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Vienna, Austria

Grosswindhager S.,Vienna University of Technology | Voigt A.,VOIGTWIPP Engineers GmbH | Kozek M.,Vienna University of Technology
Proceedings of 2012 International Conference on Modelling, Identification and Control, ICMIC 2012 | Year: 2012

This paper presents a concept for controlling the supply temperature in district heating networks (DHN) using model predictive control. Due to the inherent nonlinearity in the response characteristics caused by varying flow rates the use of Fuzzy Direct Matrix Control (DMC) is proposed. The fuzzy regions of the local Finite Impulse Response (FIR) models are determined by an axis-orthogonal, incremental partitioning scheme. It is demonstrated that the Fuzzy DMC performs well for the case study considered. In addition, different set point strategies are applied and the results are evaluated with respect to operational cost. In this context it is shown that the trade-off between pumping and heat loss cost plays an important role in minimizing overall cost. © 2012 Huazhong Univ of Sci & Tec. Source

Wenger M.,Vienna University of Technology | Hametner R.,Vienna University of Technology | Zoitl A.,Vienna University of Technology | Voigt A.,VOIGTWIPP Engineers GmbH
IEEE International Conference on Emerging Technologies and Factory Automation, ETFA | Year: 2011

Operating industrial plants at their maximum energy and resources efficiency often means to operate them close at their design limits. Due to strict safety and process parameter constraints, it is challenging to control them close to the optimal setpoints. Currently process performance improvement projects are realized by application of PC based model predictive controllers as add-ons. Applications of up-to-date optimizing control algorithms are characterized by high requirements on software architecture, numerical computing power and realtime behavior. These requirements are not accomplishable running optimizing control algorithms on Programmable Logic Controllers (PLCs) by current available software architectures. Therefore the aim of the Embedded Energy Efficiency Industrial Controller Platform (E3ICP) project is the development of an infrastructure for running state-of-the-art model predictive controllers on industrial embedded PLCs. © 2011 IEEE. Source

Grosswindhager S.,Vienna University of Technology | Voigt A.,VOIGTWIPP Engineers GmbH | Kozek M.,Vienna University of Technology
Proceedings of the IASTED International Conference on Modelling and Simulation | Year: 2011

A mathematical physical model for dynamic simulation of flow and temperature in district heating networks (DHN) is proposed. The network structure is described by means of a graph-theoretical approach where the network elements are pipe sections, consumers and heat sources. The governing equations for hydraulic flows and heat distribution through pipe networks are presented. In addition, proper orthogonal decomposition (POD) is outlined and applied for obtaining a reduced model representation of the hydraulic equations. It is shown that the proposed methods are suitable for predicting flow and temperature values at each consumer with minimal average error and can therefore be used as a conceptual tool for operational optimization of district heating networks. Source

Voglauer B.,Lenzing AG | Voigt A.,VOIGTWIPP Engineers GmbH
IFAC Proceedings Volumes (IFAC-PapersOnline) | Year: 2011

For a large scale product stock level control, an automatic Model Predictive Controller (MPC) was implemented. Due to the stable operation of a heavy delayed and disturbed process a significant optimization of operational performance was achieved. The closed loop control system needs to deal with large time delays of actuators (more than 2 hours), semi-parallel batch in between production steps and uncertain and inaccurate predicted (expected) disturbance trajectories. The optimal production rate to keep the product stock close to the optimal desired level for maximum inventory is determined by introducing a so called "Expected Disturbance Value" (EDV) and additional "Unexpected Disturbance Value" (UEDV) as input for the predictive controller. The controller design is based on a validated 1st principle model of the process. The resulting closed loop performance of the Dynamic Matrix Controller showed to be outstanding due to the time optimal production control, compensating expected and unexpected disturbance, retaining the process close to the setpoint in many practical production situations despite unstable process behaviour. Automatic handling of various absolute - and rate constraints on manipulated and controlled variables have been implemented and a robust controller tuning was achieved. © 2011 IFAC. Source

Schuster A.,Vienna University of Technology | Kozek M.,Vienna University of Technology | Voglauer B.,Lenzing AG | Voigt A.,VOIGTWIPP Engineers GmbH
Mathematical and Computer Modelling of Dynamical Systems | Year: 2012

A dynamic model of a through-air-drying process for viscose staple fibres is presented in this article. In this process fibres formed to a porous web are transported through a convective dryer that consists of numerous rotating drum sieves. Finally, the fibres pass through two remoistening drums. The structure of the model is modular and scalable. On applying spatial discretization the originally partial differential system equations (conservation of mass and energy) turn into a system of ordinary differential equations. Drying rates and heat transfer rates are calculated using phenomenological equations for heat and mass transfer. Kinetics of drying is separated into three phases, where viscose fibres are hygroscopic. The process model is able to simulate transient behaviour of the dryer like changes of the incoming fibre moisture, changes of the drying air temperature and humidity and changes of the thickness of fibre layer on the drums. Stationary validation of the longitudinal fibre moisture distribution along the dryer shows good accordance with measurement data at different operating points, for example, different temperature profiles. Dynamic data like temperature transients are utilized for both model fitting and validation of the dynamic model. For the remoistening process and disturbance behaviour concerning the thickness of the fibre web, black box models have been identified. Results of a successful application of the model in a predictive control algorithm are shown. © 2012 Copyright Taylor and Francis Group, LLC. Source

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