Economic Nonlinear Model Predictive Control of a U-loop Bioreactor

Tobias Kasper Skovborg Ritschel; Dimitri Boiroux; Marcus Krogh Nielsen; Jakob Kjøbsted Huusom; Sten Bay Jørgensen; John Bagterp Jørgensen

Economic Nonlinear Model Predictive Control of a U-loop Bioreactor

Tobias Kasper Skovborg Ritschel, Dimitri Boiroux, Marcus Krogh Nielsen, Jakob Kjøbsted Huusom, Sten Bay Jørgensen, John Bagterp Jørgensen

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Abstract

In this paper, we present an algorithm for economic nonlinear model predictive control (NMPC) of singlecell protein production in a U-loop bioreactor. The model of the U-loop bioreactor consists of both stochastic ordinary and partial differential equations. Using a typical ﬁnite-volume discretization, the model contains 87 state variables. The NMPC algorithm is based on the continuous-discrete extended Kalman ﬁlter and a simultaneous collocation method. We present a closed-loop simulation which demonstrates the computational feasibility of real-time implementation of the NMPC algorithm for startup and steady state operation of the U-loop reactor.

Original language	English
Title of host publication	Proceedings of 2020 European Control Conference
Publisher	IEEE
Publication date	2020
Pages	208-213
ISBN (Electronic)	978-1-7281-8813-3
Publication status	Published - 2020
Event	2020 European Control Conference - Virtual event, Saint Petersburg, Russian Federation Duration: 12 May 2020 → 15 May 2020 https://ecc20.eu/

Conference

Conference	2020 European Control Conference
Location	Virtual event
Country/Territory	Russian Federation
City	Saint Petersburg
Period	12/05/2020 → 15/05/2020
Internet address	https://ecc20.eu/

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title = "Economic Nonlinear Model Predictive Control of a U-loop Bioreactor",

abstract = "In this paper, we present an algorithm for economic nonlinear model predictive control (NMPC) of singlecell protein production in a U-loop bioreactor. The model of the U-loop bioreactor consists of both stochastic ordinary and partial differential equations. Using a typical ﬁnite-volume discretization, the model contains 87 state variables. The NMPC algorithm is based on the continuous-discrete extended Kalman ﬁlter and a simultaneous collocation method. We present a closed-loop simulation which demonstrates the computational feasibility of real-time implementation of the NMPC algorithm for startup and steady state operation of the U-loop reactor. ",

author = "Ritschel, {Tobias Kasper Skovborg} and Dimitri Boiroux and Nielsen, {Marcus Krogh} and Huusom, {Jakob Kj{\o}bsted} and J{\o}rgensen, {Sten Bay} and J{\o}rgensen, {John Bagterp}",

year = "2020",

language = "English",

pages = "208--213",

booktitle = "Proceedings of 2020 European Control Conference",

publisher = "IEEE",

address = "United States",

note = "2020 European Control Conference , ECC{\textquoteright}20 ; Conference date: 12-05-2020 Through 15-05-2020",

url = "https://ecc20.eu/",

}

TY - GEN

T1 - Economic Nonlinear Model Predictive Control of a U-loop Bioreactor

AU - Ritschel, Tobias Kasper Skovborg

AU - Boiroux, Dimitri

AU - Nielsen, Marcus Krogh

AU - Huusom, Jakob Kjøbsted

AU - Jørgensen, Sten Bay

AU - Jørgensen, John Bagterp

PY - 2020

Y1 - 2020

N2 - In this paper, we present an algorithm for economic nonlinear model predictive control (NMPC) of singlecell protein production in a U-loop bioreactor. The model of the U-loop bioreactor consists of both stochastic ordinary and partial differential equations. Using a typical ﬁnite-volume discretization, the model contains 87 state variables. The NMPC algorithm is based on the continuous-discrete extended Kalman ﬁlter and a simultaneous collocation method. We present a closed-loop simulation which demonstrates the computational feasibility of real-time implementation of the NMPC algorithm for startup and steady state operation of the U-loop reactor.

AB - In this paper, we present an algorithm for economic nonlinear model predictive control (NMPC) of singlecell protein production in a U-loop bioreactor. The model of the U-loop bioreactor consists of both stochastic ordinary and partial differential equations. Using a typical ﬁnite-volume discretization, the model contains 87 state variables. The NMPC algorithm is based on the continuous-discrete extended Kalman ﬁlter and a simultaneous collocation method. We present a closed-loop simulation which demonstrates the computational feasibility of real-time implementation of the NMPC algorithm for startup and steady state operation of the U-loop reactor.

M3 - Article in proceedings

SP - 208

EP - 213

BT - Proceedings of 2020 European Control Conference

PB - IEEE

T2 - 2020 European Control Conference

Y2 - 12 May 2020 through 15 May 2020

ER -

Economic Nonlinear Model Predictive Control of a U-loop Bioreactor

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