Prediction interval-based modelling of polymerization reactor: a new modelling strategy for chemical reactors

Precise and reliable modelling of polymerization reactor is challenging due to its complex reaction mechanism and non-linear nature. Researchers often make several assumptions when deriving theories and developing models for polymerization reactor. Therefore, traditional available models suffer from high prediction error. In contrast, data-driven modelling techniques provide a powerful framework to describe the dynamic behaviour of polymerization reactor. However, the traditional NN prediction performance is significantly dropped in the presence of polymerization process disturbances. Besides, uncertainty effects caused by disturbances present in reactor operation can be properly quantified through construction of prediction intervals (PIs) for model outputs. In this study, we propose and apply a PI-based neural network (PI-NN) model for the free radical polymerization system. This strategy avoids assumptions made in traditional modelling techniques for polymerization reactor system. Lower upper bound estimation (LUBE) method is used to develop PI-NN model for uncertainty quantification. To further improve the quality of model, a new method is proposed for aggregation of upper and lower bounds of PIs obtained from individual PI-NN models. Simulation results reveal that combined PI-NN performance is superior to those individual PI-NN models in terms of PI quality. Besides, constructed PIs are able to properly quantify effects of uncertainties in reactor operation, where these can be later used as part of the control process. © 2014 Taiwan Institute of Chemical Engineers.

Solving fuzzy programming with a consistent fuzzy number ranking

 Some illustrative examples are provided to identify the ineffective and unrealistic characteristics of existing approaches to solving fuzzy linear programming (FLP) problems (with single or multiple objectives). We point out the error in existing methods concerning the ranking of fuzzy numbers and thence suggest an effective method to solve the FLP. Based on the consistent centroid-based ranking of fuzzy numbers, the FLP problems are transformed into non-fuzzy single (or multiple) objective linear programming. Solutions of FLP are then crisp single or multiple objective programming problems, which can respectively be obtained by conventional methods.

The association between audit committee effectiveness and audit risk

Audit committees (ACs) are expected to play a key role in improving financial statement integrity and as a consequence reduce audit risk. Companies reporting conformity with regulations can have an AC that appears effective but is not actually effective in substance. We surveyed audit partners and managers to identify their indicators of actual AC effectiveness (auditor-chosen list). We hypothesize a negative association between AC effectiveness and audit risk, only when an auditor-chosen list, rather than extent of conformity with regulations, is used to measure effectiveness. Results support our expectations.

The need to revise the current methods to measure and assess static recrystallization behavior

Heterogeneous deformation developed during "static recrystallization (SRX) tests" poses serious questions about the validity of the conventional methods to measure softening fraction. The challenges to measure SRX and verify a proposed kinetic model of SRX are discussed and a least square technique is utilized to quantify the error in a proposed SRX kinetic model. This technique relies on an existing computational-experimental multi-layer formulation to account for the heterogeneity during the post interruption hot torsion deformation. The kinetics of static recrystallization for a type 304 austenitic stainless steel deformed at 900 °C and strain rate of 0.01s^-1 is characterized implementing the formulation. Minimizing the error between the measured and calculated torque-twist data, the parameters of the kinetic model and the flow behavior during the second hit are evaluated and compared with those obtained based on a conventional technique. Typical static recrystallization distributions in the test sample will be presented. It has been found that the major differences between the conventional and the presented technique results are due to the heterogeneous recrystallization in the cylindrical core of the specimen where the material is still partially recrystallized at the onset of the second hit deformation. For the investigated experimental conditions, the core is confined in the first two-thirds of the gauge radius, when the holding time is shorter than 50 s and the maximum pre-strain is about 0.5.

Labeled random finite sets and the bayes multi-target tracking filter

An analytic solution to the multi-target Bayes recursion known as the δ-Generalized Labeled Multi-Bernoulli ( δ-GLMB) filter has been recently proposed by Vo and Vo in [“Labeled Random Finite Sets and Multi-Object Conjugate Priors,” IEEE Trans. Signal Process., vol. 61, no. 13, pp. 3460-3475, 2014]. As a sequel to that paper, the present paper details efficient implementations of the δ-GLMB multi-target tracking filter. Each iteration of this filter involves an update operation and a prediction operation, both of which result in weighted sums of multi-target exponentials with intractably large number of terms. To truncate these sums, the ranked assignment and K-th shortest path algorithms are used in the update and prediction, respectively, to determine the most significant terms without exhaustively computing all of the terms. In addition, using tools derived from the same framework, such as probability hypothesis density filtering, we present inexpensive (relative to the δ-GLMB filter) look-ahead strategies to reduce the number of computations. Characterization of the L1-error in the multi-target density arising from the truncation is presented.