Lng/H2-Rich Syngas Centric Quad-Generation Plant with Hydrogen-Ready Self-Sustaining Feeder; Economic Evaluation, Cuckoo Search, and Mlp Neural Networks

Abstract

The rising global demand for sustainable energy, coupled with the urgent need for low-carbon hydrogen production, presents a significant challenge for modern energy systems. Existing multi-generation frameworks often suffer from inefficient thermal resource utilization and high computational costs during optimization. To address these gaps, this study proposes a novel, thermodynamically integrated liquefied natural gas (LNG)-based quadgeneration system that synergistically combines LNG cold energy recovery with steam methane reforming, gas turbine power, steam Rankine and Kalina cycles, and a proton exchange membrane electrolyzer. To ensure highefficiency performance, a surrogate-aided optimization plan, utilizing a Multi-Layer Perceptron model tuned with Cuckoo Search algorithm, is developed to navigate the complex decision space. Simulation results demonstrate that the optimized system achieves a remarkable exergy efficiency of 56.50% and a primary energy saving ratio of 34.43%, while maintaining competitive specific CO2 emissions of 0.297 kgCO2/kWh. Economically, the optimal configuration yields a total unit cost of product of 10.84 $/GJ and an annual profit of 31.01 M $, with a dynamic payback period (DPP) of 3.34 years. Comprehensive sensitivity and uncertainty analyses revealed that LNG flow rate and gas turbine outlet pressure are the primary drivers of overall system performance. These findings indicate that the proposed framework offers a robust, technically feasible, and economically viable solution for industrial-scale quad-generation, providing a scalable pathway for enhancing resource efficiency and promoting sustainable hydrogen production in future energy infrastructures.