Open Access

ARTICLE

Realization of Deep Learning Based Embedded Soft Sensor for Bioprocess Application

V. V. S. Vijaya Krishna^1,*, N. Pappa¹, S. P. Joy Vasantharani²

1 Department of Instrumentation Engineering, MIT, Anna University, Chennai, India
2 Department of Electronics Engineering, MIT, Anna University, Chennai, India

* Corresponding Author: V. V. S. Vijaya Krishna. Email:

Intelligent Automation & Soft Computing 2022, 32(2), 781-794. https://doi.org/10.32604/iasc.2022.022181

Received 30 July 2021; Accepted 02 September 2021; Issue published 17 November 2021

Abstract

Industries use soft sensors for estimating output parameters that are difficult to measure on-line. These parameters can be determined by laboratory analysis which is an offline task. Now a days designing Soft sensors for complex nonlinear systems using deep learning training techniques has become popular, because of accuracy and robustness. There is a need to find pertinent hardware for realizing soft sensors to make it portable and can be used in the place of general purpose PC. This paper aims to propose a new strategy for realizing a soft sensor using deep neural networks (DNN) on appropriate hardware which can be referred as embedded soft sensor (ESS). The work focuses on developing an ESS for estimating lactose concentration in a simulated and experimental bioreactor using DNN and realizing it on the Zynq based System on Chip (SoC). Deep neural network is developed for the process with certain number of hidden layers. The model parameters of the process is represented at input layer and lactose concentration is considered at output layer. The performance of the ESS has been observed with the number of hidden layers and different activation functions. Then the optimized neural network is chosen for realizing on hardware. Comparison is made among the values obtained from hardware realization, software simulation and laboratory analysis. Output analysis shows that the values obtained through hardware realization are closer to the values obtained through laboratory analysis. From the results it can be concluded that Deep learning provides a better way, alternative to traditional techniques for realizing ESS on hardware. From the proposed work, it can be shown that if any sensor is unavailable for measuring any parameter then this ESS can be used to measure the values. Since this ESS is realized on reconfigurable hardware like SoC, it can be portable and flexible to measure values.

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Keywords

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