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CHAPTER 5: Mean-line Modelling and Optimization of Organic RIT and Integration with Cycle Analysis Programme and it is initiated with the creation of an initial population whose elements are randomly selected in the whole design space. Different procedures are then applied in order to successively generate new population containing better elements. The performance of an individual is measured by its fitness. Pairs of individuals are selected from this population based on their objective function values. Then each pair of individuals undergoes a reproduction mechanism to generate a new population in such a way that fitter individuals will spread their genes with higher probability. The children replace their parents and as this proceeds, inferior traits in the pool die out due to the lack of reproduction. At the same time, strong traits tend to combine with other strong traits to produce children who perform better. This procedure is repeated for the next generation until the maximum specified number of generations is reached. GA can deal with complex optimization problems such as multi-dimensional, non- continuous, and non-linear problems. Moreover, GA locates the global optimal values reliably from a population of solutions even if many local optima exist and prevents the convergence to sub-optimal solutions. This distinguishes GA from the traditional optimization techniques that rely on the initial guesses, while GA is far less sensitive to the initial conditions enforced on it. GA will eventually reject any solution that does not show enough promise which helps to provide more flexibility and robustness during the optimization (Gen et al. 2000). In the present thesis GA was employed to conduct optimization of various objective functions (OF): 𝑂𝐹1 = 𝜂𝑡h𝑒𝑟𝑚𝑎𝑙,𝑐𝑦𝑐𝑙𝑒 Equation 5-30 𝑂𝐹2 = 𝜂𝑠𝑡𝑎𝑔𝑒,𝑡𝑠 Equation 5-31 172 | P a g ePDF Image | SMALL-SCALE RADIAL INFLOW TURBINE FOR WHR ORC
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