Peter Arva

Modified Gath--Geva clustering for fuzzy segmentation of multivariate time-series

Partitioning a time-series into internally homogeneous segments is an important data-mining problem. The changes of the variables of a multivariate time-series are usually vague and do not focus on any particular time point. Therefore, it is not practical to define crisp bounds of the segments. Although fuzzy clustering algorithms are widely used to group overlapping and vague objects, they cannot be directly applied to time-series segmentation, because the clusters need to be contiguous in time. This paper proposes a clustering algorithm for the simultaneous identification of local probabilistic principal component analysis (PPCA) models used to measure the homogeneity of the segments and fuzzy sets used to represent the segments in time. The algorithm favors contiguous clusters in time and is able to detect changes in the hidden structure of multivariate time-series. A fuzzy decision making algorithm based on a compatibility criteria of the clusters has been worked out to determine the required number of segments, while the required number of principal components are determined by the screeplots of the eigenvalues of the fuzzy covariance matrices. The application example shows that this new technique is a useful tool for the analysis of historical process data.

Process analysis and product quality estimation by self-organizing maps with an application to polyethylene production

The huge amount of data recorded by modern production systems definitely have the potential to provide information for product and process design, monitoring and control. This paper presents a soft-computing (SC)-based approach for the extraction of knowledge from the historical data of production. Since Self-Organizing Maps (SOM) provide compact representation of the data distribution, efficient process monitoring can be performed in the two-dimensional projection of the process variables. For the estimation of the product quality, multiple local linear models are identified, where the operating regimes of the local models are obtained by the Voronoi diagram of the prototype vectors of the SOM. The proposed approach is applied to the analysis of an industrial polyethylene plant. The detailed application study demonstrates that the SOM is very effective in the detection of the typical operating regions related to different product grades, and the model can be used to predict the product quality (melt index and density) based on measured process variables.

Fuzzy Clustering Based Segmentation of Time-Series

The segmentation of time-series is a constrained clustering problem: the data points should be grouped by their similarity, but with the constraint that all points in a cluster must come from successive time points. The changes of the variables of a time-series are usually vague and do not focused on any particular time point. Therefore it is not practical to define crisp bounds of the segments. Although fuzzy clustering algorithms are widely used to group overlapping and vague objects, they cannot be directly applied to time-series segmentation. This paper proposes a clustering algorithm for the simultaneous identification of fuzzy sets which represent the segments in time and the local PCA models used to measure the homogeneity of the segments. The algorithm is applied to the monitoring of the production of high-density polyethylene.

Monitoring process transitions by Kalman filtering and time-series segmentation

Abstract The analysis of historical process data of technological systems plays important role in process monitoring, modelling and control. Time-series segmentation algorithms are often used to detect homogenous periods of operation-based on input–output process data. However, historical process data alone may not be sufficient for the monitoring of complex processes. This paper incorporates the first-principle model of the process into the segmentation algorithm. The key idea is to use a model-based non-linear state-estimation algorithm to detect the changes in the correlation among the state-variables. The homogeneity of the time-series segments is measured using a PCA similarity factor calculated from the covariance matrices given by the state-estimation algorithm. The whole approach is applied to the monitoring of an industrial high-density polyethylene plant.

Semi-mechanistic models for state-estimation - Soft sensor for polymer melt index prediction

Nonlinear state estimation is a useful approach to the monitoring of industrial (polymerization) processes. This paper investigates how this approach can be followed to the development of a soft sensor of the product quality (melt index). The bottleneck of the successful application of advanced state estimation algorithms is the identification of models that can accurately describe the process. This paper presents a semi-mechanistic modeling approach where neural networks describe the unknown phenomena of the system that cannot be formulated by prior knowledge based differential equations. Since in the presented semi-mechanistic model structure the neural network is a part of a nonlinear algebraic-differential equation set, there are no available direct input-output data to train the weights of the network. To handle this problem in this paper a simple, yet practically useful spline-smoothing based technique has been used. The results show that the developed semi-mechanistic model can be efficiently used for on-line state estimation.

Process-data-warehousing-based operator support system for complex production technologies

Process manufacturing is increasingly being driven by market forces, customer needs, and perceptions, resulting in more and more complex multiproduct manufacturing technologies. The increasing automation and tighter quality constraints related to these processes make the operators job more and more difficult. This makes decision support systems (DSSs) for the operator more important than ever before. A traditional operator support system (OSS) focuses only on specific tasks that are performed. In the case of complex processes, the design of an integrated information system is extremely important. The proposed data-warehouse-based OSS makes possible linking complex and isolated production units based on the integration of the heterogenous information collected from the production units of a complex production process. The developed OSS is based on a data warehouse designed by following the proposed focus-on-process data-warehouse-design approach, which means stronger focus on the material and information flow through the entire enterprise. The resulting OSS follows the process through the organization instead of focusing separate tasks of the isolated process units. For human-computer interaction, front-end tools have been worked out, where exploratory data analysis and advanced multivariate statistical models are applied to extract the most informative features of the operation of the technology. The concept is illustrated by an industrial case study, where the OSS is designed for the monitoring and control of a high-density polyethylene (HDPE) plant

Synthesis of engineering objects by recursive fuzzy valuation of crisp combinations

Abstract Different types of fuzzy information in the cybernetical system of engineering synthesis are analysed. A family of relational algebrae is introduced for the representation of possible variants. Fuzzy valuated relational algebrae are defined for the formalization of informational transfer accompanying the composition and decomposition of combinations. A recursive algorithm comprising the selection of an appropriate maximum combination, the evaluation of the selected object and the consecutive valuation of the containing minimum elements is constructed.

A new route for the synthesis of amphiphilic and water-soluble ligands: mono- and di-tertiary phosphines having an alkylene sulfate chain

Cyclic sulfates react with LiPPh2 to form a series of new amphiphilic or water-soluble ligands: monotertiary phosphines with one or two alkylene sulfate chains and ditertiary phosphines with one or two hydrophilic tails attached to bridgehead carbon atom; the application of zwitterionic RhI complexes (sulfatephos)2Rh(cod) and (sulfatediphos)Rh(cod) in liquid biphasic catalysis has been demonstrated for the hydroformylation of styrene and oct-1-ene.

Effects of catalyst activity profiles on the scale-up of polymerization reactors

Abstract The aim of this paper is to demonstrate a method that is able to transform information observed by laboratory experiments with catalysts possessing changing activity to continuous industrial reactor. The method is based on the fact that the change of catalyst activity and quality properties of the polymer can be determined as functions of the catalyst age based on laboratory polymerization experiments carried out for different time periods. This information integrated with the residence time distribution of the industrial reactor can be used to estimate the average properties of the polymer powder produced in the continuous industrial reactor. The method is demonstrated in case of ethylene polymerization.

Effects of catalyst activity profiles on the operating conditions of an industrial polymerization reactor

Abstract The aim of this paper is to analyze how different catalyst activity profiles influence the operating strategies of industrial reactors. Based on this analysis a method that can be used to transform information given by laboratory reactor experiments into a form which can be used to estimate the productivity of the catalyst and the quality properties of polymers under industrial conditions is proposed. The whole approach is demonstrated in case of the production of high-density polyethylene production.