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
Round 1
Reviewer 1 Report
Application of machine learning (ML) and artificial intelli- 2 gence (AI)-based tools for modelling and enhancing sustaina- 3 ble optimization of the classical/photo-Fenton processes for the 4 landfill leachate treatment
Abstract:
The authors mansions “GA” however was not defined before
What does mean p-Fenton and c-Fenton? photo and classical?. Please clarify
How it is possible that c-Fenton was more efficiency that p-Fenton?
Introduction
I suggest change “Leachate from landfills” for “landfill leachate”
Before to use Fenton or photo-Fenton process is need to carry out a pretreatment, that allow enhance the removal of pollutants, did you considered the important stage?
Yu said that photo-Fenton process has low costs, it is incorrect, energy and reagent consume are involved in its high operational costs
In the operations mansions in the lines 82-91 you didn’t talked about photons or UV irradiation, being very important parameters
In Table 2, when you said “lambs” you mean “lamps”. If yes, then correct please
What are the wavelength of the lamps?
What does t?????? – u????t mean in equation 1 and 2?
The tables 2, 3 and 4 have an inadequate format
?2?2 “2” must be subindex as ?2?2
The analysis of pH was unneeded because of it is very well known what the optimal pH of Fenton and photo-Fenton process is around of 3. The analysis of the concentration of ?2?2 is more interesting and useful
Author Response
Please see the attachment.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
Reviewer comments:
1. Why authors used both RBFN and FFNN because RBF has localized basis functions (e.g. Gaussian) whereas FFNN has global basis functions (sigmoid). RBFs can be solved using linear regression while FFNNs require non-linear regression.
2. In the Fig.1 mark the parts and specification.
3. In the literatures try to add the combination of Machine learning based Fenton processes for the landfill leachate treatment.
4. According to Table 2 the no. of datasets are very minimum. Try to include more data to find the better accuracy of the model.
5. Figure 5 is giving the software outputs. Try to add more scientific explanation about these figures in the text.
6. From the Table 4 most of the desirability values are 100%. How?
7. What is the use of Objective Function Values mentioned in Table.4
8. Fig.2 mentioned SVR, where the authors used this technique.
9. Fig.2,3 and 4 are general explanation of those techniques, and it can be removed.
10. It has been 329 observed that p-Fenton process perform better than c-Fenton process in the treatment of 330 landfill leachate under optimum conditions. Justify with literature.
Author Response
Please see the attachment.
Author Response File: Author Response.pdf
Round 2
Reviewer 1 Report
The quality of the manuscript has increased significantly, I consider it acceptable and publishable
Reviewer 2 Report
Author updated all the reviewer queries are satisfied.
