Control
Mir Mohammad Khalilipour; Farhad Shahraki; Jafar Sadeghi; kiyanoosh Razzaghi
Abstract
Objective: The objective of this research is to optimize the crude distillation unit (CDU) in oil refineries by reducing energy consumption and improving operational efficiency through the application of a Proportional-Integral-Plus (PIP) control system within a Non-Minimal State Space (NMSS) framework. ...
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Objective: The objective of this research is to optimize the crude distillation unit (CDU) in oil refineries by reducing energy consumption and improving operational efficiency through the application of a Proportional-Integral-Plus (PIP) control system within a Non-Minimal State Space (NMSS) framework. Material and Method: Simulations of the CDU were carried out using Aspen Plus for modeling the distillation process and MATLAB for implementing the PIP control structure. The controller was tuned by an economic cost function, optimizing key parameters such as furnace duty, side-draw rates, and condenser heat removal. The PIP control system was compared to traditional control methods, with performance evaluated under various disturbances, including feed rate, temperature, and composition changes. Results: The PIP control strategy significantly improved the CDU’s performance, reducing operating costs by up to 100% compared to traditional control methods optimized by the Integral of Time-weighted Absolute Error (ITAE). The PIP system demonstrated superior disturbance handling and energy efficiency while maintaining product quality. Conclusion: The findings indicate that the PIP control system is a highly effective tool for optimizing energy consumption and process stability in modern refineries, especially under fluctuating operational conditions. Its application could lead to substantial cost savings and improved efficiency in CDU operations.
Control
Fereshte Tavakoli Dastjerd; Farhad Shahraki; Jafar Sadeghi; Mir Mohammad Khalilipour; Bahareh Bidar
Abstract
The design and development of data-driven soft sensors is important to predict the concentration of perilous pollutants in industry effluents to protect environmental health. The aim of this research is to design a tail gas quality warning system in the sulfur recovery unit (SRU) based on H2S and SO2 ...
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The design and development of data-driven soft sensors is important to predict the concentration of perilous pollutants in industry effluents to protect environmental health. The aim of this research is to design a tail gas quality warning system in the sulfur recovery unit (SRU) based on H2S and SO2 concentration soft sensor utilizing multi-state-dependent modeling method. The SRU in the petrochemical plant of ERG PETROLI, located in Italy, is selected as the study region for implementation of the warning system. The generalized random walk- multi-state-dependent parameter method (GRW-MSDP) for soft sensor design is proposed. The GRW-MSDP estimation system is based on multi-state-dependent modeling method by utilizing the extension of the generalized random walk model. The method has been developed by utilizing the algorithms of extended Kalman filter (EKF) and fixed interval smoothing (FIS). The quality warning system of tail gas based on the estimated concentrations of SO2 and H2S sends instructions to adjust the ratio of air to feed flow in the reaction furnace of SRU by plant operators. The results indicate that the proposed estimation system can be efficient in dealing with process non-linearity, high-dimensional values, and random missing data. The comparative discussion of GRW-MSDP technique performance with different soft sensing methods shows that the designed soft sensor model is more reliable with fewer input variables, lower complexity and relatively higher prediction accuracy. Furthermore, the great efficiency of the designed quality warning system is obvious from the good accuracy and F1-score values of 99.4% and 0.8951, respectively.
Control
Bahareh Bidar; Mir Mohammad Khalilipour; Farhad Shahraki; Jafar Sadeghi
Abstract
The changes in the crude oil flow rate to an atmospheric distillation unit can influence the quality of the products. This paper presents a modification method for soft sensing model including an update term, which makes it compatible with industrial variations. A modified soft sensing structure is adopted ...
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The changes in the crude oil flow rate to an atmospheric distillation unit can influence the quality of the products. This paper presents a modification method for soft sensing model including an update term, which makes it compatible with industrial variations. A modified soft sensing structure is adopted using lookup table (LUT) method where steady-state soft sensing models are performed. The steady-state soft sensing models is proposed based on local instrumental variable (LIV) technique for an industrial atmospheric distillation unit (ADU) at Shiraz refinery, Iran. The LIV-based soft sensors are utilized tray temperature measurements to monitor ASTM-D86 index of side products for nominal flow rate (60,000bbl/day). Lookup tables have been developed based on the difference between the predicted values of ASTM-D86 index and corresponding simulation values to make update terms in different feed flow rates. The results present improvement in the predictions of LIV-based soft sensors as well as acceptable control performance in feed flow rate variations. The comparison of soft sensing results with/without lookup tables demonstrates that the proposed update term helps to predict product quality more precisely and is suitable for advanced monitoring scheme due to no complexity and low computational time.
Control
Mir Mohammad Khalilipour; Farhad Shahraki; Jafar Sadeghi; kiyanoosh Razzaghi
Abstract
The paper presents the computer simulation of a multivariate control algorithm. Industrial relevance is given as the problem is derived from a real plant, an industrial athmospheric distillation column at Shiraz refinery. The control performance of the industrial column using relative gain array (RGA) ...
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The paper presents the computer simulation of a multivariate control algorithm. Industrial relevance is given as the problem is derived from a real plant, an industrial athmospheric distillation column at Shiraz refinery. The control performance of the industrial column using relative gain array (RGA) and relative normalized gain array (RNGA) configurations has been examined for nominal operating capacity and 10% increase in capacity. The results show that RNGA structure remains stable and has an acceptable control performance for both nominal and increased capacity. RGA structure unlike RNGA changes at the increased capacity and has some difficulties in crude oil switch scenario. It has also been shown that the furnace duty will increase considerably in the case of using RGA for operating at increased capacity. The results indicate that RNGA represents better decision for loop pairing and control structure selection and can solve control issues of industrial distillation column specifically for uncertain feed condition.