To the chemist or engineer in academia or discovery chemistry, optimization of a process often means devising conditions which give the best yield or lowest impurity amount.

To the industrial chemist / engineer involved in R&D, scale-up and production, optimization may refer to a range of goals relating to yield, quality, cost, efficiency or throughput – especially more than one outcome has to be optimized at the same time.

Critical factors may include:

• Catalyst (type, quantity, purity)
• Reagent (type, quantity, purity)
• Solvent (type, quantity, purity)
• Rate of addition and/or mixing, mass transfer
• Reaction temperature
• Reaction pressure (if a gas phase is involved)
• Concentration
• Reaction time (before quenching or exiting the reactor)

These factors are important in the reaction step, but it is also important to optimize the product isolation (workup or downstream processing), so additional factors may include:

• Quench conditions (time, temperature etc.)
• Extraction conditions (solvent, amount, temperature, time)
• Distillation conditions
• Crystallization conditions.

Each of the above factors may be optimized following development of a suitable and robust model.

In optimization studies, ‘the parameter which is to be optimized’ is called the objective function or goal function.  This parameter can be maximized (e.g. in the case of yield) or minimized (e.g. in the case of campaign time).  Defining a relevant objective function is an important step in making the most of an optimization exercise.

The Optimization module will search for an optimum on the response surface of the objective function (e.g. yield) and will return to the user the conditions that produce the optimum. 

You can also explore and define a Design Space or Response Surface over an entire region of interest, using e.g. Full factorial from inside Reaction Lab or from the DynoChem ribbon.