Software sensors for biomass concentration in a SSC process using artificial neural networks and support vector machine

Abstract

The lack of sensors for some relevant state variables in fermentation processes can be coped by developing appropriate software sensors. In this work, NARX-ANN, NARMAX-ANN, NARX-SVM and NARMAX-SVM models are compared when acting as software sensors of biomass concentration for a solid substrate cultivation (SSC) process. Results show that NARMAX-SVM outperforms the other models with an SMAPE index under 9 for a 20 % amplitude noise. In addition, NARMAX models perform better than NARX models under the same noise conditions because of their better predictive capabilities as they include prediction errors as inputs. In the case of perturbation of initial conditions of the autoregressive variable, NARX models exhibited better convergence capabilities. This work also confirms that a difficult to measure variable, like biomass concentration, can be estimated on-line from easy to measure variables like CO₂ and O₂ using an adequate software sensor based on computational intelligence techniques.

Multi-Phase Support Vector Regression Soft Sensor for Online Product Quality Prediction in Glutamate Fermentation Process

“…To date, many soft sensor methods have been presented for quality prediction objective, including Artificial Neural Network (ANN), Partial Least Squares (PLS) and Support Vector Machine (SVM) (Acuña et al, 2014;Facco et al, 2009;Wang et al, 2014). Recently, SVR, an extension of SVM, has also been receiving increasing attention to solve nonlinear estimation problems.…”

Soft Sensor Modeling of Product Concentration in Glutamate Fermentation using Gaussian Process Regression

“…Soft sensor appeared as a reliable and helpful method to perform on-line observation of difficult to measure the primary variables in the past decades (Acuña et al, 2014). It generally makes use of available process measurement data or prior knowledge on process mechanism to build predictive model for estimating the primary variables that cannot be easily measured by hardware based sensor in a real-time fashion (Kaneko and Funatsu, 2014;Chen et al, 2014;Shokri et al, 2015;Liu et al, 2015).…”

“…It generally makes use of available process measurement data or prior knowledge on process mechanism to build predictive model for estimating the primary variables that cannot be easily measured by hardware based sensor in a real-time fashion (Kaneko and Funatsu, 2014;Chen et al, 2014;Shokri et al, 2015;Liu et al, 2015). Many soft sensor methods have been presented, including Partial Least Squares (PLS), Artificial Neural Networks (ANN), Support Vector Machine (SVM) and Gaussian Process Regression (GPR) (Baffi et al, 1999;Wang et al, 2014;Acuña et al, 2014;Chen et al, 2014).…”