The control system design of methanol reforming hydrogen based on kingview and PLC
We designed the methanol reforming the computer control system of hydrogen combining process characteristics. The combination by SIEMENS S7–200 and "kingview" software not only have better met the control demands of the hydrogen production units with carbinol method and PSA hydrogen purification processes, but also reduce the cost and improve the operability, making the technology is more and more widely used in industrial production.
Some insights from recent applications of HRA methods in PSA effort and plant operation feedback in Czech Republic
Abstract The methods of human reliability analysis (HRA) form an integral part of probabilistic safety assessment. These methods are currently still under development and there is significant lack of data needed for applications. As a consequence, no unique formal approach for solution of many aspects related to plant staff human performance does exist. In this contribution, some examples of fruitful work are given, which reflect the needs and possibilities specific for HRA field. In the first two examples, methodology, course and results of analysis of concrete matters related to pre-accident errors and shutdown control room crew performance are described. The last example is oriented to one potential human performance related data source-observation of control room crew full-scope simulator exercise. Some conclusions of general validity are presented in the final part of the contribution.
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.