Journal Article10.1002/AIC.690350711
Adaptive extremum control using approximate process models
Melinda P. Golden,B. Erik Ydstie +1 more
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TL;DR: In this paper, an online adaptive optimization technique incorporating a priori knowledge in the form of approximate steady-state models is proposed, which is self-tuning in the sense that it converges to the optimal performance provided that a matching condition is satisfied and that the data are persistently exited.
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Abstract: The proposed on-line adaptive optimization technique incorporates a priori knowledge in the form of approximate steady-state models. The steady-state geometric characteristics of the model are periodically recalculated using a Hammerstein system and recursive least squares. The algorithm is self-tuning in the sense that it converges to the optimal performance provided that a matching condition is satisfied and that the data are persistently exited. Simulation and experimental studies performed on a continuous fermentation system have been conducted to illustrate the performance of the optimization algorithm and demonstrate the viability of adaptive extremum control.
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Citations
Adaptive extremum seeking control of continuous stirred tank bioreactors with unknown growth kinetics
TL;DR: An adaptive extremum seeking control scheme for continuous stirred tank bioreactors using Lyapunov's stability theorem and adaptive learning technique to construct a seeking algorithm that drives the system states to the desired set-points that maximize the value of an objective function.
186
Nonlinear control strategies for continuous fermenters
Michael A. Henson,Dale E. Seborg +1 more
TL;DR: In this paper, the dilution rate and feed substrate concentration are considered as manipulated inputs in single-input/single-output strategies for productivity control in continuous fermenters and compared theoretically and via simulation.
164
Modifier Adaptation for Real-Time Optimization—Methods and Applications
Alejandro Marchetti,Alejandro Marchetti,Grégory François,Timm Faulwasser,Timm Faulwasser,Dominique Bonvin +5 more
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TL;DR: An overview of the recent developments of modifier-adaptation schemes for real-time optimization of uncertain processes that have the ability to reach plant optimality upon convergence despite the presence of structural plant-model mismatch is presented.
Silicon solar cell production
TL;DR: The complete production process for solar cells is described, challenges relevant to systems engineering are highlighted, and overviews work in three distinct areas: the application of real time optimization in silicon production, the development of scale-up models for a fluidized bed poly-silicon process and a new process concept for silicon wafer production.
121
Continuous optimization using a dynamic simplex method
Qiang Xiong,Arthur Jutan +1 more
TL;DR: The traditional Nelder–Mead simplex method is modi5ed and extended to allow tracking of moving optima, which results in a so-called dynamic simplex algorithm, which demonstrates the capability and exibility of this new direct search algorithm in trackingMoving optima in multiple dimensions.
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References
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Michael J. Rolf,Henry C. Lim +1 more
TL;DR: An adaptive on-line optimization method is developed for continuous bioreactors, based on dynamic model identification, that is fast, adaptive, and requires no detailed model.
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Adaptive steady-state optimization of biomass productivity in continuous fermentors.
TL;DR: An adaptive steady‐state optimization algorithm is presented and applied to the problem of optimizing the production of biomass in continuous fermentation processes and is used to drive a methylotroph single‐cell production process to its optimum.
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On‐line optimizing control of a nonadiabatic fixed bed reactor
K. S. Lee,Won-Kyoo Lee +1 more
TL;DR: In this article, a scheme of on-line optimizing control is presented for a nonadiabatic fixed-bed reactor which experiences a highly exothermic reaction, with an objective function consisting of a net profit by producing maleic anhydride plus a penalty term on high bed temperature, it was clearly shown that the reaction conditions were driven to the expected optimum region.
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Simultaneous estimation of first and second derivatives of a cost function
David Clarke,K.R. Godfrey +1 more
TL;DR: In this paper, a correlation method for estimating the second derivative, as well as the first derivative, of a cost function which is quadratic in an input parameter is described, using a 3-level msequence perturbation signal, enabling the cost-function minimum to be reached in a single step in a noise free system.
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