Design of state estimators for the inferential control of an industrial distillation column

Application of Artificial Neural Networks onBuilding Energy Estimation Altun, Murat; Ersöz, Ahmet Bahaddin; Akçamete Güngör, Aslı; Pekcan, Onur (null; 2017-12-11) Abstract -The decision makers in the building design stage need to give more importance for energy efficient solutions to improve the performance the building in its operational phase. In practice, they are expected to generate alternative designs in the early design stage and use complicated building simulation tools to estimate building energy use of these alternatives more accurately; however, this is a time-consuming procedure and it is difficult to evaluate different alternatives in the limited time of...
Optimization of well placement geothermal reservoirs using artificial intelligence Akın, Serhat; Kök, Mustafa Verşan (2010-06-01) This research proposes a framework for determining the optimum location of an injection well using an inference method, artificial neural networks and a search algorithm to create a search space and locate the global maxima. A complex carbonate geothermal reservoir (Kizildere Geothermal field, Turkey) production history is used to evaluate the proposed framework. Neural networks are used as a tool to replicate the behavior of commercial simulators, by capturing the response of the field given a limited numb...
Prediction of Highway Bridge Performance by Artificial Neural Networks and Genetic Algorithms Tokdemir, Onur Behzat; MOHAMMADİ, Jamshid (2000-09-20) Bridge management systems (BMS) comprise various techniques need to help make decisions on the type of works that need to be performed to maintain the serviceability of a bridge and to extend its useful life. These decisions rely on current and future bridge conditions therefore it is essential for a BMS to accurately predict the future bridge performance, or in other words to assess the extent of bridge deterioration. Numerous deterioration models are reported in the literature. Most of these methods were ...
Development of algorithms and software for forecasting, nowcasting and variability of TEC Tulunay, E; Senalp, ET; Cander, LR; Tulunay, YK; Bilge, AH; Mizrahi, E; Kouris, SS; Jakowski, N (2004-01-01) Total Electron Content (TEC) is an important characteristic of the ionosphere relevant to communications. Unpredictable variability of the ionospheric parameters due to various disturbances limits the efficiencies of communications, radar and navigation systems. Therefore forecasting and nowcasting of TEC are important in the planning and operation of Earth-space and satellite-to-satellite communication systems. Near-Earth space processes are complex being highly nonlinear and time varying with random varia...
Use of artificial neural networks for the prediction of time-dependent air speed variation in metro stations KOC, Gencer; Sert, Cüneyt; Albayrak, Kahraman (2018-09-01) In this study, the time-dependent, induced air speeds at critical sections of underground metro stations are assessed using a novel one-dimensional data-driven approach. For this purpose, three artificial neural networks are used, each trained for the most basic configuration of a single train moving in a single tunnel. The first two are trained to provide the maximum and time averaged values of the induced air speeds while the train is moving inside the approach tunnel or the station. The third one is used...

Citation Formats

A. BAHAR, E. GUNER, C. Özgen, and U. Halıcı, “Design of state estimators for the inferential control of an industrial distillation column,” 2006, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/54790.