A Cluster-Based Technique for Identifying and Grouping Oily Waste Types Generated From Marine Oil Spill Response Operations

In the event of a marine oil spill and its subsequent response operations, different types oily wastes are generated in large quantities, their management is significant challenge that responders face. The goal this study to develop comprehensive pattern recognition modeling framework for deriving grouping set unique clusters separate from each other. main idea group based on characteristics, such as percentage oil, water, mineral matter, organic matter. Each cluster has relatively homogeneous pollution characteristics. Prior implementing analysis technique, it important evaluate transform raw waste data using well-defined criteria. An advanced machine learning fuzzy C-means clustering algorithm, employed classify wastes. Kolmogorov–Smirnov tests examine statistical significance clustered data. Results show heterogeneous diversity seven identified relation cluster-based method presented article an integral part integrated optimization-based model which will provide valuable inputs adjustment existing practices, enhancement short-term control strategies, development long-term policies. output would better tool characterization sorting steps required immediately recovered support downstream efforts. This result also supports overall minimizing impact environment by ensuring maximum amount can be recycled or disposed environmentally friendly fashion. Moreover, properly classified, sorted, labeled greatly help with packaging, transportation, tracking waste, result, minimize total time costs, under constraints involving storage transport capacities, pre-treatment treatment facility environmental regulatory compliance, well other operational logistic constraints.