RESUMEN
An experimental study was conducted in a sieve tray column. This study used a simulated flue gas consisting of 30% CO2 and 70%. A 10% mass fraction of methyl diethanolamine (MDEA) aqueous solution was used as a solvent. Three ramp-up tests were performed to investigate the effect of different load changes in inlet gas and solvent flow rate on CO2 absorption. The rate of change in gas flow rate was 0.1 Nm3/h/s, and the rate of change in MDEA aqueous solution was about 0.7 NL/h/s. It was found that different load changes in inlet gas and solvent flow rate significantly affect the CO2 volume fraction at the outlet during the transient state. The CO2 volume fraction reaches a peak value during the transient state. The effect of different load changes in inlet gas and solvent flow rate on the hydrodynamic properties of the sieve tray were also investigated. The authors studied the correlation between the performance of the absorber column for CO2 capture during the transient state and the hydrodynamic properties of the sieve tray. In addition, this paper presents an experimental investigation of the bubble-liquid interaction as a contributor to entropy generation on a sieve tray in the absorption column used for CO2 absorption during the transient state of different load changes.
RESUMEN
CONTEXT: Systemic treatment of metastatic renal cell carcinoma (mRCC) with targeted therapies became widely accepted; however, there are few patients who greatly benefit from immunochemotherapy (ICT). It is crucial to recognize these patients for individual treatment. OBJECTIVES: Definition of protein patterns in tissue and serum from mRCC-patients to predict benefit from ICT. MATERIALS AND METHODS: Twenty-five tissue samples and 59 sera were analysed by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS). Protein peaks of interest were identified by 2D-PAGE and peptide mass fingerprinting. Validation was carried out by Western Blot and ELISA. RESULTS: Protein patterns associated with therapy response were determined. Caveolin-1 (CAV-1) and plasminogen activator inhibitor 1 (PAI-1) were identified in tissue; serum amyloid A (SAA) and transthyretin (TTR) were found in serum. CONCLUSION: Individual prediction of therapy benefit and selecting patients for ICT based on molecular biological profiles appear to be feasible in the future.