Global land use implications of biofuels: State of the art conference and workshop on modelling global land use implications in the environmental assessment of biofuels

Background, Aims and Scope On 4¿5 June 2007, an international conference was held in Copenhagen. It provided an interdisciplinary forum where economists and geographers met with LCA experts to discuss the challenges of modelling the ultimate land use changes caused by an increased demand for biofuels. Main Features The main feature of the conference was the cross-breeding of experience from the different approaches to land use modelling: The field of LCA could especially benefit from economic modelling in the identification of marginal crop production and the resulting expansion of the global agricultural area. Furthermore, the field of geography offers insights in the complexity behind new land cultivation and practical examples of where this is seen to occur on a regional scale. Results Results presented at the conference showed that the magnitude and location of land use changes caused by biofuels demand depend on where the demand arises. For instance, mandatory blending in the EU will increase land use both within and outside of Europe, especially in South America. A key learning for the LCA society was that the response to a change in demand for a given crop is not presented by a single crop supplier or a single country, but rather by responses from a variety of suppliers of several different crops in several countries. Discussion The intensification potential of current and future crop and biomass production was widely discussed. It was generally agreed that some parts of the third world hold large potentials for intensification, which are not realised due to a number of barriers resulting in so-called yield gaps. Conclusions Modelling the global land use implications of biofuels requires an interdisciplinary approach optimally integrating economic, geographical, biophysical, social and possibly other aspects in the modelling. This interdisciplinary approach is necessary but also difficult due to different perspectives and mindsets in the different disciplines. Recommendations and Perspectives The concept of a location dependent marginal land use composite should be introduced in LCA of biofuels and it should be acknowledged that the typical LCA assumption of linear substitution is not necessarily valid. Moreover, fertiliser restrictions/accessibility should be included in land use modelling and the relation between crop demand and intensification should be further explored. In addition, environmental impacts of land use intensification should be included in LCA, the powerful concept of land use curves should be further improved, and so should the modelling of diminishing returns in crop production

Remarks on Raasch’s Hook

Finite Element’s designers have always been seeking for benchmarks to judge the capability and potentiality of a numerical method. Considering shell elements many benchmark tests have been established over the years. The Raasch challenge problem, a clamped curved hook with a tip in-plane shear load, acts as a very interesting benchmark of shell elements. The structure consists of two cylindrical shells with different curvatures. In this paper the problem is also modelled as a curved beam with a rectangular cross-section. The beam model is investigated analytically. Thus an analytical expression for the tip deflection can be obtained. Further on numerical calculations with 4-node-shell elements based on a director theory are carried out and verify the elements applicability

Postbuckling of a Circular Plate - Comparing Different Solutions

Azisymmetric problems have been often investigated in the past. Since the problem is one-dimensional, the boundary problem is suitable for analytical investigations and acts as a benchmark for numerical methods. The postbuckling of an elastic circular plate under azisymmetric loading is investigated. An analytical description is given. Solutions by means of the perturbation method and the finite element method (axisymmetric shell element) are introduced. Numerical results are presented

First sequence-confirmed case of infection with the new influenza A(H1N1) strain in Germany

Here, we report on the first sequence-confirmed case of infection with the new influenza A(H1N1) virus in Germany. Two direct contacts of the patient were laboratory-confirmed as cases and demonstrate a chain of direct human-to-human transmission

Retention of mouth-to-mouth, mouth-to-mask and mouth-to-face shield ventilation

Background: Retention of mouth-to-mouth, mouth-to-mask and mouth-to-face shield ventilation techniques is poorly understood.Methods: A prospective randomised clinical trial was undertaken in January 2004 in 70 candidates randomly assigned to training in mouth-to-mouth, mouth-to-mask or mouth-to-face shield ventilation. Each candidate was trained for 10 min, after which tidal volume, respiratory rate, minute volume, peak airway pressure and the presence or absence of stomach inflation were measured. 58 subjects were reassessed 1 year later and study parameters were recorded again. Data were analysed with ANOVA, \textgreekq2 and McNemar tests.Results: Tidal volume, minute volume, peak airway pressure, ventilation rate and stomach inflation rate increased significantly at reassessment with all ventilation techniques compared with the initial assessment. However, at reassessment, mean (SD) tidal volume (960 (446) vs 1008 (366) vs 1402 (302) ml; p<0.05), minute volume (12 (5) vs 13 (7) vs 18 (3) l/min; p<0.05), peak airway pressure (14 (8) vs 17 (13) vs 25 (8) cm H2O; p<0.05) and stomach inflation rate (63% vs 58% vs 100%; p<0.05) were significantly lower with mouth-to-mask and mouth-to-face shield ventilation than with mouth-to-mouth ventilation. The ventilation rate at reassessment did not differ significantly between the ventilation techniques.Conclusions: One year after a single episode of ventilation training, lay persons tended to hyperventilate; however, the degree of hyperventilation and resulting stomach inflation were lower when a mouth-to-mask or a face shield device was employed. Regular training is therefore required to retain ventilation skills; retention of skills may be better with ventilation devices

Autonomous Optimization and Control for Central Plants with Energy Storage

A model predictive control (MPC) framework is used to determine how to optimize the distribution of energy resources across a central energy facility including chillers, water heaters, and thermal energy storage; present the results to an operator; and execute the plan. The objective of this MPC framework is to minimize cost in real-time in response to both real-time energy prices and demand charges as well as allow the operator to appropriately interact with the system. Operators must be given the correct intersection points in order to build trust before they are willing to turn the tool over and put it into fully autonomous mode. Once in autonomous mode, operators need to be able to intervene and impute their knowledge of the facilities they are serving into the system without disengaging optimization. For example, an operator may be working on a central energy facility that serves a college campus on Friday night before a home football game. The optimization system is predicting the electrical load, but does not have knowledge of the football game. Rather than try to include every possible factor into the prediction of the loads, a daunting task, the optimization system empowers the operator to make human-in-the-loop decisions in these rare scenarios without exiting autonomous (auto) mode. Without this empowerment, the operator either takes the system out of auto mode or allows the system to make poor decisions. Both scenarios will result in an optimization system that has low “on time†and thus saves little money. A cascaded, model predictive control framework lends itself well to allowing an operator to intervene. The system presented is a four tiered approach to central plant optimization. The first tier is the prediction of the energy loads of the campus; i.e., the inputs to the optimization system. The predictions are made for a week in advance, giving the operator ample time to react to predictions they do not agree with and override the predictions if they feel it necessary. The predictions are inputs to the subplant-level optimization. The subplant-level optimization determines the optimal distribution of energy across major equipment classes (subplants and storage) for the prediction horizon and sends the current distribution to the equipment level optimization. The operators are able to use the subplant-level optimization for “advisory†only and enter their own load distribution into the equipment level optimization. This could be done if they feel that they need to be conservative with the charge of the tank. Finally, the equipment level optimization determines the devices to turn on and their setpoints in each subplant and sends those setpoints to the building automation system. These decisions can be overridden, but should be extremely rare as the system takes device availability, accumulated runtime, etc. as inputs. Building an optimization system that empowers the operator ensures that the campus owner realizes the full potential of his investment. Optimal plant control has shown over 10% savings, for large plants this can translate to savings of more than US $1 million per year

Model Predictive Control for Central Plant Optimization with Thermal Energy Storage

An optimization framework is used in order to determine how to distribute both hot and cold water loads across a central energy plant including heat pump chillers, conventional chillers, water heaters, and hot and cold water (thermal energy) storage. The objective of the optimization framework is to minimize cost in response to both real-time energy prices and demand charges. The linear programming framework used allows for the optimal solution to be found in real-time. Real-time optimization lead to two separate applications: A planning tool and a real-time optimization tool. In the planning tool the optimization is performed repeatedly with a sliding horizon accepting a subset of the optimized distribution trajectory horizon as each subsequent optimization problem is solved. This is the same strategy as model predictive control except that in the design and planning tool the optimization is working on a given set of loads, weather (e.g. TMY data), and real-time pricing data and does not need to predict these values. By choosing the varying lengths of the horizon (2 to 10 days) and size of the accepted subset (1 to 24 hours), the design and planning tool can be used to find the design year’s optimal distribution trajectory in less than 5 minutes for interactive plant design, or the design and planning tool can perform a high fidelity run in a few hours. The fast solution times also allow for the optimization framework to be used in real-time to optimize the load distribution of an operational central plant using a desktop computer or microcontroller in an onsite Enterprise controller. In the real-time optimization tool Model Predictive Control is used; estimation, prediction, and optimization are performed to find the optimal distribution of loads for duration of the horizon in the presence of disturbances. The first distribution trajectory in the horizon is then applied to the central energy plant and the estimation, prediction, and optimization is repeated in 15 minutes using new plant telemetry and forecasts. Prediction is performed using a deterministic plus stochastic model where the deterministic portion of the model is a simplified system representing the load of all buildings connected to the central energy plant and the stochastic model is used to respond to disturbances in the load. The deterministic system uses forecasted weather, time of day, and day type in order to determine a predicted load. The estimator uses past data to determine the current state of the stochastic model; the current state is then projected forward and added to the deterministic system’s projection. In simulation, the system has demonstrated more than 10% savings over other schedule based control trajectories even when the subplants are assumed to be running optimally in both cases (i.e., optimal chiller staging, etc.). For large plants this can mean savings of more than US $1 million per year

Raman solitons in transient SRS

We report the observation of Raman solitons on numerical simulations of transient stimulated Raman scattering (TSRS) with small group velocity dispersion. The theory proceeds with the inverse scattering transform (IST) for initial-boundary value problems and it is shown that the explicit theoretical solution obtained by IST for a semi-infinite medium fits strikingly well the numerical solution for a finite medium. We understand this from the rapid decrease of the medium dynamical variable (the potential of the scattering theory). The spectral transform reflection coefficient can be computed directly from the values of the input and output fields and this allows to see the generation of the Raman solitons from the numerical solution. We confirm the presence of these nonlinear modes in the medium dynamical variable by the use of a discrete spectral analysis.Comment: LaTex file, to appear in Inverse Problem