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Articles by S.M. Mohamed Esivan
Total Records ( 2 ) for S.M. Mohamed Esivan
  R. Rashid , S.M. Mohamed Esivan , S.R. Radzali and A. Idris
  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.
  R. Rashid , S.R. Radzali , B. Abdul Rahman and S.M. Mohamed Esivan
  Measurement of biological variables in a process is a key to efficient control and supervision of the bioprocess. In a process of protein production such as erythropoietin (EPO), it is crucial but difficult to measure EPO concentration using direct or on-line measurements. EPO concentration is usually measured through laboratory analysis where expensive costs of test kit, tedious and long time analysis are the biggest obstacles. Artificial neural network software sensor was developed to estimate EPO concentration based on other measured variables such as biomass, substrate or by-product in EPO production. Radial Basis Function was utilized to map nonlinear mapping between the input and output parameters. This study deals with effect of input numbers and spread constant on radial basis performance. It is found that different number of inputs and spread constant significantly affect the performance of the predictive model. The high values of coefficient of determination, R from regression analysis also proved that this model successfully mapping the nonlinear relationship between the input and output variables.
 
 
 
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