An intelligent data-driven investigation and optimization integrated with an eco-friendly thermal design approach for a marine diesel engine to study its waste-to-liquefied hydrogen generation potential

Abstract

The waste heat generated by vehicles, especially heavy-duty engines, has motivated the current research to propose a novel thermal design for a ship's engine. Hence, the suggested system comprises an organic-flash power plant, a bi-evaporator cooling subsystem, a thermal desalination cycle, a water electrolysis-based hydrogen generation unit, and a Claude unit for liquefying hydrogen. The innovative aspects associated with the proposed design include utilizing a novel cascade heat recovery process aimed at reduced irreversibility for the engine's waste and the integration of bi-evaporator technology and Claude unit to facilitate the process of waste-to-liquefied hydrogen generation for a marine engine for improved handling. In addition, the research includes an intelligent study that comprehensively analyzes and optimizes the proposed system in terms of long-term sustainability and economic feasibility. For this purpose, a data-driven optimization technique is implemented using artificial neural networks in combination with multi-objective grey wolf optimization. The aims of the research encompass the optimization of the sustainability index and unit cost of liquefied hydrogen. The research findings indicate that the vapor generator’s terminal temperature difference exerts the most substantial influence on performance metrics, as evidenced by a mean sensitivity index of 0.368. Also, the mentioned objective functions exhibit values of 1.139 and 7.60 $/kg, respectively. Besides, the optimum exergy destruction rate is 124.3 kW, the total investment cost rate is 4.94 $/year, the specific cost of products is 74.87 /GJ, the payback period is 2.2 years, and the net present value is 7.48 M$.