ABSTRACT
The tightening CII regulations has brought challenges to the maritime industry. For a huge number of aging ships, retrofit is vital for stakeholders to achieve IMO’s targeted carbon emission reduction of 20-30 https://www.w3.org/1998/Math/MathML" display="inline"> % by 2030. At present, the retrofitting approaches generally includes wind assisted ship propulsion, air lubrication biofouling management, electrification, low carbon emission fuel, etc. Considering emission reduction effect and economic efficiency, a combined retrofit policy is crucial for each ship in demand. Considering that the requirements of CII regulations are tightening gradually, adopting optimal retrofit approaches at the right time based on ship’s decarbonization conditions is vital for stakeholders. Deep reinforcement learning (DRL) is introduced to acquire the optimal ship retrofit policy for lowest life-cycle cost under the supervision of CII regulations. Herein, a retrofit strategy framework based on CII is established for decision-making support in the context of carbon reduction.
