Comparative assessment of gasification based coal power plants with various CO2 capture technologies producing electricity and hydrogen

Seven different types of gasification-based coal conversion processes for producing mainly electricity and in some cases hydrogen (H2), with and without carbon dioxide (CO2) capture, were compared on a consistent basis through simulation studies. The flowsheet for each process was developed in a chemical process simulation tool “Aspen Plus”. The pressure swing adsorption (PSA), physical absorption (Selexol), and chemical looping combustion (CLC) technologies were separately analyzed for processes with CO2 capture. The performances of the above three capture technologies were compared with respect to energetic and exergetic efficiencies, and the level of CO2 emission. The effect of air separation unit (ASU) and gas turbine (GT) integration on the power output of all the CO2 capture cases is assessed. Sensitivity analysis was carried out for the CLC process (electricity-only case) to examine the effect of temperature and water-cooling of the air reactor on the overall efficiency of the process. The results show that, when only electricity production in considered, the case using CLC technology has an electrical efficiency 1.3% and 2.3% higher than the PSA and Selexol based cases, respectively. The CLC based process achieves an overall CO2 capture efficiency of 99.9% in contrast to 89.9% for PSA and 93.5% for Selexol based processes. The overall efficiency of the CLC case for combined electricity and H2 production is marginally higher (by 0.3%) than Selexol and lower (by 0.6%) than PSA cases. The integration between the ASU and GT units benefits all three technologies in terms of electrical efficiency. Furthermore, our results suggest that it is favorable to operate the air reactor of the CLC process at higher temperatures with excess air supply in order to achieve higher power efficiency

A subspace-based perspective on spatial filtering performance with distributed and co-located microphone arrays

Commonly used data-dependent spatial filters depend on the acoustic transfer functions and the power spectral density (PSD) matrices of the desired and the undesired signals. Assuming a low-rank model of the spatial PSD matrix of the signals, and a particular spatial filter, the performance in terms of a given objective measure can often be described analytically. In this paper, we propose to use the similarity between the desired and undesired signal subspaces obtained from the sample spatial PSD matrices, as an indicator of the achievable spatial filtering performance. The subspace similarity is expressed as a distance function on the Grassmann manifold, computed using the principal angles between the subspaces. Particularly, subspace distances and spatial filtering performance are compared when using distributed arrays and co-located microphones. Experimental results demonstrate the relation between these two different measures for different array configurations and reverberation levels

Recursive implementations of informed spatial filters

Informed spatial filters (ISFs) have been shown to provide high-quality speech acquisition in dynamic scenarios due to their ability to almost instantaneously adapt the filter coefficients based on the statistics of the desired and undesired signals. In most contributions, ISFs have been implemented in closed form as minimum variance distortionless response (MVDR), or minimum-mean-squared error filters. The goal in this paper is to discuss and evaluate recursive implementations of ISFs. We show that the implementations in a generalized sidelobe canceller (GSC) structure are not equivalent to the closed form MVDR, due to the fact that the filter coefficients of both implementations are updated at each time-frequency bin. The complexity of the implementations is discussed and experimental evaluation is performed for different dynamic scenarios where the goal is to extract a desired speaker in the presence of interfering speakers