Process Prediction - Process intensification of chemical plants by model-based estimation & prediction of critical system parameters

In the process industry, a variety of thermal unit operations is applied in which gases or vapor mixtures must be cooled or condensed. If the temperature unintentionally falls below the water dew point - depending on the gas composition - acids can be produced and cause damage to essential components, such as heat exchangers in crude oil distillation columns. This kind of corrosion is called “pitting” and can lead to complete destruction of the component. One possible strategy to avoid pitting is to use corrosion inhibitors. However, such inhibitors imply a significant increase of costs and are also very difficult to use under varying process conditions. Due to this, most processes operate with a thermal safety distance (in terms of pressure and temperature) to the water dew point. A major drawback of this approach is that the capacity of the facility is not fully utilized. Therefore, the aim of the project is the research of the method and simulation of the application of an innovative application in terms of a process prediction method to determine the lowest possible temperature of the heat exchanger surface in a crude oil distillation column and the water dew point in order to fully utilize the optimization potential of the process. This process prediction method will be researched based on a real reference facility and validated within the relevant environment. The proposed solution approach is the research of a model to calculate the phase equilibrium in a multi-component-mixture using rigorous thermodynamics. In the course of this, the relevant process parameters will be collected and used as the basis of a simulation model of the reference facility, considering all relevant facility components that are needed for a complete mass and energy balance. Heat exchanger performance will be determined by means of CFD simulations and integrated into the simulation model. An interface between the process prediction model and the process control system will be implemented. The current process parameters will be transmitted to the model in terms of real-time data, and recommended process parameters generated by the model will be transferred back to the process control system in view of real-time adjustment and optimization of the process. The anticipated results comprise the definite prevention of “pitting” and an increase of efficiency by at least 1 percentage point. Due to permanent monitoring of critical process parameters by the new approach, the overall availability of the process is expected to increase significantly. By reduction of a.m. thermal safety distance to the water dew point, a decrease of condenser temperature by 5 K is expected, which is the basis for shifting the production from gasoline towards diesel, implying higher profit margins as direct financial benefit.