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

Juan Crescenciano Cruz-Victoria, Alma Rosa Netzahuatl-Muñoz and Eliseo Cristiani-Urbina ()
Additional contact information
Juan Crescenciano Cruz-Victoria: Programa Académico de Ingeniería Mecatrónica, Universidad Politécnica de Tlaxcala, Avenida Universidad Politécnica No. 1, San Pedro Xalcaltzinco, Tepeyanco 90180, Tlaxcala, Mexico
Alma Rosa Netzahuatl-Muñoz: Programa Académico de Ingeniería en Biotecnología, Universidad Politécnica de Tlaxcala, Avenida Universidad Politécnica No. 1, San Pedro Xalcaltzinco, Tepeyanco 90180, Tlaxcala, Mexico
Eliseo Cristiani-Urbina: 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

Sustainability, 2024, vol. 16, issue 7, 1-25

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.

Keywords: LSTM; Bi-LSTM; chromium; SHAP; modeling (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/16/7/2874/pdf (application/pdf)
https://www.mdpi.com/2071-1050/16/7/2874/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:16:y:2024:i:7:p:2874-:d:1366845

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().