Design, process simulation and construction of a 100 kW pilot-scale CO2 membrane rig: Improving in situ CO2 capture using selective exhaust gas recirculation (S-EGR)

Carbon capture and storage (CCS) from natural gas-fired systems is an emerging field and many of the concepts and underlying scientific principles are still being developed. Preliminary studies suggest this approach can boost the CO2 content in the feed gas up to 3 times compared to the ‘no recycle’ case (CO2 concentration increased to 18% vs. 6%), with a consequent reduction in flow to the post-combustion capture unit by a factor of three compared to conventional, non-S-EGR. For this project, Cranfield University developed a pilot-scale 100 kW CO2 membrane rig facility in order to investigate simultaneously EGR and S-EGR technologies, the latter being achieved by using a CO2 sweep air polymeric membrane. A bench-scale membrane rig has also been developed to investigate the permeability and selectivity of different polymeric membranes to CO2. Currently a small-scale polydimethylsiloxane (PDMS) membrane module is also being investigated to study its selectivity/permeability. The tests include exploring the performance improvement of the PDMS membrane using different operating conditions with a view to developing scale-up procedures for the membrane unit for the actual 100 kW pilot-scale rig. Process simulations were performed using Aspen Plus software to predict the behaviour of the pilot-scale rig using a model developed based on empirical parameters (i.e., mass transfer coefficient of CO2 through the membrane and permeance), measured in the bench-scale membrane test unit. The results show that CO2 concentrations of up to 14.9% (comparable to CO2 level in coal combustion) can be achieved with 60% EGR, with a 90% CO2 removal efficiency of the membrane units. However, the results generated with the membrane model in which specific permeance values to PDMS were applied, predicted concentrations of CO2 in flue gases up to 9.8% (v/v) for a selective recycle of 60%. The study shows that the S-EGR technique is an effective method that can provide similar conditions to that of a coal-fired power plant for the post-combustion capture system operating on natural gas-fired units, but also highlights the fact that more research is required to find more suitable materials for membranes that optimise the CO2 removal efficiencies from the flue gas

OxyCAP UK: Oxyfuel Combustion - academic Programme for the UK

The OxyCAP-UK (Oxyfuel Combustion - Academic Programme for the UK) programme was a £2 M collaboration involving researchers from seven UK universities, supported by E.On and the Engineering and Physical Sciences Research Council. The programme, which ran from November 2009 to July 2014, has successfully completed a broad range of activities related to development of oxyfuel power plants. This paper provides an overview of key findings arising from the programme. It covers development of UK research pilot test facilities for oxyfuel applications; 2-D and 3-D flame imaging systems for monitoring, analysis and diagnostics; fuel characterisation of biomass and coal for oxyfuel combustion applications; ash transformation/deposition in oxyfuel combustion systems; materials and corrosion in oxyfuel combustion systems; and development of advanced simulation based on CFD modelling

Candidate knowledge? Exploring epistemic claims in scientific writing:a corpus-driven approach

In this article I argue that the study of the linguistic aspects of epistemology has become unhelpfully focused on the corpus-based study of hedging and that a corpus-driven approach can help to improve upon this. Through focusing on a corpus of texts from one discourse community (that of genetics) and identifying frequent tri-lexical clusters containing highly frequent lexical items identified as keywords, I undertake an inductive analysis identifying patterns of epistemic significance. Several of these patterns are shown to be hedging devices and the whole corpus frequencies of the most salient of these, candidate and putative, are then compared to the whole corpus frequencies for comparable wordforms and clusters of epistemic significance. Finally I interviewed a ‘friendly geneticist’ in order to check my interpretation of some of the terms used and to get an expert interpretation of the overall findings. In summary I argue that the highly unexpected patterns of hedging found in genetics demonstrate the value of adopting a corpus-driven approach and constitute an advance in our current understanding of how to approach the relationship between language and epistemology

Το λογισμικό SpatialAnalyzer & εφαρμογές του σε προβλήματα βιομηχανικής γεωδαισίας

This paper reports a first study exploring genomic prediction for adaptation of sorghum [Sorghum bicolor (L.) Moench] to drought-stress (D-ET) and nonstress (W-ET) environment types. The objective was to evaluate the impact of both modeling genotype × environment interaction (G×E) and accounting for heterogeneous variances of marker effects on genomic prediction of parental breeding values for grain yield within and across environment types (ETs). For this aim, different genetic covariance structures and different weights for individual markers were investigated in best linear unbiased prediction (BLUP)-based prediction models. The BLUP models used a kinship matrix combining pedigree and genomic information, termed K-BLUP. The dataset comprised testcross yield performances under D-ET and W-ET as well as pedigree and genomic data. In general, modeling G×E increased predictive ability and reduced empirical bias of genomic predictions for broad adaptation across both ETs vs. models that ignored G×E by fitting a main genetic effect only. Genomic predictions for specific adaptation to D-ET or W-ET were also improved by K-BLUP models that explicitly accommodated G×E and used data from both ETs relative to prediction models that used data from the targeted ET exclusively or models that used all the data but assumed no G×E. Allowing for heterogeneous marker variances through weighted K-BLUP produced clear increments (43–72%) in predictive ability of genomic prediction for grain yield in all adaptation scenarios. We conclude that G×E as well as locus-specific genetic variances should be accommodated in genomic prediction models to improve adaptability of sorghum to variable environmental conditions

OxyCAP UK: Oxyfuel Combustion - academic Programme for the UK

The OxyCAP-UK (Oxyfuel Combustion - Academic Programme for the UK) programme was a £2M collaboration involving researchers from seven UK universities, supported by E.On and the Engineering and Physical Sciences Research Council. The programme, which ran from November 2009 to July 2014, has successfully completed a broad range of activities related to development of oxyfuel power plants. This paper provides an overview of key findings arising from the programme. It covers development of UK research pilot test facilities for oxyfuel applications; 2-D and 3-D flame imaging systems for monitoring, analysis and diagnostics; fuel characterisation of biomass and coal for oxyfuel combustion applications; ash transformation/deposition in oxyfuel combustion systems; materials and corrosion in oxyfuel combustion systems; and development of advanced simulation based on CFD modelling

Quinoa Phenotyping Methodologies: An International Consensus

Quinoa is a crop originating in the Andes but grown more widely and with the genetic potential for significant further expansion. Due to the phenotypic plasticity of quinoa, varieties need to be assessed across years and multiple locations. To improve comparability among field trials across the globe and to facilitate collaborations, components of the trials need to be kept consistent, including the type and methods of data collected. Here, an internationally open-access framework for phenotyping a wide range of quinoa features is proposed to facilitate the systematic agronomic, physiological and genetic characterization of quinoa for crop adaptation and improvement. Mature plant phenotyping is a central aspect of this paper, including detailed descriptions and the provision of phenotyping cards to facilitate consistency in data collection. High-throughput methods for multi-temporal phenotyping based on remote sensing technologies are described. Tools for higher throughput post-harvest phenotyping of seeds are presented. A guideline for approaching quinoa field trials including the collection of environmental data and designing layouts with statistical robustness is suggested. To move towards developing resources for quinoa in line with major cereal crops, a database was created. The Quinoa Germinate Platform will serve as a central repository of data for quinoa researchers globally

Genome-based trait prediction in multi- environment breeding trials in groundnut

Genomic selection (GS) can be an efficient and cost-effective breeding approach which captures both small- and large-effect genetic factors and therefore promises to achieve higher genetic gains for complex traits such as yield and oil content in groundnut. A training population was constituted with 340 elite lines followed by genotyping with 58 K ‘Axiom_Arachis’ SNP array and phenotyping for key agronomic traits at three locations in India. Four GS models were tested using three different random cross-validation schemes (CV0, CV1 and CV2). These models are: (1) model 1 (M1 = E + L) which includes the main effects of environment (E) and line (L); (2) model 2 (M2 = E + L + G) which includes the main effects of markers (G) in addition to E and L; (3) model 3 (M3 = E + L + G + GE), a naïve interaction model; and (4) model 4 (E + L + G + LE + GE), a naïve and informed interaction model. Prediction accuracy estimated for four models indicated clear advantage of the inclusion of marker information which was reflected in better prediction accuracy achieved with models M2, M3 and M4 as compared to M1 model. High prediction accuracies (> 0.600) were observed for days to 50% flowering, days to maturity, hundred seed weight, oleic acid, rust@90 days, rust@105 days and late leaf spot@90 days, while medium prediction accuracies (0.400–0.600) were obtained for pods/plant, shelling %, and total yield/plant. Assessment of comparative prediction accuracy for different GS models to perform selection for untested genotypes, and unobserved and unevaluated environments provided greater insights on potential application of GS breeding in groundnut

Turbulence and Fossil Turbulence in Oceans and Lakes

Turbulence is defined as an eddy-like state of fluid motion where the inertial-vortex forces of the eddies are larger than any of the other forces that tend to damp the eddies out. Energy cascades of irrotational flows from large scales to small are non-turbulent, even if they supply energy to turbulence. Turbulent flows are rotational and cascade from small scales to large, with feedback. Viscous forces limit the smallest turbulent eddy size to the Kolmogorov scale. In stratified fluids, buoyancy forces limit large vertical overturns to the Ozmidov scale and convert the largest turbulent eddies into a unique class of saturated, non-propagating, internal waves, termed fossil-vorticity-turbulence. These waves have the same energy but different properties and spectral forms than the original turbulence patch. The Gibson (1980, 1986) theory of fossil turbulence applies universal similarity theories of turbulence and turbulent mixing to the vertical evolution of an isolated patch of turbulence in a stratified fluid as its growth is constrained and fossilized by buoyancy forces. These theories apply to the dynamics of atmospheric, astrophysical and cosmological turbulence.Comment: 31 pages, 11 figures, 2 tables, see http://www-acs.ucsd.edu/~ir118 Accepted for publication by the Chinese Journal of Oceanology and Limnolog