Bifunctional organorhodium solid acid catalysts for methanol carbonylation

Robust, bifunctional catalysts comprising Rh(CO)(Xantphos) exchanged phosphotungstic acids of general formulas [Rh(CO)(Xantphos)]+n[H3–nPW12O40]n− have been synthesized over silica supports which exhibit tunable activity and selectivity toward direct vapor phase methanol carbonylation. The optimal Rh:acid ratio = 0.5, with higher rhodium concentrations increasing the selectivity to methyl acetate over dimethyl ether at the expense of lower acidity and poor activity. On-stream deactivation above 200 °C reflects Rh decomplexation and reduction to Rh metal, in conjunction with catalyst dehydration and loss of solid acidity because of undesired methyl acetate hydrolysis, but can be alleviated by water addition and lower temperature operation.

A learning feed-forward current controller for linear reciprocating vapor compressors

Direct-drive linear reciprocating compressors offer numerous advantages over conventional counterparts which are usually driven by a rotary induction motor via a crank shaft. However, to ensure efficient and reliable operation under all conditions, it is essential that motor current of a linear compressor follows a sinusoidal current command with a frequency which matches the system resonant frequency. The design of a high-performance current controller for linear compressor drive presents a challenge since the system is highly nonlinear, and an effective solution must be low cost. In this paper, a learning feed-forward current controller for the linear compressors is proposed. It comprises a conventional feedback proportional-integral controller and a feed-forward B-spline neural network (BSNN). The feed-forward BSNN is trained online and in real time in order to minimize the current tracking error. Extensive simulation and experiment results with a prototype linear compressor show that the proposed current controller exhibits high steady state and transient performance. © 2009 IEEE.

A hybrid current controller for linear reciprocating vapor compressors

Direct-drive linear reciprocating compressors offer numerous advantages over conventional counterparts which are usually driven by a rotary induction motor via crank shaft However, to ensure efficient and reliable operation under all conditions, it is essential that the motor current of the linear compressor follows a sinusoidal command profile with a frequency which matches the system resonant frequency. This paper describes a hybrid current controller for the linear compressors. It comprises a conventional proportional-integral (PI) controller, and a B-spline neural network compensator which is trained on-line and in real-time in order to minimize the current tracking error under all conditions with uncertain disturbances. It has been shown that the hybrid current controller has a superior steady-state and transient performance over the conventional carrier based PI controller. The performance of the proposed hybrid controller has been demonstrated by extensive simulations and experiments. It has also been shown that the linear compressor operates stably under the current feedback control and the piston stroke can be adjusted by varying the amplitude of the current command. © 2007 IEEE.