gPROMS activities: Optimisation

Finding the optimal answer directly rather than by trial and error

Optimisation is a key technology for process organisations to create value and competitive advantage in process design and operations.

In particular, large-scale optimisation based on high-fidelity models has the ability to create significant value from 'already-optimised' processes.

gPROMS family products take full advantage of the optimisation capabilities in the gPROMS platform, which make it possible to apply rigorous optimisation to the design and operation of individual unit operations (e.g. reactors or distillation columns), plant sections (e.g. multiple distillation column sequences), entire plants integrating reaction, separation and utility sections, or even multi-site applications.

The power of gPROMS's equation-oriented approach and resulting much faster solution times open the door to a wide range of advanced optimisation applications that have not been feasible in the past.

Optimisation – what does it mean?

"Process optimisation" is a much-used term in the process industries. However, it is often used simply to mean 'process improvement', which is usually achieved by manually varying some equipment parameters and process conditions to improve the design or operation.

In the gPROMS context, optimisation means formal mathematical optimisation, where the optimiser searches the decision space for the combination of decision variables that give the best-possible results.

gPROMS platform optimisation capabilities

Key capabilities of the gPROMS platform's optimisation facilities include:

  • Steady-state optimisation. Determine the optimal values of multiple decision variable such that the value of the objective function (typically an economic function) is maximised or minimised.
  • Dynamic optimisation (sometimes referred to as optimal control). Optimise the dynamic or transient behaviour of a system – for example, to minimise the time for a batch process to reach a certain state subject to constraints.

Because of the power of its equation-oriented framework and optimisation solvers, gPROMS allows many decision variables to be varied simultaneously. Plant-wide optimisation studies have included up to 50 continuous and integer decisions at a time; multi-site optimisations typically involve 50–100 decision variables.

gPROMS platform optimisation – how it works

To define an optimisation run in gPROMS, you need to create an objective function (or, alternatively, simply select a suitable variable from within the model) to maximise or minimise, then select decision variables and, optionally, define constraints.

Objective function

The objective function is defined by any equation of the form "obj_fun = expression", where the expression can be composed of any variables in the model. The objective function is often an economic objective that sums the values and costs.


gPROMS optimisation objective function


Decision variables

Any specified variable in the model can be used as a decision variable. Decision variables can be:

  • Continuous variables, in which the value can vary in a continuous manner over the course of the optimisation (e.g. a distillation column diameter)
  • Integer variables, in which the decision variable may take only integer or discrete values (e.g. standard pipe diameters, or number of stages in a distillation column).


gPROMS optimisation decision variables




It is possible define many different types of constraint, including final and interior-point constraints in dynamic optimisation.


gPROMS optimisation constraints


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