Embedded Soft Sensor for Bioreactor Application using Soft Processor of Zynq SoC

doi:10.23940/ijpe.24.09.p4.563571

Int J Performability Eng ›› 2024, Vol. 20 ›› Issue (9): 563-571.doi: 10.23940/ijpe.24.09.p4.563571

Previous Articles Next Articles

Embedded Soft Sensor for Bioreactor Application using Soft Processor of Zynq SoC

V S Vijaya Krishna V^*

ECE Department, Presidency University, Bangalore, India

Submitted on ; Revised on ; Accepted on
Contact: *E-mail address: vijayakrishna@presidencyuniversity.in

Abstract

Abstract: Soft sensors offer an alternative approach for measuring process variables that cannot be directly obtained from hardware sensors, crucial for optimizing process outputs. This paper outlines a method for implementing an embedded soft sensor to estimate lactose concentration in a bioreactor using the MicroBlaze Soft Processor found in the Zynq SoC. The study focuses on employing the Extended Kalman Filter (EKF) algorithm for this purpose. The methodology involves designing a block using the MicroBlaze soft processor core in VIVADO, utilizing the Zybo board as the hardware platform. The embedded soft sensor is developed as a software application in C, configured to operate on the soft processor within the Zynq device. A comparative analysis is conducted among lactose output values obtained from simulation, hardware implementation, and true values derived from laboratory analysis. This approach underscores the efficacy of embedded soft sensors in enhancing process monitoring and optimization.

V S Vijaya Krishna V. Embedded Soft Sensor for Bioreactor Application using Soft Processor of Zynq SoC [J]. Int J Performability Eng, 2024, 20(9): 563-571.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

References

[1] Ali A.A.S., Djelouat H., Amira A., Bensaali F., Benammar M. and Bermak A., 2017. Electronic nose system on the zynq soc platform.Microprocessors and Microsystems, 53, pp.145-156.
[2] Bachtadze N., Kulba V.V., Lototsky V.A. and Maximov M.E., 2007. Identificaton-based approach to soft sensors design. IFAC Proceedings Volumes,40(3), pp.87-92.
[3] Caponetto R., Dongola G., Gallo A. and Xibilia M.G., 2009, July. FPGA based soft sensor for the estimation of the kerosene freezing point. In 2009 IEEE International Symposium on Industrial Embedded Systems(pp. 228-236). IEEE.
[4] Krishna V.V.,2014. Performance Optimization of CNFET-based Domino Logic circuits‖. International Journal of Advanced Research in Electronics and Communication Engineering,3(9), pp.975-978.
[5] De Assis, A.J. and Maciel Filho, R., 2000. Soft sensors development for on-line bioreactor state estimation. Computers & Chemical Engineering,24(2-7), pp.1099-1103.
[6] Fortuna L., Rizzo A., Sinatra M. and Xibilia M.G., 2003. Soft analyzers for a sulfur recovery unit. Control Engineering Practice,11(12), pp.1491-1500.
[7] Kadlec P., Gabrys B. and Strandt S., 2009. Data-driven soft sensors in the process industry. Computers & chemical engineering,33(4), pp.795-814.
[8] Mouloud A., Marc C., Radu P., 2015. Soft sensor application in the pulp and paper industry assessment study.
[9] Singh J., Kaushik N. and Biswas S., 2014. Bioreactors-technology & design analysis. The scitech journal,1(6), pp.28-36.
[10] Gerson D.F., Kole M.M., Ozum B. and Oguztoreli M.N., 1988. Substrate concentration control in bioreactors. Biotechnology and Genetic Engineering Reviews,6(1), pp.67-150.
[11] Longhi L.G., Luvizetto D.J., Ferreira L.S., Rech R., Ayub M.A. and Secchi A.R., 2004. A growth kinetic model of Kluyveromyces marxianus cultures on cheese whey as substrate. Journal of Industrial Microbiology and Biotechnology,31(1), pp.35-40.
[12] Longhi L.G.S., Marcon S.M., Trierweiler J.O. and Secchi A.R., 2002. State estimation of an experimental bioreactor using the extended Kalman filtering technology. IFAC Proceedings Volumes,35(1), pp.379-382.
[13] Grewal, M.S. and Andrews, A.P., 2014. Kalman filtering: Theory and Practice with MATLAB. John Wiley & Sons.
[14] Kleinbauer R.,2004. Kalman filtering implementation with Matlab.
[15] Musulin E., Benqlilou C., Bagajewicz M.J. and Puigjaner L., 2005. Instrumentation design based on optimal Kalman filtering. Journal of Process Control,15(6), pp.629-638.
[16] Ribeiro M.I.,2004. Kalman and extended kalman filters: Concept, derivation and properties. Institute for Systems and Robotics,43(46), pp.3736-3741.
[17] Ning M., Shaojun W., Yeyong P. and Yu P., 2014, December. Implementation of LS-SVM with HLS on Zynq. In 2014 International Conference on Field-Programmable Technology (FPT)(pp. 346-349). IEEE.
[18] Vivado Design Suite User Guide, 2019. Embedded Processor Hardware Design, UG940(v2019.1).
[19] Vivado Design Suite User Guide, 2021. High-Level Synthesis, UG902(v2020.1).
[20] Kale V.,2016. Using the microblaze processor to accelerate cost-sensitive embedded system development. Xilinx, WP469, 1, p.1.

Embedded Soft Sensor for Bioreactor Application using Soft Processor of Zynq SoC

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 0

Recommended 0