Application of Machine Learning (ML) and Artificial Intelligence (AI)-Based Tools for Modelling and Enhancing Sustainable Optimization of the Classical/Photo-Fenton Processes for the Landfill Leachate Treatment

Hüseyin Cüce () and Duygu Özçelik
Additional contact information
Hüseyin Cüce: Department of Geomatic Engineering, Giresun University, Giresun 28200, Türkiye
Duygu Özçelik: Department of Environmental Engineering, Institute of Science, Nevşehir Hacı Bektaş Veli University, Nevşehir 50300, Türkiye

Sustainability, 2022, vol. 14, issue 18, 1-16

Abstract: This study presents a machine learning (ML)/artificial intelligence (AI)-based perspective to reliably predict and enhance the treatment efficiency of landfill leachate by classical-Fenton (c-Fenton) and photo-Fenton (p-Fenton) processes. This experiment also sought to lower treatment costs by evaluating the impact of using different numbers of UV-c (254 nm) lamps during p-Fenton processes, as well as to develop a sustainable process design for landfill leachate. In the modeling stage, the radial basis function neural network (RBFN), the feed forward neural network (FFNN), and the support vector regression (SVR) were used and the results were evaluated in a broad scanning. Our experimental results, optimized with the help of genetic algorithm (GA), showed an increasing trend in treatment efficiency and a decreasing trend in chemical usage amounts for p-Fenton oxidation. The results indicate that both treatment techniques performed (classical and p-Fenton) within 1 h contact time showed a very high pollutant removal with a reduction in COD of approximately 60% and 80%, respectively, during the first 30 min of processing. Additionally, it was noted that the COD elimination for the c-Fenton and the p-Fenton was significantly finished in first 15 min, 52% and 73%, respectively. According to the results of the optimization model, there is an increase from 62 to 82 percent under eight UV lamps compared to seven UV lamps when considering the impact of the number of UV lamps on the treatment efficiency in p-Fenton. It has been noted that when the results are taken as a whole, the better modeling abilities of ML-based models, particularly the RBFN and the FFNN, come to the fore. From a different angle, the FFNN and the RBFNN have both shown percentile errors that are extremely close to zero when MAPE values, a percentile error measure independent of the unit of the data set, are evaluated alone. Except for two tests whose desirability levels are still around 99.99%, all experiments attained outstanding desirability levels of 100.00%. This serves as more evidence for the higher modeling performance of these ML-based approaches.

Keywords: machine learning-based modeling; genetic algorithm; neural network; leachate; photo-Fenton (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/14/18/11261/pdf (application/pdf)
https://www.mdpi.com/2071-1050/14/18/11261/ (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:14:y:2022:i:18:p:11261-:d:910006

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 ().