1. Mongstad refinery crude oil tower
- a particularly complex distillation column:
Developed by
TEK F&T OGF Process Control
An MPC simulation model of the tower, which when combined with measurements of pressure, temperature and liquid loading in the column, has helped to make the column more robust with respect to accepting varying raw material quality and other aspects that are detrimental to processing
Benefits:
Better quality control of the products (i.e. heavy naphtha, kerosine and gas oil) which, in turn, is beneficial to further processing and the blending of end products.
Increased flexibility to switch between various products
WHAT is SEPTIC?
Tool for on-line estimation and Predictive control (MPC)
WHY?
Statoil R&D has several present and future projects within model based predictive control, including development of
fundamental mathematical models
control algorithms
estimators
methods for Real Time Optimisation (RTO)
Increased customer focus on state estimation
A standard tool reduces the costs with each implementation, and supports knowledge transfer
HOW?
Software package developed in C++
User interface with configuration files (measurements, inputs, outputs, parameters, couplings, tuning) and graphical presentation
Makes use of external- or self developed models
SFA separation train:
Trip on Snorre – one minute later this leads to lack of feed at SFA.
MPC at SFA is activated. New setpoints for the indicated FIC and LIC are optimised and calculated such that the sepration train avoids trip.
Benefits:
Approximately 20 SFA trips due to Snorre trips before implementation. None afterwards.
1-2 MNOK increased income for each trip avoided

2. Model Predictive Control in Statoil
In-house tool Septic, Statoil Estimation and Prediction Tool for Identification and Control
25 MPC applications with Septic within Statoil
Experimental step response models, built-in functionality for model gain scheduling
Flexible control priority hierarchy
Quality control by inferential models built from laboratory data or on-line analysers
DCS/PCDA interfaces currently in Septic: Honeywell TDC3000 (CM90 on Vax computer), ABB Bailey via InfoPlus (AspenTech), ABB Bailey via ABB OPC server, ABB Bailey via Matrikon OPC server, Kongsberg Simrad AIM1000 (integrated).
Septic also supports mechanistic type models, generally nonlinear models, for applications with wide operating regimes.
Refining and Gas plant applications
RCCU reactor/regenerator section (1)
RCCU main fractionator (1)
CDU atmospheric distillation (1)
CDU feed preheat heat exchanger network (MPC)
CDU feed preheat heat exchanger network (RTO)
CDU heater pass balancing
Coker Unit main fractionator (2 drums, 24-hours cycle) (1)
CokerNaphtha/CokerLGO splitter (1)
Visbreaker feed maximisation (1)
Visbreaker main fractionator (1)
LPG/Naphtha splitters (4)
C3/C4 splitters (3)
iC4/nC4 splitter (1)
Medium/Heavy Naphtha splitter (1)
Light/Medium Naphtha splitters (3)

3. Hva oppnås med bedre regulering?
Ved redusert varians, kan settpunkt flyttes nærmere øvre spesifiserte verdi

4. MPC: Generelt system v prosess u y x Forstyrrelse, DVR
Regulert variabel, CVR
Pådrag, MVR u
tilstand x y
med bibetingelser
tidsvektorer:

5. MPC: Generelt system Framtidige måling, "optimal" regulering
(closed-loop respons)
Referanse
Framtidige måling basert på pådrag fra forrige
beregning (open-loop respons)
Framtidig "optimalt" pådrag
Interpolert "optimalt" pådrag fra forrige
beregning
Nåtid t
Prediksjonshorisont

6. Control priorities MV ROC Limits MV High/Low Limits
CV hard constraints
CV soft constraints, CV setpoints, MV ideal values: Priority level 1
CV soft constraints, CV setpoints, MV ideal values: Priority level 2
CV soft constraints, CV setpoints, MV ideal values: Priority level n
CV soft constraints, CV setpoints, MV ideal values: Priority level 99
Sequence av steady-state QP-solutions  all priority levels handled
Then a single dynamic QP to meet the possibly adjusted steady-state goals

7. MPC – Fundamental models (first principles)
Open loop response is predicted by nonlinear model
MV assumption : Interpolation of optimal predictions from last sample
Linearisation by MV step change
One step for each MV blocking parameter (increased transient accuracy)
QP solver as for experimental models (step response type models)
Closed loop response is predicted by nonlinear model
Compute linearisation error (difference open-loop + QP from simulated nonlinear closed-loop response)
Above threshold ---> closed-loop to "open-loop" and iterate solution
QP solution ---> defines line search direction with nonlinear model
Possibly closed-loop to "open-loop" and iterate

8. MPCT101 – Crude tower Model Predictive Control application

9. MPCT101 – Crude Tower – Manipulated, Controlled and Disturbance Variables

10. MPCSPLT – T-108 (LPG/Naphta), T-112 (Light/Medium Naphta), T-113 (Light/Medium Naphta)

11. T-108 – Manipulated, Controlled and Disturbance variables

12. T-112 - Manipulated, Controlled and Disturbance variables

13. T-113 – Manipulated, Controlled and Disturbance variables

14. T-112 and T-113, bottom C6-, inferentials (red) and analyzers (AI063 and AI104)