The Yoon-Nelson model serves as a widely used tool for describing the breakthrough behavior of organic micropollutants within fixed bed adsorbers. This study aims to augment its modeling efficacy through two proposed refinements found in the literature: a logarithmic transformation and the incorporation of steric hindrance effects. We systematically evaluated the original Yoon-Nelson model alongside the modified versions, using breakthrough data associated with micropollutant adsorption on solid materials. Three distinct cases were scrutinized: (1) caffeine adsorption on activated carbon; (2) tetracycline adsorption on hierarchical porous carbon; and (3) diclofenac adsorption on organoclay. While all three models demonstrated comparable performance with highly symmetric breakthrough data in case 1, their efficacy diverged significantly when confronted with strongly asymmetric breakthrough data in cases 2 and 3. The original Yoon-Nelson model and the logarithmically modified version fell short in accurately representing these intricate breakthrough curves. In contrast, the version incorporating steric hindrance effects showcased substantial accuracy, outperforming other models in capturing the complexities of asymmetric breakthrough data. This advancement markedly enhances the modeling accuracy and versatility of the Yoon-Nelson model, particularly in assessing the dynamic behavior of organic micropollutants within fixed bed adsorbers.
This critique examines a review article in this journal on adsorption techniques for removing metal ions from wastewater. The article is marred by several flaws, including tortured phrases, unsubstantiated quotes, incoherent statements, and factual inaccuracies. These problems weaken the article's clarity and reliability, raising doubts about the authors' understanding of the subject. As a result, the review's credibility is compromised, limiting its value as a reliable resource for researchers. This critique highlights these issues, stressing the importance of accuracy and rigor in scientific writing.
Addressing inaccuracies in review articles is essential to prevent the proliferation of misinformation. This communication is dedicated to rectifying factual errors identified in a recent review article featured in this journal, with a specific emphasis on addressing errors related to the Temkin, Flory-Huggins, Sips, and Baudu isotherm models. By elucidating and clarifying these inaccuracies, we aim to uphold the integrity of scientific discourse and ensure the accurate dissemination of information within the scholarly community.
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 degreesC), 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.
High performance sorbents for flue gas desulfurization can be synthesized by hydration of coal fly ash, calcium sulfate, and calcium oxide. In general, higher desulfurization activity correlates with higher sorbent surface area. Consequently, a major aim in sorbent synthesis is to maximize the sorbent surface area by optimizing the hydration conditions. This work presents an integrated modeling and optimization approach to sorbent synthesis based on statistical experimental design and two artificial intelligence techniques: neural network and genetic algorithm. In the first step of the approach, the main and interactive effects of three hydration variables on sorbent surface area were evaluated using a full factorial design. The hydration variables of interest to this study were hydration time, amount of coal fly ash, and amount of calcium sulfate and the levels investigated were 4-32 h, 5-15 g, and 0-12 g, respectively. In the second step, a neural network was used to model the relationship between the three hydration variables and the sorbent surface area. A genetic algorithm was used in the last step to optimize the input space of the resulting neural network model. According to this integrated modeling and optimization approach, an optimum sorbent surface area of 62.2m(2)g(-1) could be obtained by mixing 13.1g of coal fly ash and 5.5 g of calcium sulfate in a hydration process containing 100ml of water and 5 g of calcium oxide for a fixed hydration time of 10 h.
The name of Herbert Freundlich is commonly associated with a power relationship for adsorbed amount of a substance (Cads) against the concentration in solution (Csln), such that Cads = KCslnn; this isotherm (together with the Langmuir isotherm) is considered to be the model of choice for correlating the experimental adsorption data of micropollutants or contaminants of emerging concern (pesticides, pharmaceuticals, and personal care products), but it also concerns the adsorption of gases on solids. However, Freundlich's 1907 paper was a "sleeping beauty", which only started to attract significant citations from the early 2000s onward; moreover, these citations were too often wrong. In this paper, the main steps in the historical developments of Freundlich isotherm are identified, along with a discussion of several theoretical points: (1) derivation of the Freundlich isotherm from an exponential distribution of energies, leading to a more general equation, based on the Gauss hypergeometric function, of which the power Freundlich equation is an approximation; (2) application of this hypergeometric isotherm to the case of competitive adsorption, when the binding energies are perfectly correlated; and (3) new equations for estimating the Freundlich coefficient KF from physicochemical properties such as the sticking surface or probability. From new data treatment of two examples from the literature, the influence of several parameters is highlighted, and the application of linear free-energy relationships (LFER) to the Freundlich parameters for different series of compounds is evoked, along with its limitations. We also suggest some ideas that may be worth exploring in the future, such as extending the range of applications of the Freundlich isotherm by means of its hypergeometric version, extending the competitive adsorption isotherm in the case of partial correlation, and exploring the interest of the sticking surfaces or probabilities instead of KF for LFER analysis.
This correspondence critically examines and rectifies modeling deficiencies identified in a recent article published in this journal. Our analysis covers a range of models and issues, including the Temkin isotherm, the Flory-Huggins isotherm, the pseudo-first-order kinetic model, the pseudo-second-order kinetic model, the intraparticle diffusion model, the Elovich kinetic model, and the computation of thermodynamic parameters. The elucidation and correction of these modeling issues contribute to a more accurate and reliable understanding of the studied phenomena, thereby enhancing the scientific rigor of the subject paper.
Despite recent interest in transforming biomass into bio-oil and syngas, there is inadequate information on the compatibility of byproducts (e.g., biochar) with agriculture and water purification infrastructures. A pyrolysis at 300°C yields efficient production of biochar, and its physicochemical properties can be improved by chemical activation, resulting in a suitable adsorbent for the removal of natural organic matter (NOM), including hydrophobic and hydrophilic substances, such as humic acids (HA) and tannic acids (TA), respectively. In this study, the adsorption affinities of different HA and TA combinations in NOM solutions were evaluated, and higher adsorption affinity of TA onto activated biochar (AB) produced in the laboratory was observed due to its superior chemisorption tendencies and size-exclusion effects compared with that of HA, whereas hydrophobic interactions between adsorbent and adsorbate were deficient. Assessment of the AB role in an adsorption-coagulation hybrid system as nuclei for coagulation in the presence of aluminum sulfate (alum) showed a synergistic effect in a HA-dominated NOM solution. An AB-alum hybrid system with a high proportion of HA in the NOM solution may be applicable as an end-of-pipe solution.