Many industries use huge amounts of synthetic dyes which may release into the wastewater in dyeing processes causing serious damage to aquatic life as they are recalcitrant, nonbiodegradable, stable to oxidizing agents, and toxic. Adsorption on activated carbon has been found to be a very efficient treatment method. In this work, a new adsorbent material composed of activated carbon and aluminum chloride (AC-Al) was prepared for the removal of a commercial anionic and anthraquinonic reactive dye, i.e., Reactive Black 5 (RB5) under various experimental conditions. Several parameters, such as the adsorbent’s dosage, initial RB5 concentration, pH, and contact time, were studied in order to determine the feasibility of AC-Al. According to the results, it was found that there was an increase in RB5 removal as the adsorbent’s dosage increased, especially, in pH 2 ± 0.1, where the removal rate increased, and reaching 100% by 1.0 g/L of AC-Al. Freundlich isotherm and pseudo-second-order kinetic models adequately fit the experimental data, indicating that favorable and heterogeneous adsorption occurred, closer to chemisorption. According to thermodynamics, it was found that the adsorption procedure was endothermic in nature (∆H0 = 62.621 kJ/mol) and spontaneous (∆G0 < 0), and according to the positive value of ∆S0 0.0293 (kJ/mol∙K), there is an increase in random interaction between solid and liquid interfaces. Finally, the AC-Al adsorbent was successfully regenerated and reused for four cycles.

/pdf/composite-activated-carbon-modified-with-alcl3-for-the-30lhv1od.pdf

Composite Activated Carbon Modified with AlCl3 for the Effective Removal of Reactive Black 5 Dye from Wastewaters

Abstract The upsurge in the discharge of lead ions (Pb 2+ ) into the environs resulting from various anthropogenic activities vis-vis its adverse effect on public health is a call for great concern. However, the adsorption technique, amongst other heavy metal removal methods, is regarded as the most promising. The present study synthesized a walnut shell-chitosan composite (WNS-CH) as an efficient biosorbent for Pb 2+ uptake and biofilter of Bacillus subtilis and Klebsiella pneumoniae . WNS-CH was characterized using SEM and FTIR. Furthermore, an intelligent and cost-effective machine learning model, an adaptive neuro-fuzzy model clustered with the grid-partitioning (GP), and fuzzy c -means (FCM) technique were developed to predict the adsorption of Pb 2+ based on relevant input parameters. The batch adsorption was carried out by varying operating parameters such as contact time, temperature, pH, adsorbent dose, and initial adsorbate concentration. The SEM images of WNS-CH showed a homogenous regular hollow ellipsoidal morphologies, while FTIR spectra showed the presence of O-H, N-H, C-N, and C-O. Under the conditions of initial pH 10, dosage 45 mg, and temperature of 40 °C, an adsorption efficiency of 94 % was obtained. The thermodynamic parameters, ∆H° and ∆G°, showed an endothermic and spontaneous process for Pb 2+ uptake. Antibacterial activities of the WNS-CH composite showed bioactivity against Bacillus subtilis and Klebsiella pneumoniae with a mean ZI of 5.3±1.16 and 6.0 ±1.00, respectively. The experimental data was best described by Freundlich isotherm ( R 2 = 0.9509) and pseudo-first-order kinetic ( R 2 = 0.9674) models indicating chemisorption and multilayer adsorption process. The best prediction of Pb 2+ adsorption was obtained with the optimal GP-clustered ANFIS model using a triangular membership function (triMF), giving Root Mean Square Error (RMSE), Mean Absolute Deviation (MAD), Mean Absolute Percentage Error (MAPE), and correlation determination ( R 1 ) values of 1.217, 0.563, 1.698, and 0.9966 respectively at the testing phase. The GP-ANFIS model shows good agreement with experimental results. This study revealed that WNS-CH composite could be regarded as a promising biosorbent for the remediation of Pb 2+ -polluted wastewater. The cost analysis demonstrated that the WNS-CH composite could serve as an alternative to commercial activated carbon. 

https://link.springer.com/content/pdf/10.1007/s13399-023-03804-x.pdf

Antimicrobial study and biosorption of Pb2+ ions onto chitosan-walnut composites: mechanistic studies and neuro-fuzzy modeling approach

Most of dyes cause various environmental and health problems due to their toxic, mutagenic, and even carcinogenic properties. Therefore, several treatment methods are used to remove dyes from wastewater. Adsorption is one of the most preferred methods due to its easy application and high efficiency. The aim of this study is to prepare and characterize KOH-activated pine cone (APC) biochar and use it as adsorbent for removal of anionic diazo dye, Congo red (CR) from aqueous solution. The various operating parameters such as pH, contact time, temperature, initial dye concentration, and adsorbent dosage are optimized in batch adsorption system. Experimental results showed that the prepared APC biochar has a surface area of 1714.5 m2/g and was achieved 94.62% CR removal efficiency at an adsorbent dosage of 2 g/L. The Freundlich, Langmuir, and Temkin adsorption models were used for the mathematical description of the adsorption equilibrium. Experimental data showed the best compatibility with the Freundlich isotherm. Batch adsorption models, based on the assumption of the pseudo first-order, pseudo second-order, and intra particle diffusion mechanisms, were applied to examine the kinetics of the adsorption. Kinetic data fitted the pseudo second-order kinetic model. Calculated thermodynamic parameters indicated the spontaneous, endothermic, and the increased randomness nature of CR adsorption. Structural and morphological changes of APC biochar after adsorption process were determined by using Fourier-transform infrared spectroscopy (FT-IR) and scanning electron microscope (SEM) analysis. The prediction of the CR adsorption capacity of the APC biochar using artificial neural network (ANN) algorithm was modeled. For this purpose, many different ANN models have been developed. Among them, ANN10 gave the best results. According to ANN10 results, root-mean-squared error (RMSE), mean bias error (MBE), mean absolute error (MAE), and correlation coefficient (R2) were estimated as 0.770, 0.310, 0.037, and 0.999, respectively. Consequently, the prediction results showed well agreement with experimental results.

Adsorption of Congo red from aqueous solution onto KOH-activated biochar produced via pyrolysis of pine cone and modeling of the process using artificial neural network

Ceren Altuncan

Papers

Adsorption of Congo red from aqueous solution onto KOH-activated biochar produced via pyrolysis of pine cone and modeling of the process using artificial neural network