Dynamic data reconciliation for sequential modular simulators: application to a mixing process

This paper describes a method for dynamic data reconciliation of nonlinear systems that are simulated using the sequential modular approach, and where individual modules are represented by a class of differential algebraic equations. The estimation technique consists of a bank of extended Kalman filters that are integrated with the modules. The paper reports a study based on experimental data obtained from a pilot scale mixing process.

Speciation and variation in the occurrence of haloacetic acids in three water supply systems in England

An investigation into the speciation and occurrence of nine haloacetic acids (HAAs) was conducted during the period of April 2007 to March 2008 and involved three drinking water supply systems in England, which were chosen to represent a range of source water conditions; these were an upland surface water, a lowland surface water and a groundwater. Samples were collected seasonally from the water treatment plants and at different locations in the distribution systems. The highest HAA concentrations occurred in the upland surface water system, with an average total HAA concentration of 21.3 μg/L. The lowest HAA levels were observed in the groundwater source, with a mean concentration of 0.6 μg/L. Seasonal variations were significant in the HAA concentrations; the highest total HAA concentrations were found during the autumn, when the concentrations were approximately two times higher than in winter and spring. HAA speciation varied among the water sources, with dichloroacetic acid and trichloroacetic acid dominant in the lowland surface water system and brominated species dominant in the upland surface water system. There was a strong correlation between trihalomethanes and HAAs when considering all samples from the three systems in the same data set (r2=0.88); however, the correlation was poor/moderate when considering each system independently.

Modeling of complex-valued Wiener systems using B-spline neural network

In this brief, a new complex-valued B-spline neural network is introduced in order to model the complex-valued Wiener system using observational input/output data. The complex-valued nonlinear static function in the Wiener system is represented using the tensor product from two univariate B-spline neural networks, using the real and imaginary parts of the system input. Following the use of a simple least squares parameter initialization scheme, the Gauss-Newton algorithm is applied for the parameter estimation, which incorporates the De Boor algorithm, including both the B-spline curve and the first-order derivatives recursion. Numerical examples, including a nonlinear high-power amplifier model in communication systems, are used to demonstrate the efficacy of the proposed approaches.

A method for merging flow-dependent forecast error statistics from an ensemble with static statistics for use in high resolution variational data assimilation

The background error covariance matrix, B, is often used in variational data assimilation for numerical weather prediction as a static and hence poor approximation to the fully dynamic forecast error covariance matrix, Pf. In this paper the concept of an Ensemble Reduced Rank Kalman Filter (EnRRKF) is outlined. In the EnRRKF the forecast error statistics in a subspace defined by an ensemble of states forecast by the dynamic model are found. These statistics are merged in a formal way with the static statistics, which apply in the remainder of the space. The combined statistics may then be used in a variational data assimilation setting. It is hoped that the nonlinear error growth of small-scale weather systems will be accurately captured by the EnRRKF, to produce accurate analyses and ultimately improved forecasts of extreme events.

Correlations of control variables in variational data assimilation

Variational data assimilation systems for numerical weather prediction rely on a transformation of model variables to a set of control variables that are assumed to be uncorrelated. Most implementations of this transformation are based on the assumption that the balanced part of the flow can be represented by the vorticity. However, this assumption is likely to break down in dynamical regimes characterized by low Burger number. It has recently been proposed that a variable transformation based on potential vorticity should lead to control variables that are uncorrelated over a wider range of regimes. In this paper we test the assumption that a transform based on vorticity and one based on potential vorticity produce an uncorrelated set of control variables. Using a shallow-water model we calculate the correlations between the transformed variables in the different methods. We show that the control variables resulting from a vorticity-based transformation may retain large correlations in some dynamical regimes, whereas a potential vorticity based transformation successfully produces a set of uncorrelated control variables. Calculations of spatial correlations show that the benefit of the potential vorticity transformation is linked to its ability to capture more accurately the balanced component of the flow.

An investigation of the impact of time of generation on carbon savings from PV systems in Great Britain

PV only generates electricity during daylight hours and primarily generates over summer. In the UK, the carbon intensity of grid electricity is higher during the daytime and over winter. This work investigates whether the grid electricity displaced by PV is high or low carbon compared to the annual mean carbon intensity using carbon factors at higher temporal resolutions (half-hourly and daily). UK policy for carbon reporting requires savings to be calculated using the annual mean carbon intensity of grid electricity. This work offers an insight into whether this technique is appropriate. Using half hourly data on the generating plant supplying the grid from November 2008 to May 2010, carbon factors for grid electricity at half-hourly and daily resolution have been derived using technology specific generation emission factors. Applying these factors to generation data from PV systems installed on schools, it is possible to assess the variation in the carbon savings from displacing grid electricity with PV generation using carbon factors with different time resolutions. The data has been analyzed for a period of 363 to 370 days and so cannot account for inter-year variations in the relationship between PV generation and carbon intensity of the electricity grid. This analysis suggests that PV displaces more carbon intensive electricity using half-hourly carbon factors than using daily factors but less compared with annual ones. A similar methodology could provide useful insights on other variable renewable and demand-side technologies and in other countries where PV performance and grid behavior are different.

Efficient non-linear data assimilation in geophysical fluid dynamics

New ways of combining observations with numerical models are discussed in which the size of the state space can be very large, and the model can be highly nonlinear. Also the observations of the system can be related to the model variables in highly nonlinear ways, making this data-assimilation (or inverse) problem highly nonlinear. First we discuss the connection between data assimilation and inverse problems, including regularization. We explore the choice of proposal density in a Particle Filter and show how the ’curse of dimensionality’ might be beaten. In the standard Particle Filter ensembles of model runs are propagated forward in time until observations are encountered, rendering it a pure Monte-Carlo method. In large-dimensional systems this is very inefficient and very large numbers of model runs are needed to solve the data-assimilation problem realistically. In our approach we steer all model runs towards the observations resulting in a much more efficient method. By further ’ensuring almost equal weight’ we avoid performing model runs that are useless in the end. Results are shown for the 40 and 1000 dimensional Lorenz 1995 model.

New advances in aircraft MRO services: data mining enhancement

Aircraft Maintenance, Repair and Overhaul (MRO) agencies rely largely on row-data based quotation systems to select the best suppliers for the customers (airlines). The data quantity and quality becomes a key issue to determining the success of an MRO job, since we need to ensure we achieve cost and quality benchmarks. This paper introduces a data mining approach to create an MRO quotation system that enhances the data quantity and data quality, and enables significantly more precise MRO job quotations. Regular Expression was utilized to analyse descriptive textual feedback (i.e. engineer’s reports) in order to extract more referable highly normalised data for job quotation. A text mining based key influencer analysis function enables the user to proactively select sub-parts, defects and possible solutions to make queries more accurate. Implementation results show that system data would improve cost quotation in 40% of MRO jobs, would reduce service cost without causing a drop in service quality.

Do Integrated Arable Farming Systems provide a more sustainable form of agricultural production: the UK case

Integrated Arable Farming Systems (IAFS) projects utilise a range of novel and different farming techniques, often associated with optimising or reducing the use of inputs. Here, data is presented from the LINK-IFS project which suggests that, although input levels are being reduced, the overall profitability of the system can be maintained. The effect of thus reduction in inputs, however, in terms of impact on key environmental indicators is unclear.

Towards a method for the economic evaluation of environmental indicators in UK Integrated Arable Farming Systems

Integrated Arable Farming Systems (IAFS), which involve a reduction in the use of off-farm inputs, are attracting considerable research interest in the UK. The objectives of these systems experiments are to compare their financial performance with that from conventional or current farming practices. To date, this comparison has taken little account of any environmental benefits (or disbenefits) of the two systems. The objective of this paper is to review the assessment methodologies available for the analysis of environmental impacts. To illustrate the results of this exercise, the methodology and environmental indicators chosen are then applied to data from one of the LINK - Integrated Farming Systems experimental sites. Data from the Pathhead site in Southern Scotland are used to evaluate the use of invertebrates and nitrate loss as environmental indicators within IAFS. The results suggest that between 1992 and 1995 the biomass of earthworms fell by 28 kg per hectare on the integrated rotation and rose by 31 kg per hectare on the conventional system. This led to environmental costs ranging between £2.24 and £13.44 per hectare for the integrated system and gains of between £2.48 and £14.88 for the conventional system. In terms of nitrate, the integrated system had an estimated loss of £72.21 per hectare in comparison to £149.40 per hectare on the conventional system. Conclusions are drawn about the advantages and disadvantages of this type of analytical framework. Keywords: Farming systems; IAFS; Environmental valuation; Economics; Earthworms; Nitrates; Soil fauna

Integrated Arable Farming Systems and their potential uptake in the UK

Integrated Arable Farming Systems are examined from the perspective of the farmer considering the use of such techniques, and data are presented which suggest that the uptake of the approach may expose the manager to a greater degree of risk. Observations are made about the possible uptake of such systems in the UK and the implications this may have for agricultural and environmental policy in general.

An analytical framework for appraising Integrated Arable Farming Systems

Integrated Arable Farming Systems are examined from the perspective of the farmer considering the use of such techniques, and data are presented which suggest that the uptake of the approach may expose the manager to a greater degree of risk. Observations are made about the possible uptake of such systems in the UK and the implications this may have for agricultural and environmental policy in general.

The study of chiral adsorption systems using synchrotron- based structural and spectroscopic techniques: stereospecific adsorption of serine on Au-modified chiral Cu{531} surfaces

We apply modern synchrotron-based structural techniques to the study of serine adsorbed on the pure andAumodified intrinsically chiral Cu{531} surface. XPS and NEXAFS data in combination with DFT show that on the pure surface both enantiomers adsorb in l4 geometries (with de-protonated b-OH groups) at low coverage and in l3 geometries at saturation coverage. Significantly larger enantiomeric differences are seen for the l4 geometries, which involve substrate bonds of three side groups of the chiral center, i.e. a three-point interaction. The l3 adsorption geometry, where only the carboxylate and amino groups form substrate bonds, leads to smaller but still significant enantiomeric differences, both in geometry and the decomposition behavior. When Cu{531} is modified by the deposition of 1 and 2ML Au the orientations of serine at saturation coverage are significantly different from those on the clean surface. In all cases, however, a l3 bond coordination is found at saturation involving different numbers of Au atoms, which leads to relatively small enantiomeric differences.