Dynamic optimization of batch processes: II. Role of measurements in handling uncertainty
By B. Srinivasan, Dominique Bonvin, Erik Visser, S. Palanki. Computers and Chemical Engineering, 2003
358 citations recorded by the source corpus.
Abstract The main bottleneck in using optimization at the industrial level is the presence of uncertainty in the form of model mismatch and disturbances. The way uncertainty can be handled constitutes the subject of this series of two papers. The first part dealt with the characterization of the nominal solution and proposed an approach to separate the constraint-seeking from the sensitivity-seeking components of the inputs. This second part reviews various strategies for optimization under uncertainty, namely the robust and measurement-based optimization schemes. A novel scheme is proposed, where optimality is achieved by tracking the necessary conditions of optimality. The different approaches are compared via the simulation of a bioreactor for penicillin production.
Subfields: Control and Systems Engineering
View Paper: https://www.semanticscholar.org/paper/ddc1b1b075f3acf5ac9811e978080f455deeb577
How researchers see this paper
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In Control and Systems Engineering
- Boris Houska
- Moritz Diehl
- Richard D. Braatz
- Zoltan K. Nagy
- Jan F. M. Van Impe
- Mattia Vallerio
- Filip Logist
- Dries Telen
Citations in the corpus
- Approximate robust optimization of nonlinear systems under parametric uncertainty and process noise (2015, methodology): “The additional term takes the uncertainty on the objective function (Srinivasan et al., 2003; Logist et al., 2011) into account and indicates a performance loss compared to the nominal case.”
- Robust multi-objective optimal control of uncertain (bio)chemical processes (2011, background): “Namely, model uncertainty as well as external process disturbances are inevitably present when modeling, controlling and optimizing practical processes (see, e.g., Srinivasan et al., 2003; Srinivasan and Bonvin, 2007; Diehl et al., 2008).”
- Distributional uncertainty analysis using power series and polynomial chaos expansions (2007, methodology): “This motivates the development of techniques to quantify the influence of parameter uncertainties on the process states and outputs, or take uncertainties into account in the design of processes or control systems [ 5 ].”