Modeling and optimization of a pharmaceutical crystallization process by using neural networks and genetic algorithms

Velásco-Mejía, A.; Vallejo-Becerra, Vanessa; Chávez-Ramírez, A.U.; Torres-Gonzaléz, J.; Reyes-Vidal, Y.; Castañeda, F.

doi:10.1016/j.powtec.2016.01.028

Cited by 58 publications

(41 citation statements)

References 26 publications

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“…The application of ANNs in the crystal growth received much attention in the last decade. Still the studies are rare [18,[23][24][25][26][27][28][29][30][31][32][33][34][35][36][37]. Only part of them were devoted to the crystal growth of semiconductors and oxides [18,[26][27][28][29][30][31][32][33]36,37].…”

Section: Ai Applications In Crystal Growth: State Of the Artmentioning

confidence: 99%

Application of Artificial Neural Networks in Crystal Growth of Electronic and Opto-Electronic Materials

Dropka

Holeňa

2020

Crystals

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In this review, we summarize the results concerning the application of artificial neural networks (ANNs) in the crystal growth of electronic and opto-electronic materials. The main reason for using ANNs is to detect the patterns and relationships in non-linear static and dynamic data sets which are common in crystal growth processes, all in a real time. The fast forecasting is particularly important for the process control, since common numerical simulations are slow and in situ measurements of key process parameters are not feasible. This important machine learning approach thus makes it possible to determine optimized parameters for high-quality up-scaled crystals in real time.

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Section: Ai Applications In Crystal Growth: State Of the Artmentioning

confidence: 99%

Application of Artificial Neural Networks in Crystal Growth of Electronic and Opto-Electronic Materials

Dropka

Holeňa

2020

Crystals

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“…Individual polarity ((5), (10), and (15)) and hydrophobicity ((6) and (16)) parameters for the solute and solvent separately yield inconclusive results in the sensitivity analysis. A potential improvement would be to construct parameters that describe these properties in a relative manner between the solvent and solute.…”

Section: Parameter Refinementmentioning

confidence: 99%

“…6 There have been a number of studies on the use of ANNs in the design and control of industrial crystallisers. [7][8][9][10][11][12][13] However, little has been reported on using this method for gaining understanding about the fundamental underlying mechanisms of crystal nucleation. In a study by Kumar,14 ANNs were used to predict the solution-solid interfacial energy for 57 different inorganic systems.…”

Section: Introductionmentioning

confidence: 99%

Rationalising crystal nucleation of organic molecules in solution using artificial neural networks

2019

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“…In recent years, ANN has become used widely for forecasting in various fields of research including finance, power generation, pharmaceutical, water and environmental resources (Li et al ., 2014, 2015; Qiu et al ., 2016; Velásco-Mejía et al ., 2016; Monteiro et al ., 2017). In agriculture, ANN is one of the main machine learning models which have been used widely (van Evert et al ., 2017).…”

Section: Introductionmentioning

confidence: 99%

Seed classification of three species of amaranth (Amaranthus spp.) using artificial neural network and canonical discriminant analysis

Bagheri

Eghbali²,

Haghighi³

2019

J. Agric. Sci.

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The current study was conducted in 2013 to identify the seeds of three species of Amaranthus, Amaranthus viridis L., Amaranthus retroflexus L. and Amaranthus albus L., by using the artificial neural network (ANN) and canonical discriminant analysis (CDA) methods. To begin with, photographs were taken of the seeds and 13 morphological characteristics of each seed extracted as predictor variables. Backward regression was used to find the most influential variables and seven variables were derived. Thus, predictor variables were divided into two sets of 13 and seven morphological characteristics. The results showed that the recognition accuracy of the ANN made using 13 and seven predictor variables was 81.1 and 80.3%, respectively. Meanwhile, recognition accuracy of the CDA using the seven and 13 predictor variables was 74.0 and 75.7%, respectively. Therefore, in comparison to CDA, ANN showed higher identification accuracy; however, the difference was not statistically significant. Identification accuracy for A. retroflexus was higher using the CDA method than ANN, while the ANN method had higher recognition accuracy for A. viridis than CDA. In addition, use of 13 predictor variables yielded a greater identification accuracy than seven. The results of the current study showed that using seed morphological characteristics extracted by computer vision could be effective for reliable identification of the similar seeds of Amaranthus species.

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Modeling and optimization of a pharmaceutical crystallization process by using neural networks and genetic algorithms

Cited by 58 publications

References 26 publications

Application of Artificial Neural Networks in Crystal Growth of Electronic and Opto-Electronic Materials

Application of Artificial Neural Networks in Crystal Growth of Electronic and Opto-Electronic Materials

Rationalising crystal nucleation of organic molecules in solution using artificial neural networks

Seed classification of three species of amaranth (Amaranthus spp.) using artificial neural network and canonical discriminant analysis

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