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Muhammad Riaz Khan

Publications and source records attributed to Muhammad Riaz Khan.

2 recordsLinked to original sources

Characterization of ultra-low carbon steel: A preliminary approach to investigate the quality and standards of locally-available steel

In the present work, experimental study has been carried out to expose the thermal, mechanical, and microstructural properties of low carbon steel as well as to inspects the influence of etchant concentration and etching time on its microstructure. Ultra-low carbon steel, in the form of a sheet, was collected from the Mughal Steel Industry, Peshawar, Pakistan. The sample was chemically etched, using Nital as an etchant, by two different methods: first, by changing the etching time while keeping the composition of etchant the same and second, by keeping the time constant while varying the etchant composition in a range of 5-14 %. The microstructure analysis revealed that ultra-fine grain can be obtained for the etchant composition of 8 % nitric acid in ethanol. Additionally, we noticed that the best etching time, for getting a clear morphology, was 90 s. The X-ray diffraction revealed mainly alpha-iron. Thermal analysis showed a minor weight loss followed by weight gain of 1.31 wt %. Contraction and expansion, observed on the TDA curve, suggested the transformation of BCC to FCC structure. Our results indicated that the specimen is highly ductile, malleable and soft.

cond-mat.mtrl-sci

Short Term Load Forecasting Models in Czech Republic Using Soft Computing Paradigms

This paper presents a comparative study of six soft computing models namely multilayer perceptron networks, Elman recurrent neural network, radial basis function network, Hopfield model, fuzzy inference system and hybrid fuzzy neural network for the hourly electricity demand forecast of Czech Republic. The soft computing models were trained and tested using the actual hourly load data for seven years. A comparison of the proposed techniques is presented for predicting 2 day ahead demands for electricity. Simulation results indicate that hybrid fuzzy neural network and radial basis function networks are the best candidates for the analysis and forecasting of electricity demand.

cs.AI