Long Short-Term Memory and Bidirectional Long Short-Term Memory Modeling and Prediction of Hexavalent and Total Chromium Removal Capacity Kinetics of Cupressus lusitanica Bark
[ad_1]
This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Open AccessArticle
1
Programa Académico de Ingeniería Mecatrónica, Universidad Politécnica de Tlaxcala, Avenida Universidad Politécnica No. 1, San Pedro Xalcaltzinco, Tepeyanco 90180, Tlaxcala, Mexico
2
Programa Académico de Ingeniería en Biotecnología, Universidad Politécnica de Tlaxcala, Avenida Universidad Politécnica No. 1, San Pedro Xalcaltzinco, Tepeyanco 90180, Tlaxcala, Mexico
3
Departamento de Ingeniería Bioquímica, Escuela Nacional de Ciencias Biológicas, Instituto Politécnico Nacional, Unidad Profesional Adolfo López Mateos, Avenida Wilfrido Massieu s/n, Delegación Gustavo A. Madero, Ciudad de México 07738, Mexico
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(7), 2874; https://doi.org/10.3390/su16072874 (registering DOI)
Submission received: 8 February 2024
/
Revised: 27 March 2024
/
Accepted: 28 March 2024
/
Published: 29 March 2024
Abstract
Hexavalent chromium [Cr(VI)] is a high-priority environmental pollutant because of its toxicity and potential to contaminate water sources. Biosorption, using low-cost biomaterials, is an emerging technology for removing pollutants from water. In this study, Long Short-Term Memory (LSTM) and bidirectional LSTM (Bi-LSTM) neural networks were used to model and predict the kinetics of the removal capacity of Cr(VI) and total chromium [Cr(T)] using Cupressus lusitanica bark (CLB) particles. The models were developed using 34 experimental kinetics datasets under various temperature, pH, particle size, and initial Cr(VI) concentration conditions. Data preprocessing via interpolation was implemented to augment the sparse time-series data. Early stopping regularization prevented overfitting, and dropout techniques enhanced model robustness. The Bi-LSTM models demonstrated a superior performance compared to the LSTM models. The inherent complexities of the process and data limitations resulted in a heavy-tailed and left-skewed residual distribution, indicating occasional deviations in the predictions of capacities obtained under extreme conditions. K-fold cross-validation demonstrated the stability of Bi-LSTM models 38 and 43, while response surfaces and validation with unseen datasets assessed their predictive accuracy and generalization capabilities. Shapley additive explanations analysis (SHAP) identified the initial Cr(VI) concentration and time as the most influential input features for the models. This study highlights the capabilities of deep recurrent neural networks in comprehending and predicting complex pollutant removal kinetic phenomena for environmental applications.
Share and Cite
MDPI and ACS Style
Cruz-Victoria, J.C.; Netzahuatl-Muñoz, A.R.; Cristiani-Urbina, E.
Long Short-Term Memory and Bidirectional Long Short-Term Memory Modeling and Prediction of Hexavalent and Total Chromium Removal Capacity Kinetics of Cupressus lusitanica Bark. Sustainability 2024, 16, 2874.
https://doi.org/10.3390/su16072874
Cruz-Victoria JC, Netzahuatl-Muñoz AR, Cristiani-Urbina E.
Long Short-Term Memory and Bidirectional Long Short-Term Memory Modeling and Prediction of Hexavalent and Total Chromium Removal Capacity Kinetics of Cupressus lusitanica Bark. Sustainability. 2024; 16(7):2874.
https://doi.org/10.3390/su16072874
Chicago/Turabian Style
Cruz-Victoria, Juan Crescenciano, Alma Rosa Netzahuatl-Muñoz, and Eliseo Cristiani-Urbina.
2024. “Long Short-Term Memory and Bidirectional Long Short-Term Memory Modeling and Prediction of Hexavalent and Total Chromium Removal Capacity Kinetics of Cupressus lusitanica Bark” Sustainability 16, no. 7: 2874.
https://doi.org/10.3390/su16072874
Article Metrics
Article metric data becomes available approximately 24 hours after publication online.
[ad_2]