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Logo Institut für Bioverfahrenstechnik der TU Braunschweig
Development of an AI-Based Online Sensor System for the Real-Time Determination of Volatile Fatty Acids
  • Production of natural products
    • Development of an AI-Based Online Sensor System for the Real-Time Determination of Volatile Fatty Acids
    • Production of Bacterial Cellulose in a Membrane-Based Bioreactor
    • Rheomorphological Interactions in Filamentous Microorganisms: From High-Throughput Screening to Scale-Up

Development of an AI-Based Online Sensor System for the Real-Time Determination of Volatile Fatty Acids

Project Description

The concentration and composition of volatile fatty acids (VFAs) are key indicators of microbial metabolism and process stability in anaerobic fermentation. However, their continuous and component-specific monitoring under real-world process conditions remains a significant analytical challenge. The sensAI collaborative project investigates how membrane-based separation, optical spectroscopy, and data-driven analysis methods can be combined into a robust online measurement system. The central scientific question is to what extent individual fatty acids—such as acetate, propionate, and butyrate—can be selectively detected in complex fermentation media and their concentrations utilized for predictive process control.

To this end, a membrane bypass system is being developed that enables the continuous provision of an analytically accessible measurement phase, even in complex fermentation matrices containing solids. The spectroscopic analysis of sequentially selected VFA is carried out primarily using spectroscopic and chemometric methods. Another research focus is on the use of machine learning and hybrid process modeling. Multivariate calibration methods and data-driven models are intended to enable the quantification of individual fatty acids from superimposed measurement signals. By linking sensor data with reaction kinetic models of microbial metabolism, predictive approaches for the early detection of critical process conditions are also being investigated. The developed technologies are being gradually validated under real-world conditions and integrated into a biogas pilot plant. The long-term goal is continuous, low-maintenance process monitoring as the basis for demand-driven substrate dosing and more stable biogas production.

Research Focus Areas and Methods

  • Development and optimization of a simple and robust membrane-based bypass or sampling system
  • Optical process analytics and spectroscopic quantification of volatile fatty acids
  • Chemometrics, multivariate data analysis, and machine learning
  • Hybrid modeling and prediction of microbial process states
  • Integration and validation in real-world bioproduction and biogas processes

Project details

Project Duration: October 1, 2026–September 30, 2029

Funding: Federal Ministry of Agriculture, Food, and Home Affairs (BMLEH), via FNR e.V.

Grant ID: 2224NR128A

Project Leader: Prof. Dr.-Ing. Nils Tippkötter

Project Coordination: Institute of Bioprocess Engineering, TU Braunschweig

Project Partner: PlanET Biogas GmbH

Associated Partner: Spectral Service AG

Scientific Collaboration: Instrumental Analysis (Prof. Monakhova) and the NOWUM-Energy Biogas Group, Aachen University of Applied Sciences

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