S Hore

Monte Carlo simulation of influence of reheat temperature on austenite grain growth in C-Mn steel

Abstract The evolution and topology of a two-dimensional austenite grain growth of C–Mn steel are simulated by the Monte Carlo (MC) model during the reheating process. Simulated microstructural maps have been generated in a square lattice (400×400) at various MC steps and temperatures. Normalised grain size distribution has also been computed at different reheating temperatures, assuming a uniform temperature distribution in the slab. Two important model parameters, namely, the grain growth exponent and the model constant, have been estimated to predict the average grain size. The activation energy for grain growth has been calculated using the Arrhenius relationship. The grain growth behaviour as a function of both MC and physical time has been computed, and the effect of temperature on the rate of growth has been analysed. The predicted temporal evolutions of grain growth and model parameters have been validated with the published literature and found to be in good agreement.

An adaptive neuro-fuzzy inference system-based modelling to predict mechanical properties of hot-rolled TRIP steel

A model based on adaptive neural network formalism coupled with fuzzy inference system has been developed to predict mechanical properties of hot-rolled TRIP steel. The developed model incorporates a wide range of data containing chemical compositions, thermo-mechanical processing parameters and mechanical properties of hot-rolled TRIP steel. A compact set of process variables has been selected as the model inputs for predicting tensile strength, yield strength, elongation and retained austenite under a given operating condition. The model predictions show that carbon, silicon and manganese content have a significant effect on the retained austenite which increases with the increased amount of these elements. The microalloying elements such as niobium and molybdenum have a little effect on the volume fraction of retained austenite. The present model provides a predictive platform for possible application of these artificial intelligence-based tools for automation, real-time process control and operator guidance in plant operation.

Efficiency Study of a Water-Only Cyclone by Experimental and Data Modeling Techniques when Cleaning Indian Coal Fines

Experiments have been conducted on a water-only cyclone in conjunction with washability studies for Patherdih and Munidih coal samples of Eastern India to characterize the cyclone efficiency. Data driven semi-empirical performance models have been developed using in-house experimental data. Washability studies on the Patherdih sample have been undertaken to determine the specific gravity of separation for a targeted coal quality. Size classification analysis has been conducted for the Munidih sample to estimate the cut-size. The Mayer and partition curves have been generated from float-and-sink analysis. Subsequently, reduced efficiency curves have been constructed using specific gravity as well as size classification data. The Rosin-Rammler and logistic distribution functions have been employed to model the data to generate the reduced efficiency curve, which characterizes the classification efficiency. Model parameters have been estimated for these distribution functions. Parametric sensitivity analysis was carried out by changing the operating parameters, namely, apex diameter, and feed inlet pressure and percentage solids in the feed to study the classification behavior. Model predictions were found to be in good agreement with the published literature. Efficiency mapping by the Rosin-Rammler distribution was found to be well suited for the Patherdih coal and both the Rosin-Rammler and logistic distributions are equally appropriate for Munidih coal.

Estimation and Analysis of Excess Oxygen Input into Ladle During Liquid Steel Tapping

Carryover of oxidising slag from primary steelmaking furnace during tapping affects the quality of liquid steel in several ways. Secondary steelmaking practices, such as deoxidation, desulphurization, vacuum degassing as well as inclusion control in liquid steel bath, are greatly influenced by the amount and characteristic of carryover slag. Both, slag volume as well as ferro alloy consumption increases due to the presence of carryover slag during ladle refining treatment. Carryover of furnace slag in ladle during tapping cannot be avoided and results in consumption of excess electrical energy. Thus, control of carry over slag mainly during tapping is essential by suitably modifying the operational practice. In the present investigation, slag carry over during liquid steel tapping operation is quantified for the processed data obtained from an integrated steel plant based on material balance and equilibrium thermodynamic study. Some of the relevant operational factors related to slag, deoxidation, temperature and solutes (C, S and N) are identified and correlated with the amount of slag carry over. Though, the slag carry over during tapping operation is gravity dependent draining phenomena, scope for controlling the same is identified & discussed in the present study.