Honeywell International Inc.

Lucy Ning Liu

Sanjay Kantilal Dave

Sriram Hallihole

Mandar Subhash Vartak

Ranganathan Srinivasan

Christopher J Webb

Michael Niemiec

Madhukar Madhavamurthy Gundappa

Gobinath Pandurangan

Rajni Jain

Ward MacArthur

Venkata Krishna Prasad Reddy Vurubindi

S Chandrakanth Vittal

Avijit Das

Sreesathya Sathyabhama Sreekantan

Roshan Yohannan

A method of generating a hybrid model predictive control (MPC) simulation model for a plant configured to run a process that processes at least one raw material to generate at least one tangible product. A predictive dynamic MPC sub-model is provided for each of plurality of process units in the plant, the plant including at least one process controller coupled to field devices coupled to the plurality of process units, where the process units comprise equipment for converting the raw material or an intermediate material formed from the raw material into to another material. A piping network diagram is obtained that provides a representation of a piping network for routing of the raw material and the intermediate material during the process. The dynamic MPC sub-models are coupled together using the piping network to generate the hybrid MPC simulation model which models the plant as a whole.

Hybrid plant MPC model including dynamic MPC sub-models

A method includes retrieving process variable values from a multivariable predictive controller. The multivariable predictive controller is executed on an embedded platform and is configured to control an industrial process. The method also includes simulating how the multivariable predictive controller would attempt to control the industrial process based on the process variable values. The method further includes transmitting simulated process variable values to the multivariable predictive controller.

Apparatus and method for performing process simulations for embedded multivariable predictive controllers in industrial process control and automation systems

A method of dynamic model selection for hybrid linear/non-linear process control includes developing a plurality of process models including at least one linear process model and at least one non-linear process model from inputs including dynamic process data from a processing system that runs a physical process. At least two of the plurality of process models are selected based on a performance comparison based on at least one metric, wherein the selected process models number less than a number of the plurality of process models received. A multi-model controller is generated that includes the selected process models. The physical process is simulated using the multi-model controller by applying the selected process models to obtain closed loop performance test data for each of the selected models. The performance test data is compared. A selected process model is then selected.

Dynamic model generation for implementing hybrid linear/non-linear controller

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Peoria, AZ, United States of America

Lucy Ning Liu