Antônio C. Zanin

A PLANNING MODEL FOR REFINERY DIESEL PRODUCTION

The main objective of this paper is to develop a nonlinear planning model for refinery production. The model is able to represent a general refinery topology and allows the implementation of nonlinear process models as well as blending relations. A real-world application is developed for the planning of diesel production in the RPBC refinery in Cubatao (SP). The resulting optimization model is solved with the generalized reduced gradient method. The optimization results were compared to the current situation, where no computer algorithm is used and the stream allocation is made based on experience, with the aid of manual calculations. Considering the market limitations for each kind of diesel oil usually supplied by the refinery, the optimization algorithm was able to define a new point of operation, increasing the production of more valuable oil, while satisfying all specification limits. This new operating point represents an increase in profitability of about US

Real time optimization (RTO) with model predictive control (MPC)

6,000,000 per year.

Tuning the Model Predictive Control of a Crude Distillation Unit

This paper studies a simplified methodology to integrate the real time optimization (RTO) of a continuous system into the model predictive controller in the one layer strategy. The gradient of the economic objective function is included in the cost function of the controller. Optimal conditions of the process at steady state are searched through the use of a rigorous non-linear process model, while the trajectory to be followed is predicted with the use of a linear dynamic model, obtained through a plant step test. The main advantage of the proposed strategy is that the resulting control/optimization problem can still be solved with a quadratic programming routine at each sampling step. Simulation results show that the approach proposed may be comparable to the strategy that solves the full economic optimization problem inside the MPC controller where the resulting control problem becomes a non-linear programming problem with a much higher computer load.

PETROBRAS Experience Implementing Real Time Optimization

Tuning the parameters of the Model Predictive Control (MPC) of an industrial Crude Distillation Unit (CDU) is considered here. A realistic scenario is depicted where the inputs of the CDU system have optimizing targets, which are provided by the Real Time Optimization layer of the control structure. It is considered the nominal case, in which both the CDU model and the MPC model are the same. The process outputs are controlled inside zones instead of at fixed set points. Then, the tuning procedure has to define the weights that penalize the output error with respect to the control zone, the weights that penalize the deviation of the inputs from their targets, as well as the weights that penalize the input moves. A tuning approach based on multi-objective optimization is proposed and applied to the MPC of the CDU system. The performance of the controller tuned with the proposed approach is compared through simulation with the results of an existing approach also based on multi-objective optimization. The simulation results are similar, but the proposed approach has a computational load significantly lower than the existing method. The tuning effort is also much lower than in the conventional practical approaches that are usually based on ad-hoc procedures.

Real Time Optimization (RTO) with Model Predictive Control (MPC)

Abstract PETROBRAS has defined Real Time Optimization (RTO) as a “High Sustainability” technology for downstream operations, due to its high economic return. Since 2001, RTO tools are being tested within the Company, either using in-house process simulators or, sometimes, using available commercial ones. This paper presents an overview of the PETROBRAS experiences on RTO, showing applications on Distillation and Fluidized Catalytic Cracking (FCC) units. Alternatives based on Sequential-modular simulators, along with reduced models (Kriging models and neural nets), as well as Equation-oriented based simulators / optimizers have been explored. The project scopes vary from covering only the Reactor / Regenerator section of a FCC unit up to a whole Crude distillation unit, including the preheat train, all distillation towers and the heat and material integration. Some of these RTO applications have been running close loop for almost 6 months, with proved expressive economical benefits. Based on the knowledge acquired during all these years, some of the future development needs for the improvement of RTO technology will be presented and discussed, as a guide for future research projects.

Robust model predictive control of an industrial partial combustion fluidized-bed catalytic cracking converter

This paper studies a simplified methodology to integrate the real time optimization of a continuous system into the model predictive controller in the one layer strategy. The gradient of the economic objective function is included in the cost function of the controller. One of the control objectives is to zero the reduced gradient of the economic objective while maintaining the system outputs inside their zones. Optimal conditions of the process at steady state are searched through the use of a rigorous nonlinear process model, while the trajectory to be followed is predicted with the use of a linear dynamic model that can be obtained through a plant step test. Moreover, the reduced gradient of the economic objective is computed taking advantage of the predicted input and output trajectories. The main advantage of the proposed strategy is that the resulting control/optimization problem can be solved with a quadratic programming routine at each sampling step. Simulation results show that the approach proposed here is comparable to the strategy that solves the full economic optimization problem inside the MPC controller where the resulting control problem becomes a nonlinear programming with a high computer load.