• International Journal of Technology (IJTech)
  • Vol 12, No 5 (2021)

City Gas Pipeline Routing Optimization Considering Cultural Heritage and Catastrophic Risk

City Gas Pipeline Routing Optimization Considering Cultural Heritage and Catastrophic Risk

Title: City Gas Pipeline Routing Optimization Considering Cultural Heritage and Catastrophic Risk
Farizal F., Muhammad Dachyar, Yunita Prasetya

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Cite this article as:
Farizal, Dachyar, M., Prasetya, Y., 2021. City Gas Pipeline Routing Optimization Considering Cultural Heritage and Catastrophic Risk. International Journal of Technology. Volume 12(5), pp. 1009-1018

Farizal F. Department of Industrial Engineering, Faculty of Engineering, Universitas Indonesia, Kampus UI Depok, Depok 16424, Indonesia
Muhammad Dachyar Department of Industrial Engineering, Faculty of Engineering, Universitas Indonesia, Kampus UI Depok, Depok 16424, Indonesia
Yunita Prasetya Department of Industrial Engineering, Faculty of Engineering, Universitas Indonesia, Kampus UI Depok, Depok 16424, Indonesia
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City Gas Pipeline Routing Optimization Considering Cultural Heritage and Catastrophic Risk

To fulfill energy needs, especially of industrial and household sectors, natural gas is more desirable since Indonesia has abundant gas and it is more environmentally friendly. The better way to deliver the gas to consumers’ sites is to use a pipeline. One important consideration in pipeline construction is route selection. This study aims to obtain the optimal gas pipeline route that has minimum investment cost while being able to serve as many consumers as possible within the available gas supply and at the same time reliable (having small catastrophic risks) and being able to conserve local cultural heritage. To this end, a mathematical model has been developed and solved. As a result, among three scenarios, the best route is scenario 3, avoiding the populated area scenario. Its total investment cost is 1,331,016,661,674.34 IDR with a pipeline length of 352.95 km, a gas supply flow rate of 6.87 MMSCFD, and a total catastrophic cost of 198,039,407,500.00 IDR. The route passes 2 subdistricts with industrial areas and 71 subdistricts with household gas consumers. None of the subdistricts with cultural heritage sites are selected.

Catastrophic cost; City gas routing; Cultural heritage; Optimization; Total investment cost


The energy supply of Indonesia’s industrial sector is still dominated by fossil energy, i.e., coal and natural gas. In 2018, the use of coal and natural gas reached 34.53% and 32.70%, respectively. Meanwhile, in the same year, 48.22% of household energy needs were fulfilled by liquified petroleum gas (LPG) (ESDM, 2018). Natural gas can be used to fulfill the energy needs of industrial and household sectors. Fulfilling industry needs with gas can be done by converting industrial boilers or heaters to gas fuel boilers (Palomino and Nebra, 2012). For the household sector, converting LPG to natural gas saves consumer expenses by 30% per MMBtu (ESDM, nd).

Indonesia has abundant natural gas reserves; estimated up to 77.29 trillion standard cubic feet (TSCF) (ESDM, 2018). Natural gas is a flexible source since it can be stored and transported in the form of liquified natural gas, medium-conditioned liquified gas, or compressed natural gas using trucks or tankers (Ríoz-Mercado and Borraz-Sánches, 2015). However, large quantities of natural gas are more economical if it is transported through pipelines (Mikolajková-Alifov et al., 2019). The Indonesian government has established a master plan for the national natural gas distribution network through pipelines.  The problem is that pipelines passing through many routes will increase investment costs.

Many studies on gas pipeline route determination have been done. Using the analytical hierarchy process method, Yildirim et al. (2017) examined factors that must be considered in selecting pipeline routes. Bawono and Kusrini (2017) studied the impact of investment schemes on city gas selling prices. Marcoulaki et al. (2012) developed a nonlinear programming model to determine the optimal pipeline route with data obtained from geographic information systems. The model was solved by the simulated annealing algorithm. Meanwhile, Sanaye and Mahmoudimehr (2013) proposed a mixed-integer nonlinear programming (MINLP) model for pipeline route selection by minimizing the total investment cost and gas pipeline operation with a minimum spanning tree (MST) and non-MST topology. Hong et al. (2019) used the MINLP model that was solved by the ant colony optimization algorithm to optimize pipeline routes with minimum construction costs. Maliki and Farizal (2019) determined pipeline routes taking into consideration environmental aspects using goal programming. Their model was solved with genetic algorithm (GA). Zarei and Amin-Naseri (2019) concluded that operating cost was the major part in the supply chain cost of the city gas network. Cornelis and Farizal (2020) developed a model that combined gas routing and plant location. Routing is also an important issue in distributing products (Sitompul and Horas, 2021). However, most models are still focused on the cost of pipes and compressors. A more comprehensive study that takes into consideration more factors that influence pipeline routing determination is needed.

This study aims to determine the optimal gas pipeline route, taking into consideration not only investment costs but also death risk costs, gas supply availability, and cultural heritage in a single integrated model. The proposed model considers industrial and household consumers simultaneously.


The three scenario results show that the proposed mathematical model is applicable for use as an alternative tool for the City of Semarang gas route determination. For Semarang, among the three scenarios run in this study, the third scenario is the best option. This scenario provides more subdistricts access to gas with reasonable investment costs and risks. At the same time, the route selected preserves the cultural heritage of the city.

The model can be duplicated to determine city gas routing for other cities, given that the data needed are available. Depending on the goal to be achieved, city decision-makers can use the BaU scenario, scenario 2, or scenario 3 to determine suitable gas routes to meet their city needs.

The proposed model has simplified the problem that the gas flow is assumed in the steady state and the city’s topology is assumed linear. Also, this study used only the ant colony algorithm. To make the model more realistic, future research may address these two issues as well as comparing various heuristic algorithms.


Bappenas, 2018. Rencana Kerja Pemerintah Tahun 2019 (Government Work Plan for 2019). Badan Perencanaan Pembangunan Nasional (National Development Planning Agency), Jakarta, Indonesia. Available Online at https://www.bappenas.go.id/files/rkp/PEMUTAKHIRAN%20RKP%20TAHUN%202019.pdf, Accessed on October 18, 2020

Bawono, A.A., Kusrini, E., 2017. The Impacts of Financing Investment Scenarios on Piped-Natural Gas Prices (GPs) for Households in Indonesia. International Journal of Technology, Volume 8(8), pp. 1402–1413

BPS Kota Semarang, 2019. Kota Semarang Dalam Angka 2019 (Semarang City in 2019 Figures). Badan Pusat Statistik Semarang (Central Bureau of Statistics of Semarang), Semarang, Indonesia. Available Online at https://semarangkota.bps.go.id/publication/2019/08/16/be2163d8fad5db43670a6dd5/kota-semarang-dalam-angka-2019.html, Accessed on February 9, 2021

Christodoulou, S.E., Ellinas, G., 2010. Pipe Routing Through Ant Colony Optimization. Journal of Infrastructure Systems, Volume 16(2), https://doi.org/http://dx.doi.org/10.1061/(ASCE)1076-0342(2010)16:2(149)

Cornelis, C.I., Farizal, 2020. Two-Step Optimization of Natural Gas Power Generation Location and Pipe Supply System. In: AIP Conference Proceedings, Volume 2230, p. 050008

Crane, 1982. Flow of Fluids through Valves, Fittings, and Pipe. Technical Paper No. 410M, Crane Co, New York, NY

DPMP, 2019. Perencanaan Penyediaan Infrastruktur Pendukung Industri di Kawasan Jawa Tengah (Planning for the Provision of Industrial Supporting Infrastructure in the Central Java Region). Dinas Penanaman Modal dan Pelayanan Terpadu Satu Pintu Kota Semarang (Semarang City Investment and One-Stop Integrated Service Office), Semarang, Indonesia. Available Online at https://web.dpmptsp.jatengprov.go.id/packages/upload/portal/files/PERENCANAAN%20PENYEDIAAN%20IN
FRASTRUKTUR%20PENDUKUNG.pdf, Accessed on February 15, 2021

DP Ruang, 2021. Bangunan Cagar Budaya (Cultural Heritage Building), Dinas Penataan Ruang Kota Semarang (Semarang City Spatial Planning Service), Semarang, Indonesia. Available Online at http://satudata.semarangkota.go.id/adm/file/20180426104140BangunanCagarBudaya1.pdf, Accessed on February 15, 2021

ESDM, 2018. Handbook of Energy & Economic Statistics. Kementerian Energi dan Sumber Daya Mineral (Ministry of Energy and Mineral Resources), Jakarta, Indonesia. Available Online at https://www.esdm.go.id/assets/media/content/content-indonesia-energy-outlook-2019-english-version.pdf, Accessed on October 18, 2020

ESDM, nd. Pembangunan Jaringan Gas Bumi Untuk Rumah Tangga (Construction of Natural Gas Network for Households). Kementerian Energi dan Sumber Daya Mineral (Ministry of Energy and Mineral Resources), Jakarta, Indonesia. Available Online at https://migas.esdm.go.id/uploads/buku-jasrgas-isi.pdf, Accessed on October 18, 2020

Hatmoko, J.U.D., Astuti, P.K., Farania, S.N., 2021. Insuring Project Risks: Contractor Expectations versus Insurance Company Policies. International Journal of Technology, Volume 12(1), pp. 90–100

Hong, B., Li, X., Di, G., Li, Y., Liu, X., Chen, S., Gong, J., 2019. An Integrated MILP Method for Gathering Pipeline Networks Considering Hydraulic Characteristics. Chemical Engineering Research and Design, Volume 152, pp. 320–335

Ma, L., Cheng, L., Li, M., 2013. Quantitative Risk Analysis of Urban Natural Gas Pipeline Networks using Geographical Information Systems. Journal of Loss Prevention in the Process Industries, Volume 26(6), pp. 1183–1192

Maliki, S., Farizal, 2019. Optimization of Gas Pipeline Route Selection with Goal Programming Considering Environmental Aspects. In: Proceeding of 1st GCC International Conference on Industrial Engineering and Operations Management, IEOM, Riyadh, Saudi Arabia

Marcoulaki, E.C., Papazoglou, I.A., Pixopoulou, N., 2012. Integrated Framework for the Design of Pipeline Systems using Stochastic Optimisation and GIS Tools. Chemical Engineering Research and Design, Volume 90(12), pp. 2209–2222

Meadows, D., Randers, J., Meadows, D., 2004. The Limits to Growth: The 30-Year Update. Chelsea Green Publishing, Chelsea, VT

Menon, E.S., 2005. Gas Pipeline Hydraulics. CRC Press, Boca Raton, FL

Mikolajková-Alifov, M., Pettersson, F., Björklund-Sänkiaho, M., Saxén,H.A., 2019. Model of Optimal Gas Supply to a Set of Distributed Consumers. Energies, Volume 12(3), pp. 1–27

Palomino, R.G., Nebra, S.A., 2012. The Potential of Natural Gas Use Including Cogeneration in Large-Sized Industry and Commercial Sector in Peru. Energy Policy, Volume 50, pp. 192–206

Profil Kota Semarang, 2018. Variety of Culture. Available Online at http://semarangkota.go.id/p/33/profil_kota, Accessed on May 6, 2021

Ríoz-Mercado, R.Z., Borraz-Sánchez, C., 2015. Optimization Problems in Natural Gas Transportation Systems: A State-of-the-Art Review. Applied Energy, Volume 147, pp. 536–555

Sanaye, S., Mahmoudimehr, J., 2013. Optimal Design of a Natural Gas Transmission Network Layout. Chemical Engineering Research and Design, Volume 91(12), pp. 2465–2476

Sitompul, C., Horas, O.M., 2021. A Vehicle Routing Problem with Time Windows Subject to the Constraint of Vehicles and Good’s Dimensions. International Journal of Technology, Volume 12(4), pp. 865–875

Yildirim, V., Yomralioglu, T., Nisanci, R., Çolak, H.E., Bediro?lu, S., Saralioglu, E.A., 2017. A Spatial Multicriteria Decision-Making Method for Natural Gas Transmission Pipeline Routing. Structure and Infrastructure Engineering, Volume 13(5), pp. 567–580

Zarei, J., Amin-Naseri, M.R., 2019. An Integrated Optimization Model for Natural Gas Supply Chain. Energy, Volume 185, pp. 1114–1130

Zhou, Y., Wu, Z., 2017. Risk Index Assessment for Urban Natural Gas Pipeline Leakage based on Artificial Neural Network. In: The 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), 29–31 July