Data-driven Development and Maintenance of Soft-Sensors

Product quality related process variables have significant role in advanced process control (APC). Online analyzers and software sensors can provide accurate and timely information for APC systems. In this paper we give an overview of data based soft-sensor development. We show that soft-sensor models of APC require maintenance and demonstrate that statistical quality control (SQC) techniques can be effectively used to automatize the related fault detection tasks.

János Abonyi, Barbara Farsang and Tibor KulcsarData-driven Development and Maintenance of Soft-Sensors, IEEE 12th International Symposium on Applied Machine Intelligense and InformaticsHerlany, Slovakia, 2014, 239-244.

Statistical Process Control Based Performance Evaluation Of On-Line Analysers

On-line analyzers can provide accurate and timely information for process control and monitoring. Statistical Process Control (SPC) techniques can be effectively utilized to support the development and maintenance of these tools. The D6299- 10 ASTM standard details how on-line analyzers should be validated. The applicability of this standard is demonstrated through the analysis of industrial data collected from an on-line gas chromatograph. The results confirm that automatized SPC can effectively improve the reliability of advanced process control systems.

Tibor Kulcsár, János Abonyi, Statistical Process ControlBased Performance Evaluation Of On-Line Analysers, Hungarian Journal Of Industry And Chemistry, Veszprém, 41, 77-82, 2013.

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.

B. Feil, J. Abonyi, P. Pach, S. Nemeth, P. Arva, M. Nemeth, G. Nagy, Semi-mechanistic models for State-Estimation – Soft sensor for polymer melt index prediction,  L. Rutkowski et al. (Eds.): ICAISC 2004, LNAI 3070, pp. 1111–1117, 2004.

Effects of Catalyst Activity Profiles on the Operating Conditions of an Industrial Polymerization Reactor

The aim of this paper is to analyze how different catalyst activity profiles influence the operating strategies of industrial polymerization reactors. Since the catalyst activity profiles may change considerably in the range of the residence-time of the reactor, the properties of the produced polymer powder are the averages of ones produced by catalyst particles with various activities. Hence, the residence-time distribution of the polymer phase has an effect on the product properties as well. Based on these phenomena a new method proposed to transform information from laboratory reactor into a form which can be used to estimate the operation condition of industrial reactor. The whole approach is demonstrated in case of the production of high-density polyethylene.

S. Németh, B. Feil, P. Árva, J. Abonyi, Effects of catalyst activity profiles on the operating conditions of an industrial polymerization reactor, Polymer-plastics technology and engineering, pp. 1301-1306. (2006)