DATA-BASED APPROACHES IMPROVING THE ACCURACY OF LIFE CYCLE PROFIT MODELS

Abstract

Changes in operational environment of the process industry such as decreasing selling prices, increased competition between companies and new legislation, set requirements for performance and effectiveness of the industrial production lines and processes. In this study, a life cycle profit model of a pulp process was constructed. This model was based on different kind of process information like consumption and production levels of material and energy flows in unit processes. However, all the information needed for correlation calculations was not directly provided by information systems of the mill. Therefore we used correlation analyses and self-organizing map to determine missing dependencies between process variables. These data-based approaches were tested using an example, in which factors affecting the alkaline chemical consumption in the bleaching stage were solved. The results show that these methods can be successfully applied to improve the accuracy of life cycle profit models. Copyright © 2002 IFAC