This study investigates the potential of leveraging solar energy to establish an integrated multi-energy production system to supply heating

cooling

and electricity for passenger trains. The proposed tri-generation system combines photovoltaic-thermal (PVT) panels

an organic Rankine cycle (ORC)

an absorption chiller

and a water heater unit. The interactions between system components are examined through an exergoeconomic model developed in EES software

supported by a detailed parametric analysis. A two-step multi-objective optimization framework is implemented

integrating response surface methodology (RSM) with an artificial neural network (ANN) to improve optimization accuracy. In this framework

five decision variables and two objective functions—exergy efficiency and system cost rate—are optimized. Economic analysis reveals that the ORC unit and PVT panels incur the highest cost rates

while exergy analysis indicates that the PVT panels

absorption chillers

and evaporators experience the most significant exergy destruction. The optimal system achieves an exergy efficiency of 10.87 % and a cost rate of 1.491 $/h. The second optimization stage using the ANN method based on 100 RSM-optimized points

results in further refinement in both exergy efficiency and cost rate. A real-world case study evaluates the feasibility of implementing the proposed system on a modern passenger train along a route with high solar energy potential

analyzing its performance during a 17-hour journey across five stations and over four seasons under varying weather conditions. The findings contribute to the development of low-carbon rail transport solutions

highlighting the need for future research on energy storage

system scalability

and real-time performance optimization.