Estimated Emissions from the Prime-Movers of Unconventional Natural Gas Well Development Using Recently Collected In-Use Data in the United States.

Natural gas from shale plays dominates new production and growth. However, unconventional well development is an energy intensive process. The prime movers, which include over-the-road service trucks, horizontal drilling rigs, and hydraulic fracturing pumps, are predominately powered by diesel engines that impact air quality. Instead of relying on certification data or outdated emission factors, this model uses new in-use emissions and activity data combined with historical literature to develop a national emissions inventory. For the diesel only case, hydraulic fracturing engines produced the most NO x emissions, while drilling engines produced the most CO emissions, and truck engines produced the most THC emissions. By implementing dual-fuel and dedicated natural gas engines, total fuel energy consumed, CO2, CO, THC, and CH4 emissions would increase, while NO x emissions, diesel fuel consumption, and fuel costs would decrease. Dedicated natural gas engines offered significant reductions in NO x emissions. Additional scenarios examined extreme cases of full fleet conversions. While deep market penetrations could reduce fuel costs, both technologies could significantly increase CH4 emissions. While this model is based on a small sample size of engine configurations, data were collected during real in-use activity and is representative of real world activity.

Development of engine activity cycles for the prime movers of unconventional natural gas well development.

With the advent of unconventional natural gas resources, new research focuses on the efficiency and emissions of the prime movers powering these fleets. These prime movers also play important roles in emissions inventories for this sector. Industry seeks to reduce operating costs by decreasing the required fuel demands of these high horsepower engines but conducting in-field or full-scale research on new technologies is cost prohibitive. As such, this research completed extensive in-use data collection efforts for the engines powering over-the-road trucks, drilling engines, and hydraulic stimulation pump engines. These engine activity data were processed in order to make representative test cycles using a Markov Chain, Monte Carlo (MCMC) simulation method. Such cycles can be applied under controlled environments on scaled engines for future research. In addition to MCMC, genetic algorithms were used to improve the overall performance values for the test cycles and smoothing was applied to ensure regression criteria were met during implementation on a test engine and dynamometer. The variations in cycle and in-use statistics are presented along with comparisons to conventional test cycles used for emissions compliance. IMPLICATIONS: Development of representative, engine dynamometer test cycles, from in-use activity data, is crucial in understanding fuel efficiency and emissions for engine operating modes that are different from cycles mandated by the Code of Federal Regulations. Representative cycles were created for the prime movers of unconventional well development-over-the-road (OTR) trucks and drilling and hydraulic fracturing engines. The representative cycles are implemented on scaled engines to reduce fuel consumption during research and development of new technologies in controlled laboratory environments.

Rate-distortion optimised video transmission using pyramid vector quantisation.

Conventional video compression relies on interframe prediction (motion estimation), intra frame prediction and variable-length entropy encoding to achieve high compression ratios but, as a consequence, produces an encoded bitstream that is inherently sensitive to channel errors. In order to ensure reliable delivery over lossy channels, it is necessary to invoke various additional error detection and correction methods. In contrast, techniques such as Pyramid Vector Quantisation have the ability to prevent error propagation through the use of fixed length codewords. This paper introduces an efficient rate distortion optimisation algorithm for intra-mode PVQ which offers similar compression performance to intra H.264/AVC and Motion JPEG 2000 while offering inherent error resilience. The performance of our enhanced codec is evaluated for HD content in the context of a realistic (IEEE 802.11n) wireless environment. We show that PVQ provides high tolerance to corrupted data compared to the state of the art while obviating the need for complex encoding tools.

A connectionist account of Spanish determiner production.

Evidence from experimental studies of Spanish children's production of determiners reveals that they pay more attention to phonological cues present in nouns than to natural semantics when assigning gender to determiners (Pérez-Pereira, 1991). This experimental work also demonstrated that Spanish children are more likely to produce the correct determiner when given a noun with phonological cues which suggest it is masculine, and more likely to assign masculine gender to nouns with ambiguous cues. In this paper, we investigate the phonological cues available to children and seek to explore the possibility that differential frequency in the linguistic input explains the priority given to masculine forms when children are faced with ambiguous novel items. A connectionist model of determiner production was incrementally trained on a lexicon of determiner-noun phrases taken from parental speech in a longitudinal study of a child between the ages of 1;7 and 2;11 (López Ornat, Fernandez, Gallo & Mariscal, 1994) preserving the type and token frequency information. An analysis of the database of parental productions revealed that while regular feminine nouns were slightly more frequent than regular masculine nouns, irregular masculine nouns outnumbered irregular feminine nouns by roughly 2 to 1. On the basis of this, we made the prediction that as the training lexicon builds up, the network will perform better overall on masculine determiners than would be predicted from their forms alone and will tend to assign masculine gender to ambiguous novel nouns in a test set. The findings indicate that, at least in the case of Spanish gender agreement for determiners and nouns, a general associative learning mechanism can account for important characteristics of the acquisition process seen in children.