A Hybrid Neural Model for the Optimization of Fed-Batch Fermentations

Costa, Aline Carvalho da; Henriques, A.S.; Alves, Tito Lívio Moitinho; Filho, Rubens Maciel; Lima, Enrique Luis

doi:10.1590/s0104-66321999000100006

Cited by 24 publications

(18 citation statements)

References 15 publications

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“…Tests were made to determine the functional expansion degree and activation function that led to the best training performance of the networks (Costa et al, 1999). For both FLNs the functional expansion degree chosen was 6 and the activation function was:…”

Section: Resultsmentioning

confidence: 99%

“…Before the functional expansion is performed, the network inputs, x e , are transformed into a greater number, n z , of auxiliary inputs, z. These auxiliary inputs are non-linear expressions of the real inputs (Costa et al, 1999). A polynomial expansion was then performed on the new inputs.…”

Section: Process Identification Using Functional Link Networkmentioning

confidence: 99%

“…A polynomial expansion was then performed on the new inputs. The generated monomials can be found in Costa et al (1999). Once the monomials are generated, the network weights, w ij , are estimated using an orthogonal least-squares estimator (Billings et al, 1989).…”

Section: Process Identification Using Functional Link Networkmentioning

confidence: 99%

“…This network has been shown to have a good non-linear approximation capability, although the estimation of its weights is linear. Due to the linear estimation, its training is rapid, requires low computational effort and the convergence is guaranteed (Costa et al, 1999). The objective of this work was to develop a nonlinear Multiple Input Multiple Output (MIMO) predictive control algorithm to control the process mentioned above, considering constraints on manipulated and controlled variables.…”

Section: Introductionmentioning

confidence: 99%

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

Meleiro

Costa

Filho

2005

Braz. arch. biol. technol.

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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.

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Section: Resultsmentioning

confidence: 99%

Section: Process Identification Using Functional Link Networkmentioning

confidence: 99%

Section: Process Identification Using Functional Link Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Meleiro

Costa

Filho

2005

Braz. arch. biol. technol.

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“…These methods can account for nonlinearities and are relatively computationally inexpensive. In case that the process model equations are not invertible, FLC (Bazaei & Majd, 2003;Hussain, Ho, & Allwright, 2001;Madar, Abonyi, & Szeifert, 2005), sliding mode control (Hussain & Ho, 2004), Model Predictive Con-trol (MPC) (Abonyi et al, 1999;Cubillos, Callejas, Lima, & Vega, 2001;Hermanto et al, 2011;Ibrehem, Hussain, & Ghasem, 2011;Klimasauskas, 1998;Tsen et al, 1996;van Can et al, 1996;Vega et al, 2000;Vega, Lima, & Pinto, 1997), predictive or optimal control (Anderson et al, 2000;Costa et al, 1998;Costa, Henriques, Alves, Maciel Filho, & Lima, 1999;Cubillos & Lima, 1997Schenker & Agarwal, 2000;Vieira et al, 2005) schema can be employed, where FLC and sliding mode control are computational less expensive while MPC or optimal control may provide better performance. When comparing the performances of control schema that utilize hybrid semiparametric models to those using either traditional control methods (such as a self-tuning PID (Xiong & Jutan, 2002), a generalized minimum variance controller (Xiong & Jutan, 2002), a FLC based on a linear model (Hussain et al, 2001) or a MPC based on a linearized model (Anderson et al, 2000)) or to non-parametric model based controllers (Cubillos et al, 2001;Hussain et al, 2001;Ibrehem et al, 2011;Schenker & Agarwal, 2000), it was mostly observed that the hybrid semiparametric model based control schema performed significantly better.…”

Section: Hybrid Semi-parametric Model Based Controller Structuresmentioning

confidence: 99%

Hybrid semi-parametric modeling in process systems engineering: Past, present and future

Stosch¹,

Oliveira²,

Peres³

et al. 2014

Computers & Chemical Engineering

308

187

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Hybrid semi-parametric models consist of model structures that combine parametric and nonparametric submodels based on different knowledge sources. The development of a hybrid semi-parametric model can offer several advantages over traditional mechanistic or data-driven modeling, as reviewed in this paper. These advantages, such as broader knowledge base, transparency of the modeling approach and cost-effective model development, have been widely recognized, not only in academia but also in the industry.In this paper, the most common hybrid semi-parametric modeling and parameter identification techniques are revisited. Applications in the areas of (bio)chemical engineering for process monitoring, control, optimization, scaleup and model-reduction are reviewed. It is outlined that the application of hybrid semi-parametric techniques does not automatically lead into better results but that rational knowledge integration has potential to significantly improve model-based process operation and design.

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Non‐Linear Predictive Control of a Three‐Phase Catalytic Reactor

Costa

Filho

2003

Can J Chem Eng

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A t the centre of a Model Predictive Control (MPC) algorithm is a dynamic model. Until recently, most industrial applications have used linear dynamic models. These models were preferred to the non-linear ones due to the difficulty in developing generic non-linear models from plant data and also because of the computational expense involved when non-linear models are used in the MPC formulation (Piché et al., 2000). The rapid development in computer capacity in the last few years, however, has led to the development of a great number of predictive control algorithms using non-linear models.One of the main problems with linear controllers is that they are not robust, i.e., they do not present good performance for different operating regions and with changing process conditions (Dutta and Rhinehart, 1999). According to Piché et al. (2000), MPC based on linear models is acceptable when the process operates at a single set point and the primary use of the controller is for the rejection of disturbances. However, many chemical processes do not operate at a single set point, as they are operated in different conditions depending on market requirements. The operation in different set points also occurs when an optimization layer is introduced in the control structure. The main characteristic of a multilayer optimization structure is that the optimization level defines the operational conditions of the process. Thus, the control system has to be designed to maintain the process operating adequately in different operational conditions. In this case, to properly control the process, a non-linear model is needed.Although the use of non-linear models may improve the control algorithm performance, the development of such models is not always an easy task. It is desirable to develop techniques to obtain reliable models in a simple and rapid way. In recent years, there has been a strong interest in the use of neural networks to describe chemical processes, due to their ability to approximate highly non-linear systems. Different structures of neural networks have been used as non-linear models in advanced control algorithms (Zhan and Ishida, 1997;Rohani et al., 1999;Kambhampati et al., 2000;Meleiro et al., 2001). Hussain (1999) presents a review of neural network applications in process control. They conclude that neural networks are capable of describing the system in a great number of cases. Besides, they are versatile, as they can be incorporated in various well-known non-linear control methods. Seborg (1999) provides a perspective on the current status of advanced process control and concludes that many industrial applications of nonlinear predictive control employ neural networks. In this work, the predictive control of a three-phase catalytic reactor is considered. A predictive control algorithm, which has a non-linear internal model represented by functional link networks, is proposed. This network structure has been shown to have a good non-linear approximation capability, with the advantage that the estimation of its we...

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A Hybrid Neural Model for the Optimization of Fed-Batch Fermentations

Cited by 24 publications

References 15 publications

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

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

Hybrid semi-parametric modeling in process systems engineering: Past, present and future

Non‐Linear Predictive Control of a Three‐Phase Catalytic Reactor

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