Tackling Waste for a Sustainable Future: A Deep Learning -Based Approach to Cargo Tank Crude Oil Washing and Residue Removal,

: Efficient waste management is pivotal in advancing sustainability within the oil and gas industry, particularly in reducing the environmental footprint of crude oil transportation. This study explores a deep learning-based approach to optimising the crude oil washing (COW) process and minimizing residue in cargo tanks. Traditional cleaning methods are often inefficient, resource-intensive, and environmentally hazardous, leading to increased operational costs and greater environmental impact compared to the proposed deep learning-based approach. By leveraging deep learning algorithms—specifically convolutional neural networks (CNNs) and long short-term memory (LSTM) networks—this research introduces an intelligent system that can predict optimal washing cycles, detect residual build-up in real-time, and automate tank cleaning processes. The proposed system utilises sensor data, thermal imaging, and historical washing records to build predictive models that enhance operational efficiency and reduce waste and emissions. Real-world case studies from tanker operations demonstrate how this AI-driven model improves residue removal efficiency by up to 30%, while also cutting water usage and chemical discharge significantly. The results underscore the potential of integrating AI and sustainable engineering to revolutionise waste reduction in crude oil logistics. This research contributes to the growing field of green AI applications in industrial operations and offers a scalable framework for other heavy industries confronting similar residue and waste challenges. Keywords: Deep learning, Crude oil washing, Residue removal, Cargo tank, Sustainability, Waste management