No abstract

During ladle stirring, a gas is injected into the steel bath to generate a mixing of the liquid steel. The optimal process control requires a reliable measurement of the stirring intensity, for which the induced ladle wall vibrations have proved to be a potential indicator. An experimental cold water ladle with two eccentric nozzles and eight mono-axial accelerometers was thus investigated to measure the vibrations. The effect of the sensors' positions with respect to the gas plugs on the vibration intensity was analyzed, and experimental data on several points of the ladle were collected for future numerical simulations. It is shown that the vibration root-mean-square values depend not only on process parameters, such as gas flow rate, water, and oil heights, but also on the radial and axial positions of the sensors. The vibration intensity is clearly higher, close to the gas plumes, than in the opposite side. If one of the nozzles is clogged, the vibration intensity close to the clogged nozzle drops drastically (À 36 to À 59%), while the vibrations close to the normal operating nozzle are hardly affected. Based on these results, guidelines are provided for an optimized vibration-based stirring.

Liquid steel is typically stirred in a vacuum tank using argon gas injection to achieve a homogeneous composition and high‐purity steel. The aim of this work is to study the effect of vessel vibration on the operational state monitoring of the gas stirring in a vacuum tank degasser. Following an extensive analysis of vibration features, the root mean square (RMS) of vertical velocity is found to be the best feature for the measurement of the stirring intensity caused by the volumetric gas injection rate into the ladle. Smoothing is conducted using a centered median filter with a window length of 21 s. In this work, the operational state monitoring of gas stirring is described using a ladle responsiveness value (LRV). This describes the ability of a ladle to generate the maximum amount of vibration with the minimum amount of argon gas. The LRV summarized for each ladle reveals significant differences between them. Correspondingly, a rolling ladle responsiveness value (rLRV) is used for online monitoring of possible gas leakages. The rLRV can also be used for the online monitoring of the stirring efficiency and as its comparison with the overall efficiency of a specific ladle or all ladles.

Hydro-pneumatic accumulators are used to improve the features of different kinds of hydraulic systems and they are common in industry and mobile applications. In order to include functionality of accumulators to hydraulic system models, an accurate yet light simulation model of hydro-pneumatic accumulator is needed. In this paper a simulation model of a piston type hydro-pneumatic accumulator is presented. The simulation model takes into account of the behavior of friction, nitrogen gas and hydraulic fluid. The simulation model was validated by comparing the simulation results to measurement results obtained from laboratory tests, and strong correlation was found between them. The model is suitable for researchers as well as for engineers in designing work in industry.

Industrial process failures can be often seen as a variance increase in a measured process variable. The objective of this research was to investigate if stochastic Autoregressive Moving Average, abbreviated as ARMA, and Generalized Autoregressive Conditionally Heteroscedastic, abbreviated as GARCH, time series modelling are feasible methods for the reliable detection of gradually increasing variance in the process variable. A case study was conducted for the reliable detection of increased pressure variance that indicates a harmful air leakage in a vacuum chamber in a paper machine. Variance in the chamber pressure was artificially gradually increased, a combined ARMA+GARCH time series model was fitted to it and the variance vector was determined. An abnormally high variance was detected from the variance vector using a specified detection limit and detection sensitivity. According to the simulation results, by controlling the variance vector extracted from the combined ARMA+GARCH time series model, a very slight variance increase in the process variable can be detected more reliably than detecting it from the moving variance vector computed directly from the process variable.