Stability of the 13C column separation in industrial mode of operation
Based on the original cryogenic column conceived and built by National Institute for Research and Development of Isotopic and Molecular Technologies Cluj-Napoca, it is necessary to implement control algorithms for the industrial mode of operation of this equipment, in order to produce some chemical compounds with higher concentration in (13C), compounds asked in various domains. The present work deals with column efficiency analysis and stability in industrial mode of operation.
Flooding process analysis by 13C cryogenic separation column
The applications of stable isotopes from different chemical elements are well-known. Based on specific properties, a great diversity of isotope concentration (separation) methods have been developed, one of them being the cryogenic separation for (13C). After a short description of the separation equipment (separation column), the authors deal with the arising of the flooding process. Based on simplified equations, the evolution of an intended flooding is studied, divided in more stages: normal mode of operation, pre-flooding, ldquodischargerdquo period, flooding period and flooding damping. The next step of study will analyze the possibilities to avoid the flooding process.
Prediction of flooding in an absorption column using neural networks
Presents the results of a project aimed at predicting the occurrence of flooding in the absorption column of the Rectisol process at Sasol Synthetic Fuels. The flooding predictor was used as a controlled variable in a multivariable controller with an objective function that maximizes the gas throughput. Thus the prediction of flooding was critical to the success of the project. The technical problem was first to identify a measured variable or a set of variables as a good precursor to flooding. Once suitable precursors had been identified, a statistical flooding predictor was developed. Data from the Rectisol process corresponding to several different flooding scenarios was analyzed. The pressure drop across a section of the column was determined to be a good indicator of flooding. In addition, several other variables were identified as possible precursors. Linear and nonlinear models (neural network models) were developed to predict the pressure drop across the section of the column using the variables identified to be good precursors to flooding. These models were implemented online in the distributed control system. Special flooding tests showed that the neural network differential pressure predictor was accurate. The linear differential pressure predictor, on the other hand, performed quite poorly. This indicated that there were sufficient nonlinear dynamical effects in the Rectisol process to warrant the use of a nonlinear model such as a neural network.