Integrated Process and Control System Model for Product Quality Control - Application to a Polypropylene Plant

In the near future of chemical industry, communication between design, manufacturing, marketing and management should be centered on modeling and simulation, which could integrate the whole product and process development chains, process units and subdivisions of the company. Solutions to this topic often set aside one or more components from product, process and control models, hence, as a novel know-how, an information system methodology was developed. Its structure integrates models of these components with a process data warehouse where integration includes information, location, application and time integrity. It supports complex engineering tasks related to analysis of system performance, process optimization, operator training systems (OTS), decision support systems (DSS), reverse engineering or software sensors (soft-sensors). The case study in this article presents the application of the proposed methodology for product quality soft-sensor application by on-line melt index prediction of an operating polymerization technology.

[1]  Efstratios N. Pistikopoulos,et al.  Recent advances in optimization-based simultaneous process and control design , 2004, Comput. Chem. Eng..

[2]  Wolfgang Marquardt,et al.  Trends in computer-aided process modeling , 1996 .

[3]  M. Morari,et al.  Implications of large RGA elements on control performance , 1987 .

[4]  Yong-Zai Lu Industrial intelligent control: fundamentals and applications , 1996 .

[5]  Masahiro Ohshima,et al.  Quality control of polymer production processes , 2000 .

[6]  M. Stadtherr Large-Scale Process Simulation and Optimization in a High Performance Computing Environment , 1997 .

[7]  Esa Alhoniemi,et al.  Self-organizing map in Matlab: the SOM Toolbox , 1999 .

[8]  Guido Dünnebier,et al.  Life Cycle Modelling in the chemical industries: Is there any reuse of models in automation and control? , 2006 .

[9]  Mark A. Kramer,et al.  Modeling chemical processes using prior knowledge and neural networks , 1994 .

[10]  J. V. Crivello,et al.  Encyclopedia of engineering materials‐part A: polymer science and technology, Nicholas P. Cheremisinoff, ed., Marcel Dekker, New York, 1988, 783 pp. Price: $185.00. , 1989 .

[11]  Marcelo Embiruçu,et al.  Modeling of end-use properties of poly(propylene/ethylene) resins , 2001 .

[12]  János Abonyi,et al.  Process analysis and product quality estimation by Self-Organizing Maps with an application to polyethylene production , 2003, Comput. Ind..

[13]  Rimvydas Simutis,et al.  Bioprocess optimization and control: Application of hybrid modelling , 1994 .

[14]  Geoff Barton,et al.  Interaction between process design and process control: economic analysis of process dynamics , 1991 .

[15]  Brian Roffel,et al.  Non-linear model based control of a propylene polymerization reactor , 2007 .

[16]  Lyle H. Ungar,et al.  A hybrid neural network‐first principles approach to process modeling , 1992 .

[17]  James Krieger Process Simulation Seen As Pivotal In Corporate Information Flow , 1995 .

[18]  Youxian Sun,et al.  Melt index prediction by neural networks based on independent component analysis and multi-scale analysis , 2006, Neurocomputing.