Showing papers by "Jyeshtharaj B. Joshi published in 2023"

Analysis of Reaction Kinetics of the Ozonation of Phenolic Compounds and Assessment of the Role of Mass Transfer in the Overall Rate

Abstract: The ozonation technique for biorefractory phenolic wastewater is preferred as a clean technology. However, its effectiveness and successful implementation on the commercial scale is crucial. The understanding of the rate-determining step and reliable reaction kinetics is the essence to the design and scale-up of ozonation reactors. In the case of phenolic wastewater, it is found that both mass transfer and chemical reaction contribute to the overall reaction rates. However, the mass transfer resistance is often significant in studies that determine the ozonation rate constant. This has resulted in discrepancies in the reported values of the intrinsic reaction kinetic parameters, and such apparent rate constants cannot be useful for suitable reactor design. The aim of this work is to analyze past works on the reaction kinetics of the ozonation of phenol, chlorophenols, and nitrophenols and determine the role of mass transfer steps in the overall reaction rate. The value of the volumetric liquid-side mass transfer coefficient (kLa) is estimated from the overall observed rates using the theory of mass transfer with chemical reaction, and these are found to be appropriate for the reactor geometry used in the respective works. In this way, our appraisal of the literature has provided key insights, which are yet unfamiliar, and renders useful the large body of data published in the literature, which otherwise remains unutilized.

Novel Correlation for Critical Speed for Solid Suspension in Stirred Tanks Developed Using Machine Learning Models Trained on Literature Data

Abstract: Critical speed for solid suspension (Njs) in stirred tanks is an important design parameter in several chemical processes. There is a need to develop a generalized correlation that applies to a broad range of literature data. Also, the literature lacks a comparative study of different machine learning (ML) models for the prediction of Njs. In this paper, 3240 data points have been extracted from 35 papers on solid suspension and Njs has been modeled as the dependent variable, initially using ML models. Three ML models (random forest regression (RF), CatBoost regression (CatB), and artificial neural network regression (ANN)) are compared. The CatB model was the most effective, resulting in R2 = 0.99 for the testing dataset, better than any empirical correlation published. Further, we have successfully used the CatB model to obtain the functional form of the behavior of various parameters. We then developed novel correlations (in closed form and with parametric uncertainty) for HE-3, PTD, and DT impellers with R2 = 0.89, 0.84, and 0.86, respectively, where the correlation constants were tuned using experimental data published in the literature. Our closed-form correlations significantly outperform earlier correlations published in the literature. Such a methodology has not been reported in the literature we have surveyed, and we believe that it is novel and original.