Optimisation of a Diesel-Electric Ship Propulsion and Power Generation System Using a Genetic Algorithm

Round 1

Reviewer 1 Report

This paper proposed an approach to optimize the design of diesel-electric propulsion systems with the minimization (G/A) of the fuel consumption while ensuring the required power and design speed of the ship.

We agree that this is quite an interesting topic. 

Some questions are as follows:

1. What is the main reason that two phases (design & off-design) are described? What are their relationships w.r.t technical and theoretical points of view?
2. What is the main contribution that authors claim? I see that a couple of problems were solved using a G/A technique, but cannot see the authors' innovative points.
3. Page 6, Line 221. Could you explain why the constraint should be an equality, in principle?
4. Some English errors were observed. I marked them in pink. Please correct them
5. Isn't it better to unify the unit notations. See Page 9: Knots & Kn.

Comments for author File: Comments.pdf

Author Response

This paper proposed an approach to optimize the design of diesel-electric propulsion systems with the minimization (G/A) of the fuel consumption while ensuring the required power and design speed of the ship.

We agree that this is quite an interesting topic. 

Some questions are as follows:

What is the main reason that two phases (design & off-design) are described? What are their relationships w.r.t technical and theoretical points of view?

Thank you for your comment. Your point is indeed a matter of work. The problem with combining design and off-design optimisation is that the results of one problem are the bounds of the other. In other words, the optimal number/type of D/G is the result of the design problem and bounds the maximum number of D/G to run in off-design conditions. The solution of the two nested problems is not straightforward, yet it might provide interesting results in cases where a very wide operating profile is operated.

What is the main contribution that authors claim? I see that a couple of problems were solved using a G/A technique, but cannot see the authors' innovative points.

Thank you for your comment. There are indeed various applications of the GA in literature, as we pointed out in the introduction. GA finds applications in a wide variety of industrial fields, and it is more or less the same technique, once the problem is properly set up. The purpose of our paper is rather methodological: our aim is to show how to approach ship propulsion optimisation as a whole, and how to take advantage of optimisation techniques to solve a relatively complex problem, which in common practice is tackled manually, with a step-by-step trial-and-error approach.

Page 6, Line 221. Could you explain why the constraint should be an equality, in principle?

Thank you for your comment. The constraint should be in principle be an equality constraint because the provided power should instantly match the loads. P_el is in fact the actual power output, not the maximum power output capacity of the installation. We edited the text to clarify this point.

Some English errors were observed. I marked them in pink. Please correct them

Thank you very much for your corrections, we addressed the issues you highlighted.

Isn't it better to unify the unit notations. See Page 9: Knots & Kn.

Thank you for your advice, we unified the notation.

Reviewer 2 Report

Broad comments. The authors have made a concise overview of the topic and a brief reference to existing literature. They have indicated the main task of the paper among its motivation. Finally, they have pointed out the key message and the potential benefits of their work. As a general drawback, I could say that there is no reference to similar approaches based on genetic algorithm-based optimization in different industrial areas. It is recommended also, that the authors clarify the research question again to improve the research contribution of the article.

Specific comments. In general, the text is very well structured and has clearly defined topics. The abstract is a very good guide for what follows. More or less all fundamental theory details that are needed are discussed and concluding remarks are sufficient. Some comments for improvement:

1. It would beneficial to clarify the reasoning behind the selection of the features (Power, SFOC, Speed) especially within the context of a real case scenario where weather and sea state conditions have a significant impact on engine’s performance.
2. The first paragraph of section 2 could be refined such that information that has already been presented in the previous section to be removed.
3. Authors are encouraged to provide more descriptive reasoning for the selection of the alternatives (line 235).
4. While the motivation to present the usability and efficiency of the model is evident, in the results section (section 5) it could be useful to have comparisons with different techniques and/or classical approaches (e.g. manual evaluation of engine load models). Authors could present the added value of their approach this way.
5. It would be fruitful to have some comments regarding the behavior of the Const. RPM curve (red line) in figure 8 (e.g. the peak at 15kn).

Author Response

Broad comments. The authors have made a concise overview of the topic and a brief reference to existing literature. They have indicated the main task of the paper among its motivation. Finally, they have pointed out the key message and the potential benefits of their work. As a general drawback, I could say that there is no reference to similar approaches based on genetic algorithm-based optimization in different industrial areas. It is recommended also, that the authors clarify the research question again to improve the research contribution of the article.

Thank you for your helpful comment, we agree that references from other areas might help to highlight the cross-sectoral nature of GAs. To this end, we added some relevant literature from other industrial areas, and added the following lines in the introduction:

For this reason, genetic algorithms find various applications in many industrial areas when it is required to deal with the selection of multiple variables affecting one complex system. Examples are the selection of a diesel engine’s optimal working parameters [30], the parameter selection of a combined cycle [31], or the optimal allocation of photovoltaic systems to maximise the performance of an electric microgrid. The optimisation of a geothermic plant design shown in Ehyaei et al. [32] is particularly relevant to the present work, as it performs a two-stage optimisation, separating the design phase from the computation of the optimal operating parameters.

Specific comments. In general, the text is very well structured and has clearly defined topics. The abstract is a very good guide for what follows. More or less all fundamental theory details that are needed are discussed and concluding remarks are sufficient. Some comments for improvement:

It would beneficial to clarify the reasoning behind the selection of the features (Power, SFOC, Speed) especially within the context of a real case scenario where weather and sea state conditions have a significant impact on engine’s performance.

You are making a very good point, which is indeed a matter of work, right now. We added the following:

Moreover, note that the presented approach is strictly related to design conditions. The influence of the weather has major effects on the propulsion system: a resistance increase due to the action of wind and/or waves causes a change in the propulsion working point, depending on the ship's control logic. If the vessel is operated at a constant speed (using cruise control), an increase in power demand and propeller revolution speed is experienced, while if, more commonly, the control system keeps the propeller's revolution speed constant, the rough weather causes an involuntary speed reduction, besides an increase of the power output. During the vessel's operation, real-time D/G optimisation might be highly beneficial in real conditions.

The first paragraph of section 2 could be refined such that information that has already been presented in the previous section to be removed.

Thank you for your comment. We removed the repeated information.

Authors are encouraged to provide more descriptive reasoning for the selection of the alternatives (line 235).

Thank you for your comment. We addressed your remark and added/edited the following part:

The ship is initially equipped by a conventional propulsion plant, composed of two four-stroke diesel engines that drive two fixed pitch propellers via independent shaft lines and gearboxes, while the electric load is provided by diesel generators. This type of propulsion system is particularly efficient for merchant ships, where no particular flexibility is required. In this study, the original propulsion plant is replaced by two alternative diesel-electric propulsion systems, which general structures are presented in Figure 1 and have been discussed earlier in this paper. The reason to consider a diesel-electric system for such kind of application is that the operating profile of a pleasure craft might include multiple speeds and low speeds for a relevant amount of time. Thus, it is reasonable to suppose that, in the future, electric or hybrid propulsion will be widely adopted in the pleasure craft field, similarly to other ship types that share similar operating requirements. Moreover, electric propulsion allows the implementation of zero-emission systems (for example, including batteries), which might reduce the craft's environmental impact.

While the motivation to present the usability and efficiency of the model is evident, in the results section (section 5) it could be useful to have comparisons with different techniques and/or classical approaches (e.g. manual evaluation of engine load models). Authors could present the added value of their approach this way.

Thank you for your comment. We addressed your suggestion in the paper. In section 5.1:

As a side note, it is worth mentioning that the above-defined design problem would consist of a fair amount of computation if tackled manually. Within the considered case, 256 engine combinations should be initially considered. For each of the considered combinations, the optimal set of working points matching the constraints should be then evaluated. Especially in the variable D/G speed case, this would result in a significant amount of computation that might be handled by, for instance, discretising each engine's load diagram and using a brute force approach to evaluate all the possible working point combinations.

And in section 5.2:

It is worth mentioning that, while the constant-speed, even load sharing optimality problem might be handled manually, the complexity increase due to the variable speed and the further addition of uneven load sharing justify the adoption of the proposed approach.

It would be fruitful to have some comments regarding the behavior of the Const. RPM curve (red line) in figure 8 (e.g. the peak at 15kn).

Thank you for your comment. We clarified the behaviour of the red line, adding the following sentence:

The sudden rise of the SFOC from 14 to 15 Kn in the constant speed power plant can be explained by referring to Figure 6a and Figure 6b. At 14 Kn, one D/G provides the required power at its best efficiency conditions. When increasing the ship’s speed from 14 to 15 Kn, the overall power requirement rises and one D/G is not able to provide sufficient power. The load is thus shared between two D/G, each one working at relatively light power and, because the revolution speed is constrained, at high SFOC (low efficiency). Note that the SFOC is a specific quantity, i.e. it is power averaged: while the fuel consumption (Figure 7) increases monotonically with the ship's speed and provides a quantitative measure of the expended energy the SFOC indicates how efficiently the engines are generating the power. This fact should be taken into account when comparing the SFOC at different power outputs.

Please also find the edits to the manuscript in the attached pdf.

Author Response File: Author Response.pdf

Reviewer 3 Report

The paper is clearly written and easy to follow. The presentation is good and I generally see no major issues.

It would be interesting to possibly calculate the emissions and to also include this criteria into the optimization cost function (although this could very well be linked to fuel consumption, but nevertheless).

Two minor issues:

• table 1: check the units, first four numbers suggest that we are looking at thousands of meters (no need for third decimal)
• knots should be written consistently (either as a full word, or a unit - Kn)

 

Author Response

The paper is clearly written and easy to follow. The presentation is good and I generally see no major issues.

It would be interesting to possibly calculate the emissions and to also include this criteria into the optimization cost function (although this could very well be linked to fuel consumption, but nevertheless).

Your suggestion will definitely be taken into account in future work. Besides CO2 emissions, which are more or less proportional to the burned fuel, other pollutants are more related to off-design conditions and could influence the optimisation significantly. 

Two minor issues:

    table 1: check the units, first four numbers suggest that we are looking at thousands of meters (no need for third decimal)

Thank you for the comment, we definitely agree with your remark. The manuscript has been corrected.

    knots should be written consistently (either as a full word, or a unit - Kn)

Thank you, we made the notation homogeneous to "Kn".

Round 2

Reviewer 1 Report

All comments were reflected. 

Yet I am not comfortable with the style using the colon after "where", in lines 205, 208 (maybe more). If the authors feel this is right, they can go ahead.