Dynamic optimisation of evacuation route in the fire scenarios of offshore drilling platforms. (1st March 2022) - Wang, Yanfu; Ma, Weikai; Wang, Tao; Liu, Jianlin; Wang, Xinjian; Sean, Malkeson; Yang, Zaili; Wang, Jin
Abstract: When fire occurs in an offshore platform, evacuation plays a vital role in safeguarding the evacuees' lives. How to efficiently evacuate to minimize the loss of life is a dynamic problem, requiring a continuing research effort with changing technologies. In this research, a dynamic optimisation model is proposed to determine the optimized evacuation route in the fire scenarios of offshore platforms. Firstly, the road network model of an offshore platform is built in a Geographic Information System (GIS) environment based on the data from a real drilling platform. Secondly, drilling platform fires are simulated using Fire Dynamics Simulation (FDS), and then the fire simulation data is input into the proposed road network model. Thirdly, the traditional Ant Colony Optimisation (ACO) Algorithm is improved by considering the influence of fire on evacuation to study the impact of high temperature, smoke and toxic gases on evacuation. Next, the improved route optimisation algorithm and a road network model of the offshore platform are integrated to formulate a dynamic route optimisation model for evacuation in fire scenarios. Finally, a case study is conducted to demonstrate the model on a drilling platform of Nan Hai in China. The results reveal that the equipment area around the fire source on the lower deck is mostly affected by the smoke. It is validated that the proposed model can be used to optimize the evacuation route to guide evacuees avoid the hazardous area according to the dynamic spread of smoke. This study can provide fast real-time guidance for the trapped evacuees during the evacuation process on offshore platforms by considering the influence of fire on evacuation. Highlights: The road network model of an offshore platform is built in a GIS environment. Drilling platform fires are simulated by FDS, and then the fire data is input into the proposed road network model. The traditional Ant Colony Algorithm is improved by considering the influence of fire on evacuation. The improved route optimisation algorithm and the road network model are integrated to propose the dynamic route optimisation model for evacuation in the fire scenarios of offshore platforms. The scientific novelty is that the proposed dynamic route optimisation model can visualize the real time impact of dynamic fires on evacuation route and optimize the evacuation route.