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Articles by R. Rashid
Total Records ( 4 ) for R. Rashid
  Algorafi M.A. , A.A.A. Ali , M.S. Jaafar , I. Othman , M.P. Anwar and R. Rashid
  Externally Prestressed Segmental (EPS) concrete box sections are widely used in the construction of bridge structures today. EPS concept has become an attractive tool for rehabilitation and strengthening of existing bridges which have insufficient strength and/or excessive deflection and cracking. Problem statement: EPS bridges are affected by combined stresses (bending, shear, normal, and torsional) at the joint interface between the segments. However, very limited researchers studied this type of bridges under combined stresses. Approach: This paper presented an experimental investigation of the structural behaviour of EPS bridge with shear key under torsion. Four beams were tested, each containing three segments that were presetressed using two external tendons. A parametric study of two different external tendon layouts as well as different levels of torsional force applied by different load eccentricities was conducted. Results: The effect of torsion was evaluated in terms of vertical deflections, concrete and tendon strains, failure loads and failure mechanisms. It was concluded that torsion has a significant effect in the structural behaviour of external prestressed segmental box girder beams. Torsion not only alters failure load of the beam but also changes the type of failure mechanism. It was also investigated that harp tendon layout results in better structural behaviour in term of deflection and tendon strain as compared with the straight tendon. Recommendations: It recommended including the effect of joint (flat and shear key) type as well as the effect of tendon layout under torsion to obtain comprehensive behavior of EPS bridge.
  F. Hamzah , A. Idris , R. Rashid and S.J. Ming
  Microwave-alkali (MW-A) pre-treated EFB consisting of 74% cellulose, 16% hemicellulose and 8% lignin was subjected to Simultaneous Saccharification and Fermentation (SSF) of lactic acid using Rhizopus oryzae NRRL 395. Two different morphologies of Rhizopus were studied for SSF of lactic acids; i.e., the clump and pellet form. Aspect Ratio (AR) was introduced to classify the pellet morphology, since cultivation of Rhizopus sp. produced non-uniform size pellets. Pellet with AR = 1 indicated a perfect pellet, while AR = 1.5 represented an ellipsoidal pellet. The AR of Rhizopus pellet used in the SSF was controlled to 1. The results showed that lactic acid production from MW-A pre-treated EFB using pellet exhibits higher lactate yield as compared to the clump Rhizopus. The lactate yield was 0.77 g g-1 EFB used after 96 h cultivation. Meanwhile, productivity of the lactic acid obtained from SSF of MW-A pre-treated EFB using pellet (AR = 1), clump (AR = 1.5) and pellet (AR = 1.5) Rhizopus oryzae NRRL 395 were 0.12, 0.089, 0.099 g L-1, respectively.
  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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