Identification and Control of Bioreactor using Recurrent Networks

Sivakumaran, N.; Radhakrishnan, T. K.; Babu, J. Sarat Chandra

doi:10.1080/10739140600963871

Cited by 8 publications

(5 citation statements)

References 6 publications

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“…One feature of Figure 4 that is not discussed in those studies is the presence of the recurrent neuron R1. Although an optional feature, recurrent neurons improve the portrayal and optimization of bioreactors with nonideal features such as incomplete dispersion and noise (Patnaik 2004;Sivakumaran et al 2006). Another reason for recycling the glucose outlet concentration is to provide feedback regulation of the glucoamylase synthesis reaction network, because this enhances stability and improves product synthesis in recombinant microbial systems (Xu and Tao 2006;Orrell and Bolouri 2004;Patnaik 2006).…”

Section: Application and Discussionmentioning

confidence: 97%

Supervisory Expert System-Based Intelligent Optimization of a Microbioreactor

Patnaik

2014

Applied Artificial Intelligence

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Microbioreactors with immobilized yeast cells are conventionally packed uniformly. A recent study has shown, however, that a topologically optimized distribution of cells yields much greater outputs of the desired product. Because topology optimization is a complex method requiring a good mathematical model, artificial intelligence (AI) has been employed here as an alternative method. For the same system-in other words, immobilized genetically modified yeast cells-an expert system selected online the better of two AI methods-a fuzzy neural network (FNN) and a genetic algorithm (GA)-according to the output of the product recombinant glucoamylase. Progressing in short time intervals enables the expert system toshift continually between the FNN and the GA, thereby maintaining optimal performance at all times. This method is more robust than topology optimization, easier to implement, does not require a mathematical model, and improves glucoamylase output even further.

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Section: Application and Discussionmentioning

confidence: 97%

Supervisory Expert System-Based Intelligent Optimization of a Microbioreactor

Patnaik

2014

Applied Artificial Intelligence

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“…RNNs could converge to stable system solutions and include the effects of response delays. These characteristics make these models especially useful in the modeling of continuous bioreactors [25].…”

Section: Neural Network Modelsmentioning

confidence: 99%

Fermentation: Metabolism, Kinetic Models, and Bioprocessing

González-Figueredo¹,

Flores-Estrella²,

Rojas-Rejón³

2019

Current Topics in Biochemical Engineering

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Biochemical and metabolic interpretation of microbial growth is an important topic in bioreactor design. We intend to address valuable information about the relation of critical operation variables and the simulation of bioprocesses with unstructured and structured kinetic models. Process parameters such as nutrient supply, pH, dissolved oxygen, and metabolic end-products directly impact the physiology and metabolism of microorganisms. Changes in the membrane as well as cell viability are of interest since protein expression and maturation in prokaryota are directly related to membrane integrity. This chapter intends to deliver an insight of different alternatives in kinetic modeling.

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“…The updated velocity of each particle can be calculated using the present velocity and the distances from p best and g best . The updated velocity and the position are given in (6) and (8), respectively. Equation 7shows the inertia weight:…”

Section: Pso Algorithmmentioning

confidence: 99%

“…for i = 1 : N If current fitness (i) < local best fitness (i); Then local best fitness = current fitness; % Replacement % local best position = current position (i); % Replacement % end Step 5 Compare the fitness of particle with its G t i and replace the global best value as given below. for i = 1 : N If current fitness (i) < global best fitness (i); Then global best fitness = current fitness; % Replacement % global best position = current position (i); % Replacement % end Step 6 Update the current velocity and position of the particles according to (6) and 8Step 7 Repeat step-2 to 6 until the predefined value of the performance index has been reached.…”

Section: Comparative Studymentioning

confidence: 99%

“…With an experimental study, the mathematical models for the different steady state operating regions are developed, and a nonlinear PI controller was implemented [5]. Sivakumaran et al have discussed recurrent neural network (RNN) based modeling method for a nonlinear bioreactor operating at single steady state and implemented a nonlinear model predictive controller to obtain satisfactory result [6]. Nagy has proposed a neural network model-based predictive control (NNMPC) strategy for a fermentation bioreactor model, and with simulation result they concluded that the proposed NNMPC provides enhanced performance compared to linear MPC and PID controller [7].…”

Section: Introductionmentioning

confidence: 99%

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Modeling, Analysis, and Intelligent Controller Tuning for a Bioreactor: A Simulation Study

Rajinikanth

Latha

2012

ISRN Chemical Engineering

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In this paper, a novel modeling technique has been attempted to develop the mathematical model for a bioreactor functioning at multiple operating regions. The first principle mathematical equations of the reactor are used with the POLYMATH software to generate essential data for the model development. A relative analysis is also carried out with the existing models in the literature. An optimal PID controller is then designed using a multiobjective particle swarm optimization algorithm. The controller tuning procedure is individually discussed for both the stable and unstable steady state regions. The controller tuned for each region is scheduled using a set-point scheduler to achieve a complete control over the bioreactor. The effectiveness of the proposed scheme has been confirmed through a comparative study with the controller tuning methods proposed in the literature. The results show that, the proposed method provides enhanced performance in effective reference tracking and load disturbance rejection with minimal ISE and IAE. Finally the proposed method is validated on the nonlinear bioreactor model in the presence of a measurement noise. The results testify that the PSO tuned PID performs well in tracking the change in biomass concentration at the entire operating region.

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Identification and Control of Bioreactor using Recurrent Networks

Cited by 8 publications

References 6 publications

Supervisory Expert System-Based Intelligent Optimization of a Microbioreactor

Supervisory Expert System-Based Intelligent Optimization of a Microbioreactor

Fermentation: Metabolism, Kinetic Models, and Bioprocessing

Modeling, Analysis, and Intelligent Controller Tuning for a Bioreactor: A Simulation Study

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