DEVELOPMENT OF METHOD FOR REMOVAL THE NITRIC OXIDES FROM PROCESS OF PRECIOUS METALS RECOVERY FROM ANODE SLIME
Nitric compounds, especially nitric oxides, are extremely toxic with many unfavorable local effects, especially in the surrounding of great pollution sources. This work contains the results of laboratory research for involving of the best absorption parameters (temperature, type and concentration of absorption liquid), as well as a selection of absorption system for completely removal the nitric oxides from process of hydrometallurgical recovery of precious metals from anode slime in Plant for Anode Slime Treatment. The basic aim is decreasing of nitric oxides emission to the law approved concentration, both in working conditions and at gas emission point of the plant.
NOx suppression with hydrogen peroxide in the metals industry
Metal dissolution processes involving nitric acid are major sources of NOx. Technologies using hydrogen peroxide have been developed and proven on an industrial scale to suppress the emission of NOx from these processes. No changes are made to the dissolution process. Case histories are presented to illustrate the effectiveness of hydrogen peroxide with particular reference to the pickling of stainless steels.
Surface roughness monitoring by singular spectrum analysis of vibration signals
Abstract This study assessed two methods for enhanced surface roughness (Ra) monitoring based on the application of singular spectrum analysis (SSA) to vibrations signals generated in workpiece-cutting tool interaction in CNC finish turning operations i.e., the individual analysis of principal components (I-SSA), and the grouping analysis of correlated principal components (G-SSA). Singular spectrum analysis is a non-parametric technique of time series analysis that decomposes a signal into a set of independent additive time series referred to as principal components. A number of experiments with different cutting conditions were performed to assess surface roughness monitoring using both of these methods. The results show that singular spectrum analysis of vibration signal processing discriminated the frequency ranges effective for predicting surface roughness. Grouping analysis of correlated principal components (G-SSA) proved to be the most efficient method for monitoring surface roughness, with optimum prediction and reliability results at a lower analytical-computational cost. Finally, the results show that singular spectrum analysis is an ideal method for analyzing vibration signals applied to the on-line monitoring of surface roughness.