Year 2008
Francis Quaicoe

Improving process economic performance using realtime optimization

During the last decades the competition in the process industry has grown partly due to globalization and company mergers. Consequently, the size of production plants have increased and many industrial process units have reached a critical level of complexity. Real time optimization methods have been developed to meet the challenge of operation and control of these complex plants. Real Time Optimization (RTO) is an approach for increasing the economic performance of a process by continuously adjusting the process variables in order to obtain the best operating conditions that increase profitability and minimize the operating costs of the process. The goal of plant-wide real time optimization is thus to determine setpoints for all supervisory and stabilizing level controllers in the plant in order to maximize the economic performance of the plant in the face of price variations of raw materials and end products.

The aim of the literature part of the thesis is to review the applications of real time optimization and to classify these applications based on the optimization methods used. In the literature part, the description of the structure and components of the RTO systems are presented, the various RTO schemes are reported, case studies on the applications of RTO in the process industry are presented, and finally the applications of RTO reviewed.

The aim of the experimental part of the thesis is to study the Supply Chain Management (SCM) forecasting models and optimization in the SAP Advanced Planner and Optimizer (APO). This was accomplished by using the SAP APO planning functions in a simulated metal processing company to create a forecast for a specific metal product in a distribution center. An additional goal was to prepare an instruction manual to be used as teaching material for SAP APO related scenarios. The various forecasting methods and models in SAP APO are presented. The results from the simulations from the supply chain planning are also presented and discussed. In addition, the results obtained from the simulations were verified using the analytical calculations confirming the efficiency of the forecasting models presented in SAP APO.

This info last modified 27 Apr 2024 by Jerri Kämpe-Hellenius