Eco idle system is a system to reduce exhaust emission, fuel consumption and number of engines spend during idling on the road by shutting off the engines automatically. The purpose of this project is to develop the simulation of an eco-idle system and install it on a motorcycle which is the system will turn off the motor after several seconds in idling mode. In this project, there are a few parts that have been used to develop this system kit for motorcycles. The circuit and coding of the system were created in Integrated Development Environment (IDE) software. Data acquisition System (DAQ) was developed, which will lead to the analysis of acceleration and deceleration of motorcycles. In this simulation, the main component is Arduino UNO. In this project, the coding has been added 4 seconds for engine to stop according to driver’s behaviour in real road conditions. After simulating the eco idle kit system, this kit needs to install to motorcycle which is YAMAHA LC135 to analyse data acceleration and deceleration of motorcycles during development of eco-idle kit system. The test methodologies and results for development of eco idle kit system which were tested in different checkpoints. Five different checkpoints were selected to conduct tests of eco idle kit system. Parking lot student UTHM Campus Pagoh served as the site of each checkpoint, which was used to evaluate the eco idle kit system. In conclusion, the simulation of the eco idle system has been successfully run by the connection between required part and coding of the program.

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Pigging operation is primarily conducted for flow assurance and integrity of the pipeline. It will ensure maximum throughput and minimise internal pipeline wall corrosion by removing debris, asphaltene, sulfate reducing bacteria (SRB) and other impurities from the pipeline. Currently at PETRONAS Peninsular Malaysia Operations (PMO), pigging operation is conducted per time-based as required by pipeline's Corrosion Management Plan (CMP). The pigging frequency will be revised should there are be any changes to the amount of water holdup inside the pipeline. Since there is no reliable way to predict how much water holdup and heavies will deposit, pigging frequency can be optimised only by learning system behavior.Once the system behavior is better understood, the condition-based pigging frequency can be optimised.