In the process control field, automation of plant operation is an important subject because plant operators are required to observe and handle many control loops during dynamic plant operation. Recently model predictive control (MPC) has attracted attention as a practical process control technique, and applications to many kinds of industrial plants are reported. MPC has features of control performance such as multivariable decoupling control, compensation of delay dynamics and constraint control. It is suitable for a higher level controller in a hierarchical process control system that realizes automation of the plant operation. In this paper, we outline those features of MPC, and propose a new MPC method derived from modification of Generalized Predictive Control (GPC) . Firstly, a Kalman filter based predictor is introduced in order to improve robustness of the predictor against noises. Secondly, a time-dependent weighting factor is newly introduced into MPC's quadratic type cost function, in order to improve transient response characteristics. Furthermore, the cost function is extended by adding a new term related to reference tracking error of the manipulation variables, in order to handle those manipulation variables when they have some redundancy. Thirdly, a parameter tuning method is proposed that adjusts the weighting factors in the cost function considering robust stability of the control system. Lastly the proposed MPC method with and without constraint conditions that are the upper/lower limits and rate limits for both manipulation variables and process control variables, is formulated. An application study of the MPC method to an ethylene plant's dynamic simulator is also described. Consequently, improvements of control performance such as decoupling control, delay dynamics compensation and disturbance rejection with feedforward control are verified.