Comparison of two different flow types on CO removal along a two-stage hydrogen permselective membrane reactor for methanol synthesis

Carbon monoxide (CO) is a gaseous pollutant with adverse effects on human health and the environment. Industrial chemical processes contribute significantly to CO accumulation in the atmosphere. One of the most important processes for controlling carbon monoxide emissions is the conversion of CO to methanol by catalytic hydrogenation. In this study, the effects of two different flow types on the rate of CO removal along a two-stage hydrogen permselective membrane reactor have been investigated. In the first configuration, fresh synthesis gas flows in the tube side of the membrane reactor co-currently with reacting material in the shell side, so that more hydrogen is provided in the first sections of the reactor. In the second configuration, fresh synthesis gas flows in the tube side of the membrane reactor counter-currently with reacting material in the shell side, so that more hydrogen is provided in the last sections of the reactor. For this membrane system, a one-dimensional dynamic plug flow model in the presence of catalyst deactivation was developed. Comparison between co-current and counter-current configurations shows that the reactor operates with higher conversion of CO and hydrogen permeation rate in the counter-current mode whereas; longer catalyst life is achieved in the co-current configuration. Enhancement of CO removal in the counter-current mode versus the co-current configuration results in an ultimate reduction in CO emissions into the atmosphere.CO removal Hydrogen-permselective membrane Two-stage membrane reactor Dynamic model Catalyst deactivation Co-current Counter-current

A comparison of methods for denoising of well test pressure data

Abstract Pressure transient data from downhole gauges are one of the key parameters in characterizing reservoir properties and forecasting future reservoir performance. Reservoir pressure is usually measured under dynamic changes. The collected data usually contain different levels of noise, particularly due to imperfections in measuring instruments and imperfect calibrations. The latter is due to changes between the laboratory environment and reservoir conditions. To have accurate descriptions of reservoir, it is essential to smooth the pressure data. Most related studies have employed the wavelet transform to reduce noise. However, there appears to be little research addressing the use of other smoothing techniques for pressure transient data. This paper, therefore, evaluates and compares the performance of three types of smoothing and noise removal methods, namely wavelet transform as a widely used filtering technique, regression-based smoothers, and autoregressive smoothing methods to reduce artificial noise added to simulated dual-porosity pressure data. Particularly, noise is more pronounced in pressure derivative, and so denoising of pressure derivative requires more effective tools. The effectiveness of the noise removing methods was compared using mean square error. The results show that the regression-based methods lead to the same or even better reduction in the noise level as compared to the wavelet domain filter, while the employed autoregressive method results in a moderate performance. We also test the performance of various combinations of the different smoothing methods to filter the same noisy data. It is shown that the combined locally weighted scatterplot smooth (LOESS) and autoregressive moving average (ARMA) gives the best smoothing performance for pressure derivative data. Application of the combined LOESS–ARMA to real field data shows promising results