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1
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Direct and inverse neural network modeling in free radical polymerization

100%
Curteanu S.
Open Chemistry
|
2004
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vol. 2
|
issue 1
113-140
EN
The first part of this paper reviews of the most important aspects regarding the use of neural networks in the polymerization reaction engineering. Then, direct and inverse neural network modeling of the batch, bulk free radical polymerization of methyl methacrylate is performed. To obtain monomer conversion, number and weight average molecular weights, and mass reaction viscosity, separate neural networks and, a network with multiple outputs were built (direct neural network modeling). The inverse neural network modeling gives the reaction conditions (temperature and initial initiator concentration) that assure certain values of conversion and polymerization degree at the end of the reaction. Each network is a multi-layer perceptron with one or two hidden layers and a different number of hidden neurons. The best topology correlates with the smallest error at the end of the training phase. The possibility of obtaining accurate results is demonstrated with a relatively simple architecture of the networks. Two types of neural network modeling, direct and inverse, represent possible alternatives to classical procedures of modeling and optimization, each producing accurate results and having simple methodologies.
2
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Modeling and simulation of free radical polymerization of styrene under semibatch reactor conditions

100%
Curteanu S.
|
EN
The first part of this approach is concerned with the elaboration of a radical polymerization model of styrenne, based on a kinetic diagram that includes chemical and thermal initiation, propagation, termination by recombination and chain transfer to the monomer. Furthermore, volume contraction during polymerization is considered, as well as the gel and glass effects. The mathematical formalism that describes the model in terms of moments is explored in detail. The model was then used to predict the changes in monomer conversion and molecular weight after intermediate addition of initiator and monomer. The results of this operation are dependent on the conditions of the reaction mass, quantity, and moment of substance addition. Therefore, the simulations were performed at different times with respect to the gel effect; before, during and after this phenomenon, and also with respect to different temperatures and initiators. Increasing the initiator concentration before the gel effect leads to an earlier appearance of the phenomenon and to a decrease in molecular weight. The ratio $$\bar M_w /\bar M_n $$ reveals a polydispersity index smaller for the intermediate addition of initiator. No significant changes take place during or after the gel effect. If along with the initiator, unreacted monomver (used to dissolve the initiator) enters the reactor, a small dip in conversion is observed. The general conclusion of this paper reveals the intermediate addition of initiator as a method to control polymer properties and to prevent the “dead-end” polymerization of styrene.
3
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Instance-based regression with missing data applied to a photocatalytic oxidation process

51%
Leon F., Piuleac C., Curteanu S., Poulios I.
Open Chemistry
|
2012
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vol. 10
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issue 4
1149-1156
EN
In this paper, a modified nearest-neighbor regression method (kNN) is proposed to model a process with incomplete information of the measurements. This technique is based on the variation of the coefficients used to weight the distances of the instances. The case study selected for testing this algorithm was the photocatalytic degradation of Reactive Red 184 (RR184), a dye belonging to the group of azo compounds, which is widely used in manufacturing paint paper, leather and fabrics. The process is conducted with TiO2 as catalyst (an inexpensive semiconductor material, completely inert chemically and biologically), in the presence of H2O2 (with the role of increasing the rate of photo-oxidation), at different pH values. The final concentration of RR184 is predicted accurately with the modified kNN regression method developed in this article. A comparison with other machine learning methods (sequential minimal optimization regression, decision table, reduced error pruning tree, M5 pruned model tree) proves the superiority and efficiency of the proposed algorithm, not only for its results, but for its simplicity and flexibility in manipulating incomplete experimental data. [...]
4
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NSGA-II-RJG applied to multi-objective optimization of polymeric nanoparticles synthesis with silicone surfactants

51%
Furtuna R., Curteanu S., Racles C.
Open Chemistry
|
2011
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vol. 9
|
issue 6
1080-1095
EN
Polydimethylsiloxane nanoparticles were obtained by nanoprecipitation, using a siloxane surfactant as stabilizer. Two neural networks and a genetic algorithm were used to optimize this process, by minimizing the particle diameter and the polydispersity, finding in this way the optimum values for surfactant and polymer concentrations, and storage temperature. In order to improve the performance of the non-dominated sorting genetic algorithm, NSGA-II, a genetic operator was introduced in this study - the transposition operator - “real jumping genes”, resulting NSGA-II-RJG. It was implemented in original software and was applied to the multi-objective optimization of the polymeric nanoparticles synthesis with silicone surfactants. The multi-objective function of the algorithm included two fitness functions. One fitness function was calculated with a neural network modelling the variation of the particle diameter on the surfactant concentration, polymer concentration, and storage temperature, and the other was computed by a neural network modelling the dependence of polydispersity index on surfactant and polymer concentrations. The performance of the software program that implemented NSGA-II-RJG was highlighted by comparing it with the software implementation of NSGA-II. The results obtained from simulations showed that NSGA-II-RJG is able to find non-dominated solutions with a greater diversity and a faster convergence time than NSGA-II. [...]
5
Content available remote

Fuzzy modeling applied to optical and surface properties of a ferrocenylsiloxane polyamide solution

51%
Pislaru M., Curteanu S., Cazacu M.
Open Chemistry
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2012
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vol. 10
|
issue 1
194-204
EN
A fuzzy model was designed to predict changes in surface tension and maximum absorbance due to self-assembly in a DMF solution of poly{1,1′-ferrocene-diamide-[1,3-bis(propylene) tetramethyl-disiloxane} as a function of temperature and concentration. The building of fuzzy rule-based inference systems appears as a grey-box because it allows interpretation of the knowledge contained in the model as well as its improvement with a-priori knowledge. The method provides accurate results and increases the efficiency of utilizing the available information in the model. Small mean squared errors (0.0064 for absorbance and 0.79 for surface tension) and strong correlations between experiment and simulated results (0.93 and 0.97, respectively) were found during model validation. The results showed that it is feasible to apply a Mamdani fuzzy inference system to the estimation of optical and surface properties of a ferrocenylsiloxane polyamide solution.
6
Content available remote

Optimization methodology based on neural networks and genetic algorithms applied to electro-coagulation processes

45%
Piuleac C., Curteanu S., Rodrigo M., Sáez C., Fernández F.
Open Chemistry
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2013
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vol. 11
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issue 7
1213-1224
EN
An optimization methodology based on neural networks and genetic algorithms was developed and used to optimize a real world process - an electro-coagulation process involving three pollutants at different concentrations: kaolin (250–1000 mg L−1), Eriochrome Black T solutions (50–200 mg L−1), and oil/water emulsion (1500–4500 mg L−1). Feed-forward neural networks using heterogeneous combination of transfer functions were developed, leading to good results in the validation stage (relative error about 8%). The parameters of the process (concentration of pollutant, time, pH0, conductivity and current density) were optimized handling the genetic algorithm parameters, in order to obtain a maximum removal efficiency for each pollutant. Therefore, the optimization methodology combines neural networks as modeling tools with genetic algorithms as solving method. Validation of the optimization results using supplementary experimental data reveals errors under 11%. [...]
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