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Journal : IPTEK Journal of Proceedings Series

The Application of Neural Network for Predicting Corrotion Rate in Metal Pipe Installation Abdullah, Zulkifli; Pratama, Detak Yan; Sawitri, Dyah; Risanti, Doty Dewi
IPTEK Journal of Proceedings Series Vol 1, No 1 (2014): International Seminar on Applied Technology, Science, and Arts (APTECS) 2013
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j23546026.y2014i1.349

Abstract

Corrotion is one of the problems that must be considered in the metal pipe installation because it can disturb the operation of the plant. The possibility of the corrotion occurrence can be predicted using neural network system. The black box system in the neural network can be used to calculate several potential causes the corrotion and to predict the corrotion rate. This study had constructed the prediction system of corrotion rate using neural network. The input of the system are material compositions, pH, flow rate and temperature. The material compositions which are used are Carbon (C), Manganese (Mn), Silicon (Si), Phosphorus (P), Sulphur (S), Chromium (Cr), Molybdenum (Mo), Aluminium (Al), Nickel (Ni) and Iron (Fe). The corrotion rate prediction network is using one hidden layer and lavenberg marquardt for the learning algorithm. The Mean Square Error (MSE) which is used to analyze the network performance indicates that both of training and validation show excellence results. The MSE of training is 0,000338971 and the validation is 0,000493117.
Prediction of Ceramic’s Mechanical Properties Based on Sintering Temperature using Neural Network Zulkifli Zulkifli; Detak Yan Pratama; Dyah Sawitri; Purwadi Agus Darwito
IPTEK Journal of Proceedings Series No 1 (2015): 1st International Seminar on Science and Technology (ISST) 2015
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (204.994 KB) | DOI: 10.12962/j23546026.y2015i1.1156

Abstract

Ceramics is one of material which apply in many area.  Thus, study of its properties is very important to fulfilled the properties requirement. The mechanical properties of ceramic such as flexural strength and hardness mainly depend on the sintering temperature and additive material. The experiments must be done to determine the best mechanical properties based on proportional sintering temperature and additive materials. Simulation for predicting mechanical properties of ceramics had been developed by using Artificial Neural Network. According to neural network simulation, the graphic of simulation result had same pattern to experimental data as the target. For predicting hardness, the Normalized Root Mean Square Error of network is 0 at training and 0.077 at validation part. This value is in line to its Coefficient Correlation which have value closed to 1. Meanwhile, the network can be used to predict flexural strength of ceramics excellently.