Artificial neural network to predict the power number of agitated tanks fed by <scp>CFD</scp> simulations

Bibeau, Valérie; Barbeau, Lucka; Boffito, Daria C.; Blais, Bruno

doi:10.1002/cjce.24870

Cited by 7 publications

(3 citation statements)

References 23 publications

(46 reference statements)

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“…The input layer received all the feature variables, while the output layer was designed to predict the torque. In this model, each neuron computed a weighted sum of its inputs, added a bias, and then applied an activation function to the result [25,38] During training, the backpropagation algorithm was employed to compute the gradient of the loss function with respect to each weight by the chain rule, propagating the error backward through the network [39]. The mean squared error (MSE) was used as a loss function to guide the optimization.…”

Section: Regression Modelsmentioning

confidence: 99%

“…The use of artificial neural networks has also proven effective in predicting the master power curve. Bibeau et al [25] implemented the cross-validation method within their ANN model to mitigate overfitting issues and to predict the power curve of a single-phase mixing tank. Additionally, artificial intelligence methods have been instrumental in enhancing the performance and efficiency of mixing systems.…”

Section: Introductionmentioning

confidence: 99%

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Application of Machine Learning Models in Coaxial Bioreactors: Classification and Torque Prediction

Rahimzadeh,

Ranjbarrad,

Ein-Mozaffari

et al. 2024

ChemEngineering

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Coaxial bioreactors are known for effectively dispersing gas inside non-Newtonian fluids. However, due to their design complexity, many aspects of their design and function, including the relationship between hydrodynamics and bioreactor efficiency, remain unexplored. Nowadays, various numerical models, such as computational fluid dynamics (CFD) and artificial intelligence models, provide exceptional opportunities to investigate the performance of coaxial bioreactors. For the first time, this study applied various machine learning models, both classifiers and regressors, to predict the torque generated by a coaxial bioreactor. In this regard, 500 CFD simulations at different aeration rates, central impeller speeds, anchor impeller speeds, and rotating modes were conducted. The results obtained from the CFD simulations were used to train and test the machine learning models. Careful feature scaling and k-fold cross-validation were performed to enhance all models’ performance and prevent overfitting. A key finding of the study was the importance of selecting the right features for the model. It turns out that just by knowing the speed of the central impeller and the torque generated by the coaxial bioreactor, the rotating mode can be labelled with perfect accuracy using k-nearest neighbors (kNN) or support vector machine models. Moreover, regression models, including multi-layer perceptron, kNN, and random forest, were examined to predict the torque of the coaxial impellers. The results showed that the random forest model outperformed all other models. Finally, the feature importance analysis indicated that the rotating mode was the most significant parameter in determining the torque value.

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Section: Regression Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning Models in Coaxial Bioreactors: Classification and Torque Prediction

Rahimzadeh,

Ranjbarrad,

Ein-Mozaffari

et al. 2024

ChemEngineering

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“…The ANN finds a more general correlation for the power consumption of mixers than the correlations found in the literature. [ 4 ]…”

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confidence: 99%

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2023

Can J Chem Eng

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Development of new correlation for the prediction of power number for closed clearance impellers using machine learning methods trained on literature data

Joshi,

Dalvi,

Vitankar

et al. 2024

Can J Chem Eng

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The accurate estimation of the power number for closed clearance impellers holds significant importance in industries such as chemical, biochemical, paper and pulp, as well as paints, pigments, and polymers. Existing state‐of‐the‐art correlations for predicting power numbers, however, are inaccurate for impeller Reynolds number . In this study, we compiled a dataset of 1470 data points from 15 research articles in the open literature, covering five types of impellers: (i) anchor; (ii) gate; (iii) single helical ribbon; (iv) double helical ribbon; and (v) helical ribbon with screw. Six machine learning models, namely artificial neural networks (ANN), CatBoost regressor, extra tree regressor, support vector regressor, random forest, and XGBoost regressor, were developed and compared. The results revealed that ANN emerged as the most efficient model, demonstrating the highest testing R2 value of 0.99 and the lowest testing MAPE of 7.3%. Further, we used the ANN model to develop a novel set of process correlations to estimate impeller power numbers for the industrially important anchor and double helical ribbon impellers: which significantly outperform the existing state‐of‐the‐art correlations available in literature.

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Artificial neural network to predict the power number of agitated tanks fed by CFD simulations

Cited by 7 publications

References 23 publications

Application of Machine Learning Models in Coaxial Bioreactors: Classification and Torque Prediction

Application of Machine Learning Models in Coaxial Bioreactors: Classification and Torque Prediction

Issue Highlights

Development of new correlation for the prediction of power number for closed clearance impellers using machine learning methods trained on literature data

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