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Distributed or networked model predictive control (MPC) can provide a computationally efficient approach that achieves high levels of performance for plantwide control, where the interactions between processes can be determined from the information exchanged among controllers. Distributed controllers may exchange information at a lower rate to reduce the communication burden. A dissipativity‐based analysis is developed to study the effects of low communication rates on plantwide control performance and stability. A distributed dissipativity‐based MPC design approach is also developed to guarantee the plantwide stability and minimum plantwide performance with low communication rates. These results are illustrated by a case study of a reactor‐distillation column network. © 2015 American Institute of Chemical Engineers AIChE J, 61: 3288–3303, 2015

Process units may operate different time scales (for example, reactors with different volumes) thus requiring plantwide control systems with multiple sampling rates to avoid over or under sampling. Moreover, it may be preferable to sample critical variables at a higher rate than non-critical ones to decrease capital. Motivated by the above considerations, this paper addresses the issue of distributed multirate model predictive control design for chemical process networks. In order to ensure the closed-loop stability and achieve a minimum performance, dissipativity-based constraints are included in the design of the individual local controllers.