Performance Evaluation of Real Industrial RTO Systems
Abstract
:1. Introduction
2. Problem Statement
3. RTO System Description
- (a)
- Steady-state detection (SSD), which states if the plant is at steady state based on the data gathered from the plant within a time interval;
- (b)
- Monitoring sequence (MON), which is a switching method for executing the RTO iteration based on the information of the unit’s stability, the unit’s load and the RTO system’s status; the switching method triggers the beginning of a new cycle of optimization and commonly depends on a minimal interval between successive RTO iterations, which typically corresponds to 30 min to 2 h for distillation units;
- (c)
- Execution of the optimization layer based on the two-step approach, thus adapting the stationary process model and using it as a constraint for solving a nonlinear programming problem representing an economic index.
- production planning and scheduling, which transfer information to it;
- storage logistics, which has information about the composition of feed tanks;
- Distributed control system (DCS) and database, which deliver measured values.
4. Industrial RTO Evaluation
4.1. Steady-State Detection
4.1.1. Tool A
4.1.2. Tool B
4.1.3. Industrial Results
4.2. Adaptation and Optimization
- the database might present lagged analyses, given the quality of oil changes with time;
- there might be changes in oil composition due to storage and distribution policies from well to final tank. Commonly, this causes the loss of volatile compounds;
- mixture rules applied to determine the properties of the load might not adequately represent its distillation profile;
- eventually, internal streams of the refinery are blended with the load for reprocessing.
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
Abbreviations
RTO | Real-time optimization systems |
MPC | Model predictive control |
MON | Monitoring sequence |
SSD | Steady-state detection |
APC | Advanced process control |
DCS | Distributed control system |
SM | Statistical method |
HM | Heuristic method |
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Tag | ||
---|---|---|
0.0 | 98.3 | |
3.2 | 81.1 | |
0.0 | 97.3 | |
0.0 | 90.9 | |
0.0 | 97.1 | |
4.8 | 99.5 | |
0.0 | 90.1 | |
6.8 | 84.4 |
Position in Rank | Tag | P50 | P90 | P10 | |
---|---|---|---|---|---|
1 | yes | 21.53 | 54.83 | 1.15 | |
2 | yes | 15.99 | 54.33 | 0.19 | |
3 | yes | 2.37 | 11.39 | 0.10 | |
4 | yes | 1.95 | 7.49 | 0.48 | |
5 | yes | 1.68 | 14.21 | 0.35 | |
6 | yes | 1.41 | 7.01 | <0.01 | |
7 | yes | 1.17 | 5.94 | 0.04 | |
8 | yes | 1.12 | 7.39 | <0.01 | |
9 | θ(8) | no | 0.96 | 4.08 | 0.09 |
10 | yes | 0.65 | 2.17 | 0.12 |
Knot | Active Constraint (%) | ||
---|---|---|---|
1 | 0.2 | 10 | 42.5 |
2 | 0.2 | 10 | 13.4 |
3 | 0.2 | 7.5 | 31.5 |
4 | 0.2 | 7.5 | 81.7 |
5 | 0.2 | 7.5 | 0.7 |
6 | 0.2 | 7.5 | 0.2 |
7 | 0.2 | 7.5 | 92.1 |
8 | 0.2 | 3 | 19.4 |
9 | 0.2 | 3 | 1.2 |
10 | 0.2 | 3 | 14.6 |
11 | 0.2 | 3 | 0.3 |
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Câmara, M.M.; Quelhas, A.D.; Pinto, J.C. Performance Evaluation of Real Industrial RTO Systems. Processes 2016, 4, 44. https://doi.org/10.3390/pr4040044
Câmara MM, Quelhas AD, Pinto JC. Performance Evaluation of Real Industrial RTO Systems. Processes. 2016; 4(4):44. https://doi.org/10.3390/pr4040044
Chicago/Turabian StyleCâmara, Maurício M., André D. Quelhas, and José Carlos Pinto. 2016. "Performance Evaluation of Real Industrial RTO Systems" Processes 4, no. 4: 44. https://doi.org/10.3390/pr4040044
APA StyleCâmara, M. M., Quelhas, A. D., & Pinto, J. C. (2016). Performance Evaluation of Real Industrial RTO Systems. Processes, 4(4), 44. https://doi.org/10.3390/pr4040044