Creating a digital twin platform for maritime decarbonization by AI-assisted CII measure prediction: A case of chemical tanker

• CII criteria for maritime and shipping is predicted using AI methods. • Ship digitalization with deep neural network and GRNN performance comparison. • GRNN outperforms DNN structure: R G R N N 2 = 0.998 > R D N N 2 = 0.994. • The B vessel system rating with 0.57 for the year 2025 is determined. Carbon emission reduction has been the focus of the International Maritime Organization (IMO), and restrictive mandates are considered by the Marine Environment Protection Committee (MEPC). The new guidelines consider carbon dioxide (CO 2 ) emissions based on the propulsion system efficiency, distance, and dead weight, which are called the carbon intensity indicator (CII). In this research, this factor was calculated based on the large available data from a chemical tanker ship to analyze the ship rating using artificial intelligence techniques. The available data, consisting of global positioning system (GPS) location, wind speed and direction, draft and trim, engine power and speed, and vessel speed, are used for the CII prediction by the artificial neural network (ANN) modeling. Two types of ANN are considered for modeling: multilayer feedforward with two hidden layers, called deep neural networks (DNN), and generalized regression neural networks (GRNN). The attained, required, and referenced CII are calculated, and the system rating is determined and compared with the predicted CII. The best performance of the DNN is achieved with 15 neurons in the first and second hidden layers. The performance of the two types of ANN is robust and close to each other. However, the GRNN has slightly better predictive efficiency, considering the faster convergence and setup configuration complexity. The GRNN model shows a mean absolute error of 0.0928 with an unacceptable prediction ratio of 0.06 % and a coefficient of determination R 2 = 0.998, which can capture the CII metric values and trend in transient mode robustly.