A Review Unveiling Various Machine Learning Algorithms Adopted for Biohydrogen Productions from Microalgae

Round 1

Reviewer 1 Report

In this manuscript, the recent applications of machine learning techniques in microalgal biohydrogen production are summarized. However, only a few references (less than 10 papers) on microalgal hydrogen production are analyzes. In contrast, the principle, advantages and disadvantages of various machine learning algorithms are excessively introduced.

Some other points are outlined below:

1 Lines 176 and 177-183, the phrase “ microalgal microalgal hydrogen” should be “microalgal hydrogen”.

2 Lines 222-223, the author's explanation for the decrease of microalgal hydrogen production caused by ethanol production is incomplete. Under anaerobic conditions, ethanol is also the acceptor of electrons produced by respiration, so the electrons used for hydrogen production are competitively reduced.

3 Line 274, the insertion format of the reference is incorrect.

4 The format of the references should be uniform and some of the references lack page numbers.

Author Response

We thank earnestly for all the comments/suggestions in improving our manuscript.

Author Response File: Author Response.pdf

Reviewer 2 Report

The topic of the review is machine learning applied to studying hydrogen production.

Also, machine learning is a powerful tool for studying the relationships among operational and performance parameters in biohydrogen production.

The paper is well written. 

In the last part of the review (section 4), I suggest integrating a comparative Table of advantages and disadvantages among ML techniques.

Also, please check Figure 1 and 3 because looks slightly blurry.

 

Author Response

We thank earnestly for all the comments/suggestions in improving our manuscript.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

The manuscript has been sufficiently improved to warrant publication in Fermentation.