Marcella Porru

A distillate composition estimator for an industrial multicomponent IC4-NC4 splitter with experimental temperature measurements

Abstract The problem of on-line estimating on the basis of temperature measurements the distillate NC4 impurity in an industrial IC4-NC4 splitter is addressed within an adjustable-structure Geometric Estimation approach, yielding: (i) suggestive sensor location guidelines drawn from detectability measures, and (ii) conclusive results obtained from estimator functioning assessment with simulated and experimental data. The resulting estimator performs the estimation task within an admissible error tolerance, and considerably less ODEs than the ones of the standard EKF technique employed in the majority of related previous studies.

Composition estimator design for industrial multicomponent distillation column

bDep. de Ingenieria de Procesos e Hidraulica, UAM, Apdo. 55534, 09340 Mexico, D.F. Mexico roberto.baratti@dimcm.unica.it The problem of on-line estimating on the basis of temperature measurements the distillate NC4 impurity in an industrial IC4/NC4 splitter (operating at Saras refinery at Sarroch in Italy) is addressed. The application of the adjustable model-based Geometric Estimation approach yields a dynamic data processor that adequately performs the estimation task in the light of a prescribed estimation tolerance with a scheme that is considerably simpler than the Extended Kalman Filter (EKF) employed in previous studies. The implementation of the proposed estimator with experimental data shows that distillate NC4 impurity can be inferred with an uncertainty similar to the one of off-line occasional determinations.

Feedforward-feedback control of an industrial multicomponent distillation column

Abstract The problem of regulating the distillate impurity content in a two-feed (paraffin-olefin) industrial multicomponent distillation column (iso-normal butane splitter) by manipulating the heat duty according to olefin-to-total feedflow load disturbance and tray temperature measurements is addressed. First, the detailed model-based nonlinear state-feedback robust control problem is addressed, identifying the solvability conditions and attainable closed-loop behavior. Then, the behavior of this controller is recovered with an output feedback PI temperature controller with dynamic feedforward setpoint compensation and antiwindup protection. The proposed approach is illustrated and tested through numerical simulations with a detailed model calibrated with industrial data, establishing the feasibility of improving the existing control scheme with PI temperature control plus manual setpoint compensation.

Monitoring of Batch Industrial Crystallization with Growth, Nucleation, and Agglomeration. Part 2: Structure Design for State Estimation with Secondary Measurements

This work investigates the design of alternative monitoring tools based on state estimators for industrial crystallization systems with nucleation, growth, and agglomeration kinetics. The estimation problem is regarded as a structure design problem where the estimation model and the set of innovated states have to be chosen; the estimator is driven by the available measurements of secondary variables. On the basis of Robust Exponential estimability arguments, it is found that the concentration is distinguishable with temperature and solid fraction measurements while the crystal size distribution (CSD) is not. Accordingly, a state estimator structure is selected such that (i) the concentration (and other distinguishable states) are innovated by means of the secondary measurements processed with the geometric estimator (GE), and (ii) the CSD is estimated by means of a rigorous model in open loop mode. The proposed estimator has been tested through simulations showing good performance in the case of mismatch in the initial conditions, parametric plant-model mismatch, and noisy measurements.

Monitoring of Batch Industrial Crystallization with Growth, Nucleation, and Agglomeration. Part 1: Modeling with Method of Characteristics

This paper develops a new simulation model for crystal size distribution dynamics in industrial batch crystallization. The work is motivated by the necessity of accurate prediction models for online monitoring purposes. The proposed numerical scheme is able to handle growth, nucleation, and agglomeration kinetics by means of the population balance equation and the method of characteristics. The former offers a detailed description of the solid phase evolution, while the latter provides an accurate and efficient numerical solution. In particular, the accuracy of the prediction of the agglomeration kinetics, which cannot be ignored in industrial crystallization, has been assessed by comparing it with solutions in the literature. The efficiency of the solution has been tested on a simulation of a seeded flash cooling batch process. Since the proposed numerical scheme can accurately simulate the system behavior more than hundred times faster than the batch duration, it is suitable for online applications such as process monitoring tools based on state estimators.

On the modeling of C7+ fractions with real components in plant simulations

Motivated by the need to reconstruct blends properties and their behavior in industrial distillation columns for control and monitoring purposes, the problem of the C7+ fractions modeling is addressed with substitute mixture of real components. The reliability of the proposed approach (comparable with the employment of hypothetical component as madein commercial simulators)is demonstrated in the context of the steady state simulation of an industrial debutanizer (located at SARLUX refinery, Italy).

Modelling of an Amine Based CO2 Absorption Plant: an Alternative Approach through the Identification of the Number of Stages

In the last decades, a huge number of studies on carbon dioxide removal have been presented in the literature. The non-equilibrium nature of the process involves an accurate definition of the parameters’ correlations and of the model’s assumptions. One of the most important assumptions when developing a process model is the definition of the number of discretization points. In this paper an alternative methodology has been presented for the definition of the number of stages of the absorption column in order to fit all the transport phenomena taking part in the carbon dioxide removal process. Then, it was developed a rigorous model of the absorption unit and finally implemented in Aspen Custom Modeler® environment to manage with a high level of complexity. The results obtained demonstrate that the number of stages is fundamental for the correct representation of the process and its incorrect evaluation could lead to huge errors in the prediction of the performance of the plant.