Distillation Control and Optimization – Part 6

Model-based multivariable controls or artificial neural networks can reap optimization rewards for complex towers with many constraints.

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Since the neural controller is an empirical, rather than a theoretical model, it is susceptible to errors if operated outside the conditions that existed during training. Operating data, therefore, must be gathered continually, and the network retrained whenever novel conditions occur in order to increase the robustness of the neural controller throughout its life of operation.

Sub-Optimization

Every distillation column is unique. The goal of optimization of a single column is to safely operate at maximum profit. To do this requires the knowledge of the market values of each product. If the products of a column are not final products, but are the feed flows to other unit processes, these market values might not be known.
In such cases, the goals of optimization change. The criterion becomes the generation of the required products at minimum operating cost. This minimum cost can be an optimum with respect to the column involved, but it is only a “sub-optimum” with respect to the total process of which the column is a part.

Figure 2
Figure2
The configuration of a back-propagation neural network (ANN) and its use as an internal model controller.
When the market values of the products are known, the column can be fully optimized, but the type of the market must also be known. If the market is limited, the goal is to generate the products at optimum separation and minimum operating cost. This cost varies as the feed flow(s) and their composition(s) vary.

When the market is unlimited and sufficient feedstock is available, the optimization of the column requires the determination of both the optimum separation and the value of the feed stream(s). As a function of these values, the goal can either be maximum loading or maximum energy efficiency.

When determining maximum loading, one of three constraints can be the limiting one. Throughput can be limited by:

  1. The maximum cooling capacity of the overhead condenser;
  2. The maximum heat input capacity of the reboiler;
  3. The maximum separation rate of the column itself.

In some cases, the constraint will change with ambient conditions, product prices and other independent variables. Therefore, the design of an optimal control system for a single column should follow three logical steps:

  1. Designing the basic controls to regulate the operating variables, such as pressures, temperatures, levels, and flows.
  2. Configuring the controls to regulate the reboiler heat input, the internal reflux flow rate, feed enthalpy, and the sources of heat to the reboiler and pre-heater(s).
  3. Determining the controls required to maintain the specified separation.

If the above control loops are provided for a single column, that column is “sub-optimized. Operation at this sub-optimum will generate products at close to the specified separation, but that separation might not be ideal with regard to the total system. If product purities are higher than specified, the operation is not considered to be sub-optimum.

The Total Model

It is possible to design a control system which will compensate for changes in any of the load variables: feed rate, composition, enthalpy, reflux and bottoms enthalpy. The goal of these systems is to eliminate interactions and to protect the column from the consequences of changes in ambient conditions. To provide a model that describes both the material and energy balance of the column, one has to develop both the steady state and the dynamic equations for the following:

  • Feed enthalpy balance,
  • Bottoms enthalpy balance,
  • Internal reflux computation,
  • Reboiler heat balance,
  • Overall material balance.


Béla Lipták is a control consultant, editor of the Instrument Engineer’s Handbook, and former adjunct professor at Yale University. He can be reached at liptakbela@aol.com.

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