AI based Modeling & Optimization of IRC-Enhanced Solar GT Trigeneration Systems using ANN-Evolutionary Algorithm

International Journal of Mechanical Engineering

© 2025 by SSRG - IJME Journal

Volume 12 Issue 9

Year of Publication : 2025

Authors : Mihir Kumar Pandey, Shrihar Pandey, Pankaj Kumar Shrivastava, Deonth Kumar, Manish Kumar, Sanjeev Kumar Sajjan

10.14445/23488360/IJME-V12I9P105

How to Cite?

Mihir Kumar Pandey, Shrihar Pandey, Pankaj Kumar Shrivastava, Deonth Kumar, Manish Kumar, Sanjeev Kumar Sajjan, "AI based Modeling & Optimization of IRC-Enhanced Solar GT Trigeneration Systems using ANN-Evolutionary Algorithm," SSRG International Journal of Mechanical Engineering, vol. 12,  no. 9, pp. 46-55, 2025. Crossref, https://doi.org/10.14445/23488360/IJME-V12I9P105

Abstract:

A trigeneration system is a unified energy framework that concurrently yields three modes of energy: electricity, heating, and cooling. Solar-based Gas Turbine (GT) trigeneration systems, particularly those incorporating intercooling and reheating, are attracting significant attention due to their enhanced performance. In the present study, solar-based GT trigeneration cycle incorporating intercooling and reheating (SBIRGT) is investigated. The SBIRGT comprises a parabolic trough collector (PTC) field, pneumatic compressor, ignition chamber, gas turbine, and a single-effect LiBr absorption cooling system. Two critical productivity indicators—power output and energy efficiency—have been analyzed. Artificial Neural Networks (ANNs) were employed to develop predictive models for these outputs. To determine the optimal values for both responses, widely recognized nature-inspired optimization techniques were applied, including Rao’s sequential optimization methods and Differential Evolution (DE). Among the four strategies explored, the most effective approach has been identified and recommended under the purview of this study.

Keywords:

ANN, Optimization, DE algorithm, Rao’s algorithm, Trigeneration system.

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