An innovative process design of seawater desalination toward hydrogen liquefaction applied to a ship's engine: An economic analysis and intelligent data-driven learning study/optimization

Abstract

Integrating heat recovery applications with heavy engines during transportation is critical for achieving sustainable production and reducing the irreversibility associated with the power production process of the engine. In this regard, the current paper introduces a novel waste heat recovery method utilizing the hot flue gas released by a 1-MW ship's engine to yield liquefied hydrogen from seawater desalination while simultaneously meeting the ship's air-conditioning requirement. In addition, a techno-economic analysis and an advanced feasibility study/optimization are conducted. The process employs reverse osmosis desalination to generate freshwater fed to a water electrolyzer for hydrogen production. The needed power is supplied from an organic flash cycle coupled with an ejector-based bi-evaporator refrigeration cycle. The first cooling loop of the refrigeration cycle offers air-conditioning, while the low-temperature loop yields cooling for the Claude hydrogen liquefaction cycle. A comprehensive feasibility assessment is conducted from the thermodynamic, economic, and environmental viewpoints, and accordingly, an intelligent data-driven learning approach is implemented for performing different multi-criteria optimization scenarios. This approach uses an artificial neural network with a multi-objective grey wolf optimization method. The LINMAP method is employed to reach the most favorable scenario and identify the optimal solution. The findings indicate that the flash temperature attains the highest mean sensitivity index measured at 0.377. Moreover, the best optimization scenario is associated with the exergy efficiency, CO2 emission reduction, and liquefied hydrogen cost as objective functions, calculated at 11.39 %, 22.31 kg/MWh, and 10.25 $/kg, respectively.