Reaction kinetics and modeling of photoinitiated cationic polymerization of an alicyclic based diglycidyl ether - Harikrishna, Reghunathan; Ponrathnam, S.; Tambe, S. S. (Nuclear Instruments & Methods in Physics Research Section B-Beam Interactions with Materials and Atoms, 2014)
Photoinitiated cationic polymerization of cycloaliphatic diepoxides had received tremendous attention, while studies with lesser polymerizable diglycidyl ethers are comparatively less reported. The present work deals with the photoinitiated cationic polymerization of cyclohexane dimethanol diglycidyl ether followed by estimation of kinetic parameters. The effects of concentration of photoinitiator and temperature on curing performance were studied using photo differential scanning calorimeter or photo DSC with polychromatic radiation. It was observed that the rate of polymerization as well as ultimate conversion increased with increasing concentration of photoinitiator and temperature. The influences of gelation as well as diffusional restrictions have remarkable effect on cure performance. The kinetic parameters as per autocatalytic kinetic model were studied by Levenberg-Marquardt nonlinear regression method instead of conventional linear method for obtaining more accurate values of apparent rate constant. It was observed that the model fits with data from initial stages to almost towards the end of the reaction. The activation energy was found to be higher than the values reported for more reactive cycloaliphatic diepoxides. The value of pre-exponential factor increased with increase in activation energy showing influence of gelation at early stages of reaction. (C) 2013 Elsevier B.V. All rights reserved.
Keywords: Autocatalytic model; kinetics; Levenberg-Marquardt method; Photopolymerization

Removal of arsenite [As(III)] and arsenate [As(V)] ions from wastewater using TFA and TAFA resins: computational intelligence based reaction modeling and optimization - Patil-Shinde, V.; Mulani, K. B.; Donde, K.; Chavan, N. N.; Ponrathnam, S.; Tambe, S. S. (Journal of environmental chemical engineering, 2016)
Being significantly toxic, removal of arsenic forms an important part of the drinking- and waste-water treatment. Tannin is a polyphenol-rich substrate that efficiently and adsorptively binds to the multivalent metal ions. In this study, tannin-formaldehyde (TFA) and tannin-aniline-formaldehyde (TAFA) resins were synthesized and employed successfully for an adsorptive removal of arsenite [As(III)] and arsenate [As(V)] ions from the contaminated water. Next, a computational intelligence (CI) based hybrid strategy was used to model and optimize the resin-based adsorption of As(III) and As(V) ions for securing optimal reaction conditions. This strategy first uses an exclusively reaction data driven modeling strategy, namely, genetic programming (GP) to predict the extent (%) of As(III)/As(V) adsorbed on TFA and TAFA resins. Next, the input space of the GP-based models consisting of the reaction condition variables/parameters was optimized using genetic algorithm (GA) method; the objective of this optimization was to maximize the adsorption of As(III) and As(V) ions on the two resins. Finally, the sets of optimal reaction conditions provided by GP-GA hybrid method were verified experimentally the results of which indicate that the optimized conditions have lead to 0.3% and 1.3% increase in the adsorption of As(III) and As(V) ions on TFA resin. More significantly, the optimized conditions have increased the adsorption of As(III) and As(V) on TAFA resin by 3.02% and 12.77%, respectively. The GP-GA based strategy introduced here can be gainfully utilized for modeling and optimization of similar type of contaminant-removal processes.