Modeling and Computations

Session Chair & Co-Chair:

• Naveen Punati, USG Corporation
• Kevin Joback, Molecular Knowledge Systems

Session Description:

This session will explore the role of computers and modeling in addressing the challenges present in chemical process design, control and equipment design. The speakers present case studies involving the advanced mathematical modeling to address the complex nonlinear nature of the process control. Focus will also be on the use of simulation science to accelerate the innovation with respect to new process equipment design.

Schedule:

Modeling and Control of Benzene Hydrogenation via Reactive Distillation

Automotive emissions are a significant contributor to degeneration of air quality. As such, specifications for automobile fuels obtained from petroleum have received increasing levels of attention from the Environmental Protection Agency (EPA). One compound that is regulated is benzene. Being a carcinogen, the United States Environmental Protection Agency (EPA) requires all refiners to limit the amount of benzene in gasoline to 0.62 vol% [1]. The main source for producing benzene in a gasoline pool is the reformer unit and thus benzene is present in significant amounts in the reformate stream. This work focusses on treatment of benzene present in the reformate stream.

One option to remove benzene is to hydrogenate in presence of a catalyst. However, a problem arises as the catalyst used for the reaction is not selective for benzene, and toluene, which is present in the reformate stream in considerable quantities, will also be hydrogenated. Toluene hydrogenation is undesirable as toluene has a high octane rating (RON) and, should be retained in the final product.

Benzene (100 RON) + (3H)₂   →   Cyclohexane (83 RON) 
Toluene (120 RON) + (3H)₂    →   Methylcyclohexane (75 RON)

In order to avoid problems related to the selectivity of the catalyst, the reformate stream is split into light and heavy components in the conventional process. As benzene is a reasonably light component, it is mostly concentrated in the distillate, which is then hydrogenated before being sent back to the gasoline pool. The downside of this process is that a high capital investment is needed. Reactive distillation offers an alternative route for solving this problem. By combining reaction with separation it is possible to selectively react one component in a specified region of the column while suppressing unwanted reactions of other components. Furthermore, additional savings can be achieved as the heat of reaction can be directly used for separation of the mixture [2, 3].While reactive distillation can have significant advantages over traditional designs, there are also downsides that need to be considered. For example, the simultaneous presence of reaction and separation phenomena can result in complex dynamic behavior. As such, it is important to conduct a thorough investigation of the dynamics of as well as of control configurations for reactive distillation columns to determine their viability for a particular process.

The reactive distillation column investigated in this work is a packed bed column with 70 theoretical stages and the mixture to be separated includes 15 components. A detailed fundamental model has been developed in gPROMS. The model consists of over 2400 differential equations and over 4500 algebraic equations. The column also involves a partial condenser and a recycle stream which pose additional challenges to the model due to the resulting different time scales of the dynamic behavior. A further complicating factor is that the compositions of the product streams are not the only important measures for performance of the process: one aim is to reduce the benzene concentration to acceptable levels, while at the same time as little toluene as possible should be converted.

The model was evaluated in a series of simulations involving commonly occurring disturbances, e.g., feed composition variations. A transfer function model was developed from the responses and a multi-loop control configuration was designed. The controllers were then applied to the rigorous column model and the column under feedback control was evaluated in a series of simulations. It was shown that the control scheme can result in good performance except for the case where significant feed composition disturbances are present. A feedforward-feedback control scheme was developed to address this point. The addition of feedforward control was able to reject feed concentration disturbances as long as the feed composition measurements only included small or no time delays. This work highlights that this type of process can be well controlled using traditional control schemes, however, good performance requires small measurement time delays .

Combining Process Simulation and CFD Simulation to Improve the Efficiency of SMR (Steam Methane Reformer Plants)

Steam methane reforming is an endothermic process carried out in nickel-based catalyst filled tubes inside a large gas fired furnace. Due to the high temperature of the combustion gas and the furnace walls, radiation is the predominant mode of heat transfer in the furnace. The furnace walls in the tubular reformer are lined with refractory, bricks and fiber materials having a relative low emissivity. By applying a “high emissivity coating” on the surface of the wall, it is possible to increase the heat transferred to the tubes (mainly radiation) and thereby improve the thermal efficiency of the furnace box. Considering the scale and the important of the industrial process even a small rise in the heat transfer to the tubes can be translated into an important increase in the hydrogen production rate or an important decrease in the required fuel consumption [1]. In this article, the impact of high emissivity coating on the global efficiency of a side-fired SMR furnace was conducted through process simulation (Aspen HYSYS) and 3D CFD simulation

Process and Environmental Simulation Tool for Sustainable Process Selection

Process simulation is the backbone for advancing a chemical concept from laboratory to commercialization. Such a tool helps us determine the technical as well as economic viability of a new concept. More recently, the sustainability of a chemical process has assumed significant importance, requiring additional modelling of environmental impacts in addition to technical and economic feasibility. Process engineering is integral to performing accurate and credible environmental calculations. Additional simulation tools are increasingly being used to assess life cycle environmental impacts of new products and processes.  In this presentation, the link between process engineering and environmental calculations will be demonstrated using SimaPro life cycle software. An example of the use of this simulation methodology will be presented for foam expansion agents used in spray foam insulation applications.