In a significant stride towards enhancing environmental protection and maritime safety, a team of researchers led by Emad A. Az-Zo’bi from the Department of Mathematics and Statistics at Mutah University has developed a novel approach to model the transport of pollutants in water. Their work, published in the journal ‘Scientific Reports’ (which translates to ‘Nature Research Reports’), introduces a new formulation of advection dispersion equations using fractional calculus, providing a more accurate and flexible description of how pollutants spread in water bodies.
At the heart of this research is the Modified Atangana–Baleanu–Caputo (MABC) fractional derivative, an advanced extension of the classical Atangana Baleanu derivative. This tool offers greater flexibility in describing memory and nonlocal effects, which are crucial for understanding the complex behavior of pollutants in water. As Az-Zo’bi explains, “The MABC derivative provides a more nuanced understanding of the advection dispersion phenomena, allowing us to capture the subtle dynamics that traditional models might miss.”
To solve these complex equations, the researchers turned to physics-informed neural networks (PINNs), a deep learning framework that incorporates physical laws into the model. This approach not only enhances the accuracy of the solutions but also speeds up the convergence process. The researchers demonstrated the effectiveness of their method through various case studies, showing that it outperforms conventional numerical and perturbative methods in terms of precision and computational efficiency.
The implications of this research for the maritime sector are substantial. Accurate modeling of pollutant transport is essential for developing effective mitigation strategies, protecting marine ecosystems, and ensuring the safety of maritime activities. By providing a more precise and efficient tool for predicting pollutant dispersion, this research can aid in the design of better environmental protection measures and emergency response plans.
Moreover, the commercial opportunities are significant. Shipping companies, port authorities, and environmental consulting firms can leverage this technology to enhance their environmental impact assessments, reduce risks, and comply with regulatory standards. As the maritime industry increasingly prioritizes sustainability, tools like the one developed by Az-Zo’bi and his team will become invaluable.
In summary, this research represents a significant advancement in the field of environmental modeling, with far-reaching implications for the maritime sector. By combining fractional calculus with deep learning, the researchers have developed a powerful tool that promises to enhance our understanding of pollutant transport and improve environmental protection efforts. As the lead author notes, “This work is a testament to the power of interdisciplinary research, bringing together mathematics, physics, and computer science to address real-world challenges.”