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phoenix810

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Ͷ¸åµ½ACS Industrial & Engineering Chemistry Research½ñÌìÊÕµ½Éó¸åÒâ¼ûÈçÏÂ:
reviewer1:
Summary Recommendation:
Publishable paper; requires substantial revisions and shortening as indicated and then
publish as a short note (see detailed comment b below). No re-review recommended.
Ã²ËÆ½ÐÎÒËõд¡£×îºóÒ»¾ä»°Ê²Ã´Òâ˼£¿¸ÄÍêÖ®ºó²»ÓÃÔÙÉóÁË£¿

Recommendation:
The authors use the single objective particle swarm algorithm (PSA) of Shi and
Eberhart to optimize a single plate-fin heat exchanger. I have reservations on this ms., but still recommend its publication as a short note (but only after a substantial re-writing and shortening as described below).

Detailed Comments:
a) Page 2: The authors mention that though several papers exist on the design of heat
exchangers (HXs), nothing much is available on their optimization [¡°... do not consist of any
optimization strategies that are needed ¡­¡±]. This is just NOT true. See, for example:
Agarwal, A. and Gupta, S. K., Jumping Gene Adaptations of NSGA-II and
Their Use in the Multi-objective Optimal Design of Shell and Tube Heat
Exchangers, Chem. Eng. Res. Des., 86, 123-139 (2008).
A. Jiminez et al., Indus. Eng. Chem. Res., recent issue
The authors need to look at papers in Chemical Engineering journals and expand their
literature survey to beyond the restricted confines of Mechanical Engineering. In fact,
a considerable amount of optimization using more than a single objective function
has also been reported, as also the multi-objective optimization (MOO) of Heat
Exchanger networks (the heuristic pinch algorithm, now using multi-objective GA),
etc. The authors seem to be blissfully unaware of all this work in the literature of heat
exchanger optimization.

b) I believe that the days of optimizing simple problems involving only a single
objective function are long over. The two articles mentioned above use the very
detailed Bell-Delaware method to estimate/size the HX (though not for plate-fin
HXs), and then to optimize it using more than one objective function (multi-objective
optimization, MOO). Indeed, the authors talk a bit about the possibility of a Pareto set
of non-dominated (equally good or optimal) solutions on page 14 of their ms.,
without realizing that this reflects two points of a Pareto set for a two-objective
function problem, and MOO algorithms can be used to study this more effectively.
Hence, I find this ms. far too elementary for publication in IECRes, except as a short
note.

c) Several references have ¡®et al.¡¯ This is possibly not acceptable to a journal, I believe,
and all authors need be mentioned.

d) The language needs substantial improvement.

reviewer2:
Comments to the Author
To demonstrate the improvement proposed, authors should compare their results against PSO methodologies proposed by others. In this paper, it seems that the convergence is achieved by fixed number of evaluations. Can this guarantee optimum for any problem? Conclusion of the paper should be improved to account physical significance of the problem.
Éó¸åÈË2ºÃÏñûʲôÒâ¼û£¬ÓÐûÓÐÈËÄܰÑCan this guarantee optimum for any problem? Õâ¾ä»°Ê²Ã´Òâ˼·­Òë¿´¿´°¡£¿

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[ Last edited by phoenix810 on 2010-5-6 at 16:26 ]
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phoenix810(½ð±Ò+1):лл²ÎÓë
Can this guarantee optimum for any problem?
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