsorbents

The thermodynamic modeling of protein adsorption on mixed-mode adsorbents functionalized with ligands carrying both hydrophobic and electrostatic groups was undertaken. The developed mixed mode isotherm was fitted with protein adsorption data obtained for five different proteins on four different mixed mode adsorbents by 96-well microtitre plate high throughput batch experiments on a robotic workstation. The developed mixed mode isotherm was capable of describing the adsorption isotherms of all five proteins (having widely different molecular masses and iso-electric points) on the four mixed mode adsorbents and over a wide range of salt concentrations and solution pH, and provided a unique set of physically meaningful parameters for each resin-protein-pH combination. The model could capture the typically observed minimum in mixed mode protein adsorption and predict the precise salt concentration at which this minimum occurs. The possibility of predicting the salt concentration at which minimum protein binding occurs presents new opportunities for designing better elution strategies in mixed mode protein chromatography. Salt-protein interactions were shown to have important consequences on mixed mode protein adsorption when they occur. Finally, the mixed mode isotherm also gave very good fit with literature data of BSA adsorption on a different mixed mode adsorbent not examined in this study. Hence, the mixed mode isotherm formalism presented in this study can be used with any mixed mode adsorbent having the hydrophobic and electrostatic functional groups. It also provides the basis for detailed modeling and optimization of mixed mode chromatographic separation of proteins.     

Photocatalytic Degradation of Oxytetracycline in Aqueous Solutions with TiO2 in Suspension and Prediction by Artificial Neural Networks

In this study, the photocatalytic degradation of oxytetracycline (OTC) in aqueous solutions has been studied under different conditions such as initial pollutant concentrations, amount of catalyst, and pH of the solution. Experimental results showed that photocatalysis was clearly the predominant process in the pollutant degradation, since OTC adsorption on the catalyst and photolysis are negligible. The optimal TiO₂ concentration for OTC degradation was found to be 1.0 g/L. The apparent rate constant decreased, and the initial degradation rate increased with increasing initial OTC concentration with the other parameters kept unchanged. Subsequently, data obtained from photocatalytic degradation were used for training the artificial neural networks (ANN). The Levenberg–Marquardt algorithm, log sigmoid function in the hidden layer, and the linear activation function in the output layer were used. The optimized ANN structure was four neurons at the input layer, eighteen neurons at the hidden layer, and one neuron at the output layer. The application of 18 hidden neurons allowed to obtain the best values for R² and the mean squared error, 0.99751 and 7.504e–04, respectively, showing the relevance of the training, and hence the network can be used for final prediction of photocatalytic degradation of OTC with suspended TiO₂.     

Dynamic modelling of milk ultrafiltration by artificial neural network

Artificial neural networks (ANNs) have been used to dynamically model crossflow ultrafiltration of milk. It aims to predict permeate flux, total hydraulic resistance and the milk components rejection (protein, fat, lactose, ash and total solids) as a function of transmembrane pressure and processing time. Dynamic modelling of ultrafiltration performance of colloidal systems (such as milk) is very important for designing of a new process and better understanding of the present process. Such processes show complex non-linear behaviour due to unknown interactions between compounds of a colloidal system, thus the theoretical approaches were not being able to successfully model the process. In this work, emphasis has been focused on intelligent selection of training data, using few training data points and small network. Also it has been tried to test the ANN ability to predict new data that may not be originally available. Two neural network models were constructed to predict the flux/total resistance and rejection during ultrafiltration of milk. The results showed that there is an excellent agreement between the validation data (not used in training) and modelled data, with average errors less than 1%. Also the trained networks are able to accurately capture the non-linear dynamics of milk ultrafiltration even for a new condition that has not been used in the training process.     

Kinetic determination of organic vapor mixtures with single piezoelectric quartz crystal sensor using artificial neural networks

A single piezoelectric quartz crystal coated with one kind of crown ether was applied to the simultaneous determination of binary acid and amine vapor mixtures. From the adsorption and desorption curves of analytes, which were somewhat different in shape, frequency shifts from ten time windows were taken as inputs for artificial neural networks (ANN). Prediction results were satisfactory for ANN in both sample sets. The average relative errors, for formic acid and acrylic acid were 5%, for n-butylamine and aniline, they were 3% with ANN respectively. The effects of number of neurons in the hidden layer of ANN on the performance of the network are also discussed.     

Solid phase microextraction/gas chromatography/mass spectrometry integrated with chemometrics for detection of Salmonella typhimurium contamination in a packaged fresh vegetable

A rapid method for detection of Salmonella typhimurium contamination in packaged alfalfa sprouts using solid phase microextraction/gas chromatography/mass spectrometry (SPME/GC/MS) integrated with chemometrics was investigated. Alfalfa sprouts were inoculated with S. typhimurium, packed into commercial LDPE bags and stored at 10 + 2 °C for 0, 1, 2 and 3 days. Uninoculated sprouts were used as control samples. A SPME device was used to collect the volatiles from the headspace above the samples and the volatiles were identified using GC/MS. Chemometric techniques including linear discriminant analysis (LDA) and artificial neural network (ANN) were used as data processing tools. Numbers of Salmonella were followed using a colony counting method. From LDA, it was able to differentiate control samples from sprouts contaminated with S. typhimurium. The potential to predict the number of contaminated S. typhimurium from the SPME/GC/MS data was investigated using multilayer perceptron (MLP) neural network with back propagation training. The MLP comprised an input layer, one hidden layer, and an output layer, with a hyperbolic tangent sigmoidal transfer function in the hidden layer and a linear transfer function in the output layer. The MLP neural network with a back propagation algorithm could predict number of S. typhimurium in unknown samples using the volatile fingerprints. Good prediction was found as measured by a regression coefficient (R² = 0.99) between actual and predicted data.     

Characterization of an unusual adsorption behavior of racemic methyl-mandelate on a tris-(3,5-dimethylphenyl) carbamoyl cellulose chiral stationary phase

An interesting adsorption behavior of racemic methyl mandelate on a tris-(3,5-dimethylphenyl)carbamoyl cellulose chiral stationary phase was theoretically and experimentally investigated. The overloaded band of the more retained enantiomer had a peculiar shape indicating a type V adsorption isotherm whereas the overloaded band of the less retained enantiomer had a normal shape indicating a type I adsorption behavior. For a closer characterization of this separation, adsorption isotherms were determined and analyzed using an approach were Scatchard plots and adsorption energy distribution (AED) calculations are combined for a deeper analysis. It was found that the less retained enantiomer was best described by a Tóth adsorption isotherm while the second one was best described with a bi-Moreau adsorption isotherm. The latter model comprises non-ideal adsorbate-adsorbate interactions, providing an explanation to the non-ideal adsorption of the more retained enantiomer. Furthermore, the possibility of using the Moreau model as a local model for adsorption in AED calculations was evaluated using synthetically generated raw adsorption slope data. It was found that the AED accurately could predict the number of adsorption sites for the generated data. The adsorption behavior of both enantiomers was also studied at several different temperatures and found to be exothermic; i.e. the adsorbate-adsorbate interaction strength decreases with increasing temperature. Stochastic analysis of the adsorption process revealed that the average amount of adsorption/desorption events increases and the sojourn time decreases with increasing temperature.     

Adsorption of sodium dodecyl benzene sulfonate onto carbonate rock: Kinetics,equilibrium and mechanistic study

In this study, kinetics, equilibrium, and mechanisms of SDBS adsorption onto carbonate rock in presence/absence of alkaline/electrolyte, which is not well discussed in the available literature, is analyzed through batch experiments. Analysis of kinetic data showed that adsorption rate of SDBS onto carbonate is controlled by both boundary layer and intraparticle diffusion, also adsorption kinetics meets pseudo second-order model. The coefficient of kinetic model is a linear function of initial and equilibrium concentrations. The adsorption isotherm experiences four distinct regions, with a rising trend in the first regions until reaching to a maximum after which decreases slightly, as the fourth region, due to micellar exclusion. The prevailing mechanisms in other regions were also discussed. Presence of alkaline changes adsorption mechanisms, so that adsorption isotherm matches well with the Langmuir model, while presence of electrolyte does not change the adsorption mechanisms, but it lessens repulsion between surfactant heads which results in a slight reduction in amount of CMC. A new three-parameter equilibrium model is presented which considers all prevailing mechanisms, and matches properly with obtained experimental data, especially the decreasing trend of fourth region which is very difficult to predict along with other regions using a unique isotherm model.     

Multi-objective simultaneous prediction of waterborne coating properties

Multi-objective simultaneous prediction of waterborne coating properties was studied by the neural network combined with programming. The conditions of network with one input layer, three hidden layers and one output layer were confirmed. The monomers mass of BA, MMA, St and pigments mass of TiO₂ and CaCO₃ were used as input data. Four properties, which were hardness, adhesion, impact resistance and reflectivity, were used as network output. After discussing the hidden layer neurons, learn rate and the number of hidden layers, the best net parameters were confirmed. The results of experiment show that multi-hidden layers was advantageous to improve the accuracy of multi-objective simultaneous prediction. 36 kinds of coating formulations were used as the training subset and 9 acrylate waterborne coatings were used as testing subset in order to predict the performance. The forecast error of hardness was 8.02% and reflectivity was 0.16%. Both forecast accuracy of adhesion and impact resistance were 100%.     

Artificial Neural Network (ANN) Method for Modeling of Sunset Yellow Dye Adsorption Using Nickel Sulfide Nanoparticle Loaded on Activated Carbon: Kinetic and Isotherm Study

In this paper, the NiS nanoparticles are prepared and characterized by x-ray powder diffraction and scanning electron microscopy. The NiS nanoparticles showed the excellent adsorption properties toward sunset yellow (UA) dye. The effect of solution pH, adsorbent dosage (0.005–0.020 g), contact time (0.5–30 minutes), and initial UA concentration (5–40 mg L^?1) on the extent of adsorption was investigated and modeled by artificial neural network. The experimental equilibrium data was analyzed by Langmuir, Freundlich, Tempkin, and D–R isothermal models. It was seen that the data was well presented by Langmuir model with a maximum adsorption capacity of 333.3 mg g^?1 at 26°C. Kinetic studies at various adsorbent dosages and initial UA concentrations show that high removal percentage (>90%) was achieved within 15 minutes. The adsorption of UA follows the pseudo-second-order rate model. The experimental data were applied to train the multilayer feed-forward neural network with three inputs and one output with Levenberg–Marquart algorithm and different numbers of neurons in the hidden layer. The minimum mean square error of 0.0003 and determination coefficient of (R²) 0.99 were found.     

人工神经网络预测煤炭成浆浓度的研究

考虑煤炭的多种理化特性建立了成浆浓度的神经网络预测模型,对其数据预处理方法、学习率和中间层节点数等进行了深入讨论。水分、挥发分、分析基碳、灰分和氧等五个因子对于煤炭成浆性的预测起到主导作用。五因子、七因子和八因子神经网络模型对煤炭成浆浓度的预测误差分别为：0.53%、0.50%和0.74%,而现有回归分析方程的误差为1.15%,故神经网络模型比回归分析方程有更好的预测能力,尤以七因子模型最佳。     

Process optimization and kinetics study for photocatalytic ciprofloxacin degradation using TiO2 nanoparticle: A comparative study of Artificial Neural Network and Surface Response Methodology

The photocatalytic degradation of ciprofloxacin was investigated by developing a predictive mathematical model using response surface methodology and an artificial neural network. The four independent variables involve solution pH, reaction time, catalyst dose, and initial antibiotic concentration considered as factors in central composite design to observe the response in the form of antibiotic degradation. Accordingly, at an optimum antibiotic concentration of 5.02 mg/L, catalyst dose of 44.51 mg/L, solution pH of 5.04, and reaction time of 75.80 min, the photocatalysis method achieved a ciprofloxacin degradation of 88.30%. The experimental outputs were very much consistent along with the predicted output of experiments through response surface methodology (R² = 0.9969) and artificial neural network (R² = 0.975). The adsorption isotherm and kinetic study reveal that Langmuir isotherm and pseudo-second-order kinetic models respectively were best fitted for degradation of ciprofloxacin through photocatalysis. The finding provides a novel method for evaluating the photocatalysis process for the optimization of ciprofloxacin antibiotic removal from pharmaceutical waste using experiments and computer simulation tools.     

Ultrasonic treatment of water contaminated with various pollutants onto copper nanowires loaded on activated carbon using response surface methodology and artificial intelligent

In this study, the potential application of copper nanowires loaded on activated carbon for simultaneous removal of Disulfine blue (DB), Crystal violet (CV) and Sunset yellow (SY) has been described. The relation between adsorption properties with variables such as solution pH, adsorbent value, contact time and initial dyes concentration was investigated and optimized. A three‐layer artificial neural network (ANN) model was utilized to predict dyes removal (%) by adsorbent following conduction of experiments. The training of network at above mention experimental data confirms its ability to forecast the removal performance with a linear transfer function (purelin) at output layer. The Levenberg–Marquardt algorithm and tangent sigmoid transfer function (tansig) with 16 neurons at the hidden layer was applied. Parameters were optimized by central composite design (CCD) combined with response surface methodology (RSM) and desirability function. The accuracy of ANN was judged according to both MSE and AAD% at optimal conditions and results indicate its superiority to RSM model in term of higher R² and lower AAD% values. This observation was also corroborated by the parity plots between the predicted and experimental values. The ANN model was better in both data fitting and prediction capability in comparison to RSM model.     

Separation mechanism of nortriptyline and amytriptyline in RPLC

The single and the competitive equilibrium isotherms of nortriptyline and amytriptyline were acquired by frontal analysis (FA) on the C18- bonded discovery column, using a 28/72 (v/v) mixture of acetonitrile and water buffered with phosphate (20 mM, pH 2.70). The adsorption energy distributions (AED) of each compound were calculated from the raw adsorption data. Both the fitting of the adsorption data using multi-linear regression analysis and the AEDs are consistent with a trimodal isotherm model. The single-component isotherm data fit well to the tri-Langmuir isotherm model. The extension to a competitive two-component tri-Langmuir isotherm model based on the best parameters of the single-component isotherms does not account well for the breakthrough curves nor for the overloaded band profiles measured for mixtures of nortriptyline and amytriptyline. However, it was possible to derive adjusted parameters of a competitive tri-Langmuir model based on the fitting of the adsorption data obtained for these mixtures. A very good agreement was then found between the calculated and the experimental overloaded band profiles of all the mixtures injected.