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A Real Time Feed Forward Control of Slug Flow in Microchannels †

Author

Listed:
  • Salvina Gagliano

    (Department of Electrical, Electronics and Computer Engineering, University of Catania, v.le A. Doria 6, 95129 Catania, Italy
    These authors contributed equally to this work.)

  • Fabiana Cairone

    (Department of Electrical, Electronics and Computer Engineering, University of Catania, v.le A. Doria 6, 95129 Catania, Italy
    These authors contributed equally to this work.)

  • Angelo Amenta

    (Department of Electrical, Electronics and Computer Engineering, University of Catania, v.le A. Doria 6, 95129 Catania, Italy
    These authors contributed equally to this work.)

  • Maide Bucolo

    (Department of Electrical, Electronics and Computer Engineering, University of Catania, v.le A. Doria 6, 95129 Catania, Italy
    These authors contributed equally to this work.)

Abstract

In this work, the authors present a feed-forward control system for two-phase microfluidic processes, widely adaptable for system-on-chip control in a wide variety of bio-chemical experimental conditions, in which two fluids interact in a micro-channel. The proposed approach takes advantage of the optical monitoring of the slugs flow and the on-line signal processing in the frequency domain for slug passage detection. The experimental characterization of the slug flows by the frequencies of the slugs passage was obtained and used to drive the pumps. The open loop control system was designed and implemented in Labview. The platform includes four modules and a GUI. The first manages the communication between the PC and the syringe pumps, while the second is used to implement the control law. The third manages signal acquisition from the photo-diodes and the last implements the soft-sensor for the signal analysis. Wide-reaching experimental design was carried out for characterization and validation of this approach.

Suggested Citation

  • Salvina Gagliano & Fabiana Cairone & Angelo Amenta & Maide Bucolo, 2019. "A Real Time Feed Forward Control of Slug Flow in Microchannels †," Energies, MDPI, vol. 12(13), pages 1-11, July.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:13:p:2556-:d:245205
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    Citations

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    Cited by:

    1. Bin Li & Zheng Cui & Qun Cao & Wei Shao, 2021. "Increasing Efficiency of a Finned Heat Sink Using Orthogonal Analysis," Energies, MDPI, vol. 14(3), pages 1-15, February.
    2. Marko Petkovšek & Mitja Nemec & Peter Zajec, 2021. "Algorithm Execution Time and Accuracy of NTC Thermistor-Based Temperature Measurements in Time-Critical Applications," Mathematics, MDPI, vol. 9(18), pages 1-16, September.
    3. Ying Zhang & Bo Yin & Yanping Cong & Zehua Du, 2020. "Multi-State Household Appliance Identification Based on Convolutional Neural Networks and Clustering," Energies, MDPI, vol. 13(4), pages 1-12, February.
    4. Salil Bharany & Sandeep Sharma & Sumit Badotra & Osamah Ibrahim Khalaf & Youseef Alotaibi & Saleh Alghamdi & Fawaz Alassery, 2021. "Energy-Efficient Clustering Scheme for Flying Ad-Hoc Networks Using an Optimized LEACH Protocol," Energies, MDPI, vol. 14(19), pages 1-20, September.
    5. José Francisco Sáez & Alfonso Baños, 2021. "Reset Control of Parallel MISO Systems," Mathematics, MDPI, vol. 9(15), pages 1-22, August.

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