ENHANCING THE PERFORMANCE OF VAPOR COMPRESSION REFRIGERATION SYSTEMS THROUGH MACHINE LEARNING AND BAYESIAN OPTIMIZATION

Abstract

The use of vapor compression refrigeration (VCR) systems in industrial cooling is essential, but the efficiency of 
their operation is usually less fortunate because of the rigidity and fixity of the operation that such control systems 
are utilized in, as it should. The paper addresses how machine learning (ML) and Bayesian optimization (BO) 
may be combined to improve VCR systems real-time optimization. Optimized floating-point parameterized 
models of Bayesian Optimized Neural Network (BONN) aims to alter major parameters in the system including 
refrigerant flow rate and compressor speed depending on the fluctuating environmental and operating conditions. 
Current study overcomes the limitations of traditional optimization techniques in offering an open-ended, adaptive 
solution that is found to predict and optimize system performance in different conditions. By so doing, better 
energy efficiency, cheaper costs of operations, and enhanced reliability of the system are expected to be attained 
through the research. The experimentation indicates that Bayesian-optimized model with greater accuracy in 
estimating energy usage and fridge refrigerants staff is substantially better than the traditional models. This paper 
will help in advancing the cooling technologies which are sustainable because it allows VCR systems to be 
adaptively controlled in real time.