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Journals / Afyon Kocatepe Üniversitesi Fen Bilimleri Dergisi / 2003 / Cilt: 3 - Sayı: 1-2

Biomass and specific growth rate estimation using artificial neural networks in industrial baker's yeast fermentation

Yapay sinir ağları ile endüstriyel ekmek mayası fermentasyonunda özgül üreme hızı ve biyokütle kestirimi

Cihan KARAKUZU
Mustafa TÜRKER
Mehmet YAKUT
Sıtkı ÖZTÜRK
Pages
51–66
Publication Date
2022-10-11
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The given results show that the ANN soft-sensors could successfully estimate biomass concentration and specific growth rate in real production media.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Control and optimisation of fermentation process are limited cause of unavailability of on-line measurement sensors for state variable suchas biomass and unutilised substrate.
📊
Key Findings & Data:This study presents artificial neural network (ANN) estimators for each one of biomass concentration and specific growth rate variables, based on process model and measurements from real production media.
💡
Conclusion & Impact:The given results show that the ANN soft-sensors could successfully estimate biomass concentration and specific growth rate in real production media.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

Control and optimisation of fermentation process are limited cause of unavailability of on-line measurement sensors for state variable suchas biomass and unutilised substrate. During a fermentation process, these variables are only measured by off-line laboratory analysis. This study presents artificial neural network (ANN) estimators for each one of biomass concentration and specific growth rate variables, based on process model and measurements from real production media. The given results show that the ANN soft-sensors could successfully estimate biomass concentration and specific growth rate in real production media. Hence, observation of these variables and installing effective on-line control structure will be possible.

Özet

Endüstriyel fermantasyon süreçlerinin kontrolü ve optimizasyonu özellikle biyokütle ve kullanılmamış hammadde gibi anahtar süreç değişkenlerinin ölçümü için on-line ölçüm yapan enstrümanların olmayışı sebebiyle sınırlıdır. Bu değişkenlerin ölçümü sadece çevrimdışı (off-line) süreçten alınan örneklerle yapılan laboratuvar analizi ile yapılabilmektedir. Bu çalışma, proses modeli ve üretim ortamından alınan ölçümlere dayalı olarak, maya derişimi (Cx) ve özgül üreme hızı $(mu)$ değişkenleri için birer yapay sinir ağı (YSA) kestirimci sunmaktadır. Verilen sonuçlar, YSA yazılım-sensörlerin üretim ortamında biyokütle derişimi ve özgül üreme hızını başarılı bir şekilde kestirdiğini göstermektedir. Böylece bu değişkenlerin gözlenmesi ve etkin on-line kontrol yapısı kurulumu mümkün olacaktır.

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Cite This Article

KARAKUZU, C., TÜRKER, M., YAKUT, M., ÖZTÜRK, S. (2022). Biomass and specific growth rate estimation using artificial neural networks in industrial baker's yeast fermentation. Afyon Kocatepe Üniversitesi Fen Bilimleri Dergisi, Cilt: 3 - Sayı: 1-2, 51–66. https://ajindex.com/articles/429545

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