No abstract

A misusage of machine learning (ML) strategies is usually observed in the process systems engineering literature. This issue is even more evident when dynamic identification is performed. The root of this problem is the gradient explode and vanishing issue related to the recurrent neural networks training. However, after the advent of deep learning, these issues were mitigated. Furthermore, the problem of data structuration is often overlooked during the machine learning model identification in this field. In this scenario, this work proposes a guideline for identifying ML models for the different applications in process systems engineering, which are usually for simulation or prediction purposes. While using the proposed guideline, the work also identifies a virtual analyzer for a pressure swing adsorption unit. In these types of adsorption separations, it is usual that the measurement of the main properties is not done online. Therefore, the virtual analyzer is another contribution of this manuscript. The overall results demonstrate that even though the test provides good performance during the ML model identification, its quality might degenerate over the application domain if the model application is overlooked.

The main goal of this work was to design a new product containing an active ingredient of the Eucalyptus Globulus tree - its essential oil. This work was divided into four steps: analysis of the raw material, chemical product design (needs, ideas, and product selection), manufacture, and economic analysis. After investigating the potential of all the ideas, the product selected was a plaster in gel, named Eucatrigel, with triple action: it protects the wound, accelerates the healing (due to the essential oil addition), and waterproofs the region. In the manufacturing step, it was defined the mass percentage of the essential oil in the gel as 0.5 %. The formula of the gel was based on a patent (US8563604B2) owned by Bausch Health Companies. A business case was set for the economic evaluation of this product; in this case, considering an initial investment of 681 k€, the expected payback period is four years, and the internal rate of return is 35 %.