RACETT Nigeria Ltd, 32 Agric Road, GRA Effurun, Nigeria
Received date: January 13, 2017; Accepted date: February 04, 2017; Published date: February 08, 2017
Citation: Ejofodomi OA (2017) Ground Robotic Oil Spill Surveillance (Gross) System for Early Detection of Oil Spills from Crude Oil Pipelines. J Appl Mech Eng 6:253. doi: 10.4172/2168-9873.1000253
Copyright: © 2017 Ejofodomi OA. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Crude oil spills have negative impacts on the environment, health, society and economy. Prompt detection of oil spills would lead to swift remediation, improvement in national economy, and a reduction in the damage to the environment. The Ground Robotic Oil Spill Surveillance (GROSS) System is a novel system for early detection of oil spills using small mobile robots. A single GROSS unit is a mechanized robot employed to patrol beside designated pipelines. A Global Positioning System (GPS) is used for autonomous navigation, an ultrasonic sensor for avoiding obstacles, and gas sensors for spill detection. Once a spill is detected, an integrated camera module is used to take pictures of the oil spill site. Using Xbee radios, spill images and GPS location are transferred wirelessly to a computer in the base station to enable surveillance teams initiate established containment protocols.
The GROSS system has been successfully tested on an underground crude oil pipeline at the Federal University of Petroleum Resources. The system’s ability to provide continuous surveillance along crude oil pipelines and wireless transmission of spill GPS location and images gives it the ability to detect oil spills quickly and ensure prompt response by surveillance teams.
Crude oil spill; Robotics systems; Pipeline surveillance; Oil spill detection
Oil spills have significant negative effects on the environment in which they occur and this fact has been documented extensively in scientific literature by Melanie , Gill et al. , Kadafa , Olawuyi , D’Andrea et al. , Mogborukor , Oyem , Chang et al. , Nwachukwu et al. , Okoye et al. , Oyebamiji et al.  and Zaki et al. . Direct or indirect contact with crude oil can result in adverse health symptoms such as headaches, fatigue and lethargy, loss of concentration and co-ordination, dizziness, nervousness, insomnia, nausea, vomiting, visual disorders, skin rashes and sores, respiratory disorders like shortness of breath, exacerbation of asthma, psychological problems and even cancer as studied by Gay et al.  and Salako et al. .
When crude oil spills occur in water, it disrupts the food chain and finds its way through the fur and skins of aquatic animals, reducing their insulation properties and making them prone to hypothermia. These animals may die if they ingest the oil. Spilled oil on land can contaminate drinking water and destroy agricultural crops. Other effects include loss of biodiversity in breeding grounds, vegetation hazards, loss of portable and industrial water resources, reduction in fishing and farming activity, poverty and rural underdevelopment . Environmental remediation (removal of pollutants or contaminants from water and soil) after oil spills is necessary, but very expensive. For example, the 1989 Exxon Valdez spill cost Exxon $4.8 billion for remediation, compensatory payments, settlements and fines .
The Niger delta region of Nigeria has been plagued with oil spills for decades. The Department of Petroleum Resources (DPR) reported 4,647 incidents that spilled 2,369,470 barrels of oil into the environment from 1976 to 1996. Majority of these spills are due to either corrosion or sabotage and theft . Prompt detection of oil spills will minimize the damage done to the environment, enable quick environmental remediation, protect the health of the indigenous communities, reduce the loss of crude oil, and prevent financial losses for oil companies, government and indigenous communities.
Several methods have been employed for spill detection, including real time remote surveillance by flying aircrafts with surveillance teams. Other methods employ various sensors such as visible sensors, infrared sensors, ultraviolet sensors, radar sensors, and laser fluorosensors [18,19]. Two prominent spill detection methods used in the Niger Delta are pipeline pressure monitoring and human surveillance teams. Both methods have their limitations. The pressure monitoring method cannot detect oil spills promptly, as pipeline pressure loss is highly unreliable in determining a pipeline release by Amnesty International . Human surveillance of the right of way (the land area around pipes and other infrastructure), by local/public contract, or company personnel, to visually identify an oil release is more likely to be successful than any other release detection method. If a spill is very close to a community it may be noticed immediately, but if it is far from communities, or if it is in water or swamp, then a spill can flow for days before being noticed .
This paper presents a system to provide constant autonomous surveillance for crude oil pipelines using ground mobile robots. This system, defined as the Ground Robotic Oil Spill Surveillance (GROSS) system, is an improved model of the system described in a previous publication . The GROSS System navigation is done by Global Positioning System (GPS) and magnetic orientation. Obstacle avoidance is made possible by an ultrasound sensor. Oil spill detection is achieved using gas sensors. An integrated camera acquires high resolution images of spill sites. GPS location and spill site images are transmitted wirelessly to a computer in the base station using Xbee Wireless Modules. By employing low cost GROSS units to patrol designated pipelines, it could improve leakage detection time and minimize damage to the environment. Once a spill is detected, the patrolling robot will alert the surveillance team to initiate established containment protocols. The presence and constant monitoring provided by these robots could act as a deterrent to saboteurs in the Niger Delta, and help in detecting oil leakage due to sabotage and theft as well as corrosion.
The GROSS system design and construction is presented in the Materials and Methods Section. The finished system was successfully tested on an underground crude oil pipeline in Effurun Nigeria. The results of the conducted test are presented in the Results section, and examined in the Discussion and Conclusion sections.
1) Chassis: The GROSS system chassis must have the ability to move on any ground terrain in which the pipeline is located (Figure 1).
The chassis is an aluminum tank with die cast zinc gears, tracks, suspension struts and wheels, weighs 3.7 kg and measures 355 mm (L) × 265 mm (W) × 130 mm (H). These metals offer good corrosion resistance and no maintenance requirement. Motion is accomplished by supplying power to the 12V motors using a 12 V 9000 mAh lead acid battery.
2) Development board: The Arduino Uno Rev 3 development board was used to program the GROSS system to perform autonomous pipeline surveillance.
3) Global positioning system (GPS): The Arduino GPS shield was used to acquire the longitude and latitude of detected spill sites for the surveillance team. Secondly, during and in between patrols, the GPS location was used to determine if the system had been physically tampered with by saboteurs.
4) Xbee: A pair of Xbee Pro 900HP wireless modules is used to establish wireless communication between GROSS system and Host PC at remote base station. Table 1 shows the parameters of the Xbee module. With a high gain antenna, this module can establish a wireless connection between the GROSS unit and the host PC up to a distance of 45 km. The GROSS system uses this connection to notify the base station at the start of its patrol and to relay the GPS location of detected spill sites to the PC of the surveillance team.
|RF Band Rate||900 MHz|
|RF Data Rate||10Kbps or 200Kbps|
|Indoor Urban Range (2.1 dB antenna)||10 Kbps: up to 2000 ft (610 m)
200 Kbps; up to 1000 ft (305 m)
|Outdoor LOS Range (2.1 dB antenna)||10 Kbps: up to 9 mi (15.5 km)
200 Kbps; up to 4 mi (6.5 km)
|Supply Voltage||2.1-3.6 V|
|Transmit Voltage||215 Ma|
|Receive Voltage||29 Ma|
Table 1: Relevant parameters of the Xbee Pro 900 Hp module.
Captured Images of the oil spill sites acquired by the GROSS unit are also transmitted to the team via this Connection 9 (Figure 2).
5) Motor shield: A MegaMoto Motor shield for Arduino is used to connect the 12V chassis motors to the 12V lead acid battery. The Moto shield is a general-purpose power amplifier designed to suit a wide range of DC loads.
The motor shield draws its power supply from the 12V lead acid battery, and feeds it to motors A and B of the robot chassis, depending on the logic signal it receives from the Arduino Uno.
6) Magnetometer: The HMC5883L magnetometer is used to ensure the GROSS system stays on course during pipeline patrol, and to ensure an accurate 180° turn in the opposite direction patrol completion, so the unit can patrol in the opposite direction.
7) Ultrasound sensor: The HC-SRO4 is an ultrasound sensor that uses time between transmitted and received waves to calculate distance between sensor and object. The sensor is mounted on the front of the GROSS unit. During patrol, if the sensor detects an obstacle less than 35 cm in front of it, the system takes a detour route to avoid the object before continuing along its patrol route.
8) Oil spill detector: The primary method by which the GROSS system detects oil spills is by means of the MQ-6 gas sesnor. When an oil spill occurs, methane is released into the environment. The unnatural increase in methane is detected by the tin oxide (SnO2) layer in the sensor, and this is reflected in the sensor’s output voltage. The MQ-6 sensor was mounted at the bottom of the GROSS unit to ensure close proximity to the ground for quick detection of spills (Figure 3).
9) Camera: The LS-Y201-Infrared camera (Figure 4a) captures high resolution pictures, and is operational day and night. Figure 4b shows the camera mounted on the GROSS system. When the system detects a spill, it stops and takes pictures of the spill site. This 320 × 240 image is transmitted wirelessly to the surveillance team in 10 seconds. Figures 4c, 4d and 4e show an image captured by a high resolution digital camera, the same image captured by the LS-Y201-Infrared camera during day, and at night, with virtually no difference between the daytime and nighttime images.
10) Solar panel: The motors of the chassis receive power from the 12 V Lead Acid Battery to perform pipeline patrol. A solar panel was used to maintain continuous power supply to the unit during and in between pipeline patrols. The panel is made from polycrystalline cells, has an output power of 8W, an output voltage of over 18V, and an output current of 450 mA.
The operation of the GROSS system is shown in Figure 5. When the GROSS unit is powered on, itobtains its current location, and the start and end points of its patrol route using GPS. North, south, east and west orientation and current orientation are acquired by the magnetometer.
The unit moves towards the end of the pipeline, while simultaneously checking for crude oil leakage using the MQ-6 sensor. If the unit detects a spill, it stops and takes pictures of the spill site. Spill images and GPS location are transmitted to host PC at the base station via Xbee modules. During patrol, the unit constantly checks to ensure it is traveling in the correct direction using the onboard magnetometer, employing a path adjustment if necessary. It also checks for the presence of obstacles using the HC-SR04 sensor. If an obstacle is detected less than 35 cm away from the unit, it takes a detour route around the obstacle before resuming its patrol. Upon arriving at End location, the unit turns 180°, and then waits a preset time interval before patrolling the pipeline again.
The GROSS unit was programmed to provide constant autonomous surveillance for a 100 m stretch of an underground pipeline in Effurun, Nigeria in approximately 4 minutes. System speed was set to 450 mm/s. While the GROSS system can provide constant surveillance for distances greater than 100 m, smaller distances translate to faster spill detection times. Multiple GROSS units can be employed to provide constant surveillance for larger distances. Time interval between patrols was set to 1 hour. Therefore every hour, the GROSS unit patrolled the 100 m stretch of the pipeline for approximately 4 minutes. For pipelines that are highly susceptible to vandalism, this time interval can be reduced to provide an almost continuous patrol by the system.
This section presents the visual and quantitative data obtained from actual field tests carried out on the GROSS system. Five major tests were conducted to determine the following: the system’s ability to autonomously and constantly patrol 100 m of a pipeline, its ability to run continuously without exhausting its power supply, its ability to detect and avoid obstacles during patrol, its ability to detect and alert the surveillance team if it experienced any physical tampering, and its ability to detect crude oil spills and transmit spill data to surveillance team at the base station.
The GROSS unit was programmed to autonomously patrol 100 m of an underground crude oil pipeline (Figure 6a). Figure 6b shows the system during its 100 m patrol of the pipeline.
It took approximately 4 minutes to complete patrol. After patrol completion, the system stopped and turned 180° (Figure 6c). It then waited for 1 hour before commencing patrol in the opposite direction. This verified the system’s ability to continuously and successfully patrol beside crude oil pipelines.
2) Power test
The voltage of the 12V lead acid battery was measured before and after 100 m patrol completion. During time interval between pipeline patrols, the solar panel charged the battery (Figure 7a). The battery voltage was measured 15 minutes and 30 minutes after patrol completion and solar charging. Solar charging was performed between 10-11 a.m. on February 11, 2016 under cloudy conditions (Figure 7b). The data obtained from this test is shown in Table 2. When the GROSS system patrolled 100 m, the battery voltage dropped by 0.27 V. Solar charging of 15 minutes resulted in 0.27 V increase. Therefore, for every 100 m patrolled, 15 minutes of solar charging is required to restore the battery back to its initial voltage. The time between patrols was set to 1 hour for this test, so the battery had sufficient time to fully recharge before resuming patrol.
|Initial Battery Voltage (V)||Patrol Time (Minutes)||Patrol Distance (m)||Battery Voltage After Patrol (V)||Solar Charging Time (minutes)||Final Battery Voltage (V)|
Table 2: Battery test for gross unit during and in between surveillance patrol.
3) Obstacle avoidance
If the HC-SRO4 sensor detected an obstacle less than 35 cm in front of the system, the unit was programmed to take a detour route to avoid the obstacle. This was successfully demonstrated by the GROS system (Figure 8).
4) Tampering detection
The GPS latitudes and longitudes of the surveillance route ranged from 5.539860 - 5.539770 and 5.825970 – 5.825250 respectively. During and in between patrols, the system was physically picked up and carried away from its assigned route (Figure 9), causing its GPS location to go beyond its surveillance range. When this occurred, the system wirelessly issued warning messages to the PC at the base station, alerting the surveillance team that it had been physically tampered with, and providing its current GPS location.
5) Oil spill detection
Crude oil spills were simulated along the underground pipeline. Approximately 1 liter of crude oil was poured in a large polythene bag that was placed along the surveillance route of the GROSS system (Figure 10). This was done to ensure crude oil was not spilled into the testing environment used in this research. The system successfully detected the simulated oil spill place along its surveillance route. It took a picture of the spill site and wirelessly transmitted the image and spill GPS location to the PC at the base station located 100 m away from the 100 m pipeline section.
Figure 10: (a) Simulated Oil Spill. (b) GROSS system patrolling surveillance route with simulated crude oil spill. (c) System detecting presence of 1 liter spill. (d) Remote Base Station with host PC and Xbee 100 m away from surveillance route receiving spill data. (e) Wirelessly transmitted spill GPS location and image data sent to PC. (f) Captured spill image sent to PC.
The GROSS system presented in this paper demonstrated the ability to provide continuous autonomous surveillance for Nigeria’s crude oil pipelines. The system was able to continuously patrol 100 m of an underground pipeline in 4 minutes. A trade-off exists between distance and time. Longer surveillance distances mean longer patrol times and longer spill detection times. Multiple GROSS units can be utilized to provide extensive surveillance coverage for crude oil pipelines with longer lengths. The solar panel provided continuous renewable power to the system. Therefore, once the system is programmed and installed along a crude oil pipeline, it can autonomously provide continuous surveillance without any external intervention. Continuous surveillance for Nigeria’s entire network of crude oil pipelines will help swiftly detect vandals and saboteurs damaging pipelines. The GROSS system is to be used both for underground and surface crude oil pipelines. It is assumed that the location of the pipeline under surveillance is known, as the oil companies operating these pipelines will possess this information. Furthermore, since the GROSS system will be utilized by oil companies, this information will be available to accurately program the GROSS system for automated pipeline surveillance. The GROSS system demonstrated the ability to detect crude oil spills as small as a single liter. This clearly shows that even if the system can detect leaks caused by small cracks in the pipeline, due to the high sensitivity of the gas sensor. For underground pipelines in which the spills may be contained within beneath the ground, it should be noted that the methane gas has the ability to diffuse through the soil and into the air. So while there may not be any physical evidence above ground that a spill has occurred, the system would still be able to detect crude oil spills contained within the soil.
100 m patrol resulted in a battery voltage drop of 0.27 V. Inbetween patrols, the solar panel charged the battery back to its original voltage in 15 minutes. This means that a GROSS unit set to patrol 100 m will need only 15 minutes between each patrol to maintain its power supply. It should be noted that during this test, the solar panel was disconnected from the unit during patrol. For real world applications, charging will also occur during patrol so time interval between patrols could be reduced to zero. This would mean that every inch of a pipeline would be checked for spills every eight minutes without expending any human effort. Superior motion and path planning algorithms were demonstrated by the system when it encountered obstacles. It also demonstrated an inbuilt tampering detection algorithm to alert the base station when saboteurs attempt to cause interference. This ability can help to alert the appropriate authorities before pipeline vandalization occurs.
The GROSS system demonstrated excellent spill detection capabilities. Spill GPS location and images were successfully transmitted to the surveillance team at the base station. Visual verification of spills helps surveillance teams to estimate spill size and initiate appropriate containment protocols. The system demonstrated the ability to detect spills as little as 1 liter, and this could mean the end of crude oil spills greater than a single liter. Spills due to pipeline vandalization and corrosion can be detected by the system.
The benefits of installing this system along the Nigeria’s network of crude oil pipelines are evident. The assurance of knowing that you can provide surveillance to every inch a nation’s pipeline every 8 minutes is a great accomplishment. You could also potentially detect vandals before they attack pipelines, and detect spills as little as a liter, without any external human effort. The GROSS system has the potential to prevent the financial losses associated with massive oil spills, and will also ensure the preservation of aquatic life and farmlands of indigenous communities.
The GROSS system presented in this paper is currently ready for wide-scale commercialization and application to Nigeria’s network of crude oil pipelines. A current additional feature being explored for inclusion into the GROSS system is active participation in containment protocols after spill detection. Each GROSS unit will be wirelessly linked to the pipeline valves in its surveillance route so it can automatically shut off the valves after spill detection. This would ensure the stoppage of a spill immediately after detection. Since the system can detect 1 liter spills, oil companies can avoid large-volume spills. Each valve is fitted with an Arduino board, a wireless Xbee module and shield for wireless communication, and a relay module that controls the power supply to the valve. By automatically opening or closing the relay, the valve is either opened or closed to allow or prevent crude oil flow. When the Arduino at a valve receives a wireless message from the patrolling GROSS unit indicating spill detection, the Arduino closes the valve by altering its power supply, thereby providing immediate cessation of the spill. Another feature being explored is camera mobility. In the current system, the camera is fixed and mounted in front of the system. By incorporating a small platform attached to a servo, the camera can span a wide angle to ensure captured images of the spill site contain pertinent visual information about the spill.
Inability to provide continuous and adequate surveillance of pipelines is one of the key reasons why crude oil spills persist in Nigeria. The GROSS system was invented to address this shortcoming and has so far demonstrated its ability to promptly detect crude oil spills, in addition to detecting the presence of vandals via its tampering detection algorithm. By installing these GROSS units beside the nation’s pipelines, crude oil spills can be detected as soon as they occur, and pipeline vandalism and sabotage can be eradicated from the nation.
Crude oil spills have been a chronic problem plaguing the Nigerian oil and gas industry for many decades. Lack of prompt detection of crude oil spills has resulted in large scale environmental pollution, decrease in national revenue, and loss of livelihood and lives. The GROSS system demonstrated the ability to provide autonomous and continuous surveillance for crude oil pipelines, detect crude oil spills as small as a single liter, and remotely provide spill location and images to surveillance teams. Using this system, every inch of the nation’s pipelines can be checked every 8 minutes for spills. This guarantees immediate detection of spills as soon as they occur, be it as a result of corrosion, sabotage, or oil production operations, and will lead to swift remediation, improvement in national economy, and a reduction in the damage to the environment.
The authors would like to thank the organizers of the Oil Trading and Logistics 2015 Expo Conference for their financial contributions towards this research.