Optimized Compressive Strength Modeling of Mixed Aggregate in Solid Sandcrete Production
Future Directions of Production Planning and Optimized ...
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Future Directions of P roduction Planning and O ptimized Energy and Process Industries This Project has received funding from the European Union’s Horizon 2020 Research and Innovation Program Under Grant Agreement nº 723523 Oil and Gas Demonstrator, TUPRAS, Turkey Aim: Energy efficient and higher quality diesel production in TUPRAS, Izmit Refinery. Diesel is produced in diesel hydroprocessing unit of the refinery. The unit has two reactors with total five catalyst beds. Three of these are for hydrodesulfurization and two of them are for hydrocracking. Three columns are used to separate LPG, LSRN, HSRN and diesel products. NIR soft sensor The NIR soft sensor is developed for predicting 17 different properties of the feed simultaneously. The properties in question are: • API • Density (kg/m 3 ) • Nitrogen (mg/kg) • Sulphur (%) • Distillation curve including T95. The distillation curve consists of 13 values where each point is predicted independently from each other. Figure 1. Estimation of feed properties based on NIR measurements Figure 2. Estimation of feed properties based on NIR measurements All models for all constituents are made with the same multivariate regression model technique, PLS (Partial Least Squares). Current state: The model development was done with collecting samples from the feed line and analyzing it with an existing offline NIR in the Tupras R&D lab. Online NIR purchasing and implementation is expected in 6 months. Expected outcome: NIR is used to detect T95 value of the feed much faster than the traditional method in use, ASTM D86. This feature enables MPC to have a feed forward structure that helps reducing the energy consumption. Table 2. RMSE of the LOO cross-validation result for labelled data (a) Diesel T95 (b) Diesel Sulfur (c) HSRN T95 (d) LSRN T95 Figure 9. The learning curves for the four different output variables, showing negative mean squared error (y-axis) vs. the number training examples (days, x-axis) used for training. The upper blue curve shows the learning curve for training data and the lower orange curve shows the learning curve for the test data. MPC structure As a part of decision support system an MPC is built. For the MPC structure step test is used to build transfer functions of the plant. MPC model is on Jmodelica environment. Integration of the models Physics based models Feed characterization Continuous lumping approach with 161 pseudo-components is used to describe the feed. The feed lump covers the boiling range of -250 o C – 550 o C. Consecutive PCs have 5 o C TBP difference in between. Sulfur distribution is bell shaped and this is proved by the laboratory analysis of the feed mixture. Hydrodesulfurization model Steady-state sulphur removal reactor model is constructed and tested with an NIR input. Figure 3. The sulphur removal along 3 HDS reactor beds Hydrocracking model Steady-state hydrocracking model is constructed and simulated with the output of the hydrodesulfurization model. Figure 4. Cracking reactions along 2 HC reactor beds Model vs. Data Figure 7. PC distribution at the exit of last reactor bed Figure 8. Comparison of bed exit temperatures Table 1. Comparison of important product properties Current state: Reactor models are in Matlab environment and their steady state version is tested with the NIR input. The separation columns are also steady state but they are on Aspen HYSYS and to test the compatibility, Matlab output is taken as an input to this model. Expected improvement: For both reaction and separation parts of the system the dynamic models will be developed. Separation model Column models are created on ASPEN HYSYS and the feed input is taken from the reactor simulation output. 0 0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 -300 -100 100 300 500 700 Weight fraction of pseudo-components TBP ( o C) HC reactions HC inlet HC 1st bed exit HC 2nd bed exit 0 0.005 0.01 0.015 0.02 0.025 0.03 0.035 -300 -100 100 300 500 700 Weight fraction of pseudo-components TBP ( o C) PC distribution data model 650 655 660 665 670 675 680 685 0 5 10 15 20 25 Bed exit temperatures ( o C) Bed length (m) Bed exit temperatures data model Decision support system Data driven models Case 1 - Quality prediction Data: 17 feed properties and process data Prediction: Diesel product T95, Diesel product sulphur content, HSRN product T95, and LSRN product T95 Case 2 - Optimization Data: 17 feed properties, remaining uncontrollable process parameters, and the targeted product quality values Optimization: Manipulated process data (reactor inlet temperatures, temperature difference in the reactor beds, quench H 2 flow etc.) Case 3 - Long-term monitoring Data: Time series analysis on process data Estimation: Catalyst life Figure 6. Separation model in Aspen HYSYS 0 0.2 0.4 0.6 0.8 1 1.2 0 2 4 6 8 10 12 14 Sulfur content % Bed length (m) Sulfur removal Bed 1 - HDS Bed 2 - HDS Bed 3 - HDS Simulation result Plant measurement Column Diesel T95 357.9 °C 358 °C Splitter LPG C3/C4 0.91 0.91 Debutanizer LSRN T95 78.20 85.30 °C Naphtha splitter HSRN T95 155.3 156.9 °C Naphtha splitter OUTPUT VARIABLES METHODS Diesel 95% Diesel Sulfur HSRN 95% LSRN 95% PMEAN 2.50 1.00 8.22 5.31 RIDGE 2.36 0.79 3.69 3.68 PLS 2.44 0.79 4.53 4.34 RANDOM FOREST 2.36 0.73 4.05 3.96 Figure 10. General model integration structure Figure 11. Detailed decision support structure Figure 5. TUPRAS, Izmit Refinery
Transcript of Future Directions of Production Planning and Optimized ...
Future Directions of Production Planning and Optimized Energy and Process Industries
This Project has received funding from the European Union’s Horizon 2020 Research and Innovation Program Under GrantAgreement nº 723523
Oil and Gas Demonstrator, TUPRAS, TurkeyAim: Energy efficient and higher quality diesel production in TUPRAS, Izmit Refinery.Diesel is produced in diesel hydroprocessing unit of the refinery. The unit has two reactors with total five catalyst beds. Three of these are for hydrodesulfurization and two of them are for hydrocracking. Three columns are used to separate LPG, LSRN, HSRN and diesel products.
NIR soft sensor The NIR soft sensor is developed for predicting 17 different properties of the feed simultaneously. The properties in question are:• API• Density (kg/m3)• Nitrogen (mg/kg)• Sulphur (%)• Distillation curve including T95. The distillation curve consists of 13 values
where each point is predicted independently from each other.
Figure 1. Estimation of feed properties based on NIR measurements
Figure 2. Estimation of feed properties based on NIR measurements
All models for all constituents are made with the same multivariate regression model technique, PLS (Partial Least Squares). Current state: The model development was done with collecting samples from the feed line and analyzing it with an existing offline NIR in the Tupras R&D lab. Online NIR purchasing and implementation is expected in 6 months.Expected outcome: NIR is used to detect T95 value of the feed much faster than the traditional method in use, ASTM D86. This feature enables MPC to have a feed forward structure that helps reducing the energy consumption.
Table 2. RMSE of the LOO cross-validation result for labelled data
(a) Diesel T95 (b) Diesel Sulfur
(c) HSRN T95 (d) LSRN T95
Figure 9. The learning curves for the four different output variables, showing negative mean squared error (y-axis) vs. the number training examples (days, x-axis) used for training. The upper blue curve shows the learning curve for training data and the lower orange curve shows the learning curve for the test data.
MPC structureAs a part of decision support system an MPC is built. For the MPC structure step test is used to build transfer functions of the plant. MPC model is on Jmodelica environment.
Integration of the models
Physics based modelsFeed characterizationContinuous lumping approach with 161 pseudo-components is used to describe the feed. The feed lump covers the boiling range of -250oC – 550oC. Consecutive PCs have 5oC TBP difference in between. Sulfur distribution is bell shaped and this is proved by the laboratory analysis of the feed mixture.
Hydrodesulfurization modelSteady-state sulphur removal reactor model is constructed and tested with an NIR input.
Figure 3. The sulphur removal along 3 HDS reactor beds
Hydrocracking modelSteady-state hydrocracking model is constructed and simulated with the output of the hydrodesulfurization model.
Figure 4. Cracking reactions along 2 HC reactor beds
Model vs. Data
Figure 7. PC distribution at the exit of last reactor bed
Figure 8. Comparison of bed exit temperatures
Table 1. Comparison of important product properties
Current state: Reactor models are in Matlab environment and their steady state version is tested with the NIR input. The separation columns are also steady state but they are on Aspen HYSYS and to test the compatibility, Matlab output is taken as an input to this model.Expected improvement: For both reaction and separation parts of the system the dynamic models will be developed.
Separation modelColumn models are created on ASPEN HYSYS and the feed input is taken from the reactor simulation output.
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Decision support systemData driven models
Case 1 - Quality predictionData: 17 feed properties and process dataPrediction: Diesel product T95, Diesel product sulphur content, HSRN product T95, and LSRN product T95
Case 2 - OptimizationData: 17 feed properties, remaining uncontrollable process parameters, and the targeted product quality valuesOptimization: Manipulated process data (reactor inlet temperatures, temperature difference in the reactor beds, quench H2 flow etc.)
Case 3 - Long-term monitoringData: Time series analysis on process dataEstimation: Catalyst life
Figure 6. Separation model in Aspen HYSYS
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Simulation result Plant measurement Column
Diesel T95 357.9 °C 358 °C Splitter
LPG C3/C4 0.91 0.91 Debutanizer
LSRN T95 78.20 85.30 °C Naphtha splitter
HSRN T95 155.3 156.9 °C Naphtha splitter
OUTPUT VARIABLESMETHODS Diesel 95% Diesel Sulfur HSRN 95% LSRN 95%
PMEAN 2.50 1.00 8.22 5.31
RIDGE 2.36 0.79 3.69 3.68
PLS 2.44 0.79 4.53 4.34
RANDOM FOREST 2.36 0.73 4.05 3.96
Figure 10. General model integration structure
Figure 11. Detailed decision support structure
Figure 5. TUPRAS, Izmit Refinery
