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Go to Editorial ManagerThe oil industry has a direct impact on the economic feasibility of other sectors and is considered to be the most important energy source used to turn the wheels of other industries. Therefore, it was necessary to pay attention and continuously develop this industry, to find the best modern techniques for designing, pre-commissioning and controlling process, to improve efficiency, preserve energy and achieve the highest production of costly components with the highest purity of the product. This study aims to provide a literary analysis of the stages of development and progress of the dynamics and control of the petroleum industry, in particular the distillation column, because it is multivariable with high interaction between control cycles, nonlinear behaviour and large gains. Control processes have undergone many developments and modernizations to achieve the best results. Various control methods have been used, ranging from simple proportional-integral-derivative controller (PID) to advanced control strategies such as model predictive control (MPC), multivariate model predictive control (MMPC), fuzzy logic control (FLC), quadratic dynamic matrix control (QDMC), artificial neural network control (ANN) and other advanced control techniques. The authors concluded from the review that the advanced control strategies superior than the conventional methods.
Distillation columns are among the most critical units in chemical processes, especially in oil refineries. This research aims to develop a mathematical model for a nonlinear multicomponent naphtha distillation column using real experimental data. The naphtha distillation column used in this research consists of a column with a diameter of 2.2 m and a height of 16.6 m, containing 20 trays of type (valve tray). The process currently operates in Al-Dora Refinery as part of the Midland Refinery Company in eastern Baghdad, Iraq. Four manipulated variables (reflux flow rate, reboiler heat duty, light compound fraction in the feed, and feed flow rate) were tested for their impact on four control variables (light compound fraction in the top product, light compound fraction in the bottom product, amount of top product, and amount of bottom product). A 4x4 transfer function is produced by representing the obtained dynamic models for the variables under study using various second-order models (having different damping factors) with dead time.