Thermal Analysis and Kinetic Modeling of Pyrolysis and Oxidation of Hydrochars
Round 1
Reviewer 1 Report
The paper investigated the kinetic behaviour during pyrolysis and oxidation of hydrochars derived from grape seeds.
Thermogravimetric analysis and differential scanning calorimetry highlighted the importance of the HTC severity on the decomposition profiles.
Comments
Please write in the introduction why the authors undertook measurement and modeling in ASPEN. What is the practical purpose of the measurements and analysis performed.
In the conclusion, please specify the novelties presented in the paper by the authors.
Author Response
Point 1: Please write in the introduction why the authors undertook measurement and modeling in ASPEN. What is the practical purpose of the measurements and analysis performed.
Response 1: We thank the reviewer for the comment. We added a sentence at the end of the section “Introduction” dealing with Aspen (from A simple… to heat of reactions).
Point 2: In the conclusion, please specify the novelties presented in the paper by the authors.
Response 2: Thanks for this comment. We added some comments regarding the novelties of the work at the beginning of the section “Conclusion” (from Indeed, there is… to critical approach).
Reviewer 2 Report
The global increase in energy demand, driven mainly by the growing global population, urbanization, and economic growth, is depleting the world's reserves of fossil fuels, already strained by their high consumption rate, heightening environmental concerns, resource depletion, and rising costs. To face the problem, different renewable energy sources have been investigated, like wind, solar, hydropower, geothermal, and biomass. Among the different biomasses, those deriving from waste or agricultural residues, agroindustrial, horticulture, and wood processing, are cheap and convenient. In this manuscript, this work examines the kinetics of pyrolysis and oxidation of hydrochars through thermal analysis. Thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC) techniques were used to investigate the decomposition profiles and develop two distributed activation energy models (DAEM) of hydrochars derived from the hydrothermal carbonization of grape seeds produced at different temperatures (180, 220, and 250°C). I am pleased to send you moderate comments. The results and theme of this paper is interesting. The layout is clear and easy to understand. Generally, this manuscript makes fair impression and my recommendation is that it merits publication in this Journal, after the following major revision:
- The detailed literature review indicates efforts made by the authors. The coherence of the related work, however, is still not clear. It may help the authors by answering the following questions: Why are these works relevant? Which specific problems were addressed? How are the previous results related with the latest work? What are the outstanding, unresolved, research issues? Which of them has been solved by the proposed study? Answering the questions leads to the novelty of the proposed work naturally. I think this is essential to keep the interest of the reader.
- Materials and Methods part, Although the results look “making sense”, the current form reads like a simple lab report. The authors should dig deeper in the results by presenting some in-depth discussion.
- In Fig. 5, the authors should give the explanations for the difference of data collected from different sources.
- In this paper, thermogravimetric analysisand differential scanning calorimetry techniques were used to investigate the decomposition profiles and develop two distributed activation energy models of hydrochars derived from the hydrothermal carbonization of grape seeds produced at different temperatures. Besides distributed activation energy models, fractal model can be used to investigate hydrochars, (see [Powder Technology, 2019, 349:92-98; International Journal of Heat and Mass Transfer, 2019, 137:365-371). Authors should introduce some related knowledge to readers. I think this is essential to keep the interest of the reader.
- Please check all Equations double.
- Regarding the DAEM, according to a Gaussian model, the severity of the treatment slightly affects kinetic parameters, with average activation energies between 193 and 220 kJ/mol. Meanwhile, the Miura-Maki model highlights the distributions of the activation energy and pre-exponential factor during the decomposition. The authors should give some explanation on above conclusions.
- In order to verify the validity of current results, authors need to compare their results with reported experimental data and models in literatures.
- It is suggested to discuss what the main advantages the proposed distributed activation energy models have.
- Please expand the motivation, the problem context, clarify the problem description, and (if possible) add specific objectives.
- Please, expand the conclusions in relation to the specific goals and the future work.
Author Response
Point 1: The detailed literature review indicates efforts made by the authors. The coherence of the related work, however, is still not clear. It may help the authors by answering the following questions: Why are these works relevant? Which specific problems were addressed? How are the previous results related with the latest work? What are the outstanding, unresolved, research issues? Which of them has been solved by the proposed study? Answering the questions leads to the novelty of the proposed work naturally. I think this is essential to keep the interest of the reader.
Response 1: We thank the reviewer for the useful comment. We added a paragrapgh inside the introduction (from Even thougt… to …biomass pyrolysis) that answers to the questions you proposed to lead to the novelty of the paper.
Point 2: Materials and Methods part, Although the results look “making sense”, the current form reads like a simple lab report. The authors should dig deeper in the results by presenting some in-depth discussion.
Response 2: We thank the reviewer for the comment. We added several comments in section 3.4.3 “Comparison and suggestions for future work” regarding some insights on understanding kinetic parameters.
Point 3: In Fig. 5, the authors should give the explanations for the difference of data collected from different sources.
Response 3: Thanks for this comment. We add a comment explaining this point in section 3.4.1 “Gaussian model”.
Point 4: In this paper, thermogravimetric analysisand differential scanning calorimetry techniques were used to investigate the decomposition profiles and develop two distributed activation energy models of hydrochars derived from the hydrothermal carbonization of grape seeds produced at different temperatures. Besides distributed activation energy models, fractal model can be used to investigate hydrochars, (see [Powder Technology, 2019, 349:92-98; International Journal of Heat and Mass Transfer, 2019, 137:365-371). Authors should introduce some related knowledge to readers. I think this is essential to keep the interest of the reader.
Response 4: Thanks for this comment. To expand the point, we added a paragraph in section 3.4.3 “Comparison and suggestions for future work” (the last highlighted paragraph, from Then, applying… to …computational models), where we explain other models that can be used, like the fractal one.
Point 5: Please check all Equations double.
Response 5: We checked all the Equations double.
Point 6: Regarding the DAEM, according to a Gaussian model, the severity of the treatment slightly affects kinetic parameters, with average activation energies between 193 and 220 kJ/mol. Meanwhile, the Miura-Maki model highlights the distributions of the activation energy and pre-exponential factor during the decomposition. The authors should give some explanation on above conclusions.
Response 6: We thank the reviewer for the comment. To expand the discussion you mentioned, we added a comment in section 3.4.3 “Comparison and suggestions for future work”, from Indeed, the… to on the distribution as highlighted in the document.
Point 7: In order to verify the validity of current results, authors need to compare their results with reported experimental data and models in literatures.
Response 7: We thank the reviewer for the comment. In section 3.4.3 “Comparison and suggestions for future work” we added a paragraph (from Since the literature to in the present work) in which we compare data with previous literature, which is actually very poor around the degradation of hydrochars. Also, it is worth mentioning that the final part of section 3.4.1 compares results from the Gaussian model with other values in literature.
Point 8: It is suggested to discuss what the main advantages the proposed distributed activation energy models have.
Response 8: Thanks for this comment. We added a sentence in “Comparison and suggestions for future work” to explain the advantages of the models used (from In general, the… to kinetics parameters).
Point 9: Please expand the motivation, the problem context, clarify the problem description, and (if possible) add specific objectives.
Response 9: Thanks for this comment. We added the context of the problem in section 1 “Introduction”, expanded the lacks in the literature, and add the aims.
Point 10: Please, expand the conclusions in relation to the specific goals and the future work.
Response 10: Thanks for this comment. In “Conclusions” we added sentences to expand the points you mentioned. We highlighted them in the manuscript.
Reviewer 3 Report
This paper deals with a topic of interest nowadays in the conditions in which the demand for energy is increasing. As a result, the use of biomass as a fuel becomes desirable, taking into account the negative impact on pollution. The paper addresses a problem of decomposition of biomass (grape seeds).The steps follows somehow a logical path. There are a large number of experimental results. The mathematical functions used to model de decomposition are presented together with the fitting parameters. From the conclusions, we find that the existent functions are not appropriate.
- the objectives of the paper are not very clear;
- It is not clear why it is necessary to match some coefficients of a model in the conditions in which there are measurements that can be used, as they are, in calculation algorithms.
- It is not clear how these models, more or less empirical, help us, given that in order to determine their coefficients, complete decomposition measurements are needed.
- Can the results be extrapolated to other types of biomass, in order to avoid complete measurements
Author Response
This paper deals with a topic of interest nowadays in the conditions in which the demand for energy is increasing. As a result, the use of biomass as a fuel becomes desirable, taking into account the negative impact on pollution. The paper addresses a problem of decomposition of biomass (grape seeds).The steps follows somehow a logical path. There are a large number of experimental results. The mathematical functions used to model de decomposition are presented together with the fitting parameters. From the conclusions, we find that the existent functions are not appropriate.
Point 1: the objectives of the paper are not very clear;
Response 2: We thank the reviewer for the comment. In Introduction we added several comments (highlighted) starting from the lack in the literature around the topic and moving towards our aims.
Point 2: It is not clear why it is necessary to match some coefficients of a model in the conditions in which there are measurements that can be used, as they are, in calculation algorithms.
Response 2: Thanks for this comments. Matching coefficients is necessary to solve the set of equations which model the decomposition: this allows computing the kinetic parameters, otherwise not directly deducible from the experimental measures. Section 2.3 explains this point.
Point 3: It is not clear how these models, more or less empirical, help us, given that in order to determine their coefficients, complete decomposition measurements are needed.
Response 3 Thanks for the comment. Actually, these models are not thought to reduce the number of measures but to deduce kinetic parameters and some insights around the decomposition. To clarify this point, we added a comment inside the introduction (the highlighted text).
Point 4: Can the results be extrapolated to other types of biomass, in order to avoid complete measurements
Response 4: Thanks for the comment. Results are very dependent on the type of feedstock used. Indeed, the amount and type of volatile matter as well as the carbonization extent highly affect the thermal decomposition, and therefore also the model results. Therefore, no, results can not be extrapolated to other biomasses (unless in the case of extremely similar substrates), but they can only be used for a comparison.
Round 2
Reviewer 2 Report
It is ok.
