02412nas a2200289   4500008004100000022001400041245015200055210006900207260007900276300001200355490000700367520132100374653002401695653003401719653002401753653002101777653002401798100001801822700002401840700001701864700002701881700001501908700002301923700001901946700002601965856013101991       2016                        eng d  a2194-574800aPrediction of reactivity ratios in free radical copolymerization from monomer resonance-polarity (Q-e) parameters: genetic programming-based models0 aPrediction of reactivity ratios in free radical copolymerization  aGENTHINER STRASSE 13, D-10785 BERLIN, GERMANYbWALTER DE GRUYTER GMBHcFEB  a361-3720 v143 a<p>The principal deficiency of the widely utilized Alfrey-Price (AP) scheme for computing reactivity ratios in the widely used free radical copolymerization is that it ignores important factors, such as the steric effects. This often leads to inaccurate reactivity ratio predictions by AP model. Accordingly, in this study, exclusively data-driven, Q-e parameter-based new models have been developed for the reactivity ratio prediction in free radical copolymerization. In the model development, a novel artificial intelligence formalism known as ``genetic programming (GP)'' that performs symbolic regression has been employed. The GP-based models possess a different functional form than AP model. Further, parameters of GP-based models were fine-tuned using Levenberg-Marquardt (LM) nonlinear regression method. A comparison of AP, GP and GP-LM as well as artificial neural network (ANN)-based models indicates that GP and GP-LM models exhibit superior reactivity ratio prediction accuracy and generalization performance (with correlation coefficient magnitudes close to or greater than 0.9) when compared with AP and ANN models. The GPbased reactivity ratio prediction models developed here due to their higher accuracy and generalization capability have the potential of replacing the widely used AP models.</p>10aAlfrey-Price scheme10afree radical copolymerization10agenetic programming10areactivity ratio10asymbolic regression1 aShrinivas, K.1 aKulkarni, Rahul, P.1 aShaikh, Saif1 aGhorpade, Ravindra, V.1 aVyas, Renu1 aTambe, Sanjeev, S.1 aPonrathnam, S.1 aKulkarni, Bhaskar, D.  uhttps://library.ncl.res.in/content/prediction-reactivity-ratios-free-radical-copolymerization-monomer-resonance-polarity-q-e-1