Statistical modeling and optimization of the bleachability of regenerated spent bleaching earth using response surface methodology and artificial neural networks with genetic algorithm

Almoruf O. F. Williams; Oluwaseun D. Akanbi

doi:10.1515/cppm-2022-0031

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Statistical modeling and optimization of the bleachability of regenerated spent bleaching earth using response surface methodology and artificial neural networks with genetic algorithm

Almoruf O. F. Williams and Oluwaseun D. Akanbi

Published/Copyright: November 14, 2022

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From the journal Chemical Product and Process Modeling Volume 18 Issue 3

Abstract

In this study, the statistical modeling and optimization of the regeneration of spent bleaching earth (SBE) for re-use in the bleaching of crude palm oil (CPO) oil was examined. Having a good model will assist with the successful optimal regeneration of SBE and hence minimize the environmental pollution associated with its current disposal method which is based on dumping as landfills. The SBE samples were de-oiled with the Soxhlet extraction method, using n-hexane for 1 h at 60 °C; treated at temperatures ranging from 300–500 °C; at carbonization time between 30 and 45 min; and with hydrochloric acid concentrations between 1 and 2 M, at a constant stirring time of 30 min, respectively. The operating conditions for the experiment were according to the Central Composite Design (CCD) experimental design using the Design Expert software version 13. The modeling and optimization of the SBE regeneration process was carried out with the Response Surface Methodology (RSM) and Artificial Neural Network (ANN) techniques. Five regression models were developed from the RSM approach and the best one selected based on model selection parameters recommended in the literature. Similarly, ten ANN models with the number of neurons in the hidden layer that varied from 2 to 16 were considered and the best one selected using the mean square error (MSE) and correlation coefficients (R) for the training, validation and testing performances. Results showed that the ANN technique led to a model with a better predictive ability than the RSM one. The optimum experimental bleachability of 71.5% for the regenerated de-oiled SBE was obtained at carbonization temperature of 500 °C, hydrochloric acid concentration of 2M and carbonization time of 45min. Using the Genetic Algorithm (GA), the ANN model resulted in an optimum bleachability of 70.87% with corresponding optimum factors at 468.19 °C, 2 M and 45 min, while the RSM approach gave an optimum bleachability of 73.52% at the corresponding factors of 498.99 °C, 1.57 M and 41.14 min for the carbonization temperature, acid concentration and carbonization time, respectively. The optimum experimental bleachability of the regenerated SBE achieved was 12.5% higher than that of virgin bleaching earth (VBE).

Keywords: artificial neural network; central composite design; genetic algorithms; regenerated spent bleaching earth (RSBE); response surface methodology; spent bleaching earth (SBE)

Corresponding author: Almoruf O. F. Williams, Chemical and Petroleum Engineeriing Department University of Lagos Faculty of Engineering, Akoka Yaba, Nigeria, E-mail: afwilliams@unilag.edu.ng

Acknowledgements

The authors are grateful to the management and staff of Nosak Farm Produce Limited (Lagos, Nigeria) for their support and access to relevant facilities during the period of this research. Our appreciations also go to the anonymous reviewers whose review comments and recommendations helped to improve the paper.

Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Research funding: None declared.
Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

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Received: 2022-06-20

Accepted: 2022-10-26

Published Online: 2022-11-14

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Articles in the same Issue

https://doi.org/10.1515/cppm-2022-0031

Keywords for this article

artificial neural network; central composite design; genetic algorithms; regenerated spent bleaching earth (RSBE); response surface methodology; spent bleaching earth (SBE)