System identification of an interacting series process for real-time model predictive control

MIMO model of an interacting series process for Robust MPC via System Identification

“…This results in the simulation errors not only for upstream outputs but also for downstream outputs, since the upstream variables affects the downstream variables directly, and vice versa. When (18) is used as the model structure to predict the output of the downstream variable, such nonlinearities in the upstream is detected by the upstream sensor or transmitter, and is used as one of the model inputs. In the same way, when (17) is used to predict the upstream variable, such nonlinearities affects first the downstream variable, then is detected by downstream sensor or transmitter, and further be used as one of the model inputs.…”

“…Wibowo et al [18] considered the problem of developing a linear model from the input-output data that have the relation as given above, which can be explained as follows:…”

Recent advances in dynamic modeling and control studies of biomass gasification for production of hydrogen rich syngas

“…However, a drawback of the methods used is that the physical insight of the process in the models is lost, which is characteristic of a black-box model. Wibowo et al 61 developed a MIMO state-space model from input–output data using a linear system identification technique. The subspace identification method using the N4SID algorithm was proposed as a more suitable method for a gaseous pilot plant than Prediction Error Methods (PEM), as indicated by smaller identification and validation errors.…”