The onset of PI is problem identification

The following case study illustrates these elements very well. This is on the production of Neu5Ac. This is an important pharmaceutical substance particularly in the field of cancer.

The onset of PI is problem identification. Problem identification incorporates the element of identifying the objective function as discussed earlier. In this case the objective function is defined in the following relation: MaxOF = yield = f (Y, X, q). The objective function is chosen to optimize the efficiency of the system outputs. Variables, logical rules and structural mechanisms are some of the metrics for evaluating PI in this process. The process model is influenced by a binary approach to decision making. Other parameters include identifying solutions and solvents that are mixed at various points to enable manipulation of the outcomes with specific emphasis on PI. Limitations may span operational constraints, among other parameters.

The yield is defined by the difference between the output product and the input product as a function of the substrate. That takes one to step two of the process.

In this step, properties such as enzyme activities and related chemical information is collected and carefully documented. Then an evaluation of the current process is done and parametric contributions to process inefficiencies identified. These parameters include waste per unit weight of the materials involved in the process, shortfalls in downstream product processing, and a synthesis of the route taken in the production process. Intensive consultations are then done on PI problems and sub-problems, based on the knowledge base of biological processes and the alternative strategies that can be followed to implement PI. This is followed by the in situ product removal that takes the data and the removed product in the system to step two of the sub problem. That is in reference to figure 1 of the model.

Then a generic model that constitutes process intensification elements is developed. It is ten synthesized, evaluated, and validated against experimental data and significance tests are performed to evaluate the degree of deviations from a predetermined value within an acceptable limit of 5%.

The superstructure provides a method of fixing binary decisions as discussed above with emphasis on the value of Y, with every viable solution of the generic model to produce Neu5Ac evaluated. Different steps are involved at this point. These include reactions, enrichments, and other down-streaming reactions. Alternatives such as boiling, evaporations and others are considered at this stage.