Parametric analysis and optimization of regenerative Clausius and organic Rankine cycles with two feedwater heaters using artificial bees colony and artificial neural network

Proposal of a combined heat and power plant hybridized with regeneration organic Rankine cycle: Energy-Exergy evaluation

“…A MH applied to preheat liquid is delivered to a steam generating boiler. Preheating the MH decreases the irreversibilities involved in the steam production therefore improves the thermodynamic efficiency of the cycle [23]. In this work, R123 is chosen as working fluid in RORC.…”

First and Second-Law Efficiency Analysis and ANN Prediction of a Diesel Cycle with Internal Irreversibility, Variable Specific Heats, Heat Loss, and Friction Considerations

“…Substituting the parameters and constants of Table 1 into the obtained equations, we can get ranges of temperature of different states, the heat added, the power output, the maximum passible power output, the firstlaw efficiency, the maximum passible thermal efficiency, and the second-law efficiency in the specified range. Figures 4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30, and 31 present selected computations for the effects of the key thermodynamic parameters. When 1 and 3 are given, 2 and 4 can be readily obtained via (8); 2 and 4 are obtained from (10).…”

“…Rashidi et al [24] illustrated the parametric analysis and optimization of entropy generation in unsteady MHD flow past a stretching rotating disk using artificial neural network (ANN) and particle swarm optimization (PSO) algorithm. In another study, Rashidi et al [25] performed the parametric study and optimization of regenerative Clausius and organic Rankine cycles with two feedwater heaters. They proposed a procedure based on the combination of ANN and artificial bees colony (ABC).…”

Effects of geometrical parameters on hydrothermal characteristics of shell-and-tube heat exchanger with helical baffles: Numerical investigation, modeling and optimization

“…Furthermore, it has the great capability of estimating non-linear correlations and is one of the most often used neural networks in engineering applications. Rashidi et al [26] carried out a parametric study and optimization of regenerative Clausius and organic Rankine cycles with two feedwater heaters based on neural network and artificial bees colony. It was found that the maximum values of the thermal and exergy efficiencies for R717 are greater than those for water.…”