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Refineries are complex industrial systems that transform crude oil into more valuable subproducts. Due to the advances in sensors, easily measurable variables are continuously monitored and several data-driven soft-sensors are proposed to control the distillation process and the quality of the resultant subproducts. However, data preprocessing and soft-sensor modelling are still complex and time-consuming tasks that are expected to be automatised in the context of Industry 4.0. Although recently several automated learning (autoML) approaches have been proposed, these rely on model configuration and hyper-parameters optimisation. This paper advances the state-of-the-art by proposing an autoML approach that selects, among different normalisation and feature weighting preprocessing techniques and various well-known Machine Learning (ML) algorithms, the best configuration to create a reliable soft-sensor for the problem at hand. As proven in this research, each normalisation method transforms a given dataset differently, which ultimately affects the ML algorithm performance. The presented autoML approach considers the features preprocessing importance, including it, and the algorithm selection and configuration, as a fundamental stage of the methodology. The proposed autoML approach is applied to real data from a refinery in the Basque Country to create a soft-sensor in order to complement the operators' decision-making that, based on the operational variables of a distillation process, detects 400 min in advance with 98.925% precision if the resultant product does not reach the quality standards.
The ortho-substituted phenyl ring is a basic structural element in chemistry. It is found in more than three hundred drugs and agrochemicals. During the past decade, scientists have tried to replace the phenyl ring in bioactive compounds with saturated bioisosteres to obtain novel patentable structures. However, most of the research in this area has been devoted to the replacement of the para-substituted phenyl ring. Here we have developed saturated bioisosteres of the ortho-substituted phenyl ring with improved physicochemical properties: 2-oxabicyclo[2.1.1]hexanes. Crystallographic analysis revealed that these structures and the ortho-substituted phenyl ring indeed have similar geometric properties. Replacement of the phenyl ring in marketed agrochemicals fluxapyroxad (BASF) and boscalid (BASF) with 2-oxabicyclo[2.1.1]hexanes dramatically improved their water solubility, reduced lipophilicity and most importantly retained bioactivity. This work suggests an opportunity for chemists to replace the ortho-substituted phenyl ring in bioactive compounds with saturated bioisosteres in medicinal chemistry and agrochemistry.
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