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Dissertation Defence: Selection and Optimization of Marine Oil Spill Response Operations using Artificial Intelligence and Soft Computing Techniques
September 29 at 2:00 pm - 6:00 pm
Saeed Mohammadiun, supervised by Dr. Rehan Sadiq, will defend their dissertation titled “Selection and Optimization of Marine Oil Spill Response Operations using Artificial Intelligence and Soft Computing Techniques” in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Civil Engineering.
An abstract for Saeed Mohammadiun’s dissertation is included below.
Examinations are open to all members of the campus community as well as the general public. Please email firstname.lastname@example.org to receive the Zoom link for this defence.
Marine oil spill incidents are detrimental to both natural environment and human health. Water quality, marine ecosystems, and shoreline condition can be deteriorated by the spilt oil. Swift and efficient response to an oil spill is extremely crucial to minimize the adverse impacts and consequences. However, the oily waste generated from response operations may also become a threat to the environment and socio-economic conditions of an affected area.
This study developed multiple tools to aid selecting suitable oil spill response methods (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 MATLABT 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. FDTs integrated with regression analysis and GPR were found to be the most effective techniques based on the prediction accuracy and robustness.
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) as well as the volume of weathered oil during the operation. The tool encompasses three main components of (1) multi-objective optimization agent; (2) oil weathering process agent; and (3) MCR and OWM operational agent. Applying the developed 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.