Modeling and Control Strategy Analysis of Gasoline Level Dynamic in Storage Tank with

Nonsimultaneous Product Filling and Withdrawal Scenario

(Case Study : Ujung Berung Depot )

Kinan Adhitya Kusumah2, Estiyanti Ekawati1,2, Sutanto Hadisupadmo1 kinan@instrument.itb.ac.id, esti@instrument.itb.ac.id, sutanto@tf.itb.ac.id

1Instrumentation and Control Research Group, Faculty of Industrial Technology, Institut Teknologi Bandung 2Center for Instrumentation and Technology Automation (CITA), Institut Teknologi Bandung

Litbang (ex. PAU) Lt.8, Institut Teknologi Bandung

Abstract— Fuel oil availability holds a vital role for economic activity of a country. Gasoline is a type of fuel oil which is quite high in demand. Most fuel engine vehicles, which in charge for commodity distribution, depends on gasoline in order to operate. In Indonesia, gasoline distribution system is controlled mostly by the government via national oil company (Pertamina). The government control most of oil activity, i.e. exploration, production and distribution. Those activities related to one another, and form a vast and complicated supply chain system. An effective and proper management of such large scale system is essential to achieve optimal result. Supply chain management mostly depends on intuition and experience, hence the system become less efficient, including the gasoline supply chain management system in Indonesia. In addition, research related to gasoline supply chain modeling which can accommodate specific condition such as, non simultaneous product filling and withdrawal, up to this time, has not yet been studied. This paper describe the dynamics of gasoline stock in a depot which satisfy the non simultaneous product filling and withdrawal scenario. Furthermore, it is available to accommodate disturbances such as transport delay and demand fluctuation. The model also shows the queue dynamics when depot is in out-of-stock condition. A two-state controller,as an initial trial to anticipate more advance control method, is proposed to overcome such phenomena.

Keywords-Gasoline supply chain system;Non-simultaneous product flow; inventory cost; back-order

I. INTRODUCTION Fuel oil transaction holds a vital role in supporting

economic activity in Indonesia. The government controls most of oil activity, i.e. exploration, production and distribution. Those activities related to one another, and form a vast and complicated supply chain system. An effective and proper management of such large-scale system is essential to achieve optimal result.

Supply chain is a network consist of facility and distribution entities who work on raw material procurement, conversion of raw material into mid-product and end product, and distributing product to consumer [2][6]. Supply chain

management mostly depends on intuition and experience, hence the system become less efficient, including the gasoline supply chain management system in Indonesia. Attain consumer demand in less time while concurrently maintain product stock at the lowest level, is the purpose of an effective supply chain management system [3][5].

The gasoline distribution system in Indonesia currently run based on standard operating procedure, flow movement guidance and operation. Research and application related to gasoline supply chain that shows the gasoline flow dynamics are still insufficient. Hence, the gasoline supply chain management system in Indonesia prone towards disturbance such as instrument malfunction and coordination drawback or paperwork issues.

On that basis, this research proposes to develop model and simulator to reveal the dynamic of gasoline supply chain system. The model is developed under several restrictions to simplify the problem, such as; the system only concerned on single product (gasoline) and the distribution area takes place in Bandung, Indonesia. Such model and simulator are expected can show the dynamics of supply chain system under real life uncertainties such as transport delay and demand fluctuation.

Seferlis and Giannelos [7] studied supply chain operational cost with a model that assumes more than one distributor as a single component. Proportional-Integral-Derivative (PID) control is used to maintain inventory level, yet the non-simultaneous scenario is not adapted. Chen and Lee [2] studied the multi-objectives optimization for multi-echelon supply chain system that accommodate demand and cost uncertainties. But the non-simultaneous scenario is not being considered. Yuzgec [9] studied the fossil fuel supply chain modeling. Model Predictive Control (MPC) is used to deal with supply scheduling, storing and crude oil refining in an oil refinery. The non-simultaneous scenario is adapted but transport delay component is absence. Aires [1] studied the supply chain system in Petrobras. The non-simultaneous scheme is well described but not the transport delay. Demand fluctuation is not accommodated. Siregar [8] managed to show

Bali, Indonesia, August 28-30, 20132013 3rd International Conference on Instrumentation Control and Automation (ICA)

86978-1-4673-5798-2/13/$31.00 ©2013 IEEE

the more detailed gasoline level dynamics in depot. In addition, the system being observed is the same as this research. Based on those researches, the purpose of this research is to model the level dynamics in storage tank with non-simultaneous product filling and withdrawal scenario. Transport delay and demand fluctuation are also being considered.

II. PROBLEM DESCRIPTION

A. Gasoline Distribution System Gasoline distribution system in Indonesia is performed

started from the upstream (exploration and production) to the downstream (refinery and distribution). On the downstream, there are three main components (node) which formed the gasoline distribution system, i.e. refinery unit (feeder, F), depot (fuel depot, FD) and fuel station (FS) (Figure 1). Gasoline produced in the refinery unit is distributed through pipeline to Depot with the aid of gas turbine pumps. Due to the distance and limited flow pumped, the transport delay is unavoidable.

There are three business processes occur in depot, i.e. receiving process, storing process and distribution process. Receiving process begin when the gasoline started being transported from the refinery unit until it reach the depot. When it reach the depot, it will then allocated to particular storage tank depends on the fluid type, this process considered as storing process. The gasoline stored in the storage tanks then will be distributed to the tank truck. The last process is known as the distribution process.

Figure 1. Components in gasoline supply chain system

B. Non Simultaneous Scheme Depot has role to store the gasoline and other fuel product.

Each product is stored on independent Storage Tank (ST). There are rules applied on when depot should distribute its stock and when it needs to re-stock. A single storage tank is not allowed to have filling and withdrawal process happen at the same time. Therefore, when a storage tank is in filling-state, the withdrawal process should happen at the other storage tank that in idle state. The same rule applied when a single tank is in

withdrawal-state, the product re-stocking should not happen on such tank. These two processes happened non-simultaneously.

III. MODEL DEVELOPMENT

A. Gasoline Level Dynamic on Storage Tank

To describe the gasoline level dynamic on single storage tank, a first-order model approach is used. First-order model approach is used because on the real condition, the storage tank component consists only one input and one output. Hence, first-order model is assumed sufficient to describe a single ST dynamics.

Figure 2. Level dynamic system

According to Figure 2, mathematical model for such system can be described as,

1 (1)

Where h is level (m), qi is inlet flow (m3/s), qo is outlet flow (m3/s) and BO is back-order (m3/s). Note that the system can have filling and withdrawal process happen simultaneously. In order to adapt the non simultaneous scheme, (1) need to be manipulated so it become,

1 (2)

For whichever ST that in filling-state, and,

1 (3)

For ST that in withdrawal states. The discrete time formulation is needed to simplify the inventory dynamics analysis[4].

B. The Non simultaneous Algorithm The modeling process considers the gasoline distribution

system as multi-layer system consists of feeder, depot and fuel station (Figure 1) [8]. In this system, filling and withdrawal process happen non-simultaneously. Each process has different algorithm.

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Figure 3. Filling process flowcha

Figure 4. Withdrawal process flowc

art

chart

1) Filling Process Before the product enteri

storage tank need to be checkempty space (ullage). Once ththen need further checking for idle-state has more priorities topacket from feeder is transferreSo maximum volume limit thanthe pump and pipe datasheet, feeder and depot, the flow canmaximum volume transferred tbe calculated. If this value is exis divided into more than algorithm is shown in Figure 3.

2) Withdrawal Process. Whenever gasoline from d

filling point, tank stock evaluahand. Storage tank that has priority to be a candidate forstate checking process is execufilling state or not. Unlike the batch, the withdrawal process the current active tank level isprocess on such tank must stopanother idle tank with the larges

IV. SIMULATIO

The model is simulated barchitecture, i.e. one feeder distributor. There are 5 storaSimulation is done from a mosampling time is chosen for 1 d

A. Output (Tank Truck ArrivalTable I describe the arrival

each day.

TABLE I. TANK TR

Tank Truck Capacity A

8000 liter 16.000 liter 24.000 liter 32.000 liter

In this system, the tank trucgasoline from ST are transferpattern is not constant for eachmore realistic condition, a randI, following formulation can bpattern,

, ∑

NTTC is the number of dcapacity, in this case NTTC is , is the truck category relat

ing the storage tank, first the ked which one has the largest

he candidate is chosen, the tank its state. Storage tank which in

be filled. Note that the gasoline ed in constant and specific flow. n can be transferred exist. From and also the distance between

n be calculated; furthermore the to storage tank per day can also xceeded, then the filling process one day. The filling process

depot needs to be transferred to ation process must be done first the largest stock is given the

r withdrawal process. Then the uted to see whether the tank is in

filling process, which occurs in occurs continuously. Hence, if

s critically low, the withdrawal p and the process is diverted to st stock.

ON AND ANALYSIS based on Depot Ujung Berung as supplier and one depot as

age tanks, each store gasoline. onth until a year timespan, and day.

l) Pattern Generation frequency of various tank trucks

RUCK ARRIVAL FREQUENCY

Arrival per day

6-8 6-8 6-8 1-3

cks arrival act as output because rred to the trucks. The arrival h day. Therefore, to simulate a

dom aspect is added. From Table be used to describe the arrival

∑ , (4)

(5)

ifferent combination of truck 4 (8 kl, 16 kl, 24 kl and 32 kl). ted to its capacity ( , is 8 kl

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truck, , is 16 kl truck, etc). And is between (minimum arrival frequen (maximum arrival frequency per daypseudo-random function is used with gaussiazero mean. Figure 5 shows an example of truwhich is identical to demand pattern of gasoli

Figure 5. Demand patern for T=60 d

B. Input (Gasoline Supply) Pattern GeneratThe gasoline supply process consists

processes, i.e. ordering process and deliveryto re-stock, Depot must first requests a supplThe request form contains information abogasoline should be transferred to depot. Afaccepted by the feeder, it takes some admprocess. Afterwards, the gasoline is transftakes approximately 4 days from requests gasoline first arrived in depot. The delay process and the product arrival is set to be randistribution with mean 4 days and varianceshows an example of order and supply patimespan.

Figure 6. Example of order and supply

C. Simulation

Figure 7 shows the inventory profile ofeach storage tank with non-simultaneous sccan be seen whenever gasoline supply arrivpriority is determined to the tank with less sto

It can also be seen that during filling continuously exist. This phenomenon is decreasing in Tank 4, even when the supply Simulation timespan is chosen for 30 days. Atransport delay is assumed random with noDue to such random characteristic, the modelMonte-Carlo method [8], i.e. for everyparameter; the system is simulated at least 10the stability.

Figure 8 describes the dynamics usingschedule such that the duration of gasoline st

0 10 20 30 40300

350

400

450

500

Dem

and

[Kl]

Time [days]

a random number ncy per day) and y). To generate , an distribution with uck arrival pattern, ine.

days

tion of two separate

y process. In order ly order to Feeder.

out how much the fter the request is ministrative works ferred to depot. It

process until the between ordering ndom with normal

e 3 days. Figure 6 attern for 30 days

pattern

f gasoline level in cenario adapted. It ve at depot, filling ock.

process, demand shown by stock

arrived (Figure 7). At this stage, total ormal distribution. l is analyzed using

y set of random 0 times to evaluate

g modified supply tay in storage tank

(idle time) is minimum. With that the idle time is relativescheduling in Figure 7. This rcost which exist when a producOn the contrary, such schefluctuation, which is unpredicta

Figure 9 shows the implemthe model. The controller is useoff controller characteristic anprocedure. The algorithm used, 1 , i0 , i

Depot will request for awhenever critical stock level (Laccepted by the feeder then wpacket to depot via pipeline Toprocess takes place is condistribution with mean 4 days The controller is purposely desThis is done because it is neceperformance when back-order aafter day-13, all storage tank order occur. After day-20, threquest to feeder, informed thestocking. After for about 4 dayand the filling process take plahas been refilled, it can be decrease abruptly. This is becday formed by unsatisfied dprevious day. However, the btime

Figure 10 shows the inventosudden overshoot in demand pfix supply for about 5000 kl wiby on-off controller. It can bovershoot, the system still can that as long as demand overshabout 5000 kl is sufficient toovershoot increase for about 3the system need larger supplystability.

50 60

such scheduling, it can be seen ely brief compare to standard related to minimizing inventory ct stay for period of time unused. eduling is prone to demand able by nature.

mentation of On-Off controller to ed due to similarity between on-nd real-life standard operating if

6 if

a gasoline delivery to feeder Level SP) is reached. The request will be processed and send the otal time necessary for the whole sidered random with normal and standard deviation 3 days.

igned to be active after days-20. essary to evaluate the controller appears. And it can be seen that are low on stock, hence, back-

he controller works by sending e feeder that the depot needs re-ys, the delivery arrived in depot ace. Immediately after the stock

seen that the inventory level cause the demand for particular emand accumulation from the

back-order is stable after some

ory level dynamics with varying pattern. Depot is set to received ith supply scheduling controlled be seen that up until 200% in

handle back-order. This means hoot less than 200%, supply for o maintain stability. When the 00% (3 times normal demand), y volume in order to maintain

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Figure 7. Inventory level dynamics, supply pattern, deman pattern and back order

Figure 8. Inventory level dynamics with modified supply schedule

Figure 9. On-off controller implemented for supply scheduling

Figure 10. Queue dynamics related to various demand fluctuation

0 5 10 15 20 25 300

5

10

15

Leve

l Tan

k [m

]

Time [days]

Tangki 1 Tangki 2 Tangki 3 Tangki 4 Tangki 5

0 5 10 15 20 25 30300

350

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Dem

and

[Kl]

Time [days]

0 50 100 150300

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Dem

and

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Time [days]

0 50 100 1500

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Bac

k O

rder

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0 10 20 30 40 50 600

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0 10 20 30 40 50 60

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and

[Kl]

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90

V. CONCLUSION The model obtained at current stage can accommodate non-

simultaneous scenario, transport delay, demand fluctuation and controller implementation. The model is open to be implemented with other control algorithm. Therefore, for further development, supply scheduling and optimum transport capacity per batch can be determined based on the model, and the solution can be obtained with optimization algorithm to obtain the optimum time related to when to begin the supplying [1].

Model Predictive Control (MPC) approach can be used to control the system due to its feature to predict future disturbance [6]. The idea is using the model to predict demand for a period of time, and then calculate and then determined the optimum supply period and volume.

ACKNOWLEDGMENT Authors would like to sincerely express our gratitude to

Ikatan Alumni (IA) ITB for the funding support through Riset Ikatan Alumni 2010 scheme.

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“Optimizing the Petroleum Supply Chain at PETROBRAS”, ESCAPE-14, Lisbon, Portugal

[2] Chen, C. L. and Lee, W.C. "Multi-objective optimization of multi-echelon supply chain networks with uncertain product demands and prices". Elseiver. Computers and Chemical Engineering 28, 2004: 1131–1144.

[3] Chen, K. K. and Chang, C.-T. "A seasonal demand inventory model with variable lead time and resource constraints." Elseiver. Applied Mathematical Modelling, 31, 2007: 2433.

[4] Lee, Y. H., Cho, M. K., Kim, S. J., and Kim, Y. B. "Supply chain simulation with discrete continuous combined modeling". Elseiver. Computer and Industrial Engineering, 43, 2002: 375–392

[5] Longo, F. and Mirabelli, G. "An advanced supply chain management tool based on modeling and simulation". Elseiver. Computers and Industrial Engineering, 54, 2008: 570-588.

[6] Lopez, E. P., Ydstie, B. E., and Grossmann, I. E. "A model predictive control strategy for supply chain optimization". Elseiver. Computers and Chemical Engineering, 27, 2003: 1201-1218.

[7] Seferlis, P. and Giannelos N. F. "A two-layered optimisation-based control strategy for multi-echelon supply chain network". Elseiver. Computers and Chemical Engineering, 28, 2004: 799-809

[8] Siregar, P., Yulia, R. Y., Wungu, T. D. K., Wantah, F. "Modeling the petroleum supply chain at Pertamina UPMS III, Indonesia". ICICI. Bandung. 2007

[9] Yuzgec, U., Palazoglu, A., and Romagnoli, J. A. " Refinery scheduling of crude oil unloading, storage and processing using a model predictive control strategy ". Elseiver. Computers and Chemical Engineering, 34, 2010: 1671-1686

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