Rashid, Roslina and Idris, Ani and S. M. M., Esivan and S. R., Radzali (2010) Software sensor for measuring lactic acid concentration: Effect of input number and node number. Journal of Applied Sciences, 10 (21). 2578 -2583. ISSN 1812-5654
Full text not available from this repository.
Official URL: http://dx.doi.org/10.3923/jas.2010.2578.2583
Artificial Neural Network (ANN) approach was applied in developing software sensor for production of lactic acid using pineapple waste from Lactobacillus delbreuckii. Lactic acid production currently is one of the significant materials in industry and production with renewable source such as pineapple waste made the production of lactic acid faced a lot of disturbances in measuring the quality of lactic acid produced. An artificial neural network (ANN) was developed to predict the concentration of lactic acid, using collected data from an offline analysis. Multi layer perceptron (MLP) was used for mapping between the input and output parameters. Two variables were used as input parameters. MSE was used to evaluate the predictive performance of MLP. Logsig and purelin was used as the activation function and Levenberg-Marquadt was utilized as the training algorithm. The result showed that having 2 inputs is better in predicting the concentration of lactic acid; instead of 1 input. The optimum structure found was 2-5-1.
|Uncontrolled Keywords:||multiple inputs, lactic acid concentration, artificial neural network, pineaple waste|
|Subjects:||Q Science > QD Chemistry|
|Deposited By:||Liza Porijo|
|Deposited On:||18 Jul 2012 02:59|
|Last Modified:||13 Feb 2017 01:00|
Repository Staff Only: item control page