A sequential convex moving horizon estimator for bioprocesses

Josh A. Taylor, Alain Rapaport, Denis Dochain

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


We design moving horizon state estimators for a general model of bioprocesses. The underlying optimization is nonconvex due to the microbial growth kinetics, which are modeled as nonlinear functions. We relax the nonconvex growth constraints so that the optimization becomes a second-order cone program, which can be solved efficiently at large scales. Unfortunately, solutions to the relaxation can be inexact and thus lead to inaccurate state estimates. To recover feasible, albeit potentially locally optimal solutions, we use the concave–convex procedure, which here takes the form of a sequence of second-order cone programs. We find that the moving horizon state estimators outperform the unscented Kalman filter on numerical examples based on the gradostat and anaerobic digestion when there is high process noise or parameter error.

Original languageEnglish (US)
Pages (from-to)19-24
Number of pages6
JournalJournal of Process Control
StatePublished - Aug 2022
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Modeling and Simulation
  • Computer Science Applications
  • Industrial and Manufacturing Engineering


  • Anaerobic digestion
  • Concave–convex procedure
  • Moving horizon state estimation
  • Second-order cone programming


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