Preliminary screening of neural network configurations for bioreactor applications

Patnaik, Pratap R.

doi:10.1007/bf00180404

Cited by 6 publications

(4 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These differences reinforce the main tenet of the COMPARE library (Patnaik 1996), that in real situations there is no direct choice of an optimal neural configuration; therefore it is important to define the performance index (or a weighted combination of indexes) and then search for the best network. Given that the main theme of this study is the maximization of PHB concentration, an Elman network with four input neurons, two recurrent neurons, four hidden neurons and two output neurons is the best, followed by a Hopfield network with the same numbers of neurons.…”

Section: Application and Discussionmentioning

confidence: 95%

See 1 more Smart Citation

Neural network designs for poly- ?-hydroxybutyrate production optimization under simulated industrial conditions

Patnaik

2005

Biotechnol Lett

View full text Add to dashboard Cite

Improvement of the fermentation efficiency of poly-beta-hydroxybutyrate (PHB) may make it competitive with chemically synthesized petroleum-based polymers. One step toward this is optimization of fluid dispersion and the feed rates to a fed-batch bioreactor. In a recent study using a fermentation model, dispersion corresponding to a Peclet number of approximately 20 was shown to maximize the productivity of PHB. Here further improvement has been investigated using neural optimization. A comparison of seven neural topologies has shown that while feed-forward and radial basis neural networks are computationally efficient, recurrent networks generate higher concentrations of PHB. All networks enhanced the productivity by 16-93% over model-based optimization.

show abstract

Section: Application and Discussionmentioning

confidence: 95%

“…The bioreactor was represented by each of seven ANN configurations from the COMPARE library (Patnaik 1996). Given a set of data, COMPARE does a preliminary screening with the ANNs in the library and generates performance indices that help to decide which network is the most promising.…”

Section: Neural Optimization Of Phb Productionmentioning

confidence: 99%

Neural network designs for poly- ?-hydroxybutyrate production optimization under simulated industrial conditions

Patnaik

2005

Biotechnol Lett

View full text Add to dashboard Cite

show abstract

“…As an aid to this, Patnaik [33] developed a library of networks used commonly for microbial processes; inherent in this library was a set of rules to screen competing networks and select a few promising ones for detailed studies. Both the library and the screening rules may be updated.…”

Section: Introduction Of the Kinetic Modelsmentioning

confidence: 99%

"Intelligent" descriptions of microbial kinetics in finitely dispersed bioreactors: neural and cybernetic models for PHB biosynthesis by Ralstonia eutropha

Patnaik

2007

Microb Cell Fact

View full text Add to dashboard Cite

BackgroundFor many microbial processes, the complexity of the metabolisms and the responses to transient and realistic conditions are difficult to capture in mechanistic models. The cells seem to have an innate intelligence that enables them to respond optimally to environmental changes. Some "intelligent" models have therefore been proposed and compared with a mechanistic model for fed-batch cultures of Ralstonia eutropha.ResultsTwo kinds of models have been proposed to describe such cellular behavior. Cybernetic models are derived through postulates of cellular intelligence and memory, and neural models use artificial intelligence through neural networks. Some competing models of both kinds have been compared for their ability to portray and optimize the synthesis of poly-β-hydroxybutyrate by Ralstonia eutropha in fed-batch cultures with finite dispersion. Neural models enabled the formation of more of the polymer than cybernetic models, with lesser utilization of the carbon and nitrogen substrates. Both types of models were decidedly superior to a mechanistic model used as a reference, thus supporting the value of intelligent descriptions of microbial kinetics in incompletely dispersed bioreactors.ConclusionNeural and cybernetic models describe and optimize unsteady state fed-batch microbial reactors with finite dispersion more effectively than mechanistic models. However, these "intelligent" models too have weaknesses, and hence a hybrid approach combining such models with some mechanistic features is suggested.

show abstract

“…The final network architecture was determined through multiple runs to test various architectures. These included multilayer perceptrons with both one and two hidden layers, which have been shown to be effective for bioreactor problems ( , ). It was found that networks with two hidden layers provided improved results without loss of generality, which agrees with previous findings for fermentation reactors ().…”

Section: Methodsmentioning

confidence: 99%

Use of Neural Network Models To Predict Industrial Bioreactor Effluent Quality

Pigram

MacDonald

2000

Environ. Sci. Technol.

View full text Add to dashboard Cite

Engineered bioreactors are useful tools for degrading wastes from crude oil refining facilities. One such bioreactor forms part of the wastewater remediation process used at a refinery in the San Francisco Bay Area. The flow rate and chemical concentrations of the waste vary, and it is necessary to be able to predict the efficiency of the reactor degradation process for this varied input. The complex biological, physical, and chemical processes of the reactor make deterministic modeling unsuitable. Therefore, predictive modeling for this system was performed using a neural network model. A predictive, time-series neural network model requires a complete data set. Often, in the case of a large industrial facility, data are missing. Various techniques can be used to reconstruct missing data, but comparisons of techniques have not been performed for large-scale remediation processes. In this manuscript, four techniques are used for reconstructing missing data to examine which ones provide superior predictive capabilities. It was found that the interpolated and moving average values methods provided the best predictions. The mean and median replacement methods, commonly used in neural network modeling, provided much poorer predictions. Another goal of this study is to determine which water quality parameters are more accurately predicted than others. In this study, pH was the most accurately predicted, while ammonia and total phenolics concentrations were the least accurately predicted.

show abstract

Preliminary screening of neural network configurations for bioreactor applications

Cited by 6 publications

References 10 publications

Neural network designs for poly- ?-hydroxybutyrate production optimization under simulated industrial conditions

Neural network designs for poly- ?-hydroxybutyrate production optimization under simulated industrial conditions

"Intelligent" descriptions of microbial kinetics in finitely dispersed bioreactors: neural and cybernetic models for PHB biosynthesis by Ralstonia eutropha

Use of Neural Network Models To Predict Industrial Bioreactor Effluent Quality

Contact Info

Product

Resources

About