석유화학공업으로 대표되는 공정산업의 연속공정들은 지난 20여 년간 모델예측제어를 중심으로 고급제어(APC)기법들이 도입되며 운전성 및 생산성 향상에 많은 진보를 이루었다. 이에 반하여 중합반응기를 비롯한 각종 회분공정에는 APC 기법의 도입이 아직 활발히 이루어지지 않고 있다. 이것은 회분공정의 독특한 문제점을 극복하며 원하는 성능을 보장할 수 있는 방법론이 제시되지 못한 데에 가장 큰 이유가 있다고 할 수 있다. 그러나 최근 이러한 문제점들을 극복할 수 있는 APC 기법들이 반복학습제어(ILC)에 근거하여 개발되며 회분공정 APC 환경에 큰 변화가 일어나고 있다. 본 논문에서는 이들 기법들이 다양한 실제 공정에 활발하게 적용되어 운전을 개선할 수 있기를 기대하며, ILC를 기반으로 한 최근의 회분공정 APC 연구동향을 이론과 실례를 통해 소개한다.

The operability and productivity of continuous processes, especially in petrochemical industries have made remarkable improvement during the past twenty years through advanced process control (APC) typified by model-based predictive control. On the other hand, APC have not been actively practiced in industrial batch processes typified by batch polymerization reactors. Perhaps the main cause for this has been the lack of reliable batch process APC techniques that can overcome the unique problems in industrial batch processes. Recently, some noteworthy progress is being made in this area. New high-performance batch process control techniques that can accommodate and also overcome the unique problems of industrial batch processes have been proposed on the basis of iterative learning control(ILC). In this review paper, recent advancement in the batch process APC techniques are presented, with a particular focus on the variations of the so called Q-ILC method, with the hope that they are widely practiced in different industrial batch processes and enhance their operations.

Batch Process Advanced Process Control Iterative Learning Control

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