An experimental study on on-line optimizing control of free radical bulk polymerization in a rheometer–reactor assembly under conditions of power failure

Modeling gel effect in branched polymer systems: Free‐radical solution homopolymerization of vinyl acetate

“…Autoacceleration phenomena, such as the gel effect in polymerization reactors, have been the subject of numerous studies 21–60. These studies could be classified as deterministic models or stochastic models based on comprehensive modeling techniques and recently reviewed in Ref.…”

Isothermal and non-isothermal polymerization of methyl methacrylate in presence of multiple initiators

“…Some models attempt to correlate kinetic constants [8,34,35], or transport properties of polymer and monomer [36][37][38] with conversion, temperature, free volume/chain end mobility [6,[37][38][39][40][41], or to view chain growth as a statistical phenomenon leading to different states based on some relevant kinetic parameters, or on diffusion/reptation theories [42][43][44][45][46][47][48]. More recently, attention was paid to non-isothermal and semibatch conditions, which are extremely important in the industrial practice [49][50][51][52][53][54]. Some models consider the presence of different initiators or mixture of initiators, and other models take into account non-isothermal conditions [25,45,[55][56][57][58][59][60].…”

A hybrid differential evolution algorithm approach towards assisted history matching and uncertainty quantification for reservoir models

“…Gradients based methods showed less performance in history matching because of their tendency to get trapped in local minima. In stochastic methods, many authors have tried different algorithms like simulated annealing (SA) (Sultan et al, 1994), neighbourhood algorithm (NA) (Subbey et al, 2003), genetic algorithms (GA) (Castellini et al, 2005;Sangwai et al, 2007), scatter search (SS) (Sousa et al, 2006;Erbas and Christie, 2007), Markov chain Monte Carlo (McMC) (Maucec et al, 2007), particle swarm optimization (PSO) (Mohamed et al, 2009), ant colony optimization (Hajizadeh et al, 2011), differential evolution (DE) (Hajizadeh et al, 2010). It is observed that the differential evolution algorithm has shown good results for the history matching but the performance of the algorithm was very much sensitive to the value of control parameters such as crossover rate.…”