Review and Analysis of Methods for Separating Plastic Micro-Particles from Pipe Systems, Taking into Account Efficiency and Automation Potential

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

See attached document

Comments for author File: Comments.pdf

Comments on the Quality of English Language

The document requires a thorough review of the style, spelling, and grammar of the language.

Author Response

Comments 1: A thorough review of the methodology used, style, and spelling of the document.

Response 1: We thank the Reviewer for this general comment. The manuscript has been carefully revised to improve methodological clarity, scientific style, and language quality. The methodology description has been refined in the Materials and Methods section, and the entire text has been edited.

Comments 2: Develop a characteriza*on of the microplas*cs studied in the samples obtained,
both before and a@er the separa*on process, to understand the method's selec*vity.
Clearly dis*nguish the following characteris*cs of the study and present them in a
problem defini*on sec*on.
• Size and shape: Size range (generally <5 mm) and morphology
(fragments, fibers, spheres, etc.).
• Polymer type: The chemical composi*on of the plas*c (e.g., PET,
polyethylene, polypropylene) and its density, which affects methods
such as buoyancy.
• Ini*al concentra*on: The amount (abundance) of microplas*cs in the
input sample.

Response 2: A clear and concise characterization of the microplastics considered in this study has been added in Section 1.1. This section now explicitly defines the particle size range (<5 mm), dominant morphologies (fragments, fibers, spherical particles), polymer types (PE, PP, PS, PET) together with their chemical composition and density, and the variability of initial microplastic concentrations depending on the environmental matrix. This characterization forms the formal problem definition and provides the basis for discussing separation selectivity.

 

Comments 3: Clearly iden*fy in the study the variables used for analysis and how they were
applied in the processes. For example:
• Separa*on method: The type of technique employed (physical,
chemical, and biological).
• Opera*ng parameters: Specific factors of the method, such as the
type and concentra*on of saline solu*on used in density separa*on,
the filter pore size, or the process dura*on.
• Environmental/sample condi*ons: Variables such as pH, temperature,
turbidity, or the presence of organic maWer in the water sample,
which can influence the process performance.
o Environmental Matrix: The complexity of the sample (marine
sediments, wastewater, soil) influences the resistance to the
separa*on process.
• Explain what you mean by efficiency metrics and describe them.

Response 3: We agree with this comment. The variables used in the analysis are now clearly identified and described in Sections 1.1 and 2, including the type of separation method (physical, chemical, or biological), operating parameters as reported in the literature, and environmental and sample conditions such as matrix type (e.g., drinking water or wastewater) that influence process performance.

 

Comments 4: Regarding measurement parameters, clearly separate the metrics used, such as:
• Degree of separa*on (removal efficiency): Percentage of microplas*cs
removed from the ini*al total.
• Final concentra*on: The amount of microplas*cs remaining in the
treated sample, o@en measured using techniques such as FTIR or
Raman spectroscopy.
• Purity of the recovered sample (if applicable): If the objec*ve is the
recovery of microplas*cs for analysis or recycling, the percentage of
non-plas*c material in the separated sample is measured.
• Process cost/*me: Prac*cal aspects such as resource consump*on,
processing *me, and economic viability of the method.
• Pay par*cular aWen*on to measuring the following study metrics by
process (physical, chemical, or biological):
o Recovery Rate: This is the percentage of microplas*cs
recovered a@er the separa*on process compared to a known
quan*ty (spiked sample).
o Separa*on Purity: Percentage of correctly iden*fied plas*c
par*cles rela*ve to the total solids recovered a@er the
removal of organic maWer.
o Enrichment Factor: Ra*o between the final microplas*c
concentra*on and the ini*al concentra*on in the original
matrix

Response 4: We agree with the Reviewer. The manuscript now clearly distinguishes between the applied performance metrics, including removal efficiency, final microplastic concentration after treatment, process cost and duration, and—where applicable—the purity of the recovered sample. These metrics are described in Sections 1.1 and 2 in accordance with the definitions adopted in the cited literature.

 

Comments 5: Control Factors Used in the Process:

• Density Separa*on Agent: Use of saturated solu*ons such as NaCl,

CaCl₂, or ZnCl₂ to adjust the specific gravity.

• Diges*on Reagents: Effec*veness of H₂O₂ or acid treatments to

remove organic interferences without degrading the polymers.

• Number of Stages: Cumula*ve efficiency increases with repeated

extrac*ons (e.g., from 61% in the first to 93% in the third).

Response 5:  We respectfully clarify that the present study is literature-based and does not include original experimental measurements. Therefore, metrics such as recovery rate, separation purity, and enrichment factor are discussed only when they are explicitly reported in the cited publications. These indicators are not recalculated or independently derived within this study.

 

 

Comments 6: Add a sec*on to the document discussing the results obtained, highligh*ng:

• An analysis of variance (ANOVA) applied to the results: Used to

determine if the differences in efficiency between different methods

or polymers are sta*s*cally significant.

• Standard Error (SE) and Standard Devia*on: Crucial for repor*ng the

precision and repeatability of the method (e.g., 99.05 } 0.82%).

• Sample Size (?): Must be sufficient to ensure that the variance

converges and the results are representa*ve of the studied

popula*on.

• For accurate iden*fica*on a@er separa*on, the use of FTIR or Raman

microspectroscopy is recommended, as these allow for sta*s*cal

valida*on of the chemical nature of the recovered fragments.

Response 6: We respectfully clarify that this manuscript does not present original experimental results or statistical replication. This has now been explicitly stated in section 2 and 3. Statistical indicators such as ANOVA results, standard error, standard deviation, and sample size are referenced only when provided in the cited literature and are not independently analyzed or recalculated in this work. Spectroscopic identification methods (FTIR/Raman) are discussed where relevant to the reviewed studies.

 

 

Comments 7: Include the following elements in your conclusions:

• Summary of Efficiency Metrics: Average Recovery Rate: Present the

average amount of microplas*cs recovered along with its precision.

For example: "The method achieved an average efficiency of 99.05 }

0.82%."

• Efficiency by Parameter: Break down the efficiency according to the

size or type of polymer, no*ng cri*cal varia*ons (for example

efficiency drops from 97% to 54% for smaller par*cles).

• Comparison of Methods: State which technique was sta*s*cally

superior (for example density separa*on with Zinc Chloride versus

NaCl).

Response 7: We agree with this suggestion. The Conclusions section has been revised to include a concise summary of reported efficiency ranges, a qualitative discussion of efficiency dependence on particle size and polymer type, and a comparative assessment of the analyzed separation methods based on the applied multi-criteria evaluation.

 

Comments 8: Highlight the following results of the sta*s*cal valida*on:

• Significance: Indicate whether the differences found between the

tested methods are sta*s*cally significant (using ?-values or ANOVA

results).

• Uncertainty and Errors: Express the results with the Standard Error

(SE) or Standard Devia*on to demonstrate the repeatability of the

process.

• Purity and Valida*on: Report the percentage of purity a@er

separa*on, validated by spectroscopic techniques (such as micro-FTIR

or Raman) to ensure that the sample is indeed plas*c.

Response 8: As clarified above, this study does not include original statistical validation. The Conclusions section now explicitly states the semi-quantitative nature of the analysis and clarifies that information regarding uncertainty, errors, and sample purity is derived exclusively from the cited literature where available.

 

Comments 9: Draw valid conclusions from your results regarding the Matrix and Applicability.

• Sample Context: Specify whether efficiency is maintained in real

environmental samples versus controlled ("spiked") samples.

• Technological Limita*ons: Recognize whether complete removal is

unaWainable with current technology and suggest data-based

improvements (e.g., repea*ng extrac*on cycles).

• Impact of External Factors: Men*on how external variables (such as

surfactant use or diges*on *me) influenced the final result.

Response 9: The Conclusion section now clearly distinguishes between performance reported for controlled (spiked) samples and real environmental matrices, identifies technological limitations of current separation methods, and discusses the influence of external factors on separation performance.

 

Comments 10: Include in your conclusions a proposal for an op*mized protocol based on the data

obtained for future standardized monitoring in industrial or natural environments.

Response 10: A literature-based, conceptual proposal for an optimized protocol has been added to the Conclusions, emphasizing inline physical separation supported by auxiliary treatment stages and automated monitoring as a feasible approach for future standardized applications.

 

Comments 11: Review your cita*on style and adhere to the guidelines of the journal and the

publisher.

Response 11: The citation style has been reviewed and corrected throughout the manuscript to ensure full compliance with the journal and publisher guidelines.

 

Reviewer 2 Report

Comments and Suggestions for Authors

Please see the attached document for comments.

Comments for author File: Comments.pdf

Author Response

Comments 1:Although the paper states a “three-stage research methodology” and uses a pairwise comparison approach to weight criteria K1–K5, the review protocol is not specified, and the rationale for selecting exactly three “representatives” per category is not fully justified. In addition, for pairwise weighting, please report normalized weights and a consistency check and/or a sensitivity analysis to show whether the final ranking is robust to reasonable weight perturbations.

Response 1: 

We thank the Reviewer for this valuable comment. The description of the three-stage research methodology has been clarified and formalized in Section 2. The applied protocol now explicitly defines: (i) classification of separation methods into physical, chemical, and biological groups; (ii) selection of three representative methods per category based on their technological maturity and frequency of use in the reviewed literature; and (iii) semi-quantitative, criteria-based evaluation adapted to continuously operating pipeline systems The rationale for selecting three representatives per category is now explicitly stated as a balance between methodological completeness and comparability across categories.

 

Comments 2: The manuscript frequently relies on “average cost” and “average efficiency” style summaries for different methods, but it remains unclear how these averages were computed across heterogeneous studies, and whether the reported values reflect comparable operating conditions for pipeline deployment. Please provide the data-extraction procedure, ranges, and uncertainty or confidence intervals; otherwise the downstream scoring can be overly subjective even if the intent is “semi-quantitative.”

Response 2: The manuscript has been revised to explicitly state that the reported cost and efficiency values are representative values selected within the ranges reported in the cited literature for comparable operating conditions. The data-extraction procedure is now described in Section 3

 

Comments 3: Given that the work targets inline piping systems with “limited installation space” and high continuity requirements, the discussion should explicitly address hydraulic/operational constraints. For the recommended acoustic separation, the manuscript already notes performance dependence on particle size and reduced efficiency for very small-range particles, this limitation should be quantified and connected to realistic microplastic size distributions and expected duty cycles.

Response 3: Hydraulic and operational constraints relevant to inline pipeline systems have been explicitly addressed in Section 3.3. The revised discussion now considers pressure losses, flow continuity, and system stability under varying flow velocities. For acoustic separation, the dependence of separation efficiency on particle size is discussed in relation to realistic microplastic size distributions reported in the literature.

 

Comments 4: You emphasize PLC integration, sensor-driven parameter adjustment, and “artificial intelligence” as future directions; however, the paper does not provide a concrete automation architecture. Please add a concise but technically grounded framework and broaden the discussion by citing recent vision–language model (VLM) work for industrial monitoring and interpretability (e.g., CNC-VLM, doi: 10.1016/j.ymssp.2025.113838) as an example of how multimodal signals could support robust supervision in Industry 4.0-oriented pipeline installations.

Response 4: We thank the Reviewer for this suggestion. A new subsection, Section 3.4, has been added to the manuscript, presenting a concise and technically grounded automation and control concept for inline acoustic separation systems. The proposed framework describes PLC-based control with real-time feedback from flow, pressure, and turbidity sensors, enabling dynamic adjustment of acoustic operating parameters.

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

See the attached document

Comments for author File: Comments.pdf

Comments on the Quality of English Language

The document requires a thorough review of the style, spelling, and grammar of the language.

Author Response

Comment 1:In Table 2.1, describe how you performed the normalization process. Explain which values dominated the criteria that scored highest during the one-to-one
comparisons.

Response 1:

The manuscript has been revised to explicitly describe the normalization process applied to the pairwise comparison matrix. The revised text explains that the raw comparison scores were aggregated by summing the values in each criterion row and subsequently normalized by dividing each row sum by the total sum of all criteria scores, resulting in a normalized weight vector with a total sum equal to 1.0. An explicit interpretation of the obtained weights has been added below Table 1, indicating that separation efficiency (K2) and automation capability (K3) dominated the comparison due to their direct influence on technical feasibility and stable operation in continuous pipeline systems.

Comment 2: Although the pairwise comparison method provides a solid foundation for the study, I believe it may be insufficient on its own for a technical decision as complex as an industrial filtration system. It is an excellent tool for prioritizing criteria (K1–K5), but
to choose the final filtration method, it is ideal to use it as part of a more robust
system.

Response 2:

The role of the pairwise comparison method has been clarified in the revised manuscript. It is now explicitly stated that this method is used exclusively to prioritize the evaluation criteria (K1–K5) and not as a standalone decision-making tool. The final ranking of separation methods is obtained through a broader criteria-based assessment that integrates literature-derived quantitative parameters, an ordinal scoring scale, and robustness verification via sensitivity analysis. This positions the pairwise comparison as one component of a more comprehensive decision-support framework.

Comment 3: The used technique is insufficient in the following areas:

1. Lack of intensity scales: The basic method is usually binary (one or the other
   wins). In filtering, a filter may be "a little more expensive" but "much more
   efficient." Simple pairwise comparison does not capture this magnitude.
2. Consistency: It does not offer a way to verify if the judgments are logical (if A>B and B > C, then A must be > C). If there are inconsistencies, the final result
   will be erroneous.
3. Nature of the data: engineering decisions will integrate quantitative data (m³/h, $) with qualitative data. The pairwise method is purely comparative
   and does not process absolute values.

Response 3a:

The revised manuscript clarifies that the pairwise comparison procedure is applied only to determine the relative importance of the evaluation criteria. Differences in performance intensity between separation methods are subsequently captured using a multi-level ordinal scoring scale (0–3), allowing relative magnitude effects to be reflected in the criteria-based assessment.

Response 3b:

To address consistency, the revised manuscript explicitly reports a sensitivity analysis in which individual criterion weights were varied by ±10% to verify the internal logical consistency of the weighting scheme and the robustness of the resulting ranking. The unchanged ranking of the two highest-scoring methods demonstrates that the results are not overly sensitive to minor variations in expert judgment.

Response 3c:

The revised manuscript now clearly distinguishes between quantitative and qualitative data handling. Quantitative engineering parameters such as unit cost, separation efficiency ranges, energy demand, and medium losses are first analysed independently based on literature data and subsequently translated into relative ordinal scores. This approach enables cross-method comparison without directly mixing heterogeneous physical units while preserving the influence of absolute engineering data on the final ranking.

Comment 4: I personally recommend using the AHP method. To make the choice technical and defensible, I suggest the authors evolve to the Analytic Hierarchy Process (AHP), which uses pairwise comparisons but in an advanced way:
a. Weighting Criteria: You use pairwise comparisons to assign a percentage
weight to K1, K2, K3, K4, and K5 (e.g., K1 is worth 30%, K2 is worth 40%, etc.).
b. Saaty Scale: Instead of choosing just one winner, a scale from 1 to 9 is used
to indicate how much better one option is than another.
c. Evaluation of Alternatives: You evaluate each filtration method (e.g., filter
press vs. centrifuge) against each weighted criterion.

Response 4:

The scope of the study has been clarified to emphasize that its objective is a comparative screening of microplastic separation methods under pipeline-specific constraints rather than the selection of a single optimized engineering solution. For this reason, a simplified and transparent evaluation framework was intentionally adopted. While inspired by hierarchical decision-making principles, the applied approach avoids full AHP formalism in order to maintain methodological clarity, reproducibility, and applicability to heterogeneous literature-based data.

Comment 5: An alternative approach is the use of MCDM (Multi-Criteria Decision Making), an engineering and management approach that allows for the simultaneous evaluation of multiple, often conflicting, criteria, such as cost (K1) versus efficiency (K2). MCDM will enable you to move from a simple pairwise comparison to a mathematically
defensible choice.

Response 5:

The manuscript now clarifies that the proposed evaluation framework constitutes a semi-quantitative, criteria-based decision-support approach consistent with the general principles of Multi-Criteria Decision Making (MCDM). Rather than implementing a specific formal MCDM algorithm, the study prioritizes transparency, engineering interpretability, and robustness when working with heterogeneous literature data.

Comment 6: The authors are again required to standardize the citations in their document. These should be numbered sequentially without repeating citations. To do this, organize the document's narrative in a way that allows readers to navigate through the cited authors in the correct order.

Response 6:

The manuscript has been fully revised to ensure that all references are numbered sequentially according to their first appearance in the text, without repetition or backtracking, in full compliance with the journal’s citation guidelines.

 

Reviewer 2 Report

Comments and Suggestions for Authors

The manuscript addresses a timely and relevant topic and provides a broad comparative overview of microplastic separation methods for pipeline systems. The reviewer appreciates the effort to formalize a three-stage methodology and to introduce semi-quantitative multi-criteria evaluation. However, several aspects still require revision to fully meet academic rigor and reproducibility standards.

1. Although the manuscript now reports representative cost and efficiency values derived from the literature, the procedure for data extraction and aggregation remains insufficiently transparent. The use of terms such as “average” or “representative” values across heterogeneous studies raises concerns about comparability.
2. The discussion of hydraulic and operational constraints for inline pipeline systems is conceptually sound, but it would be strengthened by a more quantitative connection to realistic operating conditions.
3. The manuscript introduces automation, PLC integration, and artificial intelligence as future directions, which is appropriate and forward-looking in section 3.4. When referring to recent developments in vision–language models, the authors should ensure that references are properly formatted, consistently cited in the text, and fully aligned with journal citation guidelines.

Author Response

Comments 1: Although the manuscript now reports representative cost and efficiency values derived from the literature, the procedure for data extraction and aggregation remains insufficiently transparent. The use of terms such as “average” or “representative” values across heterogeneous studies raises concerns about comparability.

Response 1: 

We thank the Reviewer for highlighting this important issue. The manuscript has been revised to explicitly clarify the origin, interpretation, and limitations of the cost and efficiency values reported in Table 2. The revised text now explains that these values are derived from a targeted review of experimental and pilot-scale studies conducted under hydraulic conditions comparable to inline pipeline systems. Rather than applying statistical averaging across heterogeneous datasets, mid-range values were selected from reported performance intervals to reflect typical operating conditions, while excluding extreme laboratory-optimized or highly site-specific configurations. To further avoid ambiguity, the column headings in Table 2 have been revised to use the term “indicative” instead of “representative”. These revisions improve transparency and ensure engineering-level comparability between the analyzed methods.

Comment 2: The discussion of hydraulic and operational constraints for inline pipeline systems is conceptually sound, but it would be strengthened by a more quantitative connection to realistic operating conditions.

Response 2:

The manuscript has been revised to strengthen the quantitative connection between the comparative analysis and realistic operating conditions. In Section 3.3, characteristic flow velocities associated with stable operation of inline acoustic separation systems (approximately 2–3 m/s) have been explicitly reported. Furthermore, the reduction in separation efficiency observed at higher flow velocities has been quantified (typically 10–25%) and linked to increased turbulence intensity, reduced particle residence time, and partial distortion of standing acoustic wave patterns, based on literature data.

Comment 3: The manuscript introduces automation, PLC integration, and artificial intelligence as future directions, which is appropriate and forward-looking in Section 3.4. When referring to recent developments in vision–language models, the authors should ensure that references are properly formatted, consistently cited in the text, and fully aligned with journal citation guidelines.

Response 3:

All references related to automation, PLC integration, and vision–language models have been carefully reviewed and corrected to ensure consistent formatting and compliance with journal citation guidelines. In addition, Section 3.4 has been revised to clarify that the cited vision–language model approaches are presented as emerging and illustrative examples rather than established industrial standards, while preserving the forward-looking perspective of the discussion.