Decolorization of crystal violet from aqueous solutions by a novel adsorbent chitosan/nanodiopside using response surface methodology and artificial neural network-genetic algorithm

Nasab, Shima Ghanavati; Semnani, Abolfazl; Teimouri, Abbas; Yazd, Mehdi Javaheran; Isfahani, Tahereh Momeni; Habibollahi, Saeed

doi:10.1016/j.ijbiomac.2018.11.148

Cited by 70 publications

(12 citation statements)

References 88 publications

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“…Moghri et al [ 69 , 70 ] predicted the fracture modulus for composites prepared using polyamide-6/nano clay using RSM and ANN methods and reported that ANN was adequate to model the data with a sample size of 20% less. On the contrary, similar effectiveness of both RSM and ANN has been reported in predicting the responses [ 29 , 79 ] while others have found that the RSM is better than ANN in modeling [ 80 ].…”

Section: Resultsmentioning

confidence: 63%

Statistical and Machine Learning-Driven Optimization of Mechanical Properties in Designing Durable HDPE Nanobiocomposites

2021

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The selection of nanofillers and compatibilizing agents, and their size and concentration, are always considered to be crucial in the design of durable nanobiocomposites with maximized mechanical properties (i.e., fracture strength (FS), yield strength (YS), Young’s modulus (YM), etc). Therefore, the statistical optimization of the key design factors has become extremely important to minimize the experimental runs and the cost involved. In this study, both statistical (i.e., analysis of variance (ANOVA) and response surface methodology (RSM)) and machine learning techniques (i.e., artificial intelligence-based techniques (i.e., artificial neural network (ANN) and genetic algorithm (GA)) were used to optimize the concentrations of nanofillers and compatibilizing agents of the injection-molded HDPE nanocomposites. Initially, through ANOVA, the concentrations of TiO2 and cellulose nanocrystals (CNCs) and their combinations were found to be the major factors in improving the durability of the HDPE nanocomposites. Further, the data were modeled and predicted using RSM, ANN, and their combination with a genetic algorithm (i.e., RSM-GA and ANN-GA). Later, to minimize the risk of local optimization, an ANN-GA hybrid technique was implemented in this study to optimize multiple responses, to develop the nonlinear relationship between the factors (i.e., the concentration of TiO2 and CNCs) and responses (i.e., FS, YS, and YM), with minimum error and with regression values above 95%.

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Section: Resultsmentioning

confidence: 63%

Statistical and Machine Learning-Driven Optimization of Mechanical Properties in Designing Durable HDPE Nanobiocomposites

2021

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“…In the present study, the experimental kinetic data for adsorption of CV and CR dyes on the CDA surface were analyzed using pseudo-first-order, 56 pseudo-second-order, 57 and intraparticle diffusion 58 models, which are expressed according to the following equations: …”

Section: Methodsmentioning

confidence: 99%

Eco-Efficient Green Seaweed Codium decorticatum Biosorbent for Textile Dyes: Characterization, Mechanism, Recyclability, and RSM Optimization

et al. 2020

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Biosorption using natural waste has emerged as a potential and promising strategy for removal of toxic dyes from wastewaters in comparison to conventional ones. Herein, the Codium decorticatum alga (CDA) was biologically identified and used as a biosorbent for anionic and cationic dyes from aqueous solutions. SEM analysis showed a rough surface with an irregular edge and shape while hydroxyl, amine, sulfur and carboxyl functional groups were identified using FTIR analysis. TGA/DTG confirmed the stability of CDA and the adsorption process. Batch studies were conducted to investigate the effect of operational factors such as initial pH, biosorbent dosage, temperature, initial concentration, and solid/liquid contact time on the biosorption of crystal violet (CV) and Congo red (CR) dyes. For both CV and CR dyes, the biosorption kinetics was accurately described by the pseudo-second-order model and the Langmuir isotherm was found to be best fitted for equilibrium data. Maximum uptake capacities have attained up to 278.46 mg/g for CV and 191.01 mg/g for CR. The CV and CR dye biosorption mechanism was ultimately manifested through the electrostatic interactions. The regeneration study showed that the CDA presents excellent reuse performance up to four consecutive cycles. The process optimization was performed using the response surface methodology based on Box–Behnken design (RSM-BDD). Accordingly, the optimum predicted removal efficiencies using RSM-BBD for CV and CR were obtained, respectively, at 96.9 and 89.8% using a CDA dose of 1.5 g/L, dye concentration of 20 mg/L, pH of 10 for CV, and pH of 4 for CR. Overall, CDA behaves as an efficient, recyclable, cheap, and eco-friendly adsorbent for cleaning-up of dyed effluents.

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“…Variables in this study included the adsorbent dosage (0.5-2 g L −1 ), initial concentration of cd (II) (10-100 mg L −1 ), contact time(5-100 min) and pH value (3)(4)(5)(6)(7)(8)(9) and the influence of these variables on Cd (II) adsorption by Fe 3 O 4 @SiO 2 @KCC-1 were investigated.…”

Section: Adsorption Experimentsmentioning

confidence: 99%

“…Therefore, it is necessary to find appropriate methods of high efficiency and good performance to eliminate these toxic metals [3]. Many efforts have been done to reduce and eliminate the heavy metals like Chemical precipitation, ion exchange, reverse osmosis, membrane processes, evaporation, solvent extraction, and adsorption [4][5][6][7][8][9][10][11][12][13][14]. Compared to other methods, adsorption method in terms of initial cost, reuse of effluent, simplicity and flexibility in design, easy operation, insensitivity to pollutants and toxic compounds, and absence of free radicals has received more attention [3].…”

Section: Introductionmentioning

confidence: 99%

Magnetic fibrous silica mesoporous as a selective and efficient system for removal of Cd(II) ions with a focus on optimization by response surface methodology

Emrani

Ebrahimi

Zhiani

et al. 2021

International Journal of Environmental Analytical Chemistry

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In this study, the removal efficiency of Cd (II) ions from aqueous solutions by Fe 3 O 4 @SiO 2 @KCC-1 as a simple and efficient adsorbent was studied. The prepared adsorbent was synthesised by using the microwave-assisted hydrothermal technique in a short period of time and characterised by SEM, FTIR, XRD, VSM, TEM, and BET analyses. The effect of various parameters on the removal percentage of the Cd (II) ions was studied by central composite design and the optimal condition of factors for maximum Cd (II) removal percentage was as follows: pH = 5.97, adsorbent dosage: 1.49 g L −1 , Cd (II) concentration: 59.29 mg L −1 , and contact time: 54.79 min, respectively. The results illustrated that the Central Composite Designs) CCD(model has an ideal ability in predicting the removal percentage of Cd (II) ions from the aqueous solutions.

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Decolorization of crystal violet from aqueous solutions by a novel adsorbent chitosan/nanodiopside using response surface methodology and artificial neural network-genetic algorithm

Cited by 70 publications

References 88 publications

Statistical and Machine Learning-Driven Optimization of Mechanical Properties in Designing Durable HDPE Nanobiocomposites

Statistical and Machine Learning-Driven Optimization of Mechanical Properties in Designing Durable HDPE Nanobiocomposites

Eco-Efficient Green Seaweed Codium decorticatum Biosorbent for Textile Dyes: Characterization, Mechanism, Recyclability, and RSM Optimization

Magnetic fibrous silica mesoporous as a selective and efficient system for removal of Cd(II) ions with a focus on optimization by response surface methodology

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