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Published on: April 22, 2016
Artificial neural network-genetic algorithm based optimization for the immobilization of cellulase on the smart
Yu Zhang1, Jingliang Xu, Zhenhong Yuan
1Key Laboratory of Renewable Energy and Gas Hydrate, Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences, Guangzhou 510640, PR China.
Bioresource Technology
|January 13, 2010
Summary
Immobilized cellulase enzyme activity was optimized using artificial neural networks (ANNs), achieving higher yields than response surface methodology (RSM). The enhanced enzyme retained over 50% activity after five reuse cycles.
Area of Science:
- Biotechnology
- Enzyme Immobilization
- Polymer Science
Background:
- Enzyme immobilization enhances stability and reusability.
- Smart polymers like Eudragit L-100 offer tunable properties for immobilization.
- Optimizing immobilization conditions is crucial for maximizing enzyme activity yield.
Purpose of the Study:
- To covalently immobilize cellulase onto Eudragit L-100.
- To develop and compare Response Surface Methodology (RSM) and Artificial Neural Network (ANN) models for predicting immobilized cellulase activity yield.
- To optimize immobilization parameters using ANN and genetic algorithms.
Main Methods:
- Covalent immobilization of cellulase using carbodiimide coupling on Eudragit L-100.
- Central Composite Design for data generation.
- Response Surface Methodology (RSM) and Artificial Neural Network (ANN) modeling.
- Genetic algorithm for optimization.
Main Results:
- ANN demonstrated superior simulation and prediction accuracy compared to RSM.
- RSM predicted a maximum activity yield of 57.56% under specific conditions.
- ANN, coupled with a genetic algorithm, predicted a higher maximum activity yield of 69.83%, with experimental validation of 66.75%.
- The ANN approach yielded a 9.7% increase in activity compared to RSM.
Conclusions:
- ANN is a more accurate predictive tool than RSM for enzyme immobilization optimization.
- Optimized immobilized cellulase exhibited significant reusability, retaining over 50% activity after five cycles.
- This study demonstrates an effective strategy for enhancing enzyme performance through smart polymer immobilization and advanced modeling techniques.
