Evaluation of groundwater hardness removal using activated clinoptilolite

Preparation of Mn/Ti-modified zeolite and its performance for removing iron and manganese

Excessive iron and manganese presented in groundwater sources may cause harm to human health that needs to be solved urgently. This research aims to develop high-performance Mn/Ti-modified zeolites using sol-gel method and hydrothermal synthesis method to remove Fe2+ and Mn2+ simultaneously. The preparation parameters were optimized by response surface methodology, and the results confirmed that the optimal preparation conditions were as follows: mass ratio of MnO2-TiO2/zeolite = 1, hydrothermal temperature = 200°C, and calcination temperature = 500°C. The results of batch adsorption experiments showed that the best removal rate of Fe2+ and Mn2+ by modified zeolite materials which was prepared under the optimum conditions reached 96.8% and 94.4%, respectively, at which the saturated adsorption capacity was 2.80 mg/g and 1.86 mg/g. Through the adsorption kinetics, thermodynamics, internal diffusion, and isothermal adsorption analyses, it is confirmed that the adsorption process of Fe2+ and Mn2+ by the modified zeolite is mainly chemical adsorption. The results of the Weber-Morris internal diffusion model prove that internal diffusion is not the only step that controls the adsorption process. In addition, combined with the characterization of the composite-modified zeolite and the adsorption experimental study, it shows that there is an autocatalytic reaction in the adsorption process.

The effects of anaerobic reactor post-treatments by rapid filtration systems and conventional techniques

Although anaerobic reactors are an excellent alternative in the treatment of domestic effluents, they have the disadvantage of requiring post-treatment. Many technologies have been studied and, recently, rapid filtration systems have been presented as a viable alternative for post-treatment. This work compared post-treatment techniques for anaerobic upflow sludge blanket (UASB) reactors by rapid filtration systems (double filtration (DF); triple filtration with clinoptilolite (TFc); and triple filtration with activated carbon (TFac)) to conventional systems (facultative pond (FP); biological filter (BF); biological filter with recirculation and decantation (BFD)), verifying their potential for improvement of the final effluent quality. The UASB effluent post-treatments by FP, BF, BFD, DF, TFc, and TFac were evaluated. The removal of turbidity in both BFD and FP post-treatments was below 75%. The DF, TFc, and TFac treatments showed over 99% removal of the same parameters. COD removal in the FP, BF, and BFD post-treatments was over 10%, while in the DF, TFc, and TFac treatments, it was over 80%. The greatest total phosphorus removal was observed in TFc and TFac, whose values were over 99%. The best removal of ammoniacal nitrogen, 99% was observed in the TFc treatment. Regarding Al, Cd, Cr, Cu, Mn, Ni, Pb, and Zn removal, all rapid filtration systems showed better performance when compared to conventional systems. The DF, TFc, and TFac systems showed over 90% removal of most metals evaluated, while the FP and BF treatments presented values below 50% for most metals, and in the BFD system, the removal values were below 80% for most metals. The results indicate that rapid filtration systems were better at removing all evaluated parameters when compared to conventional systems.

Zn2+ and Cd2+ removal from wastewater using clinoptilolite as adsorbent

Many industries discharge wastewater from processing into surface and underground waterways, and then, these waste waters must therefore be treated in order to remove heavy metals. The most common treatment used is the activated carbon adsorption, a particularly competitive and effective process; however, the use of activated carbon is not suitable due to the high costs. Then, in order to minimize processing cost, recent investigations have been focused on the use of low-cost adsorbents as zeolites. In particular, clinoptilolite is known to have high selectivity for certain heavy metals. In this paper, the capability of clinoptilolite as a low-cost adsorbent for the removal of zinc and cadmium ions from wastewater was analyzed in a batch system. Preliminary characterization was performed on adsorbent material in order to evaluate the chemical-physical structure. Tests in batch for analyzing adsorbing capacity of clinoptilolite were carried out varying zinc and cadmium concentrations between 10 and 200 mg/L with different amounts of sorbent in the solution (10-60 g/L). For both zinc and cadmium ions, complete adsorption was reached when the concentration was equal to 10 mg/L and adsorption capacity decreased increasing metals amount. In particular, clinoptilolite permitted high Cd²⁺ abatement, probably due to its greater affinity with adsorbent in the single system. Binary system was then analyzed, and, contrary to previous tests, the adsorbent in the simultaneous presence of the two metals demonstrated a greater affinity toward zinc, showing a higher percentage of absorption, due to a different absorption mechanism in the presence of two ions.

Susceptibility Prediction of Groundwater Hardness Using Ensemble Machine Learning Models

Groundwater resources, unlike surface water, are more vulnerable to disturbances and contaminations, as they take a very long time and significant cost to recover. So, predictive modeling and prevention strategies can empower policymakers for efficient groundwater governance through informed decisions and recommendations. Due to the importance of groundwater quality modeling, the hardness susceptibility mapping using machine learning (ML) models has not been explored. For the first time, the current research aimed to predict groundwater hardness susceptibility using the ML models. The performance of two ensemble models of boosted regression trees (BRT) and random forest (RF) is investigated through the arrangement of a comparative study with multivariate discriminant analysis (MDA). According to the hardness values in 135 groundwater quality monitoring wells, the hard and soft water are determined; then, 11 predictor variables including distance from the sea (DFS), land use, elevation, distance from the river (DFR), depth to groundwater (DTGW), pH, precipitation (PCP), evaporation (E), groundwater level (GWL), curvature, and lithology are used for predicting the groundwater hardness susceptibility map. Results indicated that the variables of DFR, DTGW, elevation, and DFS had a higher contribution to the modeling process. So, the high harness areas are mostly related to low elevations, low DTGW, and proximity to river and sea, which facilitate the percolation conditions for minerals containing calcium or magnesium into groundwater.