Comparative Analysis of a novel Cascade Transcritical Carbon Dioxide cycle and Split Transcritical Carbon Dioxide cycle integrated with Advanced Absorption Refrigeration system

Abstract

A transcritical carbon dioxide (t-CO2) Rankine cycle is capable of achieving high efficiency for waste heat recovery (WHR) from a gas turbine, despite being simpler and more compact than a steam/water cycle. Regarding the Waste Heat Recovery (WHR) system, it is crucial to optimize the net output power by integrating the necessary components. The waste heat utilization efficiency is combined with the thermal efficiency of the cycle. A basic T-CO2 Rankine cycle employed for a high temperature source is unable to completely harness the waste heat due to the fact that the working fluid is prepared to a high temperature by the recuperator in order to obtain a superior cycle efficiency. In order to utilize the unused waste heat in a simple cycle, one option is to incorporate a cascade cycle with a low-temperature (LT) loop alongside the high-temperature (HT) loop. Another option is to implement a split cycle, where the flow after the pump is divided and preheated separately by the recuperator and LT heater before being used by the HT heater. This study provides a comparative analysis of three cycles, focusing on the energy and exergy studies of their respective systems. The findings indicate that a split cycle has the capacity to generate the most amount of power among the three systems examined, across a broad spectrum of operating conditions. The rationales for this are elucidated extensively. This research aims to address this significant problem by optimizing waste heat recovery (WHR) strategies. By effectively capturing and utilizing waste heat, we can reduce overall energy consumption and reliance on fossil fuels. Also, we can Increase the efficiency of industrial processes and power generation as well as mitigate greenhouse gas emissions and contribute to climate change mitigation. However, technical limitations of modeling and matching appropriate WHR technologies to diverse waste heat sources with varying temperatures and flow rates can be a significant challenge for the proposed solutions. This study looks at various configurations of Supercritical Carbon dioxide Rankine cycles and compares their performance which leads to positive findings in favor of the split configuration. The other configurations investigated were simple and cascade cycles. These advanced configurations of Rankine cycles can yield never-before-achieved performance for power cycles. However, regardless of their efficiency, there is always some waste heat that is discharged into the environment. This study aims to capture the waste heat through a novel system. The novel system involves the Rankine cycle integrated as the top cycle acting as the source of waste heat with an advanced absorption refrigeration system as the bottom cycle. The fitness and constraints of the overall system is investigated and compared with prior findings and an attempt to justify the performance is the domain of this work. The following work owing to being a preliminary study for the final work, the study for now validates developed models against the reference models obtained from literature review. Such validation facilitates the undertaking of the integration task. The fitness and constraint modelling of the novel integrated system yield unexpected result owing to erratic governing equations of the performance parameters. However, validated state point calculations are enough to lay the groundwork for the tuning of the performance evaluation of the novel system. This paper tries to present a comparative study of two different configurations of the novel system, each cycle integrating four cycles in total. Hence, the complexity of such modelling depends on a number of parameters. And such models can project different behavior when evaluated under a broad range of working parameters of different components involved in the system. These parameters can be tweaked to facilitate multivariable optimization of desired performance parameters and fitness constraints. Such work further needs the support of strong optimization algorithm paired with machine learning. Hence, the domain of the present work can be further broadened to determine the optimal working conditions of the novel systems.