The physical, chemical and mineralogical characterization of the constituents of magnesium-rich synthetic gypsum produced in a rare earth-refining plant located in Gebeng, Pahang, Malaysia was conducted through elemental chemical analysis, scanning electron microscopy with Energy Dispersive X-ray (EDX)-analyzer, thermal analysis, X-ray fluorescence and X-ray diffraction. The crystalline nature of the by-product was studied using FTIR spectroscopy. Elemental analysis confirmed the presence of Ca and Mg, which are essential macronutrients required by plants and this Ca alongside the high pH (9.17) of MRSG may confer on the material a high acid neutralization capacity. From the result, it was observed that the studied by-product is a heterogeneous crystalline material comprising of gypsum (CaSO4.2H2O) and other major components such as calcium (magnesium) compounds (hydroxide, oxide, silicates, and carbonate) and sulfur. These aggregates may contribute to give an acid neutralization capacity to MRSG. The XRD study of MRSG indicated a high content of gypsum (45.4%), shown by the d-spacing of 7.609 Å (2-theta 11.63) in the diffractogram. The infrared absorption spectra of MRSG indicate close similarities to mined gypsum. The results of the characterization indicated that MRSG has valuable properties that can promote its use in amending soil fertility constraints on nutrient-deficient tropical acid soils.
Flood is a major disaster that happens around the world. It has caused the loss of many precious lives and destruction of large amounts of property. The possibility of flood can be determined depends on many factors that consist of rainfall, water flow rate and water level. This project aims to design a water level prediction system which is used to analyze the Kelantan River water level based on Sokor River, Galas River and Lebir River flow rate and rainfall of at Ldg. Kuala Nal and Ldg. Kenneth. The system utilizes neural networks in predicting the water level for 5 hours ahead. This system has 5 inputs and 1 output prediction. This prediction system focusses on comparing the conventional method and the Neural Network Autoregressive with Exogenous Input (NNARX) system in determining the possibility of flood. The result shows that the NNARX can predict the water level of Kelantan River much more better compared to conventional method. The performance of the system is based on the value of the means square error (MSE). The MSE of the conventional method is 0.2550 meanwhile for NNARX is 1.342 × 10−4.
Municipal authorities face many challenges in maintaining an effective water distribution service (WDS) because of aging infrastructure and financial limitations. Effective leak detection methods minimize the cost of pipe inspection and reduce the cost of leaks caused by deterioration in the pipe. The pressure residual procedure for leakage detection was performed by comparing the pressure data of certain district meter area (DMA) inner nodes with an estimation using the simulation of a mathematical network model. The effects of leakage in WDSs indicate that the pressure exponent is dependent on the geometry of the orifice, which is considered a constant factor in the applied method. This paper investigates the influence of pipe material in the pressure residual method and determines the element change in the sensitivity matrix. For accuracy, the geographic information technology (spatial data) was used to calculate the connection density around the exact nodes in the network model. The influence of the external load (soil movement and excavation work) on the metallic pipe material is beyond the scope of this paper, because leaks caused by these factors are often obvious and are accompanied by a huge pressure drop inside the distribution network.
In the PUSPATI TRIGA reactor (RTP), many variables and instruments need to be monitored to make sure it is functioning and running accordingly. The late detection of faults may result in accidents and affect workers’ safety and health. Therefore, an intelligent fault detection system is needed to detect faults in the process plant and alert for any safe point breach. This work was carried out to discover the use of an artificial neural network (ANN) to model and develop a fault detection programme in the RTP cooling system. Using actual data from the reactor to train the multilayer network model with backpropagation algorithm. Referring to the real data from the reactor, the simulation results demonstrate a good correlation between the proposed model using ANN and the real plants with a residual mean of below 1%. The preliminary results for fault detection show that ANN was able to predict the value of failure in residual factor by comparing the normal state and fault state of the plant. The proposed model using ANN method proofed that it could quickly diagnose the single fault and perform for any given failure. The research outcome could contribute to the improvement in frontier technologies and advanced manufacturing in Malaysia.
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