Sushant Poudel
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Sushant Poudel has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Felix Krujatz
Professorship Automatic Control & System Dynamics, Chemnitz University of Technology, 09126 Chemnitz, Germany
2 shared publications - Luis Porras Reyes
Institute of Technical Chemistry, Leibniz University of Hanover, 30167 Hanover, Germany
2 shared publications - Mark D. Komiskey
Institute of Natural Materials Technology, Technical University Dresden, 01069 Dresden, Germany
2 shared publications - Richard Bleisch
Institute of Natural Materials Technology, Technische Universität Dresden, 01069 Dresden, Germany
2 shared publications - Sascha Beutel
Institute of Technical Chemistry, Leibniz University of Hanover, 30167 Hanover, Germany
2 shared publications - Stefan Streif
Technische Universitt Chemnitz, Chemnitz, Germany
2 shared publications
Latest Publications
- Open Access
A Flow-Through Multi-Wavelength Sensor with Machine-Learning Calibration for Real-Time Monitoring of Microalgae Cultivation Processes
Acquiring real-time biological data is essential for effective control of microalgae cultivation processes, yet routine monitoring still depended on laborious offline analyses that relied on time-consuming wet-chemical techniques. This study introduces a flow-through, multi-wavelength visible-light (VIS) sensor for real-time monitoring of biomass and pigment concentrations in microalgae cultivation processes. Based on 209 experimental data points, six machine-learning regression models were developed to estimate dry biomass, chlorophyll , chlorophyll , total chlorophyll, total carotenoid, and astaxanthin concentrations. Validation under realistic continuous operation showed that potential remains within improved hydrodynamics and operation automatics to reduced biofilm formation. The independent dataset demonstrated that biomass and astaxanthin predictions were within ±10% of offline reference measurements. The proposed low-cost and versatile multi-wavelength sensor platform, together with machine-learning-based calibration, provides a practical soft-sensor concept for real-time monitoring of microalgal bioprocesses and offers a foundation for future integration of model-based and predictive control strategies.Open AccessA Flow-Through Multi-Wavelength Sensor with Machine-Learning Calibration for Real-Time Monitoring of Microalgae Cultivation Processes
Acquiring real-time biological data is essential for effective control of microalgae cultivation processes, yet routine monitoring still depended on laborious offline analyses that relied on time-consuming wet-chemical techniques. This study introduces a flow-through, multi-wavelength visible-light (VIS) sensor for real-time monitoring of biomass and pigment concentrations in microalgae cultivation processes. Based on 209 experimental data points, six machine-learning regression models were developed to estimate dry biomass, chlorophyll , chlorophyll , total chlorophyll, total carotenoid, and astaxanthin concentrations. Validation under realistic continuous operation showed that potential remains within improved hydrodynamics and operation automatics to reduced biofilm formation. The independent dataset demonstrated that biomass and astaxanthin predictions were within ±10% of offline reference measurements. The proposed low-cost and versatile multi-wavelength sensor platform, together with machine-learning-based calibration, provides a practical soft-sensor concept for real-time monitoring of microalgal bioprocesses and offers a foundation for future integration of model-based and predictive control strategies. - Open Access
- 15 June 2026
- Published by MDPI AG in Preprints.org
A Flow-Through Multi-Wavelength Sensor with Machine-Learning Calibration for Real-Time Monitoring of Microalgae Cultivation Processes
Acquiring real-time biological data is essential for effective control of microalgae cultivation processes, yet routine monitoring still depended on laborious offline analyses that relied on time-consuming wet-chemical techniques. This study introduces a flow-through, multi-wavelength visible-light (VIS) sensor for real-time monitoring of biomass and pigment concentrations in microalgae cultivation processes. Based on 209 experimental data points, six machine-learning regression models were developed to estimate dry biomass, chlorophyll α, chlorophyll β, total chlorophyll, total carotenoids, and astaxanthin concentrations. Validation under realistic continuous operation with an independent dataset demonstrated that biomass and astaxanthin predictions were within ± 10% of offline reference measurements. The proposed low-cost and versatile multi-wavelength platform, together with machine-learning-based calibration, provides a practical soft-sensor concept for real-time monitoring of microalgal bioprocesses and offers a foundation for future integration of model-based and predictive control strategies.
- 15 June 2026
- Published by MDPI AG in Preprints.org
Open AccessA Flow-Through Multi-Wavelength Sensor with Machine-Learning Calibration for Real-Time Monitoring of Microalgae Cultivation Processes
Acquiring real-time biological data is essential for effective control of microalgae cultivation processes, yet routine monitoring still depended on laborious offline analyses that relied on time-consuming wet-chemical techniques. This study introduces a flow-through, multi-wavelength visible-light (VIS) sensor for real-time monitoring of biomass and pigment concentrations in microalgae cultivation processes. Based on 209 experimental data points, six machine-learning regression models were developed to estimate dry biomass, chlorophyll α, chlorophyll β, total chlorophyll, total carotenoids, and astaxanthin concentrations. Validation under realistic continuous operation with an independent dataset demonstrated that biomass and astaxanthin predictions were within ± 10% of offline reference measurements. The proposed low-cost and versatile multi-wavelength platform, together with machine-learning-based calibration, provides a practical soft-sensor concept for real-time monitoring of microalgal bioprocesses and offers a foundation for future integration of model-based and predictive control strategies.