Non-linear multivariable predictive control of an alcoholic fermentation process using functional link networks

Abstract

In this work a MIMO non-linear predictive controller was developed for an extractive alcoholic fermentation process. The internal model of the controller was represented by two MISO Functional Link Networks (FLNs), identified using simulated data generated from a deterministic mathematical model whose kinetic parameters were determined experimentally. The FLN structure presents as advantages fast training and guaranteed convergence, since the estimation of the weights is a linear optimization problem. Besides, the elimination of non-significant weights generates parsimonious models, which allows for fast execution in an MPC-based algorithm. The proposed algorithm showed good potential in identification and control of non-linear processes.

Constructive learning neural network applied to identification and control of a fuel-ethanol fermentation process

“…In previous works, Meleiro et al (2002Meleiro et al ( , 2003Meleiro et al ( , 2005Meleiro et al ( , 2007 have already investigated the capability of advanced nonlinear approaches for process identification and predictive control, though restricted to a simpler control configuration for this fermentation process (Costa et al, 2002;Meleiro et al, 2005), or considering a different fermentation process. The control methodology proposed here considers constraints on manipulated and controlled variables.…”

Soft-sensor modeling for l-lysine fermentation process based on hybrid ICS-MLSSVM

“…Measurement of these key variables is necessary to control and optimize the fermentation process in real-time to enhance the productivity. However, it is hard to measure cell concentration, substrate concentration, product concentration during fermentation process in real-time due to the highly time-varying, non-linear and uncertain nature of the fermentation process 5 – 7 .…”

Non-linear dynamics of chromosome condensation induced by colcemid

“…Many biological processes have been demonstrated to be non-linear, such as genomic and intergenic regions and heartbeats intervals (Peng et al 1994;Peng et al 1995), or the industrial processes such as alcoholic fermentation (Meleiro et al 2005). This is a field of research called as non-linear dynamics and the analyses of such non-linear processes disclose the fractal properties of these phenomena, such as scaling, self-similarity and fractal dimension (Liebovitch 1998;Stam 2005;Kunicki et al 2009).…”