Técnicas de inteligência artificial para a geração dinâmica de set points para uma coluna de destilação

Artificial Intelligence techniques are applied to improve performance of a simulated oil distillation system. The chosen system was a debutanizer column. At this process, the feed, which comes to the column, is segmented by heating. The lightest components become steams, by forming the LPG (Liquefied Petroleum Gas). The others components, C5+, continue liquid. In the composition of the LPG, ideally, we have only propane and butanes, but, in practice, there are contaminants, for example, pentanes. The objective of this work is to control pentane amount in LPG, by means of intelligent set points (SP s) determination for PID controllers that are present in original instrumentation (regulatory control) of the column. A fuzzy system will be responsible for adjusting the SP's, driven by the comparison between the molar fraction of the pentane present in the output of the plant (LPG) and the desired amount. However, the molar fraction of pentane is difficult to measure on-line, due to constraints such as: long intervals of measurement, high reliability and low cost. Therefore, an inference system was used, based on a multilayer neural network, to infer the pentane molar fraction through secondary variables of the column. Finally, the results shown that the proposed control system were able to control the value of pentane molar fraction under different operational situations

Human immunodeficiency virus type 1 fitness is a determining factor in viral rebound and set point in chronic infection.

Human immunodeficiency virus type 1 (HIV-1) isolates from 20 chronically infected patients who participated in a structured treatment interruption (STI) trial were studied to determine whether viral fitness influences reestablishment of viremia. Viruses derived from individuals who spontaneously controlled viremia had significantly lower in vitro replication capacities than viruses derived from individuals that did not control viremia after interruption of antiretroviral therapy (ART), and replication capacities correlated with pre-ART and post-STI viral set points. Of note, no clinically relevant improvement of viral loads upon STI occurred. Virus isolates from controlling and noncontrolling patients were indistinguishable in terms of coreceptor usage, genetic subtype, and sensitivity to neutralizing antibodies. In contrast, viruses from controlling patients exhibited increased sensitivity to inhibition by chemokines. Sensitivity to inhibition by RANTES correlated strongly with slower replication kinetics of the virus isolates, suggesting a marked dependency of these virus isolates on high coreceptor densities on the target cells. In summary, our data indicate that viral fitness is a driving factor in determining the magnitude of viral rebound and viral set point in chronic HIV-1 infection, and thus fitness should be considered as a parameter influencing the outcome of therapeutic intervention in chronic infection.

Robust integration of real time optimization with linear model predictive control

Here, we study the stable integration of real time optimization (RTO) with model predictive control (MPC) in a three layer structure. The intermediate layer is a quadratic programming whose objective is to compute reachable targets to the MPC layer that lie at the minimum distance to the optimum set points that are produced by the RTO layer. The lower layer is an infinite horizon MPC with guaranteed stability with additional constraints that force the feasibility and convergence of the target calculation layer. It is also considered the case in which there is polytopic uncertainty in the steady state model considered in the target calculation. The dynamic part of the MPC model is also considered unknown but it is assumed to be represented by one of the models of a discrete set of models. The efficiency of the methods presented here is illustrated with the simulation of a low order system. (C) 2010 Elsevier Ltd. All rights reserved.

Stable Model Predictive Control for Integrating Systems with Optimizing Targets

This paper concern the development of a stable model predictive controller (MPC) to be integrated with real time optimization (RTO) in the control structure of a process system with stable and integrating outputs. The real time process optimizer produces Optimal targets for the system inputs and for Outputs that Should be dynamically implemented by the MPC controller. This paper is based oil a previous work (Comput. Chem. Eng. 2005, 29, 1089) where a nominally stable MPC was proposed for systems with the conventional control approach where only the outputs have set points. This work is also based oil the work of Gonzalez et at. (J. Process Control 2009, 19, 110) where the zone control of stable systems is studied. The new control for is obtained by defining ail extended control objective that includes input targets and zone controller the outputs. Additional decision variables are also defined to increase the set of feasible solutions to the control problem. The hard constraints resulting from the cancellation of the integrating modes Lit the end of the control horizon are softened,, and the resulting control problem is made feasible to a large class of unknown disturbances and changes of the optimizing targets. The methods are illustrated with the simulated application of the proposed,approaches to a distillation column of the oil refining industry.

Robust model predictive control with zone control

Model predictive control (MPC) is usually implemented as a control strategy where the system outputs are controlled within specified zones, instead of fixed set points. One strategy to implement the zone control is by means of the selection of different weights for the output error in the control cost function. A disadvantage of this approach is that closed-loop stability cannot be guaranteed, as a different linear controller may be activated at each time step. A way to implement a stable zone control is by means of the use of an infinite horizon cost in which the set point is an additional variable of the control problem. In this case, the set point is restricted to remain inside the output zone and an appropriate output slack variable is included in the optimisation problem to assure the recursive feasibility of the control optimisation problem. Following this approach, a robust MPC is developed for the case of multi-model uncertainty of open-loop stable systems. The controller is devoted to maintain the outputs within their corresponding feasible zone, while reaching the desired optimal input target. Simulation of a process of the oil re. ning industry illustrates the performance of the proposed strategy.

A stable MPC with zone control

Several MPC applications implement a control strategy in which some of the system outputs are controlled within specified ranges or zones, rather than at fixed set points [J.M. Maciejowski, Predictive Control with Constraints, Prentice Hall, New Jersey, 2002]. This means that these outputs will be treated as controlled variables only when the predicted future values lie outside the boundary of their corresponding zones. The zone control is usually implemented by selecting an appropriate weighting matrix for the output error in the control cost function. When an output prediction is inside its zone, the corresponding weight is zeroed, so that the controller ignores this output. When the output prediction lies outside the zone, the error weight is made equal to a specified value and the distance between the output prediction and the boundary of the zone is minimized. The main problem of this approach, as long as stability of the closed loop is concerned, is that each time an output is switched from the status of non-controlled to the status of controlled, or vice versa, a different linear controller is activated. Thus, throughout the continuous operation of the process, the control system keeps switching from one controller to another. Even if a stabilizing control law is developed for each of the control configurations, switching among stable controllers not necessarily produces a stable closed loop system. Here, a stable M PC is developed for the zone control of open-loop stable systems. Focusing on the practical application of the proposed controller, it is assumed that in the control structure of the process system there is an upper optimization layer that defines optimal targets to the system inputs. The performance of the proposed strategy is illustrated by simulation of a subsystem of an industrial FCC system. (C) 2008 Elsevier Ltd. All rights reserved.

Closed-loop model re-identification of processes under MPC with zone control

This work deals with a procedure for model re-identification of a process in closed loop with ail already existing commercial MPC. The controller considered here has a two-layer structure where the upper layer performs a target calculation based on a simplified steady-state optimization of the process. Here, it is proposed a methodology where a test signal is introduced in a tuning parameter of the target calculation layer. When the outputs are controlled by zones instead of at fixed set points, the approach allows the continuous operation of the process without an excessive disruption of the operating objectives as process constraints and product specifications remain satisfied during the identification test. The application of the method is illustrated through the simulation of two processes of the oil refining industry. (c) 2008 Elsevier Ltd. All rights reserved.

Aestivation in the South American lungfish, Lepidosiren paradoxa: Effects on cardiovascular function, blood gases, osmolality and leptin levels

The African (Protopterus sp.) and South American lungfish (Lepidosiren paradoxa) inhabit shallow waters, that seasonally dry out, which induces aestivation and cocoon formation in Protopterus. Differently, L. paradoxa has no cocoon, and it aestivates in a simple burrow. In water PaCO(2) is 21.8 +/- 0.4 mmHg (mean values +/- S.E.M.; n = 5), whereas aestivation for 20 days increased PaCO(2) to as much as 37.6 +/- 2.1 mmHg, which remained the same after 40 days (35.8 +/- 3.3 mmHg). Concomitantly. the plasma [HCO(3)(-)]-values for animals in water were 22.5 +/- 0.5 mM, which after 20 days increased to 40.2 +/- 2.3 mM and after 40 days to 35.8 +/- 3.3 mM. Initially in water, PaO(2) was 87.7 +/- 2.0 mmHg, but 20 days in aestivation reduced the value to 80.5 +/- 2.2 and later (40 days) to 77.1 +/- 3.0 mmHg. Meanwhile, aestivation had no effect on pHa and hematocrit. The blood pressures were equal for animals in the water or in the burrow (P(mean) similar to 30 mmHg), and cardiac frequency (f(H)) fell from 31 beats min(-1) to 22 beats min(-1) during 40 days of aestivation. The osmolality (mOsm kg H(2)O(-1)) was elevated after 20 and 40 days of aestivation but declined upon return to water. The transition front activity to aestivation involves new set-points for the variables that determine the acid-base status and PaO(2) of the animals, along with a reduction of cardiac frequency. (C) 2008 Elsevier B.V. All rights reserved.

Supervisory control of wastewater treatment plants by combining principal component analysis and fuzzy c-means clustering

In this paper a methodology for integrated multivariate monitoring and control of biological wastewater treatment plants during extreme events is presented. To monitor the process, on-line dynamic principal component analysis (PCA) is performed on the process data to extract the principal components that represent the underlying mechanisms of the process. Fuzzy c-means (FCM) clustering is used to classify the operational state. Performing clustering on scores from PCA solves computational problems as well as increases robustness due to noise attenuation. The class-membership information from FCM is used to derive adequate control set points for the local control loops. The methodology is illustrated by a simulation study of a biological wastewater treatment plant, on which disturbances of various types are imposed. The results show that the methodology can be used to determine and co-ordinate control actions in order to shift the control objective and improve the effluent quality.

A framework for extreme-event control in wastewater treatment

In this paper an approach to extreme event control in wastewater treatment plant operation by use of automatic supervisory control is discussed. The framework presented is based on the fact that different operational conditions manifest themselves as clusters in a multivariate measurement space. These clusters are identified and linked to specific and corresponding events by use of principal component analysis and fuzzy c-means clustering. A reduced system model is assigned to each type of extreme event and used to calculate appropriate local controller set points. In earlier work we have shown that this approach is applicable to wastewater treatment control using look-up tables to determine current set points. In this work we focus on the automatic determination of appropriate set points by use of steady state and dynamic predictions. The performance of a relatively simple steady-state supervisory controller is compared with that of a model predictive supervisory controller. Also, a look-up table approach is included in the comparison, as it provides a simple and robust alternative to the steady-state and model predictive controllers, The methodology is illustrated in a simulation study.

Algoritmo de controlo inteligente de microprodução para regulação de tensão em redes de baixa tensão

Com o constante desenvolvimento da sociedade, o consumo de energia elétrica tem aumentado gradualmente, por outro lado a preocupação com o meio ambiente e a necessidade de um desenvolvimento sustentável, faz com que a legislação atual favoreça a introdução de fontes de energia de origem renovável em detrimento de fontes de energia de origem fóssil. Cada vez mais têm surgido incentivos para a implementação de pequenos sistemas de produção em instalações de utilização, estes consumidores/produtores são denominados de prosumers, sendo este tipo de produtores ligados à rede elétrica de baixa tensão. Com a introdução deste tipo de produtores é necessário dotar a rede elétrica de meios que permitam ao operador da rede monitorizar e controlar em tempo real o estado da rede assim como destes novos produtores. No âmbito desta dissertação, foi desenvolvido um algoritmo de controlo inteligente de microprodução. Avaliando o consumo, a produção, entre outros parâmetros de gestão da rede, este algoritmo calculará um conjunto de set-points que deverão ser enviados para os microprodutores de modo a limitar a potência injetada na rede e assim controlar a tensão. Também foi realizado um estudo económico do impacto que as medidas propostas teriam do ponto de vista dos gestores da rede bem como do ponto de vista dos microprodutores.

Energy-aware task mapping onto heterogeneous platforms using DVFS and sleep states

Heterogeneous multicore platforms are becoming an interesting alternative for embedded computing systems with limited power supply as they can execute specific tasks in an efficient manner. Nonetheless, one of the main challenges of such platforms consists of optimising the energy consumption in the presence of temporal constraints. This paper addresses the problem of task-to-core allocation onto heterogeneous multicore platforms such that the overall energy consumption of the system is minimised. To this end, we propose a two-phase approach that considers both dynamic and leakage energy consumption: (i) the first phase allocates tasks to the cores such that the dynamic energy consumption is reduced; (ii) the second phase refines the allocation performed in the first phase in order to achieve better sleep states by trading off the dynamic energy consumption with the reduction in leakage energy consumption. This hybrid approach considers core frequency set-points, tasks energy consumption and sleep states of the cores to reduce the energy consumption of the system. Major value has been placed on a realistic power model which increases the practical relevance of the proposed approach. Finally, extensive simulations have been carried out to demonstrate the effectiveness of the proposed algorithm. In the best-case, savings up to 18% of energy are reached over the first fit algorithm, which has shown, in previous works, to perform better than other bin-packing heuristics for the target heterogeneous multicore platform.

An Open Source BI Approach: Concept Proof Tracking Fleet

É possível assistir nos dias de hoje, a um processo tecnológico evolutivo acentuado por toda a parte do globo. No caso das empresas, quer as pequenas, médias ou de grandes dimensões, estão cada vez mais dependentes dos sistemas informatizados para realizar os seus processos de negócio, e consequentemente à geração de informação referente aos negócios e onde, muitas das vezes, os dados não têm qualquer relacionamento entre si. A maioria dos sistemas convencionais informáticos não são projetados para gerir e armazenar informações estratégicas, impossibilitando assim que esta sirva de apoio como recurso estratégico. Portanto, as decisões são tomadas com base na experiência dos administradores, quando poderiam serem baseadas em factos históricos armazenados pelos diversos sistemas. Genericamente, as organizações possuem muitos dados, mas na maioria dos casos extraem pouca informação, o que é um problema em termos de mercados competitivos. Como as organizações procuram evoluir e superar a concorrência nas tomadas de decisão, surge neste contexto o termo Business Intelligence(BI). A GisGeo Information Systems é uma empresa que desenvolve software baseado em SIG (sistemas de informação geográfica) recorrendo a uma filosofia de ferramentas open-source. O seu principal produto baseia-se na localização geográfica dos vários tipos de viaturas, na recolha de dados, e consequentemente a sua análise (quilómetros percorridos, duração de uma viagem entre dois pontos definidos, consumo de combustível, etc.). Neste âmbito surge o tema deste projeto que tem objetivo de dar uma perspetiva diferente aos dados existentes, cruzando os conceitos BI com o sistema implementado na empresa de acordo com a sua filosofia. Neste projeto são abordados alguns dos conceitos mais importantes adjacentes a BI como, por exemplo, modelo dimensional, data Warehouse, o processo ETL e OLAP, seguindo a metodologia de Ralph Kimball. São também estudadas algumas das principais ferramentas open-source existentes no mercado, assim como quais as suas vantagens/desvantagens relativamente entre elas. Em conclusão, é então apresentada a solução desenvolvida de acordo com os critérios enumerados pela empresa como prova de conceito da aplicabilidade da área Business Intelligence ao ramo de Sistemas de informação Geográfica (SIG), recorrendo a uma ferramenta open-source que suporte visualização dos dados através de dashboards.

The effect of antiretroviral treatment of different durations in primary HIV infection.

OBJECTIVES: To compare immunological, virological and clinical outcomes in persons initiating combination antiretroviral therapy (cART of different durations within 6 months of seroconversion (early treated) with those who deferred therapy (deferred group). DESIGN: CD4 cell and HIV-RNA measurements for 'early treated' individuals following treatment cessation were compared with the corresponding ART-free period for the 'deferred' group using piecewise linear mixed models. Individuals identified during primary HIV infection were included if they seroconverted from 1st January 1996 and were at least 15 years of age at seroconversion. Those with at least 2 CD4 less than 350 cells/microl or AIDS within the first 6 months following seroconversion were excluded. RESULTS: Of 348 'early treated' patients, 147 stopped cART following treatment for at least 6 (n = 38), more than 6-12 (n = 40) or more than 12 months (n = 69). CD4 cell loss was steeper for the first 6 months following cART cessation, but subsequent loss rate was similar to the 'deferred' group (n = 675, P = 0.26). Although those treated for more than 12 months appeared to maintain higher CD4 cell counts following cART cessation, those treated for 12 months or less had CD4 cell counts 6 months after cessation comparable to those in the 'deferred' group. There was no difference in HIV-RNA set points between the 'early' and 'deferred' groups (P = 0.57). AIDS rates were similar but death rates, mainly due to non-AIDS causes, were higher in the 'deferred' group (P = 0.05). CONCLUSION: Transient cART, initiated within 6 months of seroconversion, seems to have no effect on viral load set point and limited beneficial effect on CD4 cell levels in individuals treated for more than 12 months. Its long-term effects remain inconclusive and need further investigation.

Contact force transitions in regrasp tasks of planar objects

This paper presents a simple and fast solution to the problem of finding the time variations of the forces that keep the object equilibrium when a finger is removed from a three contact point grasp or a finger is added to a two contact point grasp, assuming the existence of an external perturbation force (that can be the object weight itself). The procedure returns force set points for the control system of a manipulator device in a regrasping action. The approach was implemented and a numerical example is included in the paper to illustrate how it works.