ايجاد و توسعه روش هاي نوين براي تلفيق داده شي ء گرا

چكيده انگليسي :

Abstract Object oriented methodology has characteristics such as data abstraction andinheritance and is widely used for analysis and modeling of complex systems andproblems Therefore object oriented viewpoint was applied on data reconciliation problemand complex data reconciliation problems were divided to simpler ones Data reconciliation as an object has attributes such as measurements and reconciledvalues and can be a basis of classes for data reconciliation problems After analysis of datareconciliation problem based on object oriented method some classes were developed forperforming data reconciliation These classes include Constraints for management ofconstraints Optimization to define and encapsulate different optimization methodsapplicable on data reconciliation problems DR the main class for data reconciliation SSDR LSSDR and NSSDR for performing steady state data reconciliation in linear andnonlinear cases and DDR LDDR and NDDR for linear and nonlinear dynamic datareconciliation Another class namely DDRMethod was also developed for applyingdifferent methods of dynamic data reconciliation by creating new classes and inheritingfrom this class Theses classes are related to each other by inheritance and aggregationmechanisms A data reconciliation software tool namely DCON is also developed based ondeveloped classes for performing different types of data reconciliation problems by usingthe developed classes described above This software tool has different parts in order toeasy enter required information and perform data reconciliation in linear nonlinear steady state and dynamic cases DDE and COM data communication mechanism are used innonlinear dynamic data reconciliation for communication of data between the software tooland simulation programs written by any programming language that support DDE orCOM Also the software is capable of connecting to real plants via serial and parallel portsand applying on line data reconciliation on real processing plants In order to show the performance of artificial neural networks for nonlinear dynamicdata reconciliation a new method namely NetDDR similar to the NARMA L2 systemidentification method based on neural networks is developed The network of this methodis trained using true and noisy values obtained by process simulation Then the trainednetwork is used for dynamic data reconciliation NetDDR method was tested by twoillustrative examples of simulation of a two component distillation column and a chemicalreactor In each time step Gaussian white noises are added to the true values obtained bysimulation in order to simulate real measurements Then noisy data are sent to DCONsoftware for performing data reconciliation In performed cases reconciled values properlyfit true values and the variance of errors of reconciled values becomes very smallcompared to the variance of measurement errors Extended Kalman Filtering EKF method is also used for dynamic data reconciliationand comparison of its results with results of NetDDR method Comparison shows thatNetDDR method produces better results than EKF method while it doesn t need anyinformation about the state variables and Jacobian matrices of state and measurementvariables This method is also faster that EKF method and it can also be used for on lineapplications as EKF method NetDDR method has many tuning parameters as EKF methodwhile tuning of its parameters is much easier than those of EKF method

فرضي، علي

ايجاد و توسعه روش هاي نوين براي تلفيق داده شي ء گرا

خلاصه سازي داده ها , مخفي سازي , ارث بري , روش فيلتر كالمن