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The worldwide need for energy as well as environmental challenges have promoted the
creation of sustainable power solutions. Various working fluid combinations are utilized for an
Organic Rankine Cycle powered Vapor Compression Cycle (ORC-VCC) to deliver cooling
applications. The selection of an appropriate working fluid significantly impacts system
performance, efficiency, and environmental impact. In this study we evaluates possible working
fluids to optimize the ORC-VCC system. Firstly, the Artificial Neural Network (ANN) derived
models for exergy destruction (𝐸𝑑𝑡𝑜𝑡), and the heat exchanger total heat transfer capacity (𝑈𝐴𝑡𝑜𝑡).
Later on, the multi-objective optimization was conducted using the acquired models for 𝐸𝑑𝑡𝑜𝑡 and
𝑈𝐴𝑡𝑜𝑡 using the Genetic Algorithm (GA) followed by Technique for Order of Preference by
Similarity to Ideal Solution (TOPSIS).The optimization result showcase Decane ORC- R600a
VCC as the optimal choice for ORC-VCC system, resulting in values of 𝐸𝑑𝑡𝑜𝑡 and 𝑈𝐴𝑡𝑜𝑡 resulting
in value of 24.50 kW and 6.71 kW/K, respectively. From the extended analysis and optimization,
the cooling capacity was found to be 41.8 kW. Meanwhile, the total cost per unit was estimated to
be approximately $78710 and the payback period is 9.7 years. The research data shows how viable
it is to implement biogas-driven ORC-VCC systems when providing air conditioning capabilities.