Linear Model-Based Predictive Control of the LHC 1.8 K Cryogenic Loop

The LHC accelerator will employ 1800 superconducting magnets (for guidance and focusing of the particle beams) in a pressurized superfluid helium bath at 1.9 K. This temperature is a severely constrained control parameter in order to avoid the transition from the superconducting to the normal state. Cryogenic processes are difficult to regulate due to their highly non-linear physical parameters (heat capacity, thermal conductance, etc.) and undesirable peculiarities like non self-regulating process, inverse response and variable dead time. To reduce the requirements on either temperature sensor or cryogenic system performance, various control strategies have been investigated on a reduced-scale LHC prototype built at CERN (String Test). Model Based Predictive Control (MBPC) is a regulation algorithm based on the explicit use of a process model to forecast the plant output over a certain prediction horizon. This predicted controlled variable is used in an on-line optimization procedure that minimizes an appropriate cost function to determine the manipulated variable. One of the main characteristics of the MBPC is that it can easily incorporate process constraints; therefore the regulation band amplitude can be substantially reduced and optimally placed. An MBPC controller has completed a run where performance and robustness has been compared against a standard PI controller (Proportional and Integral)

Superconductivity and charge carrier localization in ultrathin La1.85Sr0.15CuO4/La2CuO4\mathbf{{La_{1.85}Sr_{0.15}CuO_4}/{La_2CuO_4}} bilayers

$\mathrm{La_{1.85}Sr_{0.15}CuO_4}$/$\mathrm{La_2CuO_4}$ (LSCO15/LCO) bilayers with a precisely controlled thickness of N unit cells (UCs) of the former and M UCs of the latter ([LSCO15\_N/LCO\_M]) were grown on (001)-oriented {\slao} (SLAO) substrates with pulsed laser deposition (PLD). X-ray diffraction and reciprocal space map (RSM) studies confirmed the epitaxial growth of the bilayers and showed that a [LSCO15\_2/LCO\_2] bilayer is fully strained, whereas a [LSCO15\_2/LCO\_7] bilayer is already partially relaxed. The \textit{in situ} monitoring of the growth with reflection high energy electron diffraction (RHEED) revealed that the gas environment during deposition has a surprisingly strong effect on the growth mode and thus on the amount of disorder in the first UC of LSCO15 (or the first two monolayers of LSCO15 containing one $\mathrm{CuO_2}$ plane each). For samples grown in pure $\mathrm{N_2O}$ gas (growth type-B), the first LSCO15 UC next to the SLAO substrate is strongly disordered. This disorder is strongly reduced if the growth is performed in a mixture of $\mathrm{N_2O}$ and $\mathrm{O_2}$ gas (growth type-A). Electric transport measurements confirmed that the first UC of LSCO15 next to the SLAO substrate is highly resistive and shows no sign of superconductivity for growth type-B, whereas it is superconducting for growth type-A. Furthermore, we found, rather surprisingly, that the conductivity of the LSCO15 UC next to the LCO capping layer strongly depends on the thickness of the latter. A LCO capping layer with 7~UCs leads to a strong localization of the charge carriers in the adjacent LSCO15 UC and suppresses superconductivity. The magneto-transport data suggest a similarity with the case of weakly hole doped LSCO single crystals that are in a so-called {"{cluster-spin-glass state}"

Non-Linear Advanced Control of the LHC Inner Triplet Heat Exchanger Test Unit

The future Large Hadron Collider (LHC) at CERN will include eight interaction region final focus magnet systems, the so-called "Inner Triplet", one on each side of the four beam collision points. The Inner Triplets will be cooled in a static bath of pressurized He II nominally at 1.9 K. This temperature is a control parameter and has very severe constraints in order to avoid the transition from the superconducting to normal resistive state. The main difference in these special zones with respect to a regular LHC cell is higher dynamic heat load unevenly distributed which modifies largely the process characteristics and hence the controller performance. Several control strategies have already been tested at CERN in a pilot plant (LHC String Test) which reproduced a LHC half-cell. In order to validate a common control structure along the whole LHC ring, a Nonlinear Model Predictive Control (NMPC) has been developed and implemented in the Inner Triplet Heat Exchanger Unit (IT-HXTU) at CERN. Automation of the Inner Triplet setup and the advanced control techniques deployed based on the Model Based Predictive Control (MBPC) principle are presented

La inclusión de la dimensión económica en la Evaluación de Impacto Ambiental

El objetivo de este trabajo es mostrar la importancia de incluir la dimensión económica en los Estudios deImpacto ambiental (EsIA). Dimensión económica que se incorpora a la evaluación “ex–ante” de inversiones a través del análisis beneficios costos (ABC) y la valoración económica de las externalidades (VEE). El trabajo muestra con ilustraciones y la presentación de dos casos las consecuencias no deseables de ignorar el ABC: a) Existencia de proyectos de eficiencia dudosa en términos de su resultado económico-social; y b) Escasez de proyectos ambientalmente eficientes, pero con escaso retorno económico privado

Applying Advanced Control Techniques for Temperature Regulation of the LHC Superconducting Magnets

The temperature of the superconducting magnets for the future LHC accelerator is a control parameter with strict operating constraints imposed by (a) the maximum temperature at which the magnets can o perate, (b) the cooling capacity of the cryogenic system, (c) the variability of applied heat loads and (d) the accuracy of the instrumentation. A temperature regulation with narrow control band can i n principle be achieved by implementing a Model Predictive Control (MPC)-type controller. For this purpose, and for investigating the behaviour of the cooling system, a simulation program has been dev eloped. A prototype MPC controller has been installed and completed its first run

On field theory quantization around instantons

With the perspective of looking for experimentally detectable physical applications of the so-called topological embedding, a procedure recently proposed by the author for quantizing a field theory around a non-discrete space of classical minima (instantons, for example), the physical implications are discussed in a ``theoretical'' framework, the ideas are collected in a simple logical scheme and the topological version of the Ginzburg-Landau theory of superconductivity is solved in the intermediate situation between type I and type II superconductors.Comment: 27 pages, 5 figures, LaTe

Real-Time optimization using the Modifier Adaptation methodology

[ES] La gestión óptima de las plantas de proceso normalmente se lleva a cabo en una capa de optimización en tiempo real (Real Time Optimization, RTO) que actúa sobre la capa de control y que toma decisiones considerando objetivos económicos en base a un  modelo del proceso, normalmente estacionario. Sin embargo, dicha operación óptima no está garantizada debido a la presencia de incertidumbre entre el modelo usado para la toma de decisiones y el proceso real. Con la idea de conducir el proceso a su punto de operación óptimo usando un modelo que se sabe incierto o erróneo, surge la metodología de adaptación de modificadores (Modifier Adaptation o MA). En dicha metodología, el problema de optimización económica de la capa RTO es modificado mediante unos términos correctores, conocidos como modificadores, estimados a partir de medidas de la planta, con el objetivo de conducir el proceso a su punto de operación óptimo. El presente artículo hace una revisión de las técnicas desarrolladas hasta el momento dentro de la metodología MA analizando sus características y modos de implementación.[EN] Optimal process operation is carried out by a Real-Time Optimization (RTO) layer which operates above the control layer and takes decisions based on steady-state plant models by considering economic objectives. However, this optimal operation is not guaranteed due to the presence of plant-model mismatch. To bring the process to the optimum operating point, the economic optimization problem solved in the RTO layer is changed following the Modifier Adaptation methodology (MA). This methodology changes the economic optimization problem solved in the RTO layer by adding some corrector terms or modifiers estimated from plant measurements to bring the process to the real optimum. This article presents a review of the different MA techniques developed until now and analyzing their features and the way to implement them.Este trabajo ha sido realizado gracias al proyecto DPI2015-70975-P del MINECO del Gobierno de España bajo la beca FPI BES-2013-062737.Rodríguez-Blanco, T.; Sarabia, D.; De Prada, C. (2018). Optimización en Tiempo Real utilizando la Metodología de Adaptación de Modificadores. Revista Iberoamericana de Automática e Informática industrial. 15(2):133-144. https://doi.org/10.4995/riai.2017.8846OJS13314415

Un Entorno de Modelado Inteligente y Simulación Distribuida de Plantas de Proceso

[ES] Se describen un conjunto de aplicaciones informáticas que abordan diferentes aspectos del modelado y simulación de procesos continuos. Primero, se presenta un prototipo universitario SIMPD, que es un generador de código de simulación de sistemas de la industria de proceso y cuyo algoritmo básico trata de emular el modo de razonamiento de un experto en modelado cuando escribe un modelo de simulación. Se compara con otros enfoques y se analizan tanto las ventajas que presenta desde la perspectiva del usuario final como los inconvenientes para el programador que trate de aumentar el conjunto de sistemas modelables. Segundo, se describe tanto una arquitectura de simulación distribuida cuyas comunicaciones se basan en el estándar de facto OPC (OLE for Process Control) como el conjunto de herramientas informáticas desarrolladas para diseñar estos escenarios de simulación. Esta arquitectura se aplica a un proceso industrial, explicando el criterio usado para dividir el modelo de simulación global.Los autores quieren agradecer el soporte financiero de la empresa Ebro Agrícolas así como a la Junta de Castilla y León por medio del proyecto “Desarrollo de un entorno de modelado inteligente y simulación distribuida de plantas de proceso”.Acebes, L.; Alves, R.; Merino, A.; De Prada, C. (2010). Un Entorno de Modelado Inteligente y Simulación Distribuida de Plantas de Proceso. Revista Iberoamericana de Automática e Informática industrial. 1(2):42-48. http://hdl.handle.net/10251/146626OJS42481