Proceedings ArticleDOI
Multiobjective intelligence optimal operation of PET polymerization
Liulin Cao,Jing Wang,Pei Jiang,Qibing Jin +3 more
- pp 336-340
TLDR
The simulation result indicates that the hybrid network model and model-based multiobjective optimal algorithm are effective in polymerizing of PET with maximum yield and the best quality.Abstract:
A multiobjective intelligence optimal approach in polymerizing of PET with maximum yield and the best quality is proposed. The hybrid neural network based on B-spline and diagonal recursive neural network is used to model the PET process qualities, i.e. the Intrinsic Viscosity and Molecular Weight distribution. Then a hybrid NSGAII-PSO optimal algorithm with penalty functions is applied to solve the multiobjective optimal problem in order to get the best operation conditions. The simulation result indicates that the hybrid network model and model-based multiobjective optimal algorithm are effective.read more
Citations
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Proceedings Article
The multi-objective optimization of esterification process based on improved NSGA-III algorithm
TL;DR: Experimental results indicate that PNSGA- III outperforms NSGA-III in terms of IGD metric and HV metric, and reaches a better diversity in the esterification problem.
Journal ArticleDOI
Aplicação de redes Neuro Fuzzy ao processamento de peças automotivas por meio de injeção de polímeros
Carlos Affonso,Renato José Sassi +1 more
TL;DR: The purpose of this paper was to use a multilayer perceptron artificial neural network and a radial basis function artificial Neural Network combined with fuzzy sets to produce an inference mechanism that could predict injection mold cycle times and confirmed neurofuzzy networks as an effective alternative to solving such problems.
References
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A fast and elitist multiobjective genetic algorithm: NSGA-II
TL;DR: This paper suggests a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties, and modify the definition of dominance in order to solve constrained multi-objective problems efficiently.
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Particle swarm optimization
TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
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On the Mathematical Modeling of Polymerization Reactors
TL;DR: The mathematical model, which can represent the detailed behavior of a polymer reactor, is an invaluable tool for developing the optimal design and optimal control system for these reactors.
Proceedings ArticleDOI
Viscosity Prediction for PET Process Based on Hybrid Neural Networks
TL;DR: The results indicated that both parallel and serial hybrid neural networks can model for complicated systems well and transfer the solution of nonlinear control strategy into solving for linear systems based on the decomposed models.
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