The operation of a paper machine relies on the close monitoring and control of several integrated units to ensure a high quality paper with the required specifications. In this paper, the retention control system in the wet-end of a paper machine is considered. The control objective is to maximize the retention of fines and fibres in the paper sheet to prevent the accumulation of micro particles in the water system. We present an adaptive extremum-seeking scheme for the optimization and control of retention in the wet-end of a paper machine. An adaptive learning technique is introduced to construct an algorithm that drives the system to the optimal retention value. Lyapunov's stability theory is used in the design of the extremum-seeking controller structure and the development of the parameter learning laws. The performance of the technique is illustrated via simulations based on a first-principles dynamic model developed previously for a micro-particulate system.