Optimum Design of a Continuous Fermentation Unit of an Industrial Plant for Alcohol Production

Neto

et al. 2007

Braz. J. Chem. Eng.

-Cashew wine of demi-sec grade was produced in a stirred batch reactor. The kinetic parameters obtained for cashew wine fermentation were Y X/S =0.061, Y P/S =0.3 and µ max =0.16 h -1 . The yield and the productivity of cashew wine were 57.7% and 0.78 g/Lh respectively. A 2 2 factorial experimental design was used for the cashew wine vinegar fermentation optimization study. The cashew wine vinegar process optimization ranges found for initial concentrations of ethanol and acetic acid as independent variables were 4.8 to 6.0% and 1.0 to 1.3% respectively.

Section: Methodssupporting

confidence: 89%

Section: Methodssupporting

confidence: 89%

Cashew wine vinegar production: alcoholic and acetic fermentation

Neto

et al. 2007

Braz. J. Chem. Eng.

Braz. arch. biol. technol.

“…The FLNs described above were used as internal models of the predictive controller. The sampling time was chosen as 12 minutes, considering that an HPLC (High Performance Liquid Chromatograph) was used to measure substrate and ethanol concentrations on-line (Andrietta, 1994). The controlled and manipulated variables constraints were as follows: Fig.…”

Section: Resultsmentioning

confidence: 99%

“…The process consists of four interlinked units: fermentor (ethanol production unit), centrifuge (cell separation unit), cell treatment unit and vacuum flash vessel (ethanolwater separation unit). This scheme simulated industrial conditions (Andrietta and Maugeri, 1994), with the difference that only one fermentor was used instead of a cascade system, besides the flash was used to extract part of the ethanol. The substrate used was sugar-cane molasses.…”

Section: Extractive Alcoholic Fermentation Processmentioning

confidence: 99%

“…Several schemes combining fermentation with a separation process have been developed (Costa et al, 2001). Silva et al (1999) have shown that a scheme combining a fermentor with a vacuum flash vessel presents several positive features and better performance than an industrial conventional process (Andrietta and Maugeri, 1994). Another important aspect to be considered in the optimization of the alcoholic fermentation process is the development of an efficient control strategy, since it can minimize costs and environmental impact by maintaining the process under optimum conditions.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Meleiro

Costa

Filho

2005

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.

Neural-Networks-Based Feedback Linearization versus Model Predictive Control of Continuous Alcoholic Fermentation Process

Mjalli

Al‐Asheh

2005

Chem. Eng. Technol.

In this work advanced nonlinear neural networks based control system design algorithms are adopted to control a mechanistic model for an ethanol fermentation process. The process model equations for such systems are highly nonlinear. A neural network strategy has been implemented in this work for capturing the dynamics of the mechanistic model for the fermentation process. The neural network achieved has been validated against the mechanistic model. Two neural network based nonlinear control strategies have also been adopted using the model identified. The performance of the feedback linearization technique was compared to neural network model predictive control in terms of stability and set point tracking capabilities. Under servo conditions, the feedback linearization algorithm gave comparable tracking and stability. The feedback linearization controller achieved the control target faster than the model predictive one but with vigorous and sudden controller moves.