NLP model based thermoeconomic optimization of vapor compression-absorption cascaded refrigeration system

This paper addresses the size and cost estimation of vapor compression-absorption cascaded refrigeration system (VCACRS) for water chilling application taking R410a and water-LiBr as refrigerants in compression and absorption section respectively which can help the design engineers in manufacturing and experimenting on such kind of systems. The main limitation in the practical implementation of VCACRS is its size and cost which are optimized in the present work by implementing Direct Search Method in non-linear programming (NLP) mathematical model of VCACRS. The main objective of optimization is to minimize the total annual cost of system which comprises of costs of exergy input and capital costs in monetary units. The appropriate set of decision variables (temperature of evaporator, condenser, generator, absorber, cascade condenser, degree of overlap and effectiveness of solution heat exchanger) minimizes the total annual cost of VCACRS by 11.9% with 22.4% reduction in investment cost at the base case whereas the same is reduced by 7.5% with 11.7% reduction in investment cost with reduced rate of interest and increased life span and period of operation. Optimization results show that the more investment cost in later case is well compensated through the performance and operational cost of the system. In the present analysis, optimum cascade condensing temperature is a strong function of period of operation and capital recovery factor. The cascading of compression and absorption systems becomes attractive for lower rate of interest and increase life span and operational period.