A GIS approach to ingest Meteosat Second Generation data into the Local Analysis and Prediction System

The Local Analysis and Prediction System (LAPS) is modified to ingest Meteosat Second Generation (MSG) data for cloud analysis. A first study is conducted to test the actual performance of the weather analysis software after new satellite bands are introduced. Results show that the system provides high quality cloud products such as cloud mask, cloud top height and cloudiness. A comparison with products from EUMETSAT’s Nowcasting SAF shows a general underestimation of the LAPS product although the results are not conclusive. The study shows the potential of MSG data in refining the mesoscale analyses produced by LAPS. Moreover the software tools, based on open source codes for geolocation and geographical information systems, written for the transformation of MSG data into input files for LAPS have demonstrated a great flexibility and ease of use. The study opens up an avenue for successive validation and refinement of the analyses together with their improved implementation for operational nowcasting and very short range forecasting applications.     

Droughts Prediction: a Methodology Based on Climate Seasonal Forecasts

This study proposes a methodology for the drought assessment based on the seasonal forecasts. These are climate predictions of atmospheric variables, such as precipitation, temperature, wind speed, for upcoming season, up to 7 months. In regions particularly vulnerable to droughts and to changes in climate, such as the Mediterranean areas, predictions of precipitation with months in advance are crucial for understanding the possible shifts, for example, in water resource availability. Over Europe, practical applications of seasonal forecasts are still rare, because of the uncertainties of their skills; however, the predictability varies depending on the season and area of application. In this study, we describe a methodology which integrates, through a statistical approach, seasonal forecast and reanalysis data to assess the climate state, i.e. drought or not, of a region for predefined periods in the next future, at monthly scale. Additionally, the skill of the forecasts and the reliability of the released climate state assessment are estimated in terms of the false rate, i.e. the probability of missing alerts or false alarms. The methodology has been first built for a case study in Zakynthos (Greece) and then validated for a case study in Sicily (Italy). The selected locations represent two areas of the Mediterranean region often suffering from drought and water shortage situations. Results showed promising findings, with satisfying matching between predictions and observations, and false rates ranging from 1 to 50%, depending on the selected forecast period.

     

Tube-based robust sampled-data MPC for linear continuous-time systems

This note extends to the continuous-time case the “tube-based” approach for the design of discrete-time robust model predictive control (MPC) algorithms developed in Mayne, Seron, and Rakovi? (2005). This extension is of interest in view of the simplicity and popularity of the method as well as of the industrial relevance of continuous-time implementations of MPC. The proposed robust control law is composed of two terms: (1) a sampled-data MPC control law and (2) a continuous-time state feedback term.     

Capillary-bridge forces between solid particles: Insights from lattice Boltzmann simulations

Liquid capillary-bridge formation between solid particles has a critical influence on the rheological properties of granular materials and, in particular, on the efficiency of fluidized bed reactors. The available analytical and semi-analytical methods have inherent limitations, and often do not cover important aspects, like the presence of non-axisymmetric bridges. Here, we conduct numerical simulations of the capillary bridge formation between equally and unequally sized solid particles using the lattice Boltzmann method, and provide an assessment of the accuracy of different families of analytical models. We find that some of the models taken into account are shown to perform better than others. However, all of them fail to predict the capillary force for contact angles larger than π/2, where a repulsive capillary force attempts to push the solid particle outward to minimize the surface energy, especially at a small separation distance. We then apply the most suitable model to study the impact of capillary interactions on particle clustering using a coupled lattice Boltzmann and Discrete Element method.     

Layout design in dynamic environments: analytical issues

The paper discusses the layout problem for a productive environment affected by variability of demand (dynamic environment). A conceptual framework of the layout flexibility is discussed, according to the concept of layout robustness. An analytical approach extends the technique adopted for the optimal layout determination from the static to the dynamic context. For an assigned layout problem, an example shows how the analytical approach may be applied to the calculation of the probability distribution of the cost function. From this basis, a measure of the layout robustness is proposed and commented on, in order to give useful advice to those in charge of layout planning and control and to highlight the potential use of the theoretical issues discussed.     

Unilateral Contact Problem for Aging Viscoelastic Beams

The frictionless unilateral contact problem of a viscoelastic Bernoulli–Euler beam resting on a viscoelastic soil is studied. The mathematical formulation involves equilibrium equations, compatibility equations, and constitutive laws, with an aging integral-type form. The unilateral nature of the contact is imposed through a compatibility inequality, which allows the determination of the contact imprint at each time. Further, the governing integro-differential equation for the unknown contact pressure is derived. As special cases, the elastic Winkler-type soil and the rigid soil conditions are discussed. A numerical approach is presented, which employs the finite difference method along space and an adaptive step-by-step algorithm along time. The procedure allows for time discontinuities of both external loads and contact pressure. Several selected numerical examples are presented and the influence of the most important material and geometrical parameters are shown. For the simplest situations, it was possible to compare the results obtained with known analytical solutions.     

Sensorless Control of Induction Machines by a New Neural Algorithm: The TLS EXIN Neuron

This paper proposes two speed observers for high-performance induction machine drives, both adopting an online adaptation law based on a new total least-squares (TLS) technique: the TLS EXIN neuron. The first is a model reference adaptive system (MRAS) observer with a neural adaptive integrator in the reference model and a neural adaptive model trained online by the TLS EXIN neuron. This observer, presented in a previous article of the authors, has been improved here in two aspects: first, the neural adaptive integrator has been modified to make its learning factor vary according to the reference speed of the drive, second, a neural adaptive model based on the modified Euler integration has been proposed to solve the discretization instability problem in field-weakening. The second observer is a new full-order adaptive one based on the state equations of the induction machine, where the speed is estimated by means of a TLS EXIN adaptation technique. Both these observers have been provided with an inverter nonlinearity compensation algorithm and with techniques for the online estimation of the stator resistance of the machine. Moreover, a thorough theoretical stability analysis has been developed for them both, with particular reference to the field-weakening region behavior for the TLS MRAS observer and to the regenerating mode at low speeds for the TLS adaptive observer. Both speed observers have been verified in numerical simulation and experimentally on a test setup, and have also been compared experimentally with the BPN MRAS observer, the classic adaptive observer and with an open-loop estimator. Results show that both proposed observers outperform all other three observers in every working condition, with the TLS adaptive observer resulting in a better performance than the TLS MRAS observer     

Quantitative Risk Assessment for Process Design Modification and Maintenance Optimization in Refineries and Petrochemical Plants

Quantitative risk assessment is methodology based on calculating probabilities and frequencies of sequential events using Boolean algebra, and it is normally used to perform safety assessments for complex interacting systems. Although quantitative risk assessment has been commonly used in aerospace and nuclear industries, it can also be used for quantifying economic risk and for estimating possibilities of potential production losses in a petrochemical or a manufacturing plant. In developing quantitative risk assessment models for petrochemical plants, component failures as well as human (operator) errors are taken into consideration in developing the plant's fault‐tree logic, in which is used to predict probabilities of future plant upsets. This paper shows how the quantitative risk assessment can be used to rank the economic importance of the production units in a refinery for prioritizing maintenance activities. In addition, two case studies are compared to demonstrate how a quantitative risk assessment model can be used as an invaluable tool in process design optimization. The quantitative risk assessment methodology developed in this work relates production losses to the performance of the major components and the process design. This application of the quantitative risk assessment provides a basis for the risk‐informed decision‐making and optimizing allocation of plant resources in support of plant operation and maintenance activities.     

Supercritical Carbon Dioxide Extraction of Carotenoids from Pumpkin (Cucurbita spp.): A Review

Carotenoids are well known for their nutritional properties and health promoting effects representing attractive ingredients to develop innovative functional foods, nutraceutical and pharmaceutical preparations. Pumpkin (Cucurbita spp.) flesh has an intense yellow/orange color owing to the high level of carotenoids, mainly α-carotene, β-carotene, β-cryptoxanthin, lutein and zeaxanthin. There is considerable interest in extracting carotenoids and other bioactives from pumpkin flesh. Extraction procedures able to preserve nutritional and pharmacological properties of carotenoids are essential. Conventional extraction methods, such as organic solvent extraction (CSE), have been used to extract carotenoids from plant material for a long time. In recent years, supercritical carbon dioxide (SC-CO₂) extraction has received a great deal of attention because it is a green technology suitable for the extraction of lipophylic molecules and is able to give extracts of high quality and totally free from potentially toxic chemical solvents. Here, we review the results obtained so far on SC-CO₂ extraction efficiency and quali-quantitative composition of carotenoids from pumpkin flesh. In particular, we consider the effects of (1) dehydration pre-treatments; (2) extraction parameters (temperature and pressure); the use of water, ethanol and olive oil singularly or in combination as entrainers or pumpkin seeds as co-matrix.     

An Interconnect Strategy for a Heterogeneous, Reconfigurable SoC

Editor's note:The diversification of the SoC market has increased the need for domain-specific programmable platforms that can be tailored to postfabrication products. This article presents a design case study on the interconnect for such a programmable platform, as part of the Morpheus project.—Li-Shiuan Peh, Princeton University