César de Prada Moraga

OPC based distributed real time simulation of complex continuous processes

Abstract This paper presents a methodology for the development of distributed process simulation using OPC (OLE for Process Control). The distributed components operate as OPC servers enclosing continuous simulations developed with the simulation language EcosimPro. The paper presents the problems related to data interchange and synchronisation in real time and the solutions adopted and gives results about the performance of OPC in this kind of applications. The methodology has been applied to a large process simulator of a beet sugar factory used for control room operator training. This includes a process simulation operating in a network of six computers, a SCADA system for operation on the process, an instructor console and the corresponding software for real time communication and synchronisation. The main advantages of this approach are: (i) independence of the development of the simulation from the communication mechanisms, (ii) access to the simulations by a wide range of applications, due to the standard provided by OPC, (iii) use of low cost conventional equipment and (iv) support for large scale simulations.

Distributed continuous process simulation : An industrial case study

This paper describes a distributed continuous simulation of an industrial scale case study using DCOM components. The proposed approach considers each part of the simulation as a DCOM component. These components contain simulations performed with an efficient modelling language, EcosimPro, while other software elements communicate and synchronize the execution of the components. The case study considers an industrial scale simulation corresponding to a beet sugar factory. Several aspects, like the effect of increasing the distribution degree or how it is restricted by the computational requirements of the simulations and the performance of the machines have been studied. The paper describes also an operators training simulator for a sugar factory, based on the distributed simulation.

EDUSCA (EDUCATIONAL SCADA): FEATURES AND APPLICATIONS

Abstract This paper presents EDUSCA, an EDUcational SCAda with OPC data access. EDUSCA was specially designed for automatic control education, research and operator training purposes. The developed system takes advantage of the facilities offered by the OPC communication architecture. The system allows the students to face real practical problems, could be used to train operators in plant supervision and also allows the implementation of research functionalities. Their main advantages of EDUSCA are: (i) it can be connected to any plant whenever it includes OPC communications; (ii) it is free of licenses, thus it can be installed in many computers as necessary; (iii) it can be adapted to each application requirements and (iv) it allows the implementation of a diverse set of functionalities, as for example, control algorithms.

INTRODUCCIÓN A LA SECCIÓN ESPECIAL DE MODELADO, CONTROL Y OPTIMIZACIÓN EN LA INDUSTRIA AZUCARERA

Introduccion a la Seccion Especial de Modelado, Control y Optimizacion en la Industria Azucarera

Nonlinear Model Predictive Control for the Superfluid Helium Cryogenic Circuit of the Large Hadron Collider

Superfluid helium is used in the cryogenic circuit that cools down and stabilizes temperature of more than 1600 high performance, main superconducting magnets of the Large Hadron Collider (LHC) - the new particle accelerator at European Organization for Nuclear Research (CERN). This paper presents a simulation study of the application of Nonlinear Model Predictive Control (NMPC) to the Superfluid Helium Cryogenic Circuit. First, the new first principles, distributed parameter model of the circuit to be used in online optimization is reviewed. Then stabilization of the superconducting magnets temperature using NMPC based on the model and Continuation/ Generalized Minimum Residual (C/GMRES) algorithm is described. Finally the small computational cost of C/GMRES solution/approximation method and resulting real-time feasibility are highlighted.

Robust integrated production-maintenance scheduling for an evaporation network

Abstract This work aims to reduce the global resource consumption in an industrial evaporation network by better tasks management and coordination. The network works in continuous, processing some products in several evaporation plants, so optimal load allocation and product-plant assignment problems appear. The plants have different features (capacity, equipment, etc.) and their performance is affected by fouling inside the heat exchangers and external factors. Hereby, the optimizer has to decide when maintenance operations have to be triggered. Therefore, a mixed production/maintenance scheduling problem arises. The plant behavior is approximated by surrogate linear models obtained experimentally, allowing thus the use of mixed-integer linear optimization routines to obtain solutions in acceptable time. Furthermore, uncertainty in the weather forecast and in the production plan is also considered via a two-stage stochastic programming approach. Finally, a trade-off analysis between other objectives of interest is given to support the decision maker.

Distributed continuous process simulation: An industrial case study

Abstract This paper presents a case study of the creation of distributed continuous simulations using DCOM components. The proposed approach considers each part of the simulation as a DCOM component and uses an efficient simulation modeling language, EcosimPro, to perform the simulations contained in each one. This procedure has been applied to an industrial scale simulation corresponding to a sugar beet factory. The effect of the distribution degree in the performance has been studied. The simulation has been used to test advanced control algorithms and also in the construction of a simulator training system.

Reasoning and planning with means-ends models in abnormal situation management

Abstract Mismanagement of abnormal operating conditions has a very costly, yet avoidable impact for the Chemical Process Industry. For abnormal situation management (ASM) is key to assess quickly the plant situation and develop a plan on-line either to restore normal operation or to bring the process to a safe state. This paper addresses the important issue of including a teleological (intentional) dimension in process modelling for ASM by explicitly relating process components to functions and goals that must be achieved. A formal modelling framework that resort to means (functions and components) and ends (a hierarchy of goals) to distinguish between normal and abnormal situations, to reason about goal achievement, to trace the causes of a unit malfunction and to plan a course of action is presented. The means-ends model (MEM) is expressed as logical axioms in a knowledge base using situations calculus . This formal representation of a MEM allows continuously assessing plant situations, doing queries about the state of goals and functions, and testing alternative course of actions for feasibility and performance.