Selection and optimization of marine oil spill response operations using artificial intelligence and soft computing techniques

Marine oil spill incidents are detrimental to both natural environment and human health. Water quality, marine ecosystems, and shoreline conditions can be deteriorated by the spilt oil. Swift and efficient response to an oil spill is crucial to minimize the adverse consequences. However, the oily waste generated from response operations may also become a challenge, requiring careful waste management strategies. Widely used oil spill response methods (OSRMs) include mechanical containment and recovery (MCR), in-situ burning, and the use of chemical dispersants. Choosing the most suitable method is a complex process depending on various factors. Among OSRMs, MCR is the most effective in removal of spilt oil from the marine environment. The management of oily wastewater generated during MCR requires careful attention, as it comprises a significant portion of overall oily waste. This study developed multiple tools to aid selecting OSRMs in harsh and remote offshore waters. These selection tools employ machine learning techniques and historical response data to predict appropriate OSRMs for new incidents. The tools were developed in MATLABTM using various artificial intelligence and soft computing techniques, such as fuzzy decision tree (FDT), Gaussian process regression (GPR), and artificial neural network, individually or in combination. FDT-based tools were also integrated with regression analysis techniques and an optimization algorithm to enhance their performance. Optimized FDTs integrated with regression analysis and GPR were found to be the most effective techniques based on the prediction power. Furthermore, this study developed an integrated optimization tool to efficiently manage the mechanical response process. This tool aims to minimize the time and cost associated with MCR and oily wastewater management (OWM) and the volume of weathered oil during the operation. The tool encompasses three components of multi-objective optimization, oil weathering process, and MCR and OWM operational agents, simulating detailed response procedures. Applying the tool to a case study in Canada led to a notable reduction in the time and cost of the entire response, and a considerable increase in the volume of recovered oil. It provides an effective approach to manage response process, and significantly reduces the environmental and socio-economic impacts of oil spill incidents.Applied Science, Faculty ofEngineering, School of (Okanagan)Graduat