Optimization Study of Carbon Emission Reduction from Ships Based on EEXI and CII Frameworks: Synergistic Application of Transformer-LSTM Model and Quadruple Waste Heat System

Amid global efforts toward energy conservation and carbon reduction in maritime transport, this study proposes an interdisciplinary optimization framework integrating predictive analytics with advanced energy systems to address challenges in vessel decarbonization. A Transformer-LSTM hybrid model is developed to forecast emission patterns using historical operational data, enabling dynamic navigation strategy adjustments. The complementary quadruple-heat supply system synergizes waste heat recovery, energy efficiency optimization, and carbon capture technologies through an integrated control architecture. Case studies demonstrate significant improvements in both Energy Efficiency Existing Ship Index (EEXI) and Carbon Intensity Indicator (CII), achieving 18.7% enhancement in energy utilization efficiency and 22.4% reduction in operational carbon intensity compared to conventional systems. The proposed solution offers a cost-effective pathway for retrofitting legacy vessels, particularly in optimizing power distribution and thermal management under varying operational conditions. Quantitative analysis reveals the system's potential to reduce annual greenhouse gas emissions by 34-41% per vessel, substantiating its contribution toward achieving IMO's 2050 decarbonization targets. This research establishes a methodological framework for intelligent energy-carbon synergy control in maritime applications, providing both theoretical advancements and practical implementation strategies for the shipping industry's low-carbon transition.