Operation optimization is an effective method to explore potential economic benefits for existing plants.The maximum potential benefit from operation optimization is determined by the distances between current operating point and process constraints,which is related to the margins of design variables.Because of various disturbances in chemical processes,some distances must be reserved for fluctuations of process variables and the optimum operating point is not on some process constraints.Thus the benefit of steady-state optimization can not be fully achieved while that of dynamic optimization can be really achieved.In this study,the steady-state optimization and dynamic optimization are used,and the potential benefit is divided into achievable benefit for profit and unachievable benefit for control.The fluid catalytic cracking unit(FCCU)is used for case study.With the analysis on how the margins of design variables influence the economic benefit and control performance,the bottlenecks of process design are found and appropriate control structure can be selected.
Industrial processes are mostly large-scale systems with high order.They use fully centralized control strategy,the parameters of which are difficult to tune.In the design of large-scale systems,the decomposition according to the interaction between input and output variables is the first step and the basis for the selection of control structure.In this paper,the decomposition principle of processes in large-scale systems is proposed for the design of control structure.A new variable pairing method is presented,considering the steady-state information and dynamic response of large-scale system.By selecting threshold values,the related matrix can be transformed into the adjoining matrixes,which directly measure the couple among different loops.The optimal number of controllers can be obtained after decomposing the large-scale system.A practical example is used to demonstrate the validity and feasibility of the proposed interaction decomposition principle in process large-scale systems.
