Equation-oriented Optimization of Cryogenic Systems for Coal Oxycombustion Power Generation

AbstractEfficient separation systems are essential to the development of economical CO2 capture system for fossil flue power plants. Air Separation Units (ASU) and CO2 Processing Units (CPU) are considering the best commercially available technologies for the O2/N2 and CO2/N2, O2, Ar separations in coal oxycombustion processes. Both of these systems operate at cryogenic temperatures and include self-integrated refrigeration cycles, making their design challenging. Several researchers have applied sensitivity tools available in the commercial flow sheet simulators to study and improve ASU and CPU systems for oxy-fired coal power plants. These studies are limited, however, as they neglect important interactions between design variables.In this paper, we apply an advanced equation-based flowsheet optimization framework to design these cryogenic separations systems. The key advantage of this approach is the ability to use state-of-the-art nonlinear optimization solvers that are capable of considering 100,000+ variables and constraints. This allows for multi-variable optimization of these cryogenic separations systems and their accompanying multi-stream heat exchangers. The effectiveness of this approach is demonstrated in two case studies. The optimized ASU designs requires 0.196 kWh/kg of O2, which are similar to a “low energy” design from American Air Liquide and outperforms other academic studies. Similarly, the optimized CPU requires 18% less specific separation energy than an academic reference case. Pareto (sensitivity) curves for the ASU and CPU systems are also presented. Finally, plans to apply the framework to simultaneously optimize an entire oxycombustion process are discussed

Multistream faster than Nyquist signaling

We extend Mazo's concept of faster-than-Nyquist (FTN) signaling to pulse trains that modulate a bank of subcarriers, a method called two dimensional FTN signaling. The signal processing is similar to orthogonal frequency division multiplex (OFDM) transmission but the subchannels are not orthogonal. Despite nonorthogonal pulses and subcarriers, the method achieves the isolated-pulse error performance; it does so in as little as half the bandwidth of ordinary OFDM. Euclidean distance properties are investigated for schemes based on several basic pulses. The best have Gaussian shape. An efficient distance calculation is given. Concatenations of ordinary codes and FTN are introduced. The combination achieves the outer code gain in as little as half the bandwidth. Receivers must work in two dimensions, and several iterative designs are proposed for FTN with outer convolutional coding

Evaluating Novel Speech Transcription Architectures on the Spanish RTVE2020 Database

This work presents three novel speech recognition architectures evaluated on the Spanish RTVE2020 dataset, employed as the main evaluation set in the Albayzín S2T Transcription Challenge 2020. The main objective was to improve the performance of the systems previously submitted by the authors to the challenge, in which the primary system scored the second position. The novel systems are based on both DNN-HMM and E2E acoustic models, for which fully-and self-supervised learning methods were included. As a result, the new speech recognition engines clearly outper-formed the performance of the initial systems from the previous best WER of 19.27 to the new best of 17.60 achieved by the DNN-HMM based system. This work therefore describes an interesting benchmark of the latest acoustic models over a highly challenging dataset, and identifies the most optimal ones depending on the expected quality, the available resources and the required latency

Transmission Experiment of Bandwidth Compressed Carrier Aggregation in a Realistic Fading Channel

In this paper, an experimental testbed is designed to evaluate the performance of a bandwidth compressed multicarrier technique termed spectrally efficient frequency division multiplexing (SEFDM) in a carrier aggregation (CA) scenario1. Unlike orthogonal frequency division multiplexing (OFDM), SEFDM is a non-orthogonal waveform which, relative to OFDM, packs more sub-carriers in a given bandwidth, thereby improving spectral efficiency. CA is a long term evolution-advanced (LTE-Advanced) featured technique that offers a higher throughput by aggregating multiple legacy radio bands. Considering the scarcity of radio spectrum, SEFDM signals can be utilized to enhance CA performance. The combination of the two techniques results in a larger number of aggregated component carriers (CCs) and therefore increased data rate in a given bandwidth with no additional spectral allocation. It is experimentally shown that CA-SEFDM can aggregate up to 7 CCs in a limited bandwidth while CA-OFDM can only put 5 CCs in the same bandwidth. In this work, LTE-like framed CA-SEFDM signals are generated and delivered through a realistic LTE channel. A complete experimental setup is described together with error performance and effective spectral efficiency comparisons. Experimental results show that the measured BER performance for CA-SEFDM is very close to CA-OFDM and the effective spectral efficiency of CA-SEFDM can be substantially higher than that of CA-OFDM

Energy efficient and latency aware adaptive compression in wireless sensor networks

Wireless sensor networks are composed of a few to several thousand sensors deployed over an area or on specific objects to sense data and report that data back to a sink either directly or through a series of hops across other sensor nodes. There are many applications for wireless sensor networks including environment monitoring, wildlife tracking, security, structural heath monitoring, troop tracking, and many others. The sensors communicate wirelessly and are typically very small in size and powered by batteries. Wireless sensor networks are thus often constrained in bandwidth, processor speed, and power. Also, many wireless sensor network applications have a very low tolerance for latency and need to transmit the data in real time. Data compression is a useful tool for minimizing the bandwidth and power required to transmit data from the sensor nodes to the sink; however, compression algorithms often add a significant amount of latency or require a great deal of additional processing. The following papers define and analyze multiple approaches for achieving effective compression while reducing latency and power consumption far below what would be required to process and transmit the data uncompressed. The algorithms target many different types of sensor applications from lossless compression on a single sensor to error tolerant, collaborative compression across an entire network of sensors to compression of XML data on sensors. Extensive analysis over many different real-life data sets and comparison of several existing compression methods show significant contribution to efficient wireless sensor communication --Abstract, page iv