PONE-D-21-17709

Analysis on Frosting of Heat Exchanger and Numerical Simulation of Heat Transfer Characteristics using BP Neural Network Learning Algorithm

PLOS ONE

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Haibin Lv

Academic Editor

PLOS ONE

Additional Editor Comments (if provided):

Based on the treatment of frosting problem of air source heat pump, the optimized dimensionless parameter neural network model proposed in this paper has good effect, small overall system error and low sensitivity, and is applicable in the actual situation. But throughout the full text, including the summary, introduction, methods, results and conclusions, the contents of each module need to be further optimized, especially some details. The equation needs to be better reflected, the drawing also needs to be further improved, the references should be reasonable and appropriate, the explanation of the relevant algorithm should be more clear, the relevant results should be based on strong data support, and the innovation of the article should be paid attention to and emphasized.

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"The authors are indebted to the financial support from the State Key Laboratory of Air-conditioning Equipment and System Energy Conservation, China, under the contract. No. ACSKL2018KT1206."

  

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Yes

Reviewer #3: Partly

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The author studied the operating conditions of the air source heat pump in a low temperature and high humidity environment, and found that equipment frosting in this extreme environment would affect the system performance of the heat exchanger and cause the deterioration of the heat exchange process. The author introduces the mechanism and process of the formation of the frost layer of the heat exchanger, as well as the changes in the performance of the equipment after frosting. In the research, the author proposed to use the combination of non-dimensional parameters and neural network to predict the frosting amount of the equipment. Through training and optimizing the designed model, based on the analysis of frost layer heat transfer energy and the theory of ideal minimum frost energy, an adaptive defrosting heat pump system is established. And in the research, the author designs the simulation model of the air source heat pump unit with defrost function. Finally, the performance of the model was tested, and simulation experiments were performed to verify the effectiveness of the design model in the actual environment. But there are some problems that need to be improved in order to be published in the journal.

The opinion of the author is as follows:

1. The abstract of the article is not very well written, so the author needs to add more introduction to the experimental method. And accurately introduce the research contribution of the article.

2. The author needs to introduce the innovative points of the article at the end of "Introduction", highlighting the contribution of the article results to related research fields.

3. In the article "Related work", the author introduces the related research on equipment frosting, but lacks a summary of the research status of the literature. The author needs to supplement and introduce the contribution of the article to the research field.

4. The formula of the article needs to be optimized. The parentheses in formulas 2, 4, 6, 7, 8, 9, 11, 12, and 13 are in italics. The author needs to optimize the display of the formula.

5. The form of expression in Figure 1 needs to be optimized.

6. The author needs to supplement the parameter settings of the neural network in the method section.

7. The author mentioned the comparison between different algorithms in "Result Analysis", whether there are references to relevant literature for these algorithms used.

8. In "Result Analysis", the author has less analysis content of the experimental results, and can't display the research results of the article well, so the relevant content needs to be further enhanced.

9. The author needs to explain what the right axis in Figures 10 and 11 represents.

Reviewer #2: This paper mainly deals with the frosting problem of air source heat pump in low temperature and high humidity environment. Based on the formation characteristics of frost layer and the frosting and defrosting performance of heat exchanger, through the improvement and optimization of traditional neural network algorithm, a mathematical model of heat exchanger is proposed, including the environmental condition model, the main components model of heat pump and the defrosting device model. Finally, through the test of the performance of the model, it is found that when the hidden layer of the neural network is 6, the overall sensitivity of the model is low, and the performance of the optimization model is improved significantly. Regression analysis shows that there is a good correlation and accuracy between the predicted value and the actual value. The simulation results of heat transfer characteristics show that the defrosting performance of the new unit is improved significantly. The proposed non dimensional parameter neural network model based on minimum ideal defrosting energy has relatively ideal minimum auxiliary heat, and the overall system error is small. However, there are still some details in this paper, which need to be further improved.

a. The description of research methods in the abstract part is not clear enough, and some of them are redundant, so it is necessary to adjust them.

b. In the chapter of related work, there is a lack of summary of relevant research work. In this part, you should sort out and summarize the advantages and disadvantages of relevant research, and then lead to the research of the article on this basis.

c. In the first section of research methods, the article mentioned "so that the optimized one can be used to predict the amount of freezing under multi working fluid conditions [26]". The references here are not appropriate. Combined with the context, this should be the expected effect of the article.

d. "To detail a more accurate BP network, a three-layer BP neural network is selected when designing the BP network.", what is the reason for selecting the three-layer BP neural network?

e. At present, the description of model algorithm optimization is not clear enough. It is necessary to provide more detailed explanation for the realization process of adaptive network control algorithm.

f. Levenberg Marquardt (LM) optimization algorithm, Bayesian regulation algorithm, and adagrad (AD), momentum stockative (MS), and root men (RM) algorithms should be briefly explained for the readability of the article, or relevant references should be provided.

g. "It is found that, when the number of hidden layers is 6, the overall sensitivity of the model is low.". The result is not rigorous enough to capture in fact from the diagram.

h. What is the difference between condition 1 and condition 2 in FIG. 10 and FIG. 11, and how to define it?

Reviewer #3: In this paper, the author mainly studies the frosting problem of air source heat pump in low temperature and high humidity environment. Aiming at this problem, a dimensionless parameter neural network model based on minimum ideal defrosting energy is proposed. According to the calculation method of minimum defrosting energy, the ideal minimum auxiliary heat is obtained. The results show that relative humidity, dry bulb temperature, pre auxiliary heat and post auxiliary heat have significant effects on frosting. The proposed control optimization system can improve the prediction efficiency and reduce the system error.

1. In the conclusion, the author only uses short language to summarize the full text of the experiment, which is lack of depth. It should be combined with specific data, images and other information to conduct in-depth summary and analysis of the full text, to make the article more in-depth.

2. In the analysis of the results, the author lacks the analysis of the data in Figure 8 and Figure 9. Suggest not only explain the meaning of the picture, but also analyze and discuss its internal relationship.

3. In the part of model testing, the author proposes that when the hidden layer is 6, the overall sensitivity of the model is low. As for how to get the data "6", the hidden layer of all algorithms in the chart is 6. Please give a supplementary explanation.

4. Hope the purpose of this study will be clear and detailed in the abstract.

5. In the introduction, I hope the author can put forward the innovation of the scheme according to the research method.

6. In the last part of the introduction, the author should introduce the purpose of this study and the expected effect, rather than the full text chapter.

7. The author uses three sections to talk about the theory of related technologies / methods, and whether these parts can be combined to make the content more compact.

8. The author has established different mathematical models for different environmental conditions. Is it necessary to study the feasibility of these models?

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Reviewer #1: Yes: Xin Gao

Reviewer #2: No

Reviewer #3: No

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Analysis on Frosting of Heat Exchanger and Numerical Simulation of Heat Transfer Characteristics using BP Neural Network Learning Algorithm

PONE-D-21-17709R1

Dear Dr. Chu,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Haibin Lv

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

Reviewer #3: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Yes

Reviewer #3: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Author designs the simulation model of the air source heat pump unit with defrost function. Finally, the performance of the model was tested, and simulation experiments were performed to verify the effectiveness of the design model in the actual environment. In this revision, authors explain and discuss my concerns in details. I am satisfied with their work and suggest to accept the paper in the phase.

Reviewer #2: The simulation results of heat transfer characteristics show that the defrosting performance of the new unit is improved significantly. The proposed non dimensional parameter neural network model based on minimum ideal defrosting energy has relatively ideal minimum auxiliary heat, and the overall system error is small. Authors improved their paper. The paper can be accepted now.

Reviewer #3: In this paper, the author mainly studies the frosting problem of air source heat pump in low temperature and high humidity environment. Aiming at this problem, a dimensionless parameter neural network model based on minimum ideal defrosting energy is proposed. According to the calculation method of minimum defrosting energy, the ideal minimum auxiliary heat is obtained. The results show that relative humidity, dry bulb temperature, pre auxiliary heat and post auxiliary heat have significant effects on frosting. The proposed control optimization system can improve the prediction efficiency and reduce the system error. I think it`s OK now.

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

Reviewer #3: No