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GIS based decision support for gas pipe maintenance
Kathrin Kirchner
Department of Business Information Systems,
Friedrich Schiller University of Jena, Germany
k.kirchner@wiwi.uni-jena.de
Abstrct
Pipelines worldwide have suffered from the effects of time and corrosion. The integrity and reliability of pipes are therefore issues of concern and focus of all pipeline operators. Geographic information systems are employed to document the state of the pipeline network. The transition from this documentary character to a GIS based decision support system gives additional use and utility to information already stored.
1 Introduction
The Middle East has huge reserves of natural gas. Growth in the gas sector is also taking place in the domestic market, where the use of natural gas has increased as well as in the export market. As a result of this product demand from local and international markets, the Middle East is well aware of transporting gas as cost effectively as possible. Maintaining the economic viability of new and existing pipeline infrastructure is important today because there are fewer margins for design, operational and financial error. As new operators come on line the existing infrastructure have to be reliable as the new pipes being built. (AME Info, 2005)
GIS has become an indispensable tool of daily work in gas utility companies. Most of them have used GIS to document their pipeline network. Despite their cost, most GIS have done little more than replace paper-drawn maps. Utilizing the data stored in a GIS for planning purposes in pipe maintenance can reap large cost savings and helps the company to make complex decisions.
A spatial decision support system (SDSS) is an interactive, computer-based system for supporting a user or a group of users in achieving a higher effectiveness of decision making while solving a semi-structured geographically related decision problem (Keenan, 2003). Adopting the SDSS paradigm will harness the power of a GIS. In this paper this concept is developed to prototypical use for the case of gas pipe maintenance.
2 Key issues in gas pipe maintenance
Reliability of supply and low operating cost are the main targets of pipeline operators. Preventive maintenance forms the substantial part of all cost components. Inspection and leakage check for a gas network is done on a regular, scheduled basis by the stuff of the utility company. Based on these information, a maintenance plan is made on a monthly or quarterly basis.
A choice of several maintenance techniques is possible, depending on characteristics like pipe material or the number of stub connections. Several in-situ techniques can be used instead of the dig-a-trench methods. They offer an additional benefit in keeping traffic and neighbour disturbances to a minimum.
Only about 1/8 of the total repair cost is on account of material and on immediate labour for pipeline replacement. Another quarter is spent for earthworks and the half of the cost goes to reconstruction of road surface, see figure 1. Pooling of activities in close local proximity thus holds large savings potential, cause a least part of side setup cost can be saved. Considering the list of preventive maintenance measures this calls for a computerized spatial decision support system.
Figure 1: Maintenance Cost
3 Architecture of a SDSS for pipe maintenance
A typical architecture of a SDSS consists of five key modules (Armstrong et. al., 1990):
For the SDSS used in the context of gas pipe maintenance a three layer architecture can be used as indicated in figure 2.
Figure 2: Architecture of Spatial Decision Support System
The SDSS blends with the existing GIS and utilizes all resources and GUIs the GIS offers. The model base for typical procedures that supports the decisions in pipe maintenance has to be developed.
4 Model base construction
In the field of gas pipe maintenance there are four fields of decision making. All of them carry spatial reference and are closely interrelated:
Figure 3: Concept of the model base
4.1 Scoring
Urgency of preventive maintenance is measured trough a scoring model. Typical relevant influencing factors are technical data (pipe diameter, building distance), economic factors like outdated pipe material and external criteria as traffic load or unapproved tree cover (DVGW, 1999). For each criteria categories are formed and a score is assigned to each of them. The pipe indicated in figure 4 earns 5 points for being small diameter, 2 for the soil type and so on. Summing all categories, there is a total of 16 urgency points. By using this score different line segments can be compared. This allows for ranking the whole distribution network on a common perspective. Most of the influence factors (such as the ones indicated on figure 4) can be taken from a spatial database immediately. The ultimate model within a company has to be geared to what data is available.
Figure 4: Scoring for an exemplary pipe segment
4.2 Selection of technology
Second part of the model base is the choice for a good maintenance technique. These techniques can be classified as repair, reconditioning or replacement measures. Repair is concentrated on leaky junctions and bushings. Reconditioning techniques try to refit existing pipes with a new inner lining for instance through hose relining. Replacement can be done with traditional open pit technology but also with trenchless methods. Only two small pits are needed to destroy the pipe by a burster to create place for a new pipe.
Each of the technologies carries preconditions for applicability, for instance soil conditions, tube material or the number of stub connections, which can be found within the spatial database. Now, given the characteristics of the line segment, infeasible techniques are filtered out in step 1 (figure 5). The remaining alternatives are now subjected to cost calculation. In preparation to cost calculation the priority metric which is calculated as urgency score points resolved divided by the cost incurred was especially useful. The number indicates where the invested money is spent in a most efficient manner.
Figure 5: Choice of maintenance technique
4.3 Cost component
Cost calculation is one of the standard tasks of controlling. Most of the cost influencing factors for all technologies can be taken directly from the GIS.
Cost can be divided into direct and indirect cost. Direct cost depend on the reconditioning technique, side characteristics and engineering work. A lot of data for calculating prices can taken from data stored in the spatial database.
In practice, indirect cost are often not considered by the utility companies, because they do not have to pay it. These are costs for traffic obstruction – coming to late to office and need more petrol – or lower sales in shops cause people cannot reach a shop because of the construction side. Indirect cost are even higher with open pit technology and so it is important to use an estimation for these cost for the maintenance technique decision process.
4.4 Cluster analysis
Pooling of construction sides helps to reduce fix costs. Standard cluster methods will not work because of two reasons:
Figure 6: Example for saving matrix
Considering the problem 2), the pooling process should be guided by the quest to maximize cost savings directly or by upgrading the priority metric as indicated in figure 5 to
priority metric, if sites combined = scoreSite1 + scoreSite2 / costSite1 + costSite2 – savings
The problem of pooling construction sites is totally different from typical clustering problems, where the majority of objective functions are based on proximity of points as measured by Euclidian distance.
DBScan (Density based spatial clustering of applications with noise, Ester et. al. 1996) is a powerful density based cluster algorithm. In its basic version points are in an Euclidian space. Cluster nuclei are formed by regions of high density (MinPts) inside an e-Neighbourhood. Upon termination of the algorithm, each point is either
Figure 7: Algorithm DBScan with MinPts=5 and e=1 unit
DBScan works well in 2- and 3-dimensional space and has a good running time of O(nlogn) where n is the number of points.
GDBScan (Sander, 1998) generalizes the concept of e-Neighbourhood by replacing the Euclidian distance with an arbitrary quasi-distance function. For the MinPts parameter the points are not simply counted. Other similarity functions can be used which may depend on spatial and non-spatial characteristics of the objects.
For the problem of gas pipe maintenance the travel distance along the pipe network can be used as pseudo-distance function. The parameter e is set to 1 km, a typical threshold for maintenance techniques. The priority metric defined above can serve as similarity function.
Preliminary results with small networks in a prototypical implementation show very promising and plausible results (see figure 8), but some work needs to be done like the choice of e and MinPts will seriously influence the solution.
Figure 8: Exemplary cluster solution in the prototypical implementation
5. Summary and Outlook
Extending GIS functionality through the model base described will assure the transition from a information to a decision support system. Maintenance of gas pipes has been used as prototype. This is an application of medium complexity with immediate utility to the end user. The problem can only be solved through close interaction with quite different fields of research: the technicalities of pipe refurbishing, economic considerations to identify key cost drivers and the algorithmic side, where new cluster algorithms had to be developed.
Gas Pipe Maintenance is only a small part of the work a public utility company faces. Next planned steps are Decision Support Systems for water, sewage and electric power companies. Water and sewage need only minimal adaptations while electricity is more complicated because of different techniques and voltage regimes.
References
GIS based decision support for gas pipe maintenance
Kathrin Kirchner
Department of Business Information Systems,
Friedrich Schiller University of Jena, Germany
k.kirchner@wiwi.uni-jena.de
Abstrct
Pipelines worldwide have suffered from the effects of time and corrosion. The integrity and reliability of pipes are therefore issues of concern and focus of all pipeline operators. Geographic information systems are employed to document the state of the pipeline network. The transition from this documentary character to a GIS based decision support system gives additional use and utility to information already stored.
1 Introduction
The Middle East has huge reserves of natural gas. Growth in the gas sector is also taking place in the domestic market, where the use of natural gas has increased as well as in the export market. As a result of this product demand from local and international markets, the Middle East is well aware of transporting gas as cost effectively as possible. Maintaining the economic viability of new and existing pipeline infrastructure is important today because there are fewer margins for design, operational and financial error. As new operators come on line the existing infrastructure have to be reliable as the new pipes being built. (AME Info, 2005)
GIS has become an indispensable tool of daily work in gas utility companies. Most of them have used GIS to document their pipeline network. Despite their cost, most GIS have done little more than replace paper-drawn maps. Utilizing the data stored in a GIS for planning purposes in pipe maintenance can reap large cost savings and helps the company to make complex decisions.
A spatial decision support system (SDSS) is an interactive, computer-based system for supporting a user or a group of users in achieving a higher effectiveness of decision making while solving a semi-structured geographically related decision problem (Keenan, 2003). Adopting the SDSS paradigm will harness the power of a GIS. In this paper this concept is developed to prototypical use for the case of gas pipe maintenance.
2 Key issues in gas pipe maintenance
Reliability of supply and low operating cost are the main targets of pipeline operators. Preventive maintenance forms the substantial part of all cost components. Inspection and leakage check for a gas network is done on a regular, scheduled basis by the stuff of the utility company. Based on these information, a maintenance plan is made on a monthly or quarterly basis.
A choice of several maintenance techniques is possible, depending on characteristics like pipe material or the number of stub connections. Several in-situ techniques can be used instead of the dig-a-trench methods. They offer an additional benefit in keeping traffic and neighbour disturbances to a minimum.
Only about 1/8 of the total repair cost is on account of material and on immediate labour for pipeline replacement. Another quarter is spent for earthworks and the half of the cost goes to reconstruction of road surface, see figure 1. Pooling of activities in close local proximity thus holds large savings potential, cause a least part of side setup cost can be saved. Considering the list of preventive maintenance measures this calls for a computerized spatial decision support system.
Figure 1: Maintenance Cost
3 Architecture of a SDSS for pipe maintenance
A typical architecture of a SDSS consists of five key modules (Armstrong et. al., 1990):
- data base system
- model base system
- display generator
- report generator
- user interface
For the SDSS used in the context of gas pipe maintenance a three layer architecture can be used as indicated in figure 2.
Figure 2: Architecture of Spatial Decision Support System
The SDSS blends with the existing GIS and utilizes all resources and GUIs the GIS offers. The model base for typical procedures that supports the decisions in pipe maintenance has to be developed.
4 Model base construction
In the field of gas pipe maintenance there are four fields of decision making. All of them carry spatial reference and are closely interrelated:
- Estimation of current state of decay of each pipe segment, depending on material or age, but also on traffic load, tree cover or building proximity
- Choice of maintenance technology which is influenced by environmental restrictions like traffic load or number of consumer stubs
- Cost incurred, depending on various cost drivers
- Site pooling, considering the cost saving perspective and the applicability of mainte¬nan¬ce methods
- a system to measure and estimate the state of decay or each segment
- a component to identify technically feasible refurbishing techniques for each pipe and each construction side
- a cost module
- and a cluster component to aggregate construction sides.
Figure 3: Concept of the model base
4.1 Scoring
Urgency of preventive maintenance is measured trough a scoring model. Typical relevant influencing factors are technical data (pipe diameter, building distance), economic factors like outdated pipe material and external criteria as traffic load or unapproved tree cover (DVGW, 1999). For each criteria categories are formed and a score is assigned to each of them. The pipe indicated in figure 4 earns 5 points for being small diameter, 2 for the soil type and so on. Summing all categories, there is a total of 16 urgency points. By using this score different line segments can be compared. This allows for ranking the whole distribution network on a common perspective. Most of the influence factors (such as the ones indicated on figure 4) can be taken from a spatial database immediately. The ultimate model within a company has to be geared to what data is available.
Figure 4: Scoring for an exemplary pipe segment
4.2 Selection of technology
Second part of the model base is the choice for a good maintenance technique. These techniques can be classified as repair, reconditioning or replacement measures. Repair is concentrated on leaky junctions and bushings. Reconditioning techniques try to refit existing pipes with a new inner lining for instance through hose relining. Replacement can be done with traditional open pit technology but also with trenchless methods. Only two small pits are needed to destroy the pipe by a burster to create place for a new pipe.
Each of the technologies carries preconditions for applicability, for instance soil conditions, tube material or the number of stub connections, which can be found within the spatial database. Now, given the characteristics of the line segment, infeasible techniques are filtered out in step 1 (figure 5). The remaining alternatives are now subjected to cost calculation. In preparation to cost calculation the priority metric which is calculated as urgency score points resolved divided by the cost incurred was especially useful. The number indicates where the invested money is spent in a most efficient manner.
Figure 5: Choice of maintenance technique
4.3 Cost component
Cost calculation is one of the standard tasks of controlling. Most of the cost influencing factors for all technologies can be taken directly from the GIS.
Cost can be divided into direct and indirect cost. Direct cost depend on the reconditioning technique, side characteristics and engineering work. A lot of data for calculating prices can taken from data stored in the spatial database.
In practice, indirect cost are often not considered by the utility companies, because they do not have to pay it. These are costs for traffic obstruction – coming to late to office and need more petrol – or lower sales in shops cause people cannot reach a shop because of the construction side. Indirect cost are even higher with open pit technology and so it is important to use an estimation for these cost for the maintenance technique decision process.
4.4 Cluster analysis
Pooling of construction sides helps to reduce fix costs. Standard cluster methods will not work because of two reasons:
- Savings are largest when using a single technology for the amalgamated sites. That is why the technology choice may be revised.
- Large clusters are often undesirable because of traffic obstruction and duration of civil works.
Figure 6: Example for saving matrix
Considering the problem 2), the pooling process should be guided by the quest to maximize cost savings directly or by upgrading the priority metric as indicated in figure 5 to
priority metric, if sites combined = scoreSite1 + scoreSite2 / costSite1 + costSite2 – savings
The problem of pooling construction sites is totally different from typical clustering problems, where the majority of objective functions are based on proximity of points as measured by Euclidian distance.
DBScan (Density based spatial clustering of applications with noise, Ester et. al. 1996) is a powerful density based cluster algorithm. In its basic version points are in an Euclidian space. Cluster nuclei are formed by regions of high density (MinPts) inside an e-Neighbourhood. Upon termination of the algorithm, each point is either
- a core point inside a cluster with at least MinPts objects in its e-Neighbourhood
- a boundary point which lies in the e-Neighbourhood of a core point
- an outlier, which is a point in a low density region (see figure 7)
Figure 7: Algorithm DBScan with MinPts=5 and e=1 unit
DBScan works well in 2- and 3-dimensional space and has a good running time of O(nlogn) where n is the number of points.
GDBScan (Sander, 1998) generalizes the concept of e-Neighbourhood by replacing the Euclidian distance with an arbitrary quasi-distance function. For the MinPts parameter the points are not simply counted. Other similarity functions can be used which may depend on spatial and non-spatial characteristics of the objects.
For the problem of gas pipe maintenance the travel distance along the pipe network can be used as pseudo-distance function. The parameter e is set to 1 km, a typical threshold for maintenance techniques. The priority metric defined above can serve as similarity function.
Preliminary results with small networks in a prototypical implementation show very promising and plausible results (see figure 8), but some work needs to be done like the choice of e and MinPts will seriously influence the solution.
Figure 8: Exemplary cluster solution in the prototypical implementation
5. Summary and Outlook
Extending GIS functionality through the model base described will assure the transition from a information to a decision support system. Maintenance of gas pipes has been used as prototype. This is an application of medium complexity with immediate utility to the end user. The problem can only be solved through close interaction with quite different fields of research: the technicalities of pipe refurbishing, economic considerations to identify key cost drivers and the algorithmic side, where new cluster algorithms had to be developed.
Gas Pipe Maintenance is only a small part of the work a public utility company faces. Next planned steps are Decision Support Systems for water, sewage and electric power companies. Water and sewage need only minimal adaptations while electricity is more complicated because of different techniques and voltage regimes.
References
- AME Info FZ LLC (Ed.): Utilisation of natural gas in the Middle East to rapidly overtake oil as governments commit to massive investment in the industry, www.ameinfo.com/60285.html, May 2005.
- Armstrong, Densham: Organization Strategies for Spatial Decision Support Systems, IJGIS, vol. 4, 1, p. 3-20, 1990.
- DVGW (Ed.): Technical Standard G 401. Entscheidungshilfen fuer die Rehabilitation von Gasverteilungsnetzen. DVGW – German Technical and Scientific Association for Gas and Water, 1999.
- Ester, Kriegel, Sander and Xu: A density based algorithm for discovering clusters in large spatial databases with noise, Proc. 2nd Int. Conf. on Knowledge Discovery and Data Mining, Portland, Oregon, 1996.
- Keenan: Spatial Decision Support Systems, in: Mora, Forgionne and Gupta (Ed.): Decision Making Support Systems. Achievements and challenges for the new decade, Idea Group Publishing, p. 28-29, 2003.
- Sander: Generalized density-based clustering for spatial data mining, Ph.D. Thesis, Munich, 1998.