Application of artificial neural networks in modeling and optimization of biofuels production

Abstract

Artificial neural networks, the artificial intelligence systems that imitate functions of biological neurons, have been widely used in different areas due to their variety and ability to conform to specificities of different applications. When it comes to application of artificial neural networks in bioprocess modeling, their task usually represents prediction or forecasting the values of dependent variables (outputs) based on given values of independent variables (inputs). Although bioprocess model is the 'black box' and remains unknown, which could represent the obstacle in bioprocess analysis, neural networks have shown better ability in prediction of bioprocess results comparing to other modeling methods, such as RSM (Response Surface Methodology) and mathematical modeling. Obtained model could be further used for bioprocess optimization, commonly performed using genetic algorithms. This study provides the review of the main characteristics and applications of artificial neural networks in modeling and optimization of biofuels (bioethanol, biogas and biohydrogen) production.

Keywords

• artificial neural network
• genetic algorithm
• bioethanol
• biogas
• hydrogen
• rsm