Ibrahim Alatawi

This work evaluates the feasibility of utilizing an organic Rankine cycle (ORC) for waste heat recovery in internal combustion engines to meet the stringent regulations for reducing emissions resulting from the combustion of fossil fuels. The turbine is the most crucial component of the ORC cycle since it is responsible for power production. In this study, a variable geometry radial inflow turbine is designed to cope with variable exhaust conditions. A variable geometry turbine is simply a radial turbine with different throat openings: 30, 60, and 100%. The exhaust gases of a heavy-duty diesel engine are utilized as a heat source for the ORC system. Different engine operating points are explored, in which each point has a different exhaust temperature and mass flow rate. The results showed that the maximum improvements in engine power and brake specific fuel consumption (BSFC) were 5.5% and 5.3% when coupled to the ORC system with a variable geometry turbine. Moreover, the variable geometry turbine increased the thermal efficiency of the cycle by at least 20% compared to the system with a fixed geometry turbine. Therefore, variable geometry turbines are considered a promising technology in the field and should be further investigated by scholars.

Previous theoretical research efforts which were validated by experimental findings demonstrated the thermo-economic benefits of the hybrid concentrated photovoltaic-thermoelectric (CPV-TE) system over the stand-alone CPV. However, the operating conditions and TE material properties for maximum CPV-TE performance may differ from those required in a standalone thermoelectric module (TEM). For instance, a high-performing TEM requires TE materials with high Seebeck coefficients and electrical conductivities, and at the same time, low thermal conductivities ( k ). Although it is difficult to attain these ideal conditions without complex material engineering, the low k implies a high thermal resistance and temperature difference across the TEM which raises the PV backplate’s temperature in a hybrid CPV-TE operation. The increased PV temperature may reduce the overall system’s thermodynamic performance. To understand this phenomenon, a study is needed to guide researchers in choosing the best TE material for an optimal operation of a CPV-TE system. However, no prior research effort has been made to this effect. One method of finding the optimum TE material property is to parametrically vary one or more transport parameters until an optimum point is determined. However, this method is time-consuming and inefficient since a global optimum may not be found, especially when large incremental step sizes are used. This study provides a better way to solve this problem by using a multiobjective optimization genetic algorithm (MOGA) which is fast and reliable and ensures that the global optimum is obtained. After the optimization has been conducted, the best performing conditions for maximum CPV-TE energy, exergy, and environmental (3E) performance are selected using the technique for order performance by similarity to ideal solution (TOPSIS) decision algorithm. Finally, the optimization workflow is deployed for 7000 test cases generated from 10 features using the optimal machine learning (ML) algorithm. The result of the optimization chosen by the TOPSIS decision-making method generated an output power, exergy efficiency, and CO2 saving of 44.6 W, 18.3%, and 0.17 g/day, respectively. Furthermore, among other ML algorithms, the Gaussian process regression was the most accurate in learning the CPV-TE performance dataset, although it required more computational effort than some algorithms like the linear regression model.