Estimation of solid vapor pressures of pure compounds at different temperatures using a multilayer network with particle swarm algorithm

Lazzús, Juan A.

doi:10.1016/j.fluid.2009.12.001

Cited by 22 publications

(15 citation statements)

References 30 publications

(75 reference statements)

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“…PSO is a population-based optimization tool, where the system is initialized with a population of random particles and the algorithm searches for optima by updating generations [20]. In each iteration, the velocity of each particle j is calculated according to the following formula [21]:…”

Section: Hybrid Algorithmmentioning

confidence: 99%

“…s k j is the current position of the particle, ψ k j is the best one of the solutions that this particle has reached, and ψ g is the best solutions that all the particles have reached. In general, the value of each component in v can be clamped to the range [−v max , +v max ] control excessive roaming of particles outside the search space [20,21]. After calculating the velocity, the new position of each particle is:…”

Section: Hybrid Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Hybrid Neural Network-Particle Swarm Algorithm to Describe Chaotic Time Series

Lazzús¹,

Salfate²,

Montecinos³

2014

NNW

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This tutorial is based on modification of the professor nomination lecture presented two years ago in front of the Scientific Council of the Czech Technical University in Prague [16].It is devoted to the techniques for the models developing suitable for processes forecasting in complex systems. Because of the high sensitivity of the processes to the initial conditions and, consequently, due to our limited possibilities to forecast the processes for the long-term horizon, the attention is focused on the techniques leading to practical applications of the short term prediction models. The aim of this tutorial paper is to bring attention to possible difficulties which designers of the predicting models and their users meet and which have to be solved during the prediction model developing, validation, testing, and applications. The presented overview is not complete, it only reflects the author's experience with developing of the prediction models for practical tasks solving in banking, meteorology, air pollution and energy sector. The paper is completed by an example of the global solar radiation prediction which forms an important input for the electrical energy production forecast from renewable sources. The global solar radiation forecasting is based on numerical weather prediction models. The time-lagged ensemble technique for uncertainty quantification is demonstrated on a simple example.

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Section: Hybrid Algorithmmentioning

confidence: 99%

Section: Hybrid Algorithmmentioning

confidence: 99%

Hybrid Neural Network-Particle Swarm Algorithm to Describe Chaotic Time Series

Lazzús¹,

Salfate²,

Montecinos³

2014

NNW

Self Cite

View full text Add to dashboard Cite

show abstract

“…According to Ghaedi et al, the hybrid approach of combining an ANN with PSO can solve the problem of dye adsorption in aqueous solution [28]. According to Lazzus, the hybrid model of ANN and PSO for solid vapor pressure prediction at different temperatures is employed successfully [29]. A hybrid of ANN and PSO (ANN-PSO) was used for the prediction of pollutant removal from water samples [30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Process Conditions for Styrene Epoxidation Based on the Artificial Intelligence Method

Huang

et al. 2019

Chem Eng & Technol

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A novel prediction and optimization method based on improved generalized regression neural network (GRNN) and particle swarm optimization (PSO) algorithm is proposed to optimize the process conditions for styrene epoxidation to achieve higher yields. This model was designed to optimize the five input parameters reaction temperature and time as well as catalyst, solvent, and oxidant dosage. The output of the improved GRNN was given to the PSO algorithm to optimize the process conditions. The optimal smoothing parameter s of GRNN was chosen from the training sample with a minimum cross validation error. Under the five optimized process conditions the maximum yield reached 95.76 %. This innovative model of improved GRNN hybrid PSO algorithm proved to be a useful tool for optimization of process conditions for styrene epoxidation.

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“…For example, the ANN-GCM method has been used to estimate some properties of ionic liquids such as density (Lazzus, 2009), melting point (Lazzus, 2012a;Gharagheizi et al, 2012a;Aguirre et al, 2012), glass transition temperature (Lazzus, 2012b;Gharagheizi et al, 2012c), thermal decomposition temperature (Lazzus, 2012c;Gharagheizi et al, 2013c), heat capacity , viscosity (Gharagheizi et al, 2012b) and surface tension . This method has been also used to predict the flash point temperature of organic compounds (Lazzus, 2010b), the solid vapor pressure of pure compounds (Lazzus, 2010a), flammability limit temperature of organic compounds (Gharagheizi, 2009;Gharagheizi et al, 2013a), vaporization enthalpy of organic compounds (Gharagheizi et al, 2011) and sublimation enthalpy of organic compounds at 298 K (Gharagheizi et al, 2013b). In our previous works, we have used the ANN-GCM method to predict the densities of hydrocarbon systems (Moosavi and Soltani, 2013a), the density of liquid alkali metals and their mixtures (Sabzevari and Moosavi, 2014) and also to predict the specific volume of polymeric systems (Moosavi and Soltani, 2013b).…”

Section: Introductionmentioning

confidence: 99%