Effect of aluminium acetyl acetonate on the hydrogen and nitrogen permeation of carbon molecular sieves membranes

With a growing interest in hydrogen as energy carrier, the efficient purification of hydrogen from gaseous mixtures is very important. This paper addresses the separation of hydrogen using Carbon Molecular Sieves Membranes (CMSM), which show an attractive combination of high permeability, selectivity and stability. Supported CMSM containing various amounts of aluminium have been prepared from novolac and aluminium acetyl acetonate (Al(acac)₃) as carbon and alumina precursors. The thickness of the CMSM layers depend on the content of Al(acac)₃ in the dipping solution, which also has influence in the pore size and pore size distribution of the membranes. The permeation properties of the membranes against the Al content in the membrane follows a volcano shape, where the membrane containing 4 wt (%) of Al(acac)₃ has the best properties and was stable during 720 h for hydrogen at 150 °C and 6 bar pressure difference. All the CMSM have permeation properties well above the Robeson Upper limit.     

A Practical Approach to Representation of Real-time Building Control Applications in Simulation

Machine Intelligence Research - Computer based automation and control systems are becoming increasingly important in smart sustainable buildings, of- ten referred to as automated buildings (ABs),...     

Developing conductive immiscible polystyrene/polypropylene blends with a percolated conducting polyaniline/polyamide filler by tuning its specific interactions with the styrene-based triblock compatibilizer grafted with maleic anhydride

An approach to induce conductivity in immiscible polystyrene/polypropylene (PS/PP) blends is described using a percolated conducting polyaniline/polyamide (PANI/PA) filler combined with a polystyrene-block-poly(ethylene-ran-butylene)-block-polystyrene-graft-maleic anhydride compatibilizer. The approach is based on the ability of the compatibilizer to concomitantly stabilize the cocontinuous morphology and to improve the state of dispersion of the PANI/PA filler. Selective localization of PANI/PA in the PS phase with improved filler dispersion is achieved with the optimal master batch (MB) preparation technique followed by its optimized sequence addition to the blend components. This results in an increase in the dc conductivity by six decades as compared with that of the neat compatibilized blend at an effective 4.8-wt % PANI concentration. An investigation of the effect of functionality and concentration of the filler and the compatibilizer on the filler connectivity in the blend is performed. The prevailing specific interactions are analyzed by Fourier transform infrared spectroscopy and thermogravimetric analysis of the MBs. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020 , 137, 48433.     

The PAR(p) Interconfigurations model used by the Brazilian Electric Sector

This study discusses the characteristics of the Periodic Autoregressive model, PAR(p), which is used to generate synthetic series of inflow energies that serve as entries for computer platforms that implement the planning and expansion of the operations of the BES – the Brazilian Electric Sector (SEB – Sistema Elétrico Brasileiro). The methodology for the design of a generating plant is presented in addition to the fundamentals of the “PAR(p) Interconfigurations” Model, which is referred to as the Inflow Energy Generation Model (IEGM) in this study. The major contribution of this study is to provide the first scientific discussion of the representation of multiple configurations using the PAR(p) model. For this purpose, several topics related to the time series are discussed, such as the definition of the model order, the matter of stationarity and the need to address possible outliers. Finally, a case study is presented, wherein the results of the estimation and generation of the described model’s scenarios are demonstrated.     

Model repair — aligning process models to reality

Process mining techniques relate observed behavior (i.e., event logs) to modeled behavior (e.g., a BPMN model or a Petri net). Process models can be discovered from event logs and conformance checking techniques can be used to detect and diagnose differences between observed and modeled behavior. Existing process mining techniques can only uncover these differences, but the actual repair of the model is left to the user and is not supported. In this paper we investigate the problem of repairing a process model w.r.t. a log such that the resulting model can replay the log (i.e., conforms to it) and is as similar as possible to the original model. To solve the problem, we use an existing conformance checker that aligns the runs of the given process model to the traces in the log. Based on this information, we decompose the log into several sublogs of non-fitting subtraces. For each sublog, either a loop is discovered that can replay the sublog or a subprocess is derived that is then added to the original model at the appropriate location. The approach is implemented in the process mining toolkit ProM and has been validated on logs and models from several Dutch municipalities.     

Performance evaluation of noise reduction method during on-line monitoring of MV switchgear for PD measurements by non-intrusive sensors

Partial Discharge (PD) measurement is a globally accepted method for insulation diagnosis of electrical assets. The consequences of insulation breakdown are well known. The trend is to move from conventional offline testing to online monitoring for insulation life prediction, which results in the inclusion of high frequency noise in the captured signals. Therefore de-noising is of paramount importance in online monitoring to obtain useful information from the signal.In this research, a 20 kV switchgear panel has been subjected to PD faults in the laboratory and measurements have been carried out by using different non-intrusive sensors including a novel sensor, the D-dot sensor and recorded by a high frequency oscilloscope. The measured results show the effective applicability of sensors for switchgear. The Discrete Wavelet Transform (DWT) has been used to de-noise PD signals in this paper. Time domain and frequency domain comparison of original and de-noised PD signals reveals the significance of this technique for online monitoring of Medium Voltage (MV) switchgear. Finally, an adaptive online de-noising concept, based on automatic de-noising is also proposed in this paper.     

Reactivity,Selectivity, and Stability of Zeolite-Based Catalysts for Methane Dehydroaromatization

Non-oxidative dehydroaromatization is arguably the most promising process for the direct upgrading of cheap and abundant methane to liquid hydrocarbons. This reaction has not been commercialized yet because of the suboptimal activity and swift deactivation of benchmark Mo-zeolite catalysts. This progress report represents an elaboration on the recent developments in understanding of zeolite-based catalytic materials for high-temperature non-oxidative dehydroaromatization of methane. It is specifically focused on recent studies, relevant to the materials chemistry and elucidating i) the structure of active species in working catalysts; ii) the complex molecular pathways underlying the mechanism of selective conversion of methane to benzene; iii) structure, evolution and role of coke species; and iv) process intensification strategies to improve the deactivation resistance and overall performance of the catalysts. Finally, unsolved challenges in this field of research are outlined and an outlook is provided on promising directions toward improving the activity, stability, and selectivity of methane dehydroaromatization catalysts.