Automatic Designing System for Piping andInstruments Arrangement including
Branches of Pipes
H jiHajimeKiKimura(K(Kyushuh UUniversity,iit JJapan））
ICCAS2011 (September 2020-22, 2011)

Overview
1. Motivation and Purpose
2. Problem Formulation
・Parameters： １）Equipment’s Locations and directions２）Piping Routes

Consider piping T-branches as equipments

・A new evaluation algorithm for Valve Operability → later
3. Multi-Objective Genetic Algorithm (MOGA)
・Coding of the piping arrangement design
・Crossover operation
・Self-organization equipment arrangement method

4. Experiments5. Conclusions and Future Works

2PumpA Ballast Pump RoomValveA handle forvalve operation
It is operatedusing a rod fromupper pathway

From upper pathway:
Gratingg

Upper Pathway

PumpLadderMotivation
3D-CAD
3D
CAD contributes
PipePiArrangement

designing efficiency

But…

Needs
Nd sophisticatedhi ti t d skills
kill
Automatic design Ｗｈｙ？

[Reason 1] Obscurity of the design evaluation
Not only to arrange shortest pipes between equipments!
ex.)) Easy
Etto operate
t valves,
leasy forf maintenance,
i tetc.
tAnswer → 1) Define numerical evaluation for all items
2) Formulate as a multi-objective optimization[[Reason 2]] A Problem in designingg g algorithmsg
It is no use that the algorithm gives only one solution!
GGeneticti
Answer → Show plural solutions
algorithm
Designer selects one of them as he needs.

Motivation
3D-CAD
3D
CAD contributes
PipePiArrangement

designing efficiency

But…

Needs
Nd sophisticatedhi ti t d skills
kill
Automatic design Ｗｈｙ？

[Reason 1] Problems in designing algorithms-Poor Performance-It is no use that the algorithm gives only one solution!
Answer1) Reconsideration of the problem formulation
Genetic2) Show plural solutions
algorithm
Designer selects one of them as he needs.

[[Reason 2]] Obscurity
y of the designg Criteria-Not only to arrange shortest pipes between equipments!
y to operatepvalves,, easy
y for maintenance,, etc.
ex.)) Easy
Answer1) Define numerical evaluation for all items
2) Formulate as a multi-objective optimization

Motivation
3D-CAD
3D
CAD contributes
PipePiArrangement

designing efficiency

But…

Needs
Nd sophisticatedhi ti t d skillskill
Automatic design Ｗｈｙ？

[Reason 1] Problems in designing algorithms-Poor Performance-It is no use that the algorithm gives only one solution!
Answer1) Reconsideration of the problem formulation
Genetic2) Show plural solutionsalgorithm
Designer selects one of them as he needs.

[[Reason 2]] Obscurity
y of the Designg Criteria-Not only to arrange shortest pipes between equipments!
y to operatepvalves,, easy
y for maintenance,, etc.
ex.)) Easy
Answer1) Define numerical evaluation for all items
2) Formulate as a multi-objective optimization

Motivation3D-CAD
3D
CAD contributes
PipePiArrangement

designing efficiency

But…

Needs
Nd sophisticatedhi ti t d skills
kill
Automatic design Ｗｈｙ？

[Reason 1] Problems in designing algorithms-Poor Performance-It is no use that the algorithm gives only one solution!
Answer1) Reconsideration of the problem formulation
Genetic
2) Show plural solutions
algorithm
Designer selects one of them as he needs.

[[Reason 2]] Obscurity
y of the Design
g Criteria-Not only to arrange shortest pipes between equipments!
y to operatepvalves,, easy
y for maintenance,, etc.
ex.)) EasyAnswer
1) Define numerical evaluation for all items
2) Formulate as a multi-objective optimization

Overview
1. Motivation and Purpose
2. Problem Formulation
・Parameters： １）Equipment’s Locations and directions２）Piping Routes

Consider piping T-branches as equipments

・A new evaluation algorithm for Valve Operability
3. Multi-Objective Genetic Algorithm (MOGA)
・Coding of the piping arrangement design
・Crossover operation
・Self-organization equipment arrangement method

4. Experiments
5. Conclusions and Future Works

11Piping and Instruments Arrangement ProblemPIDPlotPlan

Ship hull

Equipments(pumps, tanks,valves, etc.)

Arrange routes of thepipes, the locations of thebranches and the valves

start
end

Constraint：T-branches, etc・・・
Obj ti Function:
ObjectiveFti
C t O
Cost,
Operabilitybilit
For practical reasons, 90°elbows are used.

Problem Formulation [Conventional]
Piping and Instruments Diagram

Pipeline
FROM – TO List

PID

Given
Equipment
Dim. & Loc.

Pl t Plan
PlotPlEquipment arrangement list

Search SpaceParameters for VALVES
locationsdirections

Minimize

Valve
Operationality(cost)

Parameters for Pipes
locationsdirectionsbranches

andCost ofMaterials

patterns
locationsProblem Formulation [Conventional]
Piping and Instruments Diagram

Pipeline
FROM – TO List

PID

Given
Equipment
Dim. & Loc.

Pl t Plan
PlotPlEquipment arrangement list

Search SpaceParameters for VALVES
locationsdirections

Minimize

Valve
Operationality(cost)

Parameters for Pipes
locationsdirections
branches

andpatterns
locations

Including branches in
Cost ofpiping makes thearrangement problem
Materials
complicated!

A New Problem Formulation
Valve 1

T1

Treat piping branches as
the same as ‘equipments’
Valve 2

T3

T2

Parameters to search:（１） Locations and directions of equipments（２） Piping routes without branches= locations of elbows (lists)

Overview
1. Motivation and Purpose
2. Problem Formulation
・Parameters： １）Equipment’s Locations and directions２）Piping Routes

Consider piping T-branches as equipments

・A new evaluation algorithm for Valve Operability → later
3. Multi-Objective Genetic Algorithm (MOGA)
・Coding of the piping arrangement design
・Crossover operation
・Self-organization equipment arrangement method

4. Experiments
5. Conclusions and Future Works

16Motivation
3D-CAD
3D
CAD contributes
PipePiArrangement

designing efficiency

But…

Needs
Nd sophisticatedhi ti t d skills
kill
Automatic design Ｗｈｙ？

[Reason 1] Problems in designing algorithms-Poor Performance-It is no use that the algorithm gives only one solution!
Answer1) Reconsideration of the problem formulation
Genetic
2) Show plural solutions
algorithm
Designer selects one of them as he needs.

[[Reason 2]] Obscurity
y of the Designg Criteria-Not only to arrange shortest pipes between equipments!
y to operatepvalves,, easy
y for maintenance,, etc.
ex.)) Easy
Answer1) Define numerical evaluation for all items
2) Formulate as a multi-objective optimization

What is “multi-objective optimization”?

Ship A

Ship B

Which solution should we choose?
Designers encounter with similar situations too often

18

What is “multi-objective optimization”?

Ship A

Ship B

Which solution should we choose?
Designers encounter with similar situations too often
Evaluation is not scalar

Multi-ObjectiveM
lti Obj tiOptimization

19

What is “multi-objective optimization”?
costfunction

f2

Candidates of Solutions

space of objective function

0

cost function

f120What is “multi-objective optimization”?
costfunction

f2

Candidates of Solutions

space of objective function

0

cost function

f121

What is “multi-objective optimization”?
costfunction

f2

Candidates of Solutions

space of objective function

0

cost function

f122

What is “multi-objective optimization”?
costfunction

f2

Candidates of Solutions

Useless

space of objective function

0

cost function

f123

What is “multi-objective optimization”?
costfunction

f2

Candidates of Solutions

Pareto optimum
solutionsUseless

space of objective function

0

cost function

f124

What is “multi-objective optimization”?
costfunction

f2

Candidates of Solutions

Pareto optimumsolutionsUselessspace of objective function0

cost functionf11)) Eliminate useless dominated solutions
2) Many optimum solutions would exist.
Finding all the Pareto optimum solutions is important.
25
Do not worry about choosing one of them.

NSGA--ⅡNSGA
NSGA-Ⅱ： Nondominated Sorting Genetic Algorithms Ⅱ
Multi-objective Genetic algorithm
１． Efficient calculation in
Nondominated Sorting
２． Crowding distance
３． Elite strategy
NondominatedSorting

Reference
Kalyanmoy Deb:A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-Ⅱ,IEEE Transactions on Evolutionary Computation
Computation, vol.
vol 6
6, No
No. 22, (2002)

Crowding distance

Rank 3

Rank 3

Rank 2

Rank 2

Rank 1

Rank 1

Elite strategy

Overview
1. Motivation and Purpose
2. Problem Formulation
・Parameters： １）Equipment’s Locations and directions２）Piping Routes

Consider piping T-branches as equipments

・A new evaluation algorithm for Valve Operability → later
3. Multi-Objective Genetic Algorithm (MOGA)
・Coding of the piping arrangement design
・Crossover operation・Self-organization equipment arrangement method

4. Experiments
5. Conclusions and Future Works27

Coding For Genetic Algorithms
Valve 1

T1

Valve 2
T2

Fixed-length gene code

T3

・Variable-length gene code
・Depends on the locationsof the connectedequipments

Parameters to search:（１） Locations and directions of equipments（２） Piping routes without branches= locations of elbows (lists)

Overview
1. Motivation and Purpose
2. Problem Formulation
・Parameters： １）Equipment’s Locations and directions２）Piping Routes

Consider piping T-branches as equipments

・A new evaluation algorithm for Valve Operability → later
3. Multi-Objective Genetic Algorithm (MOGA)
・Coding of the piping arrangement design
・Crossover operation
・Self-organization equipment arrangement method

4. Experiments
5. Conclusions and Future Works

29

Crossover Operation in the Piping Arrangement
Prepare two solutions as parents
S l t llocations
Selecttiandd directions
di ti
off equipmentsitwith connected pipes

Valve 1
T1
T1

Valve 1

Valve 2
T2

Parent A

T3
Valve 2

T2
T3

Parent B

The pipes are cut off onpositions of arbitrarypyelbows in the parent.

Valve 1T1ChildT2
T3

Valve 2

Valve 1
T1
T1

Valve 1

Valve 2
T2

Parent A

T3
Valve 2

T2
T3

Parent B

Connect broken pipes
within 3 elbows

Valve 1T1ChildT2
T3

Valve 2

Valve 1
T1
T1

Valve 1

Valve 2
T2

Parent A

T3
Valve 2

T2
T3

Parent B

Overview
1. Motivation and Purpose
2. Problem Formulation
・Parameters： １）Equipment’s Locations and directions２）Piping Routes

Consider piping T-branches as equipments

・A new evaluation algorithm for Valve Operability → later
3. Multi-Objective Genetic Algorithm (MOGA)
・Coding of the piping arrangement design
・Crossover operation
・Self-organization equipment arrangement method

4. Experiments
5. Conclusions and Future Works

33

To use Genetic Algorithms,
feasible initial solution candidates are needed.
However, if we generate initial populations by random
equipment arrangement…

Ex.1

Ex.2

To use Genetic Algorithms,
feasible initial solution candidates are needed.
However, if we generate initial populations by random
equipment arrangement…

The piping tends to fullyp
over the design
g spacepspread

Ex.1

Ex.2

To use Genetic Algorithms,
feasible initial solution candidates are needed.
However, if we generate initial populations by random
equipment arrangement…

The piping tends to fullypover the design
g spacepspreadInefficient!
Ex.1

Ex.2

Generating good initial populations:Self-organization
gEquipment
q pArrangement
gEquipment 2

Equipment 1

Equipment 2

Equipment 1
Target equipment(a) Target has two destinations

Equipment 3

Targetg equipmentq p(b) Target has three destinations

Generating good initial populations:Self-organization
gEquipment
q pArrangement
gEquipment 2

Equipment 2

Equipment 1

Middlepoint

Center of gravity

Equipment 1
Target equipment(a) Target has two destinations

Equipment 3

Targetg equipmentq p(b) Target has three destinations

Generating good initial populations:Self-organization
gEquipment
q pArrangement
gEquipment 2

Equipment 2

Equipment 1

Middlepoint

Center of gravity

Equipment 1
Target equipment(a) Target has two destinations

Equipment 3

Targetg equipmentq p(b) Target has three destinations

Repeat these operations at all equipments in random order→ The equipments form into connected order.

Self-organizationDemo

13 floatingg equipments
q p
5 fixed equipments
19 pipesSelf-organizationDemo

32 floatingg equipments
q p
4 fixed equipments
72 pipesGenerating good initial populations:Self-organization
gEquipment
q pArrangementg
Example 1

Feature：Example 2

Various solutions are found when the order of theoperation is different.

Problems: It cannot take care of ‘valve operability’, or etc..
→ Use Genetic Algorithms
It cannot draw pipelines if there are too many obstacles→ Make use of Dijkstra method (on going)

Overview
1. Motivation and Purpose
2. Problem Formulation
・Parameters： １）Equipment’s Locations and directions２）Piping Routes

Consider piping T-branches as equipments

・A new evaluation algorithm for Valve Operability → later
3. Multi-Objective Optimization Algorithm (MOGA)
・Coding of the piping arrangement design
・Crossover operation
・Self-organization equipment arrangement method

4. Experiments
5. Conclusions and Future Works

43

Motivation
3D-CAD
3D
CAD contributesPipePiArrangement

designing efficiency

But…

Needs
Nd sophisticatedhi ti t d skills
kill
Automatic design Ｗｈｙ？

[Reason 1] Problems in designing algorithms-Poor Performance-It is no use that the algorithm gives only one solution!
Answer1) Reconsideration of the problem formulation
2) Show plural solutions
Designer selects one of them as he needs.

[[Reason 2]] Obscurity
y of the Design
g Criteria-Not only to arrange shortest pipes between equipments!
y to operatepvalves,, easy
y for maintenance,, etc.
ex.)) Easy
Answer1) Define numerical evaluation for all items
2) Formulate as a multi-objective optimizationValve Operability
Evaluation of the space from pathways to valves
Good Arrangement

Fair Arrangement(2) Crew needs to get
down to go through thenarrow place

Accessible,
But…

Accessible

The valve can be
operated by hands(1) The valve can be
operated by some tools

Valve Operability
Evaluation of the space from pathways to valves
Good Arrangement

Fair Arrangement(2) Crew needs to get
down to pass throughthe narrow place

Accessible,
But…

Accessible

The valve can be
operated by hands(1) The valve can be
operated by a rod

Valve Operability
Evaluation of the space from pathways to valves
Bad Arrangementg

Not Accessible!

All pipes and valvesmust be arranged not
only to put without
interference each otherbut also to make spacefrom pathways to valves
so that crew can accessthe valves.
valvesI li it andd Obscure
Obf !
Implicitso far!

To apply optimization algorithms,
Numerical evaluation for the valve
operationality is neededneeded.

Evaluation Algorithm for Valve Operability
Accessibility
Crew can move to a position where the valve can beoperated by hands or by some tools.

Possibilit of Val
PossibilityValvee Handling
The valve can be operated by hands.

The design space is partitioned into regular grids,
and recognize
gaccessible segments
gEvaluation

Recursive Fill Algorithm

Valve operability is calculated in thisgrid
id space byb summingi up thehminimum distance from each valve toaccessible segments that are locatedin the direction of the axis of thevalve’s handle or four directions
perpendicular to that axis.

Finding Accessible Segments:g
Recursive Fill Algorithm
ValvesWorker segment Matrix:
Imitating shape of the crew(worker)

SweepPathway
Obstacles:
Pipes, hull,pump, etc.

Finding Accessible Segments:g
Recursive Fill Algorithm
ValvesWorker segment Matrix:
Imitating shape of the crew
(worker)

Pathway
Obstacles:
Pipes, hull,pump, etc.

Finding Accessible Segments:g
Recursive Fill Algorithm
ValvesWorker segment Matrix:
Imitating shape of the crew(worker)

Pathway
Obstacles:
Pipes, hull,pump, etc.

Finding Accessible Segments:g
Recursive Fill Algorithm
ValvesWorker segment Matrix:
Imitating shape of the crew(worker)

Pathway
Obstacles:
Pipes, hull,pump, etc.

Finding Accessible Segments:g
Recursive Fill Algorithm
Able to handle by hand

Crew can movethis swept area

Able to handle by a rod
Distance (cost)= 3 segments

Obstacles:
Pipes, hull,pump, etc.

Divide into regulargrids, and judge
all segments.
Find inaccessible
segments using
the recursive fillalgorithm

Evaluate all valves,valvesand sum upRouting to all valvesfrom passage space inaccessible segments

Features of the Evaluation Algorithm
Accessible 1. Crew can move to a p
position wherethe valve can be operated by hands.
2. Crew can move to a position wherethe valve can be operated by a rod,but cannot be operated by hands.

GoodCost = 0Fair
Cost= distance

Inaccessible
BadCost= 10000

Crew cannot move to a position wherethe valve can be operated because
obstacles surround valves.

Expert’sp
Obscure or Implicitp
Criterion ofSumming overall valves

Valve-Operability is clearly numerically defined.

Material Cost
Material Cost Function

Weight of the kth pipe
Length of the kth pipe
Diameter of the kth pipe
Number of pipes

Overview
1. Motivation and Purpose
2. Problem Formulation
・Parameters： １）Equipment’s Locations and directions２）Piping Routes

Consider piping T-branches as equipments

・A new evaluation algorithm for Valve Operability → later
3. Multi-Objective Optimization Algorithm (MOGA)
・Coding of the piping arrangement design
・Crossover operation
・Self-organization equipment arrangement method

4. Experiments(1) 5 valves(2) 7valves

5. Conclusions and Future Works

58

Simulation Setting (PID)

Five valves

Simulation Setting （Geometrical conditions）
Connectors
Ctfor piping

Ship hullPassagespacePump
Design space is 5ｍ×5ｍ×2ｍ

Results (5 valves）valves）
Number of evaluate solutions: 20000 times
Calculation time: 10 days by Intel Core2Quad 2.664GHz

Routes aregenerated overpipes or valves

Material Cost = 2.67
Number of elbows = 18
Cost of Valve Operability = 140.6
140 6

Material Cost
= 2.595
Number of Elbows = 13
C t off V
Cost
Valvel O
Operability
bilit = 171.0
171 0

3-objective optimization

61

Grating

【NoteNote】】 Solutions in previous methods

MaterialMt i l CostC t = 8.12
8 12
Cost of Valve Operationality = 0

Material
Mt i l CostC t= 5.50
5 50
Cost of Valve Operationality = 10001

Design space = 5m x 5m x 5m

63

Simulation Setting （PID）

7 valves

Results （7 valves）
valves）
Number of evaluate solutions: 20000 times
Calculation time: 7 days by Intel Core2Quad 2.664GHz

Material Cost = 2.7975
Number of elbows = 24
Cost of Valve Operability = 270.8
270 8

Material Cost= 2.6475
Number of Elbows = 22
C t off V
CostValvel O
Operability
bilit = 286.0
286 03-objective optimization

65

Best solution on number of elbows （7 valves）

Number of evaluations

Best solution on Material cost （7 valves）

Number of evaluations

Best solution on Valve Operability Cost （7 valves）

Number of evaluations

Pareto solutions in Material-ValveOperability space

Material cost
Number of evaluations

Valve Operability cost

Number of Pareto solutions （7 valves）

Number of ppareto solutions

Number of evaluations

Overview
1. Motivation and Purpose
2. Problem Formulation
・Parameters： １）Equipment’s Locations and directions２）Piping Routes

Consider piping T-branches as equipments

・A new evaluation algorithm for Valve Operability → later
3. Multi-Objective Optimization Algorithm (MOGA)
・Coding of the piping arrangement design
・Crossover operation
・Self-organization equipment arrangement method

4. Experiments(1) 5 valves(2) 7valves

5. Conclusions and Future Works71

ConclusionsConclusions
1. Supposition in Automatic Pipe Arrangement :Make obscure criteria to be clear
Treat as multi-objective problem

2. Valve Operability Evaluation Algorithm is proposed.
3 An Implementation of Multi-objective GA for pipe arrangement is proposed3.
proposed.

【A new GA for piping arrangement】●Problem formulation: Piping branches as equipments→ Simplify the piping encoding●A new Gene encoding and crossover operation for GAs→ Simple and Intuitively appropriate●Self-organization equipment arrangement
to generate good initial populations

Open Source

Remarks

Proposed System
Multi-ObjectiveOptimization
Algorithm

zPipe
p diagramgzEquipment Arrangement list
zFrom-To list (Pipeline list)
zGeometric shapes of Hull,
Equipments, and pipes

Expert s knowledge forExpert’s
generating plans is stored

XML file
Locations and directions ofPipes and Valves
Viewer

Evaluation Algorithms
Expert’s knowledge for
evaluating plans is stored

XML fileX3D file

CAD Operator

CAD System