gPROMS Process example

Advanced applications: batch process optimization

The dynamics of batch processing make recipe optimization a challenging task unsuited to traditional steady-state simulation tools.

gPROMS Process combines dynamics, high-fidelity process models and optimization capabilities to allow you to truly optimize batch processes.

By optimizing process design and operating policy you can enhance product quality, minimise batch time, increase asset utilisation and easily develop recipes for different equipment or product qualities.

The process

The example below shows an industrial batch esterification process. The unoptimized process results in around 60 tonnes per batch with 96.2 wt% purity, over a batch time of 8 hours and 10 minutes.

Figure 1 – Batch process flowsheet & recipe

The challenge

There is significant scope for optimizing the process to minimise batch time (in order to maximize throughput), minimise cost of raw materials and utilities and maximize profitability, while simultaneously improving product purity.

Some key constraints and potential decision variables are shown on the diagram in red and green respectively.

Figure 2 – Batch process, showing potential decision variables in green and constraints in red

The approach

The batch process was simulated then optimized in the following steps:

Step 1 – Create the process model

The process flowsheet, shown in Figure 3, was created using gPROMS Process’s drag & drop flowsheeting capabilities.

Initial reaction kinetic parameters were taken from literature. For additional accuracy the paraeters were refitted to recent experimental data using the gPROMS^® parameter estimation capability.

Figure 3 – batch process flowsheet

Step 2 – Create the operating policy

The batch operating policy was defined using the gPROMS graphical TASK language. The schedule is shown on the right.

Step 3 – Simulate a batch

The simulation was then executed, with the operating policy defined in Step 2 applied to the process flowsheet to dynamically simulate a batch. The plots below show the purity and batch temperature over the course of the simulation.

It can be seen that the batch takes around 8 hours and 20 minutes (just under 30,000 seconds) to complete.

Figure 4 – Selected results from the batch simulation

Step 4 – Define and run the batch optimization

The simulation results reflect the process designers’ best first guess at a schedule. In many cases, if this produces product satisfactorily (as is the case here) it will be applied and used without modification.

However many schedules can be significantly improved by using optimization techniques to minimise batch time while maintaining or improving quality.

The optimization was set up as follows:

1. Define the objective function. The objective specificed in the gPROMS optimization setup was to minimise the operating cost.
2. Define constraints. These were the items shown in red in figure 2 – e.g. batch vessel maximum temperature and pressure, product purity, etc.
3. Choose decision variables. These are taken from the items shown in green in figure 2 – for example feed addition rates, steam rates and so on.

The optimization was then executed.

The results

The optimization results show that it is indeed possible to improve the operation significantly.

Optimization shows that batch time can be reduced to just over 7 hours, a saving of over an hour. At the same time product purity increases from 96.2% to 97.1%, providing a higher-value product.

Figure 5 – Selected results from the batch process optimization

The profiles for the optimal case vs the original simulation case are shown above.

The changes doubled the profit margin for the product.