K. H. Chu

Improved fixed bed models for metal biosorption

This paper describes the application of two new mathematical models, derived from an existing model with two adjustable parameters, to simulate the breakthrough curves of metal biosorption in fixed bed columns. No new adjustable parameters are introduced into the modified models. The models offer a fast and accurate alternative to the conventional mass balance-based models which are much more complicated mathematically. The major characteristic of this empirical modeling approach is that it requires experimental breakthrough data for model calibration. Modeling results suggest that the new models are capable of describing symmetric and asymmetric experimental breakthrough curves selected from the biosorption literature. Compared to the original model, the modified models show significant improvements for modeling breakthrough curves obtained with columns packed with native biomass but yield only small improvements on deviations from breakthrough data obtained with a column packed with immobilized biomass.

Removal of Arsenate from Aqueous Solution by Adsorption onto Titanium Dioxide Nanoparticles

Titanium dioxide (TiO2) was investigated for the removal of pentavalent arsenate from aqueous solution. Kinetic results revealed that arsenate adsorption was almost instantaneous. The extent of arsenate adsorption decreased with increasing pH owing to the decrease of positively charged binding sites on the TiO2 surface. Adsorption isotherms measured at pH 3 and 7 generally followed the Langmuir model. The maximum uptake capacity ranged from 8 mg g−1 at pH 3 to 2.7 mg g−1 at pH 7. Addition of phosphate resulted in a significant reduction in arsenate adsorption, indicating that phosphate—a molecular analogue of arsenate—competes with arsenate for the same surface binding sites. By contrast, bicarbonate had little effect on arsenate adsorption, whereas sulfate exhibited a moderate suppression effect. A considerable reduction in arsenate adsorption was also observed in the presence of relatively high concentrations of background electrolytes (>50 mmol L−1).

Quantitative Analysis of Copper Biosorption by the Microalga Chlorella vulgaris

In this study we have investigated the kinetics of copper removal by inactivated biomass of Chlorella vulgaris, a green microalga, in batch systems. A dual resistance rate model incorporating intrinsic adsorption kinetics and film diffusion was used to assess the relative importance of the two rate processes under varying experimental conditions. Intraparticle diffusion was not accounted for in this model since metal uptake by nonliving algal biomass is a passive nonmetabolically mediated process and is, therefore, a surface binding phenomenon. Modeling results have found that film diffusion appears to be the ratelimiting step at low initial metal concentrations. On the other hand, both the intrinsic adsorption kinetics and film diffusion are likely to control the overall rate of adsorption at high initial metal concentrations. The model described in this study can thus be used for predicting if and under which conditions the metal adsorption process could sufficiently be described by single resistance mo...

Predictive modeling of competitive biosorption equilibrium data

This paper compares regression and neural network modeling approaches to predict competitive biosorption equilibrium data. The regression approach is based on the fitting of modified Langmuir-type isotherm models to experimental data. Neural networks, on the other hand, are non-parametric statistical estimators capable of identifying patterns in data and correlations between input and output. Our results show that the neural network approach outperforms traditional regression-based modeling in correlating and predicting the simultaneous uptake of copper and cadmium by a microbial biosorbent. The neural network is capable of accurately predicting unseen data when provided with limited amounts of data for training. Because neural networks are purely data-driven models, they are more suitable for obtaining accurate predictions than for probing the physical nature of the biosorption process.

Removal of aqueous pentachlorophenol by horseradish peroxidase in the presence of surfactants

An important issue in the oxidation of pentachlorophenol (PCP) by the enzyme horseradish peroxidase (HRP) is enzyme inactivation during the reaction. This study was initiated to investigate the ability of two nonionic surfactants (Tween 20 and Tween 80) to mitigate HRP inactivation. The surfactants were tested at concentrations below and above their critical micelle concentrations (CMCs). Enhancement of PCP oxidation was observed at sub-CMCs, indicating effective protection of HRP by the two surfactants. Maximum levels of PCP removal were observed when the concentrations of Tween 20 and Tween 80 were 40 and 50% of the CMCs, respectively. At supra-CMCs, both surfactants caused a noticeable reduction in the extent of PCP removal.

Studies on fixed bed biosorption and elution of copper using polyvinyl alcohol-immobilized seaweed biomass

ABSTRACT Biosorption of copper by inactivated biomass of the brown marine alga Sargassum baccularia immobilized in polyvinyl alcohol (PVA) beads was investigated. PVA-immobilized biomass beads were packed in a laboratory-scale fixed-bed column and subjected to three consecutive cycles of copper loading and elution. Bound copper was eluted with solutions containing a range of ethylenediaminetetraacetic acid (EDTA) concentrations. Up to 100% of the bound copper was consistently recovered from immobilized biomass using an aqueous solution containing 4 mM EDTA in repeated loading/elution cycles. The PVA-immobilized biomass beads were shown to be robust and stable with little decrease in the copper uptake capacity under dynamic flow conditions. The excellent reusability of the new biosorbent could lead to the development of a viable metal removal technology.

Protein adsorption on Ion exchange resin: Estimation of equilibrium isotherm parameters from batch kinetic data

The simple Langmuir isotherm is frequently employed to describe the equilibrium behavior of protein adsorption on a wide variety of adsorbents. The two adjustable parameters of the Langmuir isotherm—the saturation capacity, orqm, and the dissociation constant,Kd—are usually estimated by fitting the isotherm equation to the equilibrium data acquired from batch equilibration experiments. In this study, we have evaluated the possibility of estimatingqm andKd for the adsorption of bovine serum albumin to a cation exchanger using batch kinetic data. A rate model predicated on the kinetic form of the Langmuir isotherm, with three adjustable parameters (qm,Kd, and a rate constant), was fitted to a single kinetic profile. The value ofqm determined as the result of this approach was quantitatively consistent with theqm value derived from the traditional batch equilibrium data. However, theKd value could not be retrieved from the kinetic profile, as the model fit proved insensitive to this parameter. Sensitivity analysis provided significant insight into the identifiability of the three model parameters.

Neural Network Modeling of the Kinetics of SO2 Removal by Fly Ash-Based Sorbent

The mechanistic modeling of the sulfation reaction between fly ash-based sorbent and SO2 is a challenging task due to a variety reasons including the complexity of the reaction itself and the inability to measure some of the key parameters of the reaction. In this work, the possibility of modeling the sulfation reaction kinetics using a purely data-driven neural network was investigated. Experiments on SO2 removal by a sorbent prepared from coal fly ash/CaO/CaSO4 were conducted using a fixed bed reactor to generate a database to train and validate the neural network model. Extensive SO2 removal data points were obtained by varying three process variables, namely, SO2 inlet concentration (500–2000 mg/L), reaction temperature (60–80°C), and relative humidity (50–70%), as a function of reaction time (0–60 min). Modeling results show that the neural network can provide excellent fits to the SO2 removal data after considerable training and can be successfully used to predict the extent of SO2 removal as a function of time even when the process variables are outside the training domain. From a modeling standpoint, the suitably trained and validated neural network with excellent interpolation and extrapolation properties could have immediate practical benefits in the absence of a theoretical model.