Mixed coagulant-flocculant optimization for pharmaceutical effluent pretreatment using response surface methodology and Gaussian process regression

Tahraoui, Hichem; Belhadj, Abd-Elmouneïm; Triki, Zakaria; Boudella, Nihel Rayen; Seder, Sarah; Amrane, Abdeltif; Zhang, Jie; Moula, Nassim; Tifoura, Amina; Ferhat, Radhia; Bousselma, Abla; Mihoubi, Nadia

doi:10.1016/j.psep.2022.11.045

Cited by 37 publications

(36 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to assess the performance of the ANN, the mean square error (MSE) was calculated to demonstrate the statistical difference between the predicted and experimental values [ 12 , 15 ]. The accuracy of the developed model was assessed by the value of the correlation coefficient R 2 [ 20 , 21 , 22 , 23 , 24 , 25 ].…”

Section: Methodsmentioning

confidence: 99%

Experimental Analysis and Neural Network Modeling of the Rheological Behavior of Xanthan Gum and Its Derivatives

et al. 2023

Self Cite

View full text Add to dashboard Cite

The main objective of this study was to create a mathematical tool that could be used with experimental data to predict the rheological flow behavior of functionalized xanthan gum according to the types of chemical groups grafted onto its backbone. Different rheological and physicochemical analyses were applied to assess six derivatives synthesized via the etherification of xanthan gum by hydrophobic benzylation with benzyl chloride and carboxymethylation with monochloroacetic acid at three (regent/polymer) ratios R equal to 2.4 and 6. Results from the FTIR study verified that xanthan gum had been modified. The degree of substitution (DS) values varying between 0.2 and 2.9 for carboxymethylxanthan gum derivatives were found to be higher than that of hydrophobically modified benzyl xanthan gum for which the DS ranged from 0.5 to 1. The molecular weights of all the derivatives were found to be less than that of xanthan gum for the two types of derivatives, decreasing further as the degree of substitution (DS) increased. However, the benzyl xanthan gum derivatives presented higher molecular weights varying between 1,373,146 (g/mol) and 1,262,227 (g/mol) than carboxymethylxanthan gum derivatives (1,326,722–1,015,544) (g/mol). A shear-thinning behavior was observed in the derivatives, and the derivatives’ viscosity was found to decrease with increasing DS. The second objective of this research was to create an ANN model to predict one of the rheological properties (the apparent viscosity). The significance of the ANN model (R2 = 0.99998 and MSE = 5.95 × 10−3) was validated by comparing experimental results with the predicted ones. The results showed that the model was an efficient tool for predicting rheological flow behavior.

show abstract

Section: Methodsmentioning

confidence: 99%

Experimental Analysis and Neural Network Modeling of the Rheological Behavior of Xanthan Gum and Its Derivatives

et al. 2023

Self Cite

View full text Add to dashboard Cite

show abstract

“…To achieve this, the study used multi-objective optimization (MOO) to solve the optimization problem. The four objectives were assigned weights, and their weighted sum was considered as a single objective, which was solved using the particle swarm algorithm available in the MATLAB Optimization Toolbox [16]. The objective functions considered in this study were the minimum conductivity (J 1 ), the minimum turbidity (J 2 ), the minimum viscosity (J 3 ), and the minimum interfacial tension (J 4 ).…”

Section: Multi-objective Optimizationmentioning

confidence: 99%

“…The objective functions considered in this study were the minimum conductivity (J 1 ), the minimum turbidity (J 2 ), the minimum viscosity (J 3 ), and the minimum interfacial tension (J 4 ). These objectives were combined into a scalar goal as follows: J = w 1 J 1 + w 2 J 2 + w 3 J 3 + w 4 J 4 (10) where w 1 , w 2 , w 3 , and w 4 are weighting factors, which can be calculated using the rank sum method [16,[43][44][45].…”

Section: Multi-objective Optimizationmentioning

confidence: 99%

See 1 more Smart Citation

Designing an Efficient Surfactant–Polymer–Oil–Electrolyte System: A Multi-Objective Optimization Study

et al. 2023

Self Cite

View full text Add to dashboard Cite

This research aimed to study the effects of individual components on the physicochemical properties of systems composed of surfactants, polymers, oils, and electrolytes in order to maximize the recovery efficiency of kerosene while minimizing the impact on the environment and human health. Four independent factors, namely anionic surfactant sodium dodecylbenzene sulfonate (X1) (SDBS), oil (X2) (kerosene), water-soluble polymer poly(ethylene glycol) (X3) (PEG), and sodium chloride (X4) (NaCl), were studied using the full factorial design (FFD) model. Four output variables, namely conductivity (Y1), turbidity (Y2), viscosity (Y3), and interfacial tension (IFT) (Y4), were taken as the response variables. All four FFD models have high coefficients of determination and low errors. The developed models were used in a multi-objective optimization (MOO) framework to determine the optimal conditions. The obtained optimal conditions are X1 = 0.01, X2 = 50, X3 = 5, and X4 = 0.1, with an error of 0.9414 between the predicted and experimental objective function values. This result shows the efficiency of the model developed and the system used for the recovery of kerosene, while also having a positive effect on the protection of the environment.

show abstract

“…Coagulants and flocculants are a section of chemicals that lead the world market for water chemicals that is worth $23.5 billion in 2018, and it is expected to reach $32.5 billion by 2020 4,5 . Despite the worldwide usage and being world market leader, the chemicals are still lacking green chemistry.…”

Section: Introductionmentioning

confidence: 99%

Treatment of palm oil mill effluents using rambutan (Nephelium lappaceum) and its modeling using artificial neural networks

Bunyamin,

Sethu,

Arumugasamy

et al. 2023

Asia-Pacific J Chem Eng

View full text Add to dashboard Cite

Palm oil extraction is one of the important industries in Malaysia. The extraction of palm oil will not only result in the production of palm oil for human consumption but also generate palm oil mill effluent (POME), which contains highly polluting properties. Synthetic coagulants such as aluminum sulfate (alum), aluminum chloride, and sodium aluminate contribute toward environmental pollution and risk to human health. Thus, natural coagulants and flocculants have been introduced throughout the globe to slowly replace synthetic coagulants. In this study, rambutan (Nephelium lappaceum) seed was used as natural coagulants combining with aloe vera as natural flocculants. The standard jar test experiments were conducted to study the effects of pH, coagulant dosages, and rapid mixing speed on the removal efficiency of chemical oxygen demand (COD), total suspension solid (TSS), and turbidity of POME. The model of the coagulation–flocculation process was evaluated by using response surface methodology (RSM) and artificial neural network (ANN). RSM associated with Box–Behnken design was used to identify the optimum parameters (inputs), which consist of pH, coagulants dosages and rapid mixing speed for the optimum removal efficiency (outputs) of COD, TSS, and turbidity of POME. In terms of removal efficiency of COD, TSS, and turbidity of POME, rambutan seed has the optimum removal efficiency of 28.48%, 56.38%, and 60.36%, respectively. ANN was utilized with the aim of evaluating the performance of 12 training algorithms to predict the output results. Levenberg–Marquardt (LM) training algorithm was verified to be the best training algorithm for coagulation–flocculation process of rambutan seed based on the lowest mean squared error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) for validation data and the correlation coefficient R of .9347.

show abstract

Mixed coagulant-flocculant optimization for pharmaceutical effluent pretreatment using response surface methodology and Gaussian process regression

Cited by 37 publications

References 59 publications

Experimental Analysis and Neural Network Modeling of the Rheological Behavior of Xanthan Gum and Its Derivatives

Experimental Analysis and Neural Network Modeling of the Rheological Behavior of Xanthan Gum and Its Derivatives

Designing an Efficient Surfactant–Polymer–Oil–Electrolyte System: A Multi-Objective Optimization Study

Treatment of palm oil mill effluents using rambutan (Nephelium lappaceum) and its modeling using artificial neural networks

Contact Info

Product

Resources

About