A multimodal modeling and monitoring approach based on maximum likelihood principal component analysis and a component‐wise identification of operating modes are presented. Analyzing each principal component individually allows separating components describing the variation within the individual modes from those capturing variation which the modes commonly share. On the basis of the former set, a Gaussian mixture model produces a statistical fingerprint that describes the production modes. The advantage of the component‐wise analysis is a simple identification of the mixture model parameters, which does not rely on the computationally cumbersome expectation maximization. The proposed method diagnoses abnormal process conditions by defining statistics relating to the components describing (1) between‐cluster variation, (2) within cluster variation, and (3) model residuals. The article demonstrates the benefits of this approach over existing work by an application to a continuous stirred tank reactor (CSTR) simulator and the analysis of recorded data from a furnace and a chemical reaction process. © 2012 American Institute of Chemical Engineers AIChE J, 59: 1557–1569, 2013

This paper presents a model to describe the dynamic behavior of bulk propylene polymerizations performed in a continuous reactor, assuming that three catalysts can be used. The model takes into account mass, energy, and statistical moment balances used to estimate the final polymer properties, including the melt index (MI) and extractables in xylene (XS). As the catalysts present different sensitivities to hydrogen, the proposed controller scheme considers that the MI control loop must be reconfigured in line and in real time when the catalysts are exchanged. Simulation results indicate that the reactor stability is maintained during specified production programs and that the desired final properties can be achieved satisfactorily. It is shown that control reconfiguration represents an alternative to overcome existing operation limits and recover operability when multiple catalysts are used at plant site.

Polymeric materials are present in various industrial sectors and in daily life, presenting advantages such as low cost and durability. Several processes for manufacturing have been developed. To achieve safety and operational goals measurement methods for proper process monitoring and effective control are needed. However, in real polymer plants, measuring devices are subject to uncertainties and are not always available. Hence, this paper proposes a virtual sensor scheme based on a particle filter and artificial neural network (ANN) that is applied to a simulated polymerization reactor. This scheme reduces uncertainties and enables the observation of latent variables. The ANN is also used for predicting the final properties of the polymer. The goal is to provide controllers with more complete and improved information. The results show that the virtual sensor scheme improves the process control, providing accurate estimates and action times that are consistent with industrial sampling intervals, which highlights its potential for practical applications.

The present work discusses the mathematical modeling and the control design of the steam reforming of natural gas. The developed model comprises a set of differential and algebraic equations, based on energy and mass balances for reactions performed in a fixed catalyst bed reactor, where natural gas and water are transformed mainly into a mixture of hydrogen and carbon oxides. Normally, after removal of hydrogen and purification of the output stream, the residual gas can be also directed to the furnace to provide heat to the reactor. This is a common practice in industrial sites in order to minimize losses. As the global reactions are exothermic, the reactor temperature may reach prohibitive high values, leading to coke formation and catalyst deactivation. For this reason, a control scheme is proposed to account for regulation of the reactor outlet temperature, using residual and fuel gas streams as manipulated variables, allowing the analysis of effect of several process variables in reactor performance. The obtained results indicate that the proposed mathematical model can accurately represent the steam reforming process and that the proposed control scheme can allow for efficient operation of the reactor, even when the residual gas stream is not sufficient to reach the desired operation temperature.

W ith the aid of various complementary methods of microstructural analysis, the precipitation, grain growth, and secondary recrystallisation behaviour of an 15Cr-15Ni-1•2Mo-T i-B (wt-%) austenitic stainless steel were studied over prolonged periods of time in the temperature range 600-1300°C. T he experimental results showed that several types of precipitates were present in the material, and that the dissolution temperatures of each of these correlated with the type and extent of grain growth which was observed. It was, therefore, concluded that in the present study secondary recrystallisation was caused directly by the interaction of precipitates with grain boundaries. Furthermore, secondary recrystallisation produced a strong, predominantly {122} 012 texture which has not previously been reported.MST /4235At the time the work was carried out Professor Padilha and Professor Randle were in the

No abstract

No abstract

The continuing development of stainless steels has resulted in complex steel compositions with substantial amounts of alloying elements. The benefits of such additions invariably come attached to unavoidable disadvantages. One of the most critical item is the potential microstructural instability of the material. Alloying elements may be in a supersaturated solid solution, in which the precipitation of carbides, nitrides, borides and intermetallic phases occurs in a wide range of temperatures. In order to dissolve the mentioned precipitates, solution annealing is commonly performed. However, at the temperature range in which this treatment is carried out, the onset of abnormal grain growth can occur. The interaction between the dissolution of these second-phase particles and the occurrence of abnormal grain growth is investigated in this work. This study also shows that the thermodynamics and the kinetics of dissolution of precipitates may be used to predict whether abnormal grain growth takes place