Research on Product Yield Prediction and Benefit of Tuning Diesel Hydrogenation Conversion Device Based on Data-Driven System
Abstract
:1. Introduction
2. Industrial Hydrocracking Process
3. Experimental Methods
3.1. Overall Framework
3.2. Deep Nerual Network
3.3. Data Collection and Preprocessing
3.4. Analysis and Selection of Modeling Variables
3.5. Genetic Algorithm
4. Results and Discussion
4.1. RLG Device Yield Prediction Model
4.1.1. Selection of DNN Model Parameters
- (1)
- Establishment of input and output neuron layers
- (2)
- Batch_Size
- (3)
- Batch Normalization
- (4)
- Determination of the number of neurons in the hidden layer
- (5)
- Choice of activation function
4.1.2. DNN Model Training
4.1.3. Model Prediction Results and Evaluation
4.2. Mathematical Model for Revenue Optimization of RLG Device
4.2.1. The Objective Function of the Optimization Problem
4.2.2. Binding Conditions
4.2.3. Genetic Algorithm to Optimize RLG Device Yield
- (1)
- Parameter setting of the genetic algorithm
- (2)
- RLG device yield optimization results
5. Conclusions
- First, by combining the reaction mechanism and characteristics of the RLG process, data such as the properties of the crude oil and process operation variables were separated and preprocessed. A three-layer DNN model with (17, 128, 64) nodes was then established. This model predicts the gasoline yield with an average absolute error of 1.58%, showing a better prediction performance.
- Then, on the basis of this predictive model, plant tuning operations were carried out with the goal of maximizing plant efficiency.
- Next, the results show that optimizing the operating conditions using the GA algorithm to meet the 3% increase in gasoline production can maximize the economic benefits of the plant.
- Finally, it was verified that the optimization value of the operating conditions is consistent with the actual situation of the RLG process, which proves that the established model has good applicability.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
LCO | Light Cycle Oil |
RLG | React LCO into Gasoline |
DNN | Deep Neural Network |
GA | Genetic Algorithm |
MDM | Mechanism-driven Model |
DDM | Data-driven Model |
FNN | Forward Neural Network |
FCC | Fluid Catalytic Cracking |
HPS | High-Pressure Separator |
LPS | Low-Pressure Separator |
MLP | Multi-Layer Perceptron |
ANN | Artificial Neural Network |
DCS | Distributed Control System |
PSO | Particle Swarm Algorithm |
SA | Simulated Annealing Algorithm |
ACO | Ant Colony Algorithms |
BN | Batch Normalization |
MAE | Mean Absolute Error |
MSE | Mean Square Error |
LPG | Liquefied Gas |
R2 | R Square |
MAPE | Mean Absolute Percentage Error |
RON | Research Octane Number |
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Category | Number | Definition |
---|---|---|
inputs | 17 | (1) Fresh diesel feed, new hydrogen, the earliest distilled light oil fraction volume, amount of pre-hydrogenated sulfur-containing light hydrocarbons in the take-up reformer, feedstock density (20 °C), feedstock total sulfur content, feedstock total nitrogen content, and 95% distillation temperature of feedstock. (2) R101 average temperature, R101 inlet hydrogen partial pressure, R101 inlet circulating hydrogen volume, R101 inlet hydrogen–oil ratio, R102 average temperature, R102 inlet hydrogen partial pressure, R102 inlet circulating hydrogen volume, R102 inlet hydrogen–oil ratio, R102 inlet hydrogen–oil ratio, actual circulating hydrogen purity, |
outputs | 5 | gasoline yield, diesel yield, LPG yield, dry gas yield. R102 temperature rise |
Program | Number of Variables | Variable Name |
---|---|---|
Input variables | 17 | (1) Fresh diesel feed volume, D-501 fresh hydrogen inlet flow rate, the earliest distilled light oil fraction volume, amount of sulfur-containing light hydrocarbons in the pre-hydrogenation of the collected reformer, feedstock density (20 °C), feedstock total sulfur content, feedstock total nitrogen content, and 95% distillation temperature of the feedstock; (2) R101 average temperature, R101 inlet hydrogen partial pressure, R101 inlet circulating hydrogen volume, R101 inlet hydrogen–oil ratio, R102 average temperature, R102 inlet hydrogen partial pressure, R102 inlet circulating hydrogen volume, R102 inlet hydrogen–oil ratio, R102 inlet hydrogen–oil ratio Actual circulating hydrogen purity |
Output variables | 5 | Gasoline yield, diesel yield, LPG yield, dry gas yield; R102 temperature rise |
Program | Gasoline Yield | Diesel Yield | LPG Yield | Dry Gas Yield | R102 Temperature Rise |
---|---|---|---|---|---|
MAE | 0.9122 | 1.0565 | 0.2382 | 0.0987 | 0.9671 |
MSE | 1.4305 | 2.0185 | 0.1030 | 0.0171 | 1.7750 |
R2 | 0.9370 | 0.9333 | 0.9469 | 0.9266 | 0.9603 |
MAPE | 0.0188 | 0.0262 | 0.1040 | 0.0467 | 0.0202 |
Program | Gasoline Yield | Diesel Yield | LPG Yield | Dry Gas Yield | R102 Temperature Rise |
---|---|---|---|---|---|
MAE | 1.5810 | 1.9810 | 0.3519 | 0.1290 | 1.5575 |
MSE | 6.8294 | 10.6909 | 0.4005 | 0.0283 | 7.7243 |
R2 | 0.7252 | 0.6929 | 0.7843 | 0.8811 | 0.7891 |
MAPE | 0.0370 | 0.0469 | 31.2087 | 0.0615 | 0.0300 |
Process Conditions | Space for Excellence |
---|---|
R101 average temperature (°C) | 360~395 |
R101 Inlet hydrogen partial pressure (MPa) | 6.5~9.1 |
R101 inlet circulating hydrogen flow rate (kN3/h) | 120~140 |
R101 inlet hydrogen-to-oil ratio | 800~1200 |
R102 average temperature (°C) | 370~415 |
R102 inlet hydrogen partial pressure (MPa) | 6.5~9.1 |
R102 inlet circulating hydrogen flow rate (kN3/h) | 130~150 |
R102 inlet hydrogen-to-oil ratio | 900~1400 |
Process Conditions | Group 1 | Group 2 | Group 3 | Group 4 | ||||
---|---|---|---|---|---|---|---|---|
Original Value | Optimization Value | Original Value | Optimization Value | Original Value | Optimization Value | Original Value | Optimization Value | |
R101 average temperature (°C) | 370.3 | 377.2 | 381.3 | 390.5 | 367.2 | 376.7 | 371.2 | 378.5 |
R101 inlet hydrogen partial pressure (MPa) | 6.9 | 6.8 | 8.5 | 9.10 | 6.7 | 6.7 | 7.9 | 7.1 |
R101 inlet circulating hydrogen flow rate (kN3/h) | 148 | 132 | 132 | 140 | 153 | 124 | 153 | 140 |
R101 inlet hydrogen-to-oil ratio | 1435 | 1200 | 1232 | 800 | 1518 | 1200 | 11,451 | 1200 |
R102 average temperature (°C) | 375.8 | 377.7 | 381.9 | 391.0 | 381.8 | 383.4 | 388.6 | 398.5 |
R102 inlet hydrogen partial pressure (MPa) | 6.85 | 6.80 | 8.48 | 9.01 | 6.63 | 6.58 | 7.78 | 6.94 |
R102 inlet circulating hydrogen flow rate (kN3/h) | 155 | 146 | 145 | 145 | 160 | 145 | 160 | 142 |
R102 inlet hydrogen-to-oil ratio | 1506 | 1400 | 1357 | 900 | 1591 | 1400 | 1516 | 1400 |
R102 temperature rise (°C) | 58.8 | 60.4 | 59.96 | 60.5 | 57.9 | 60.0 | 61.0 | 63.0 |
Gasoline yield% | 53.86 | 57.01 | 52.10 | 55.31 | 49.10 | 52.11 | 49.71 | 53.62 |
Plant revenue (CNY/ton) | 6240 | 6382 | 6030 | 6381 | 6269 | 6331 | 6083 | 6302 |
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Zheng, Q.; Fan, Y.; Zhou, Z.; Jiang, H.; Zhou, X. Research on Product Yield Prediction and Benefit of Tuning Diesel Hydrogenation Conversion Device Based on Data-Driven System. Energies 2023, 16, 5332. https://doi.org/10.3390/en16145332
Zheng Q, Fan Y, Zhou Z, Jiang H, Zhou X. Research on Product Yield Prediction and Benefit of Tuning Diesel Hydrogenation Conversion Device Based on Data-Driven System. Energies. 2023; 16(14):5332. https://doi.org/10.3390/en16145332
Chicago/Turabian StyleZheng, Qianqian, Yijun Fan, Zhi Zhou, Hongbo Jiang, and Xiaolong Zhou. 2023. "Research on Product Yield Prediction and Benefit of Tuning Diesel Hydrogenation Conversion Device Based on Data-Driven System" Energies 16, no. 14: 5332. https://doi.org/10.3390/en16145332