Principal Research Results

Optimal Rotation Planning Method for Gas Turbine Hot Gas Path Parts

BackgroundGas turbine hot gas path parts, e.g. blades, vanes, and combustors, are used in severe high temperature conditions, and areinspected, repaired and changed according to a parts rotation schedule. Since the prices of these parts are very expensive, and operation hours between checks differ from unit to unit, a smart rotation schedule, i.e. parts assignment to units that consumes the life ofparts efficiently, reduces maintenance costs significantly.

ObjectivesCRIEPI has developed a system that supports management of gas turbine hot gas path parts. The objective of the research isto develop a method for planning economical rotation schedules that is incorporated into the developed user support system.

Principal Results
1. Development of an optimal rotation planning method
We have developed a new rotation planning method for gas turbine hot gas parts. In a rotation schedule (Fig. 1) obtained by
the method, individual parts are exchanged in consideration of their remaining lifetime in addition to the basic rotation that is donewith parts groups (Fig. 2). Thus, the proposed method can plan a rotation schedule that consumes the life of individual parts efficiently
and can reduce management costs, compared to existing methods that plan rotation schedule without exchange of individual parts.
It is very hard to plan an economical rotation schedule with exchange of individual parts because the number of parts used in a gas turbine is huge. However, the developed method plans optimal rotation schedule efficiently as follows:(i) Based on graph theory, the method calculates a required number of parts groups that are indispensable for shortening abort periodof gas turbine units.
(ii) Using a hybrid algorithm that is a combination of a heuristic algorithm and a genetic algorithm, the method finds a rotation schedule that minimizes management costs.
2. Verification of effectiveness of the proposed method
In order to evaluate an effectiveness of developed method, we made numerical experiments and compared the developed
method with existing methods. In the numerical experiments, as shown in Table 1, we could reduce more than 40% of costs by using
the developed method, and ascertained effectiveness of developed method.

Future Developments
We are making the proposed method fit for practical use. Furthermore, we extend the method so that we can plan a rotation
schedule in which parts failure is taken into account.
Main Researcher: Ken-ichi Tokoro,
Research Scientist, Information Systems Sector, System Engineering Research Laboratory
Reference
Ken-ichi Tokoro, 2004, “Development of management support system for gas turbine hot gas path parts - Optimal parts rotation
scheduling method by a combination of a heuristic method and a genetic algorithm -,’’ CRIEPI Report (in Japanese)8A. Cost reduction and ensuring reliability

A partParts rotation that is done with parts group
Exchange of individual part

A parts group

Parts groups under repair②The parts group, which has been repaired, stands byfor operating as a backup parts group.

①The parts group, which wasoperating, is removed andrepaired when the unit ischecked.
③When a unit is checked, a partsgroup is selected from the backup
group, and it is installed in the unit.

Parts groupin operation

Backup parts groups④The parts that run out their lifetime
during the operation hour of the unit areremoved from the group, and they are
dealed with as idle parts.
⑤If the parts that have enouch lifetime
for the operation hour of the unit exist in
the idle parts, they are substituted for theremoved parts.

⑥The parts running
short are newly
purchased.

New parts

Idle parts

Fig.1 Parts rotation obtained by the proposed method

Power unit 1

First stage
blades groupFirst stage
blade 1

Power unit 2

Second stage
blades groupFirst stage
blade 2

Power unit n

CombustorgroupFirst stage
blade nPower units

Parts groups

Individual parts

Power Plant

Fig.2 Grouping of parts
Table 1 Comparison of management cost (billion yen)

Proposed method*1（Mean ）（Max ）（Min ）

Method 1*2

Method 2*3

47.86

50.03

45.49

95.78

85.46

Parts purchase
costs

36.33

38.99

33.65

86.94

77.28

Repair costs

10.50

10.29

10.64

7.46

7.03

Transportation
costs

1.03

0.83

1.20

1.38

1.15

Total
Break
down

Existing method*1 The rotation schedule obtained by the genetic algorithm (GA) varies. Thus, we ran the algorithm 10 times, andshow the mean, maximum, and minimum value of management costs.
*2 The method installs a parts group in ascending order of their stored time in warehouse.
*3 The method installs a parts group in descending order of their lifetime.

9