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This paper considers the optimal plate fin design and control for central processing unit (CPU) heat sink processes. First, we apply a finite element method to investigate the heat transfer phenomena of a heat sink process. To have a better heat dispersion performance, a real-coded genetic algorithm is then utilized to search for an optimal set of plate-fin shape parameters. The objective function to be minimized is the entropy generation rate which can take simultaneously the two major factors, heat transfer rate and air resistance, into consideration in the design. The present optimization scheme is able to achieve a better design for heat dispersion than existing methods. To attenuate environmental and time-varying disturbances, a direct adaptive control scheme is then developed for the CPU heat sink process. It is based on using a bounded single neuron controller (SNC) along with a parameter tuning algorithm to regulate the temperature of a selected control point. Extensive comparisons of the SNC-based control performance with the on-off control as well as a PI controller show that the proposed scheme provides excellent control performance despite the existence of unexpected process uncertainties.