Please use this identifier to cite or link to this item:
|Title:||Hybrid Model for Monitoring and Optimization of Distillation Columns|
|Abstract:||Distillation columns are primary equipment in petrochemical, gas plants and refineries. Distillation columns energy consumption is estimated to be 40% of the total plant energy consumption. Optimization of distillation columns has potential for saving large amount of energy and contributes to plant wide optimization. Currently rigorous tray to tray models are used to describe columns separation with high accuracy. Rigorous distillation models are being used as part of design, optimization and as a part of on-line real-time optimization applications. Due to large number of nonlinear equations, rigorous distillation models are not suitable for inclusion in optimization models of complex plants (e.g. refineries), since they would make the model too large. For this reason, current practice in plant-wide optimization for planning or for scheduling is to include simplified model. Accuracy of these simplified models is significantly lower than the accuracy of the rigorous models, thereby causing discrepancy between production planning and RTO decisions. This work describes reduced size hybrid model of distillation columns, suitable for use as stand-alone tool for individual column or as part of a complete plant model, either for RTO or for production planning. Hybrid models are comprised of first principles material and energy balances and empirical models describing separation in the column. Hybrid models can be used for production planning, scheduling and optimization. In addition this work describes inferential model development for estimating streams purity using real time data. Inferential model eliminates the need for Gas Chromatography GC analyzers and can be used for monitoring and control purposes. Predictions from the models are sufficiently accurate and small size of the models enable significant reduction in size of the total plant models.|
|Appears in Collections:||Open Access Dissertations and Theses|
Files in This Item:
|Aljuhani_Fahad_M_201512_MASc.pdf||2.43 MB||Adobe PDF||View/Open|
Items in MacSphere are protected by copyright, with all rights reserved, unless otherwise indicated.