•The ability of activated carbon of loofa to bind MB was tested.•SEM study showed that the activated carbons were porous with well developed pores.•The maximum % removal of MB obtained was 99% at ...50°C for AC1.•The optimum pH value was determined to be 2.0 for AC1 and to be 10.0 for AC2.
The vegetable sponge of cylindrical loofa (CL), a natural product which grows in the north of Algeria, was used to prepare activated carbons. Two activated carbons, AC1 and AC2, by two physiochemical activation methods to be used for methylene blue removal from wastewater. The surface structure of AC1, AC2 and CL were analyzed by scanning electron microscopy. Adsorption isotherm of methylene blue onto the prepared activated carbons was determined by batch tests. The effects of various parameters such as contact time, initial concentration, pH, temperature, adsorbent dose and granulometry were investigated, at agitation rate 150rpm. The results showed that the equilibrium uptake increased with increasing initial MB concentration. The maximum % removal of MB obtained was 99% at 50°C for AC1 and 82% at 30°C for AC2. The increase in initial pH in the ranges of 2–10 increases the yields removal of MB on AC2. The pseudo-first-order and pseudo-second-order kinetic models were applied to test the experimental data. The latter provided the best correlation of the experimental data compared to the pseudo-first-order model.
Despite their indisputable importance around the world, the pesticides can be dangerous for a range of species of ecological importance such as honeybees (
Apis mellifera
L.). Thus, a particular ...attention should be paid to their protection, not only for their ecological importance by contributing to the maintenance of wild plant diversity, but also for their economic value as honey producers and crop-pollinating agents. For all these reasons, the environmental protection requires the resort of risk assessment of pesticides. The goal of this work was therefore to develop a validated QSAR model to predict contact acute toxicity (LD
50
) of 111 pesticides to bees because the QSAR models devoted to this species are very scarce. The analysis of the statistical parameters of this model and those published in the literature shows that our model is more efficient. The QSAR model was assessed according to the OECD principles for the validation of QSAR models. The calculated values for the internal and external validation statistic parameters (
Q
2
and
r
m
2
¯
)
are greater than 0.85. In addition to this validation, a mathematical equation derived from the ANN model was used to predict the LD
50
of 20 other pesticides. A good correlation between predicted and experimental values was found (
R
2
=
0.97 and RMSE = 0.14). As a result, this equation could be a means of predicting the toxicity of new pesticides.
The activity coefficient at infinite dilution of water in ionic liquids is a thermodynamic property of a paramount importance in separation processes. However, accurate modeling of this parameter ...remains a challenging task due to the highly nonlinear behavior of the water/ionic liquid systems. Also, available models consider a large number of inputs that are usually difficult to access and require complicated use techniques. Therefore, the main objective of this paper is to use artificial intelligence techniques to propose models (based on a reduced number of inputs that are easily accessible, and to improve the accuracy of the correlative performance for activity coefficient at infinite dilution of water in ILs). The present work features the application of artificial neural networks, support vector machine and least square support vector machine, among data-driven methods, for modeling the activity coefficient at infinite dilution of water in 53 ionic liquids. Overall, the models proposed are able to accurately correlate 318 experimental data points gathered from the literature. According to the results, the ANN is more powerful and effective computational learning machine than the two remaining ones. The correlation coefficients
R
2
and deviations expressed as an average absolute relative deviation for the neural network model are estimated to be 0.99997 and 0.56%, respectively. Furthermore, the neural network model’s interpolation and extrapolation capabilities are demonstrated, and its accuracy is compared to other proposed models in the literature based on multi-linear regression, least squares support vector machine and another feedforward neural network. This work also includes a graphical user interface for the proposed model, as well as an inputs’ sensitivity analysis.
The removal of pharmaceutical actives compounds (PhACs) by nanofiltration (NF) and reverse osmosis (RO) of paramount importance in membrane separation processes. However, modeling remains a difficult ...approach due to the strongly nonlinear performance of the removal mechanisms of organic molecules by NF/RO. The present work features the application of neural networks based on quantitative structure–activity relationship (single neural networks “QSAR-SNN” and bootstrap aggregated neural networks “QSAR-BANN
(Staking of 30 networks)
") for prediction of the removal of 23 pharmaceutical active compounds (PhACs). Overall, the models proposed are able to accurately correlate 599 experimental data points gathered from the literature. According to the results, the QSAR-BANN
(Staking of 30 networks)
is a more powerful and effective computational learning machine than the QSAR-SNN. The regression coefficients “
R
2
” and the root mean squared error “
RMSE
” for the QSAR-BANN
(Staking of 30 networks)
model are estimated to be 0.9672 and 3.2810%, respectively. Moreover, QSAR-BANN
(Staking of 30 networks)
model capabilities is showed to describe the removal of PhACS by NF/RO and its precision is compared to proposed previous models, where this comparison showed the superiority of our BANN model. The work with one class of organic compounds (PhACs) is more suitable for prediction performances NF/RO by QSAR-BANN model.
The current global concern is how to replace polluting fossil fuels with sustainable green fuels. On this basis, in this study, a characterization protocol for sewage sludge in Algeria was carried ...out. Their objective was to study the process of anaerobic digestion for biogas production by analogy to experiments that have already been made in the literature. The performance of the proposed models has been validated with experimental data from the scientific literature. Biodegradable organic matter levels are high (160–500 mg L−1) giving an acceptable yield for biogas production. The modeling of cumulative biogas production was carried out by Gompertz and models were proposed for different products. It has been observed that there is a better agreement of the models proposed with the experimental data with a maximum value of r2 = 0.9996. The modeling of the degradation of organic matter was carried out by the first order‐model, and dimensionless models were proposed. The latter gave a good agreement with the experimental data better than the model in the literature, with a maximum value of r2 = 0.9985. The proposed models to study the anaerobic digestion process of organic matter, as well as those related to the process of cumulative biogas production, are universal (without units of measurement), which will help producers in this field to improve production, as well as reduce the investment cost through preliminary simulations.
A characterization protocol for sewage sludge in Algeria was carried out. Study on the anaerobic digestion process for biogas production has been made. The volume of biogas produced was modeled by that of Gompertz and the models proposed. Degradation of organic matter was made by the first order model, and models proposed. Proposed models give better smoothing curves compared to those in the literature.
•QSAR model for acute oral toxicity of pesticides is developed and proposed.•This model has been developed and validated on the basis of the OECD principles for QSAR acceptance in regulation.•The new ...nonlinear model showed excellent robustness, fitness and predictive ability.•The majority of pesticides were located within the AD, further indicated the reliability of the prediction.
Quantitative Structure Activity Relationship (QSAR) models are expected to play an important role in the risk assessment of chemicals on humans and the environment. In this study, we developed a validated QSAR model to predict acute oral toxicity of 329 pesticides to rats because a few QSAR models have been devoted to predict the Lethal Dose 50 (LD50) of pesticides on rats. This QSAR model is based on 17 molecular descriptors, and is robust, externally predictive and characterized by a good applicability domain. The best results were obtained with a 17/9/1 Artificial Neural Network model trained with the Quasi Newton back propagation (BFGS) algorithm. The prediction accuracy for the external validation set was estimated by the Q2ext and the root mean square error (RMS) which are equal to 0.948 and 0.201, respectively. 98.6% of external validation set is correctly predicted and the present model proved to be superior to models previously published. Accordingly, the model developed in this study provides excellent predictions and can be used to predict the acute oral toxicity of pesticides, particularly for those that have not been tested as well as new pesticides.
Bottom Ash of Expired Drug Incineration (BAEDI) is used in this study for the removal of Sr(II). In the kinetic study, the experimental results are tested. The results obtained show that the ...pseudo-second order and ninth order models fit the data. Thermodynamic study suggests that the process is: spontaneous, endothermic and with a raised affinity for Sr
2+
. The results obtained (
ΔH°
= 37.58 kJ mol
–1
,
E
a
= 21.35 kJ mol
–1
) show that the adsorption of Sr
2+
is a physical nature. Statistical analysis and the parameters obtained, revealed that the models used are suitable for the data. The maximum adsorption capacity (
q
m
), calculated by using Langmuir model, is 38 mgg
–1
.
Abstract
Currently there are several wastewater treatments processes, and several adsorbent materials consist of separating and purifying the various industrial effluents. In this work an artificial ...neural network (ANN) was developed to describe the dynamic adsorption of sodium decanesulfonate using actived carbon obtained by the calcination of mineral biomass under different conditions. Three inputs (time, mass of adsorbent and fixed bed height) were used in the input layer, three neurons in the hidden layer and one in the output layer for the reduced concentration. The Levenberg Marquardt back-propagation algorithm was applied. The tangent sigmoid and linear transfer functions are used for the hidden layer and the output layer respectively. The results showed a correlation coefficient R
2
= 0.9965 with root mean squared error RMSE = 0.0276. An interpolation and an extrapolation stage are made to test the accuracy of the network. The results showed a high correlation coefficient R
2
= 0.9969 and 0.984 respectively for the interpolation and the extrapolation. These results show the robustness and the high capacity of ANN to describe the dynamic adsorption of sodium decanesulfonate onto actived carbon.
Purpose: The purpose of this study was to develop a robust and externally predictive in silico QSAR-neural network model for predicting plasma protein binding of drugs. This model aims to enhance ...drug discovery processes by reducing the need for chemical synthesis and extensive laboratory testing. Methods: A dataset of 277 drugs was used to develop the QSAR-neural network model. The model was constructed using a Filter method to select 55 molecular descriptors. The validation set’s external accuracy was assessed through the predictive squared correlation coefficient Q2 and the root mean squared error (RMSE). Results: The developed QSAR-neural network model demonstrated robustness and good applicability domain. The external accuracy of the validation set was high, with a predictive squared correlation coefficient Q2 of 0.966 and a root mean squared error (RMSE) of 0.063. Comparatively, this model outperformed previously published models in the literature. Conclusion: The study successfully developed an advanced QSAR-neural network model capable of predicting plasma protein binding in human plasma for a diverse set of 277 drugs. This model’s accuracy and robustness make it a valuable tool in drug discovery, potentially reducing the need for resource-intensive chemical synthesis and laboratory testing.
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