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Processes
Volume 11
Issue 1
10.3390/pr11010085
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Article
Peer-Review Record

Research on Performance Optimization of Liquid Concentration Detection Systems Based on Turbulence Elimination

Processes 2023, 11(1), 85; https://doi.org/10.3390/pr11010085
by Zhiyang Li, Haizeng Liu *, Chao Wang, Jianye Chen and Qingsong Zhang
Reviewer 1:
M.Z. Sahdan
Reviewer 2:
Ramesh Singh
Processes 2023, 11(1), 85; https://doi.org/10.3390/pr11010085
Submission received: 3 December 2022 / Revised: 16 December 2022 / Accepted: 21 December 2022 / Published: 28 December 2022

Round 1

Reviewer 1 Report

The conclusion shall be improved by removing the numbering and make it simpler

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Reviewer 2 Report

In this study, the authors proposed a method for concentration detection by differential pressure based on turbulence elimination to improve the reliability of concentration detection results. The simulation modelling was carried out using Fluent software and the results obtained were also compared with experimentation. The results are well presented and discussed. The assumptions made along with the selected boundary conditions for the simulation of the fluid flow are acceptable. Overall, the manuscript is concise and well written. Below are some minor  comments for the consideration of the authors:

1) In Table 1, any reason for selecting the minimum angle for the inlet as 30 deg?

2) Lines 180 and 187, did the authors compare the turbulence intensity  using a higher TES length above 150 mm?

3) Line 257, any reason why the variations in detection error for low-density liquid are found to be greater than the high-density liquid?

4) Line 259, referring to Fig. 11, there is a steep change in the error for the lower density fluids (1.0 and 1.3) if compared to the high-density fluid (1.7) between 0.8 and 0.9 m/s. Why is this the case?

Author Response

Please see the attachment.

Author Response File: Author Response.docx

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