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agree on a useful and solvable design optimization problem, which the optimizer then
formulates as a BLMOO problem.
An important subtask of the optimization problem definition is process operation
modeling. Modeling the operational part of the design problem is much more difficult
than the structural part due to its dynamic and stochastic nature.of the modeler. In this
subtask the designer and the optimizer can rely on the expertise of the modeller. The
modeller is expected to have expertise about both mathematical modeling and the
designed process itself, i.e. its chemical and physical characteristics. Based on his
expertise the modeler should be able to create such operational models that are suita-
ble to be used in optimization. The suitability of the models will be assessed by the
optimizer and the designer.
The stage of problem-solving is focused on the optimizer. However, cooperation
with the other stakeholders is likely to be needed also in this stage. In the beginning of
this stage the data and models required in the optimization are expected to be trans-
ferred to the optimizer in a form which he can utilize. Depending on the utilized
MOO method, different type and amount of cooperation with designer will be needed
also during the actual problem-solving. According to an interview (see chapter “Inter-
views”) industrial experts seem to favor optimization methods which lead to represen-
tations of Pareto optimal designs.
The last stage of optimizing design is result interpretation. Also this stage is
performed in cooperation between the designer and the optimizer. The optimizer pre-
pares result presentations, which indicate Pareto optimal designs and help the design-
er evaluate the impact of his preferences on the design. The designer is expected to
study the design optimization result, assess its reliability and make decision about
possible changes to his design. This is not necessary a straightforward task and is
likely to require assistance from the optimizer and the modeler. The reliability of the
optimization result is dependent on used operational models and data. Sensitivity
analysis of the result might also be needed. In the end, the designer can adopt changes
to his design or reject the optimization results and reformulate the optimization prob-
lem with the optimizer.
3.4
Data, Knowledge and Models
The optimizing design requires additional knowledge, data and models than the state-
of-the-art approaches to process design. The new requirements originate from the
need to solve the process design BLMOO problem. The new requirements for know-
ledge, data and models in optimizing design are summarized in Table 1. In addition to
these, the previous requirements are still valid, e.g. designer knowledge for process
design, use of design data and design models.
The expertise and knowledge of the stakeholders involved in optimizing design is
complementary. The designer has knowledge about industrial processes and their
design, customer requirements and evaluation of process designs. Meanwhile, the
modeler is expected have knowledge about similar processes and their mathematical
modeling.
The knowledge of the optimizer concerns about optimization and acting as an ana-
lyst in a decision-making process of MOO. However, during the activities of the
optimizing design combination of the knowledge of different stakeholders and
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