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An adaptive predictive control with recurrent neural network prediction for industrial processes is presented. The neural predictive control law with integral action is derived based on the minimization of a modified predictive performance criterion. The stability and steady-state performance of the closed-loop control system are well studied. Numerical simulations reveal that the proposed control gives satisfactory tracking and disturbance rejection performance for two illustrative nonlinear systems with time-delay. Experimental results for temperature control of a variable-frequency oil-cooling process show the efficacy of the proposed method for industrial processes with set-points changes and load disturbances.