Thermoeconomic and environmental analyses based single objective optimization of subcooled compression-absorption cascaded refrigeration system using evolutionary techniques

ABSTRACT

This article analyses a subcooled compression-absorption cascaded refrigeration system (SCRS) in comparison with a standalone compression-absorption cascaded refrigeration system (CRS) based on thermoeconomic optimization. Three different absorbent solution/solution mixture-refrigerant combinations have been tested as working pairs in vapor absorption refrigeration system (VARS), whereas four environmentally friendly refrigerants have been utilized in vapor compression refrigeration system (VCRS). The total annual cost of the system is formulated using energy, exergy, and economic performance of the system and is minimized for optimal operational parameters by implementing five recent evolutionary optimization techniques. The effect of compression subcooling on various operational parameters of the cascaded system is reported. Amid all absorbent solution/solution mixture-refrigerant-refrigerant combinations, R290 combined with (CaCl₂-LiBr-LiNO₃)-H₂O provides the minimum total annual cost of 13,164.8 US$ yr⁻¹ and 15,401.9 US$ yr⁻¹ for CRS and SCRS, respectively. Sanitized Teaching Learning Based Optimization (sTLBO) and Coyote Optimization Algorithm (COA) obtain the optimal total annual cost for most VARS-VCRS absorbent solution/solution mixture-refrigerant-refrigerant combinations. Though the coefficient of performance (COP) of an optimal SCRS is about 250% higher than its optimal CRS, higher compressor work results in a higher penalty cost for the optimal SCRS, which is almost 190% higher than the optimal CRS. A quick convergence is observed for sTLBO and COA.

KEYWORDS:

• Evolutionary techniques
• exergy destruction
• subcooled compression-absorption cascaded refrigeration system
• thermoeconomic optimization
• total annual cost

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Data are available on request due to privacy/ethical restrictions.

Supplementary Material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/15567036.2023.2244909

Additional information

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Notes on contributors

Makkitaya Swarna Nagraj

Makkitaya Swarna Nagraj is a research scholar in the Chemical Engineering department at IIT Guwahati, Guwahati (India). She works in the area of thermal engineering and optimization of refrigeration and air-conditioning.

Debasis Maharana

Debasis Maharana is a research scholar in the Chemical Engineering department at IIT Guwahati, Guwahati (India). He works in the area of modeling and optimization of complex engineering problems.

Prakash Kotecha

Dr. Prakash Kotecha is an associate professor in the Chemical Engineering department at IIT Guwahati, Guwahati (India). He works in the area of applied optimization and computational intelligence.

R. Anandalakshmi

Dr. R. Ananadalakshmi is an associate professor in the Chemical Engineering department at IIT Guwahati, Guwahati (India). She works in the area of thermal engineering, refrigeration and air-conditioning, solar thermal energy conversion, and energy-efficient design of thermal systems.