Roberto Baratti

River flow forecast for reservoir management through neural networks

Abstract River flow forecasts are required to provide basic information for reservoir management in a multipurpose water system optimisation framework. An accurate prediction of flow rates in tributary streams is crucial to optimise the management of water resources considering extended time horizons. Moreover, runoff prediction is crucial in protection from water shortage and possible flood damages. In this paper, a neural approach is used to model the rainfall-runoff process when different time step durations have to be considered in reservoir management. Numerical comparisons with observed data are provided for runoff prediction in the Tirso basin at the S.Chiara section in Sardinia (Italy).

Development of a composition estimator for binary distillation columns. Application to a pilot plant

Abstract A nonlinear extended Kalman filter, which infers the compositions of the streams leaving a binary distillation column from temperature measurements, is developed. The accuracy of the distillation column model on which the estimator is based, is discussed in connection with the reliability of the obtained estimates. The estimator performance is checked by comparison with the dynamic behavior of a distillation pilot plant, where the separation of a binary mixture of ethanol and water takes place.

A composition estimator for multicomponent distillation columns — development and experimental test on ternary mixtures

Abstract A nonlinear extended Kalmanfilter has been developed to infer the outlet composition of a ternary distillation column from temperature measurements. The reliability of the developed estimator is discussed with respect to disturbances on either steam, reflux and feed flowrates. It is shown that the accuracy of the adopted vapor–liquid equilibrium model plays a crucial role. The performance of the estimator is tested by comparison with actual outlet compositions measured in a pilot plant, where the separation of a ternary mixture of ethanol, tert -butanol and water is carried out.

Monitoring Pollutant Emissions in a 4.8 MW power Plant through Neural Network

Abstract The prime aim of this paper is to address the relevant issues associated to the development of neural-based software sensors for monitoring the pollutant emissions coming out from combustion chambers. The objective is to prove the potential of software sensors as alternative monitoring systems to conventional analytical equipment. The preliminary results refer to a 4.8 MW power pilot plant operating at the Enel Santa Gilla Research Center in Cagliari (Italy).

An extended Kalman filtering approach with a criterion to set its tuning parameters; application to a catalytic reactor

Abstract In this work, the problem of tuning Kalman filters is addressed. Such tuning usually consists of finding the values of the model and measurements covariance matrices by trial and error methods till satisfactory results have been obtained. Here, we propose a new method to endow the model covariance matrix with physical meaning, enabling a more systematic gain tuning procedure. Simulation examples for a simulated non-isothermal continuous reactor and for an experimental reactor where carbon monoxide oxidation takes place are presented.

Reconstruction of chaotic time series by neural models: a case study

Abstract This work analyses the problems related to the reconstruction of a dynamical system, which exhibits chaotic behaviour, from time series associated with a single observable of the system itself, by using feedforward neural network model. The starting network architecture is obtained setting the number of input neurons according to the Takens’ theorem, and then is imporved by slightly increasing the number of inputs. The choice of the number of the hidden neurons is based on the results obtained testing different net structures. The effectiveness of the method is demonstrated by applying it to the Brusselator system (Phys. Lett. 91 (1982) 263).

CATALYTIC CONVERTER DESIGN FOR MINIMISATION OF COLD-START EMISSIONS

Abstract The axial catalyst distribution in a monolithic converter that minimises cold-start pollutant emissions is investigated numerically, under the constraint of fixed total catalyst surface area. Various warm-up mechanisms can be present during the transient period. The catalyst distribution affects greatly which mechanisms prevail. For the optimal distribution, a large amount of catalyst is required in the upstream section of the monolith This ensures that the hot spot is kept at the monolith inlet throughout the warm-up period, and hence heat transfer by convection dominates. A result of practical significance is that the evolution and the steady-state value of the temperature of the exhaust gas stream at the monolith inlet do not affect significantly the form of The optimal distribution. Even though the local catalyst surface area of the optimal distribution in the downstream section of the converter is reduced as compared to the uniform distribution, steady-state performance is not adversely affe...

On the topological modeling and analysis of industrial process data using the SOM

In this paper, we overview and discuss the implementation of topology-based approaches to modeling and analyzing industrial process data. Emphasis is given to the representation of the data obtained with the self-organizing map (SOM). The methods are used in visualizing process measurements and extracting relevant information by exploiting the topological structure of the observations. Benefits of the SOM with industrial data are presented for a set of process measurements measured in an industrial gas treatment plant. The practical goal is to identify significant operational modes and most sensitive process variables before developing an alternative control strategy. The results confirmed that the SOM-based approach is capable of providing valuable information and offers possibilities for direct application to other process monitoring tasks.

Design and experimental verification of a nonlinear catalytic reactor estimator

Abstract A procedure for the implementation of a nonlinear extended Kalman filter to infer the outlet concentration of a catalytic reactor from temperature measurements is developed. The role of the reactor model in determining the reliability of the conversion estimates is discussed in detail. The estimator performance is checked by comparison with the dynamic experimental behavior of a catalytic reactor where the oxidation of CO to CO 2 is carried out on a Pt—alumina-supported catalyst. The application of the developed estimation procedure to reactors of interest in applications is discussed.

Monitoring of a CO oxidation reactor through a grey model-based EKF observer

Abstract Often, in real applications it is difficult to dispose of a simple, yet, representative kinetic model because of the complexity of the reactions taking place. To overcome this limitation a hybrid modelling approach is proposed for the identification of the dynamic behaviour of chemical reactors. In particular, the tools of neural network modelling have been exploited to represent the kinetic reaction data. The “neural reaction rate model” is integrated within a first principles model that constitutes the basis of a nonlinear observer extended Kalman filter (EKF) for an heterogeneous gas–solid reactor where the catalytic oxidation of carbon monoxide takes place. The outlined procedure shows that artificial neural networks (ANN) can be effectively used to formulate lumped reaction rates because of their capability in capturing the essential characteristics of the functional relationship among the state variables.