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The development of a nonlinear dynamic model of an industrial process is summarized. The model includes additive stochastic terms and not all the state variables were accessible. Nonlinear state estimation was approximated by a linearized Kalman filter and the control algorithm was from dynamic programming. The development of software and hardware for a remote on-line computer control experiment is then described, in which the industrial plant was connected to a process control computer some 130 miles away by a regular telephone channel.