(92a) Framework for Operability Assessment of Production Facilities: An Application to a Primary Unit of a Crude Oil Refinery

Nowadays due to environmental concerns, the high cost of energy and the tight product specifications the existing production facilities have been pushed to their capacity design limits. However, in order to enhance the performance of any process with some complexity involves a large number of process variables that must be modified in a coordinated fashion. Production engineers have improved several processes after a great deal of effort and trial and error experimentation. It is costly in terms of engineering hours and production lost, but it can also compromise operating personnel safety and equipment integrity. Therefore, there is a need of methodologies and prediction tools to assist the production engineers in their tasks.

Although, the maximization of production has been traditionally the most important target and concern in the day to day operation of many production facilities, recently, some other issues related with the overall impact of waste streams and emissions are considered more seriously due to environmental regulations. It is common to find production facilities operating in an economically viable fashion, conversely, these processes still struggle to embrace all the restrictions imposed for the up to date environmental requirements. Also, a growing competitive international market is demanding the enhancement of existing plants and the construction of innovative facilities with the state-of-the-art technology. This work focuses on the development of a methodology for the optimization, control and operability of existing production facilities. Nevertheless, the ideas and concepts presented in this work can be extended to the design and plant synthesis. Existing production facilities present an interesting challenge from the operability point of view. The main constraint is the fact that many production managers are not willing to change the plant layouts. Modifications in an existing plant are usually costly in time down and capital expenditure. However, there is room for improvement when choosing the set points optimally. The optimal operating conditions are not unique and they are subject to the constant fluctuations in the raw material quality and sustained unknown disturbances on the process. Secondly, in existing plants there are a priori limitations related with the capacity and performance of equipment units involved in the process. Considering the above limitations related with the equipment and environmental regulations, it is possible to put forward a methodology that embraces the optimization and control of an existing production facility reducing its environmental impact.

In this work, the methodologies of Life Cycle Assessment (LCA) and Environmental Damage Assessment (EDA) are applied within the optimization problem. The LCA evaluates the environmental impact of a process from the raw material to a final product. The EDA can supply the necessary information about the damage caused by the process to the environment. Basically we are looking at damages involving human health, ecosystem quality and resources usage.

The model-based methodology proposed in this work, starts with the implementation of first principle models for the process units on consideration. Secondly; steady-state and dynamic simulations are performed for model validation purposes. It is followed by rigorously posing the optimization problem, that is, objective function and constraints. In this assessment, besides the common raw materials and energy costs penalties; the so-called triple-bottom-line constraints are incorporated in the objective function. The triple-bottom-line constraints are related with sustainable and environmental costs respectively. The solution to the optimization problem gives the optimal set points for the process variables under steady-state conditions. At this stage, the influences of exogenous disturbances are not taken into account since these are mathematically feasible solutions only. Subsequent to the solution of the optimization problem, it is the critical importance an assessment of process controllability in view of the fact that optimal set points may be difficult to maintain under sustained disturbances or process variability. The process controllability feature relays on a back-off strategy where the constraints are restrained avoiding process excursions that violate a hard constraint compromising the plant stable operation when subject to disturbances. Another equally critical concern addressed in this work is related with the transition task, that is, the trajectory followed by a process when changing from an operating regime to another one. Optimal trajectories are calculated using a model predictive control strategy (MPC) which can handle constraints and presents good robustness features against model mismatch and perturbations.

The present framework is implemented in HYSYS® and a user friendly front end in MS Excel® where the-state-of-art optimizer is implemented. An application to a primary unit of a crude oil refinery is used for benchmarking the methodology above mentioned. Although the primary unit does not have waste streams neither emissions, the optimization of this section has a remarkable effect on the up and down streams units reducing the overall environmental impact of the process. The operability assessment of primary unit presents several challenges due to that the process is extremely interactive due to a high degree of energy integration. It is an example where steady-state optimization was performed and controllability issues were no part of the design. Optimal set points are calculated and implemented in a closed loop fashion driving the process along an optimal trajectory without violating any of the constraints. The assessment compares the impact of the primary unit operating at the current conditions and at the optimal conditions. When implementing the optimal set points, there is a reduction in the amount energy usage and it implies a reduction in the production of NOx, CO2 and SO2 to the atmosphere. The presented methodology is a promising approach for the optimization of existing production facilities and synthesis of future chemical plants