Identification and predictive control of a multistage evaporator

J.C. Atuonwu, Y. Cao, G.P. Rangaiah, M.O. Tadé

Research output: Contribution to journalArticleAcademicpeer-review

21 Citations (Scopus)


A recurrent neural network-based nonlinear model predictive control (NMPC) scheme in parallel with PI control loops is developed for a simulation model of an industrial-scale five-stage evaporator. Input–output data from system identification experiments are used in training the network using the Levenberg–Marquardt algorithm with automatic differentiation. The same optimization algorithm is used in predictive control of the plant. The scheme is tested with set-point tracking and disturbance rejection problems on the plant while control performance is compared with that of PI controllers, a simplified mechanistic model-based NMPC developed in previous work and a linear model predictive controller (LMPC). Results show significant improvements in control performance by the new parallel NMPC–PI control scheme
Original languageEnglish
Pages (from-to)1418-1428
JournalControl Engineering Practice
Issue number12
Publication statusPublished - 2010


  • recurrent neural-networks
  • automatic differentiation
  • multivariable processes
  • system-identification
  • models
  • reactor
  • backpropagation
  • temperature
  • inverse
  • time


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