Model-free framework for dynamic characterization and optimization of plant-wide operation

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In today's dynamic market and complex manufacturing environment for chemical process industries, operating conditions of all units of a plant must be carefully coordinated in order to remain near an economic optimum. Real time optimization (RTO) solves the problem of coordinating various plant units and has attracted the attention of process industry. The conventional RTO system is based on a steady-state flow-sheet model of a plant, and its formulation can be extremely limited for most integrated plants with their units interacting across different time scales. Other approaches utilizing dynamical behaviour of an entire plant are unlikely to scale to real plant-wide problems in any foreseeable future because of exorbitant computational requirements. Contrary to the paucity of scalable plant-wide dynamics and dynamic optimization scheme for practical systems, the rapid growth of distributed control systems and data acquisition tools has resulted in the availability of massive and high dimensional data. Such data capture the history of various operation modes and events. The objective of my research is to develop fundamental theories and computational frameworks for deriving an improved plant-wide operation strategy by extracting valuable information from historical plant operation data without constructing a plant-wide dynamic model. It would be expected that applications of the suggested framework to the decision hierarchy in process industry will result in significantly improved process and economic performance.