A multi-mode operational simulation and 4E analysis approach to ammonia production in a solar- and energy storage–driven system with LS-boosting machine learning for multi-criteria optimization and a case study

Abstract
The development of sustainable ammonia production through solar-driven integrated systems faces several critical challenges, including effective subsystem integration, validation under realistic operating conditions, and determination of the most suitable operational strategy. To overcome these limitations, this study proposes an innovative solar-assisted ammonia production system based on parabolic trough solar collectors and performs a quasi-steady-state simulation that accounts for daily solar irradiance fluctuations under three operational modes. Afterward, a comprehensive 4E assessment framework is employed, encompassing energy, exergy, exergoeconomic, and exergoenvironmental analyses, with the latter based on life cycle assessment methodology. In addition, a hybrid optimization approach, integrating least-squares boosting machine learning and a multi-objective grey wolf optimizer (MOGWO), is applied to identify the optimum performance. The selected objective functions are ammonia production rate, total investment cost rate, and total exergoenvironmental impact rate. The optimization results yield optimal values of 1457 kg/day, 197.5 $/h, and 383.9 mPts/h, respectively. To evaluate practical applicability, the optimized system is investigated under the climatic conditions of Yazd and Bandar Abbas. Under optimal operating conditions, the above-mentioned objectives are found to be 1068.08 kg/day, 184.37 $/h, and 339.29 mPts/h for Yazd, respectively, while corresponding values for Bandar Abbas are 735.91 kg/day, 172.82 $/h, and 306.95 mPts/h.

Author
Mohammad Kaveh

DOI
https://doi.org/10.1016/j.energy.2026.141959

ISSN
1873-6785

Publish Date: 2026-08-11