Adsorption of arsenic and fluoride: Modeling of single and competitive adsorption systems.

The elevated co-occurrence of arsenic and fluoride in surface and groundwater poses risks to human health in many parts of the world. Using single and competitive batch equilibrium adsorption studies, this research focuses on As(V) and F adsorption by activated carbon and its modeling. BET, XRD, FESEM, EDS, and FTIR analysis were used to discern the structural characteristics of activated carbon. The influence of dosage, pH, and contact time were also investigated in single and simultaneous adsorption systems. The maximum adsorption capacity of activated carbon for arsenic and fluoride were found to be 3.58 mg/g and 2.32 mg/g, respectively. Kinetics studies indicated that pseudo-second-order kinetic model fit better than pseudo-first-order, Elovich, and intraparticle diffusion kinetic models. The non-linear regression analysis of Langmuir, Freundlich, Toth, Redlich Petersons, and Modified Langmuir Freundlich models was used to determine single-component asorption model parameters. Additionally, the simultaneous adsorption was rigorously modeled and compared using the Extended Langmuir (EL), Extended Langmuir Freundlich (ELF), Modified Competitive Langmuir (MCL), and Jeppu Amrutha Manipal Multicomponent (JAMM) isotherm models, and competitive mechanisms were interpreted for the simultaneous adsorption system. Further, the model performances were evaluated by statistical error analysis using the normalized average percentage error (NAPE), root mean square errors (RMSE), and the correlation coefficient (R2). According to the modeling results, single equilibrium data fitted better with the Modified Langmuir Freundlich isotherm model, with a higher R2 of 0.99 and lower NAPE values of 3.8 % and 1.28 % for As(V) and F, than other models. For the binary adsorption, the Extended Langmuir Freundlich isotherm model demonstrated excellent fit with lowest errors. All the competitive isotherm models fit the As(V) and F simultaneous sorption systems reasonably well. Furthermore, the research unveiled a nuanced hierarchy of isotherm fitting, with ELF > EL > MCL > JAMM in varying arsenic at a constant fluoride concentration, and ELF > JAMM > EL > MCL in varying fluoride at a constant arsenic concentrations. In addition, competitive studies divulged crucial insights into selective adsorption, as As(V) exhibits a pronounced adsorption selectivity over F on activated carbon. In essence, As(V) showed a more pronounced antagonistic behavior over F, whereas F exhibited a much lesser competitive behavior in the adsorption of arsenic.

Enhanced adsorption of phenolic compounds using biomass-derived high surface area activated carbon: Isotherms, kinetics and thermodynamics.

The progress of industrial and agricultural pursuits, along with the release of inadequately treated effluents especially phenolic pollutant, has amplified the pollution load on environment. These organic compounds pose considerable challenges in both drinking water and wastewater systems, given their toxicity, demanding high oxygen and limited biodegradability. Thus, developing an eco-friendly, low-cost and highly efficient adsorbent to treat the organic pollutants has become an important task. The present investigation highlights development of a novel adsorbent (CFPAC) by activation of Cassia fistula pod shell for the purpose of removing phenol and 2,4-dichlorophnenol (2,4-DCP). The significant operational factors (dosage, pH, concentration, temperature, speed) were also investigated. The factors such as pH = 2 and T = 20°C were found to be significant at 1.6 g/L and 0.6 g/L dosage for phenol and 2,4-DCP respectively. Batch experiments were further conducted to study isotherms, kinetic and thermodynamics studies for the removal of phenol and 2,4-DCP. The activated carbon was characterised as mesoporous (specific surface area 1146 m2/g, pore volume = 0.8628 cc/g), amorphous and pHPZC = 6.4. At optimum conditions, the maximum sorption capacity for phenol and 2,4-DCP were 183.79 mg/g and 374.4 mg/g respectively. The adsorption isotherm was better conformed to Redlich Peterson isotherm (phenol) and Langmuir isotherm (2,4-DCP). The kinetic study obeyed pseudo-second-order type behaviour for both the pollutants with R2 > 0.999. The thermodynamic studies and the value of isosteric heat of adsorption for both the pollutants suggested that the adsorption reaction was dominated by physical adsorption (ΔHx < 80 kJ/mol). Further, the whole process was feasible, exothermic and spontaneous in nature. The overall studies suggested that the activated carbon synthesised from Cassia fistula pods can be a promising adsorbent for phenolic compounds.

Development of a Multicomponent Adsorption Isotherm Equation and Its Validation by Modeling.

Researchers have made significant efforts over the past few decades to understand adsorption by developing various simple adsorption isotherm models. However, though many contaminants usually occur as multicomponent mixtures in nature, multicomponent adsorption isotherms have received limited attention and remain an area of inadequate research. We have presented here in a new multicomponent adsorption isotherm model, named the Jeppu Amrutha Manipal Multicomponent (JAMM) isotherm, that can alleviate this problem. We first developed the JAMM multicomponent isotherm using our experimental data sets of arsenic and fluoride competitive adsorption on activated carbon. We then tested the JAMM multicomponent isotherm for a case study of cadmium and zinc competitive adsorption. Next, we further assessed the JAMM isotherm using another competitive adsorption case study of copper and chromium. Through extensive validation studies and error analysis, the JAMM isotherm was able to demonstrate its efficacy in predicting the adsorption behavior in several multicomponent adsorption systems accurately. The main advantage of JAMM isotherm over other multicomponent isotherms is that it utilizes and leverages the single-component adsorption parameters to simulate multicomponent isotherms. The proposed JAMM analytical isotherm model furthermore incorporates the interaction between the components, a mole fraction parameter, and a heterogeneity index, providing a more comprehensive modeling framework for multicomponent adsorption. The mole fraction term was introduced for the distribution of adsorption sites based on the relative number of molecules of each component. An additional term for interaction coefficient was introduced for the representation of interactions. During the validation of JAMM with three experimental case studies with negligible, small, and high competition systems of adsorbates, impressive predictions were exhibited, with the average normalized absolute percentage error as 6.05% and average R2 as 0.86, highlighting the model's robustness, versatility, and reliability. We propose that the new JAMM isotherm modeling framework might profoundly help in chemical engineering, environmental engineering, and materials science applications by providing a potent tool for analyzing and predicting multicomponent adsorption systems.

Development and Validation of Advanced Nonlinear Predictive Control Algorithms for Trajectory Tracking in Batch Polymerization.

In this work, a computationally efficient nonlinear model-based control (NMBC) strategy is developed for a trajectory-tracking problem in an acrylamide polymerization batch reactor. The performance of NMBC is compared with that of nonlinear model predictive control (NMPC). To estimate the reaction states, a nonlinear state estimator, an unscented Kalman filter (UKF), is employed. Both algorithms are implemented experimentally to track a time-varying temperature profile for an acrylamide polymerization reaction in a lab-scale polymerization reactor. It is shown that in the presence of state estimators the NMBC performs significantly better than the NMPC algorithm in real time for the batch reactor control problem.

Data-Driven Modeling of a Pilot Plant Batch Reactor and Validation of a Nonlinear Model Predictive Controller for Dynamic Temperature Profile Tracking.

Batch process plays a very crucial and important role in process industries. The increased operational flexibility and trend toward high-quality, low-volume chemical production has put more emphasis on batch processing. In this work, nonlinearities associated with the batch reactor process have been studied. ARX and NARX models have been identified using open-loop data obtained from the pilot plant batch reactor. The performance of the batch reactor with conventional linear controllers results in aggressive manipulated variable action and larger energy consumption due to its inherent nonlinearity. This issue has been addressed in the proposed work by identifying the nonlinear model and designing a nonlinear model predictive controller for a pilot plant batch reactor. The implementation of the proposed method has resulted in smooth response of the manipulated variable as well as reactor temperature on both simulation and real-time experimentation.

Continuous Fixed-Bed Column Studies on Congo Red Dye Adsorption-Desorption Using Free and Immobilized Nelumbo nucifera Leaf Adsorbent.

The adsorption of Congo red (CR), an azo dye, from aqueous solution using free and immobilized agricultural waste biomass of Nelumbo nucifera (lotus) has been studied separately in a continuous fixed-bed column operation. The N. nucifera leaf powder adsorbent was immobilized in various polymeric matrices and the maximum decolorization efficiency (83.64%) of CR occurred using the polymeric matrix sodium silicate. The maximum efficacy (72.87%) of CR dye desorption was obtained using the solvent methanol. Reusability studies of free and immobilized adsorbents for the decolorization of CR dye were carried out separately in three runs in continuous mode. The % color removal and equilibrium dye uptake of the regenerated free and immobilized adsorbents decreased significantly after the first cycle. The decolorization efficiencies of CR dye adsorption were 53.66% and 43.33%; equilibrium dye uptakes were 1.179 mg g-1 and 0.783 mg g-1 in the third run of operation with free and immobilized adsorbent, respectively. The column experimental data fit very well to the Thomas and Yoon-Nelson models for the free and immobilized adsorbent with coefficients of correlation R2 ≥ 0.976 in various runs. The study concludes that free and immobilized N. nucifera can be efficiently used for the removal of CR from synthetic and industrial wastewater in a continuous flow mode. It makes a substantial contribution to the development of new biomass materials for monitoring and remediation of toxic dye-contaminated water resources.

Comparative study of using five different leaf extracts in the green synthesis of iron oxide nanoparticles for removal of arsenic from water.

Greenleaf extracts have been used as reducing agents for the synthesis of various nanoparticles because of their high antioxidant capacity and environmentally benign reducing properties. Five different plant species were chosen for this comparative study of the synthesis of iron oxide nanoparticles for arsenic adsorption. Based on the excellent reducing properties reported in previous studies, the following plant leaves were selected: black tea leaves (Camellia sinensis), oak tree leaves (Quercus virginiana), green tea leaves (C. sinensis), pomegranate leaves (Punica granatum), and eucalyptus leaves (Eucalyptus globulus). Iron nanoparticles were synthesized using the green synthesis method with the above leaves. The adsorption capacity of the nanoparticles was determined by carrying out kinetic and adsorption isotherm studies. Eucalyptus leaf nanoparticles were determined to be having the highest arsenic adsorption capacity of 39.84 mg/g, followed by oaktree leaf nanoparticles of adsorption capacity 32.05 mg/g. This indicates that locally available and nonagricultural trees are better suited for green synthesis of iron nanoparticle for arsenic remediation compared to green tea, or back tea leaves. The experiments revealed that the adsorption kinetics followed the pseudo-second-order rate equation and that the Langmuir equation could best describe adsorption isotherm data. The nanoparticles were characterized using SEM coupled with EDS, XRD, BET surface area, and UV Spectroscopy. The SEM images indicated that the iron oxide nanoparticles had spherical morphology with particle diameter around 10-100 nm and were amorphous in structure. The elemental analysis done by Energy dispersive spectroscopy (EDS) showed their weight percentage of C, O, Fe, S to be 44.70%, 32.80%, 20.56%, and 0.65%, respectively.HighlightsIron nanoparticles were synthesized by five different leaf extracts of locally available plants with high reported antioxidant capacity.The five green-synthesized nanoparticles were characterized using EDS, XRD, FTIR, BET, and UV spectrometry.The adsorption behavior of the five nanoparticles was studied using kinetic and adsorption isotherm experiments.The best adsorbing nanoparticles were determined to be from oakleaf and eucalyptus leaf extracts, which are nonagricultural tree leaves, and can be obtained easily.The oak leaves of Quercus virginiana species were used for the first time for the synthesis of iron oxide nanoparticles and they showed promising results in the form of high adsorption capacity for the removal of As (V).

A modified Langmuir-Freundlich isotherm model for simulating pH-dependent adsorption effects.

Analytical isotherm equations such as Langmuir and Freundlich isotherms are widely used for modeling adsorption data. However, these isotherms are primarily useful for simulating data collected at a fixed pH value and cannot be easily adapted to simulate pH-dependent adsorption effects. Therefore, most adsorption studies currently use numerical surface-complexation models (SCMs), which are more complex and time consuming than traditional analytical isotherm models. In this work, we propose a new analytical isotherm model, identified as the modified Langmuir-Freundlich (MLF) isotherm, which can be used to simulate pH-dependent adsorption. The MLF isotherm uses a linear correlation between pH and affinity coefficient values. We validated the proposed MLF isotherm by predicting arsenic adsorption onto two different types of sorbents: pure goethite and goethite-coated sand. The MLF model gave good predictions for both experimental and surface complexation-model predicted datasets for these two sorbents. The proposed analytical isotherm framework can help reduce modeling complexity, model development time, and computational efforts. One of the limitations of the proposed method is that it is currently valid only for single-component systems. Furthermore, the model requires a system-specific pH. vs. affinity coefficient relation. Despite these limitations, the approach provides a promising analytical framework for simulating pH-dependent adsorption effects.

A modified batch reactor system to study equilibrium-reactive transport problems.

It is difficult to design column experiments to study transport processes involving slow geochemical reactions that require long residence times to reach equilibrium. We propose a sequential equilibration reactor (SER) setup to study such equilibrium geochemical reactive transport problems. The proposed system consists of sequentially operated batch reactors that directly mimic typical one-dimensional grids used in numerical reactive transport models. The SER experimental setup has the characteristics of batch experiments and provides complete control over the reaction time; in addition, the setup also includes certain simple transport features. We conducted several single-reactor and multiple-reactor SER experiments to investigate arsenic adsorption and transport on iron-oxide coated sand, at different pH, solid-solution ratio, and initial arsenic concentration conditions. The data generated from the experiments are compared against predictions from a geochemical transport code (PHREEQCI) that used previously developed surface complexation model parameters to describe the reaction system. The model predictions matched the SER experimental data well. The proposed SER system provides a flexible alternative to column experiments and allows better control over system parameters such as pH, reaction time, and solid-solution ratio.

Development of a scalable model for predicting arsenic transport coupled with oxidation and adsorption reactions.

Understanding the fundamentals of arsenic adsorption and oxidation reactions is critical for predicting its transport dynamics in groundwater systems. We completed batch experiments to study the interactions of arsenic with a common MnO2(s) mineral, pyrolusite. The reaction kinetics and adsorption isotherm developed from the batch experiments were integrated into a scalable reactive transport model to facilitate column-scale transport predictions. We then completed a set of column experiments to test the predictive capability of the reactive transport model. Our batch results indicated that the commonly used pseudo-first order kinetics for As(III) oxidation reaction neglects the scaling effects with respect to the MnO2(s) concentration. A second order kinetic equation that explicitly includes MnO2(s) concentration dependence is a more appropriate kinetic model to describe arsenic oxidation by MnO2(s) minerals. The arsenic adsorption reaction follows the Langmuir isotherm with the adsorption capacity of 0.053micromol of As(V)/g of MnO2(s) at the tested conditions. The knowledge gained from the batch experiments was used to develop a conceptual model for describing arsenic reactive transport at a column scale. The proposed conceptual model was integrated within a reactive transport code that accurately predicted the breakthrough profiles observed in multiple column experiments. The kinetic and adsorption process details obtained from the batch experiments were valuable data for scaling to predict the column-scale reactive transport of arsenic in MnO2(s)-containing sand columns.