397f Reducing on-Line Computational Requirement for Real-Time Dynamic Optimization of a Nonlinear Integrated Plant

Nonlinear Integrated Plant Thidarat Tosukhowong and Jay H. Lee An integrated plant with large material and energy recycle loops is known to exhibit a multiple timescale phenomena, in which the fast dynamics arise from the change in the unit operations and the slow dynamics are induced by interactions from the recycle streams. When the recycle-to-feed ratio is very large, the system becomes extremely ill-conditioned and the settling time of the entire plant will be much longer than the residence time of each unit operation. If the conventional centralized nonlinear model predictive control scheme is used for control and economic optimization, a small sampling time and very large prediction horizon will be required to satisfy both the control and plant-wide economic objective, resulting in unwieldy computational requirement, which is often unachievable in real-time application. In addition, if the control and optimization objectives are included in the same optimizer, the economic performance can be severely compromised for the plant with many real-time disturbances [1]. Based on this consideration, this work adopts a two-layer optimization scheme, in which the plantwide economic real-time optimizer (RTO) functions on top of the control layer and calculates the optimal setpoints for the controllers in the plant. Previous work [2] suggested that an RTO based on the slow-scale reduced order dynamic model of a linear plant and a slower sampling time (hours) outperforms the conventional steady-state RTO and other coincidence point dynamic RTO method. The remaining challenge is how to effectively formulate and solve the nonlinear dynamic real-time optimization of the plant-wide economics at a chosen time scale.