Quantum Chemical Studies on Solvents for Post-Combustion Carbon Dioxide Capture: Calculation of pKa and Carbamate Stability of Disubtituted Piperazines

Piperazine is a widely studied solvent for post-combustion carbon dioxide capture. To investigate the possibilities of further improving this process, the electronic and steric effects of CH3, CH2F, CH2OH, CH2NH2, COCH3, and CN groups of 2,5-disubstituted piperazines on the pKa and carbamate stability towards hydrolysis are investigated by quantum chemical methods. For the calculations, B3LYP, M11L, and spin-component-scaled MP2 (SCS-MP2) methods are used and coupled with the SMD solvation model. The experimental pKa values of piperazine, 2-methylpiperazine, and 2,5-dimethylpiperazine agree well with the calculated values. The present study indicates that substitution of CH3, CH2NH2, and CH2OH groups on the 2- and 5-positions of piperazine has a positive impact on the CO2 absorption capacity by reducing the carbamate stability towards hydrolysis. Furthermore, their higher boiling points, relative to piperazine itself, will lead to a reduction of volatility-related losses

Learning Control of a Flight Simulator Stick

Aimportant part of a flight simulator is its\ud control loading system, which is the part\ud that emulates the behaviour of an aircraft as\ud experienced by the pilot through the stick.\ud Such a system consists of a model of the\ud aircraft that is to be simulated and a stick that is\ud driven by an electric motor. To make the\ud simulation as realistic as possible, the\ud simulator stick should behave in the same way\ud as the stick in the real aircraft.\ud However, due to the properties of the motor\ud and the stick, small irregularities can be felt\ud when the stick is moved, which do not occur in\ud a real aircraft. Probable causes of these\ud irregularities are cogging in the motor and\ud small imperfections in the transmission.\ud Both disturbances have a reproducible nature.\ud Because the disturbances are reproducible,\ud feedback error learning control is used for\ud control. The learning controller consists of two\ud neural networks. One neural network is used to\ud compensate the unknown friction and is\ud operated in feed-forward. The other neural\ud network compensates cogging and\ud imperfections in the transmission and is\ud operated in feedback. Experimental results\ud showed that the learning controller is able to\ud compensate the disturbances