No abstract

Flavor is the focal point in the flavor industry, which follows social tendencies and behaviors. The research and development of new flavoring agents and molecules are essential in this field. However, the development of natural flavors plays a critical role in modern society. Considering this, the present work proposes a novel framework based on scientific machine learning to undertake an emerging problem in flavor engineering and industry. It proposes a combining system composed of generative and reinforcement learning models. Therefore, this work brings an innovative methodology to design new flavor molecules. The molecules were evaluated regarding synthetic accessibility, the number of atoms, and the likeness to a natural or pseudo-natural product. This work brings as contributions the implementation of a web scraper code to sample a flavors database and the integration of two scientific machine learning techniques in a complex system as a framework. The implementation of the complex system instead of the generative model by itself obtained 10% more molecules within the optimal results. The designed molecules obtained as an output of the reinforcement learning model’s generation were assessed regarding their existence or not in the market and whether they are already used in the flavor industry or not. Thus, we corroborated the potentiality of the framework presented for the search of molecules to be used in the development of flavor-based products.

Big Data Analytics plays a crucial role in Industry 4.0 by offering tools to improve the decision-making process. These tools comprise data management infrastructures and analytical methods. Among the economic sectors, the chemical process industry already holds mature data management structures but poorly explored analytical tools. In this sense, this work proposes an online analytical tool that can deal with Big Data to be used for identifying abnormal operations in chemical processes. It deals with a modified dynamic sensitivity matrix (DSM) and Gram−Schmidt orthogonalization (GSO) to prioritize process variables under abnormal behavior and scaling the impact they have on plant performance. In order to evaluate the effectiveness of the proposed method, the synthesis of n-propyl-propionate in a challenging simulated moving-bed reactor (SMBR) process is the object of this study. Simulations are carried out within a software-in-the-loop mode through the integration between Matlab and gPROMs. The results show that the proposed algorithm correctly prioritized the variables concerning their impact on the process performance during abnormal behavior. The capabilities of the proposed method were tested in two campaigns: considering abnormalities in two and three operating variables, respectively. In both, the method correctly prioritized their correction order based on their impact on the process performance. Finally, a "what-if" scenario shows that choosing the correction order at random leads to abnormal behavior for longer periods of time.