Impact of Design of Experiments in the optimisation of catalytic reactions in academia

Design of Experiments (DoE) is extensively and routinely used in industry; however, in the last decades, it has been gaining increasing interest in organic synthesis in academia. The use of Chemometrics is an attractive strategy to find the real optimum in chemical reactions, especially when affected by several variables. DoE has been applied in a growing number of synthetic transformations over the years, where it can undoubtedly help in the process optimisation, saving costs and time. This review concisely discusses the chemometric basis of Design of Experiments and highlights several examples in which DoE has been applied in organic synthesis. Table of contents 1 Introduction 2 Chemometric basis of DoE 3 DoE applied in catalysis: examples 4 Conclusions

Extraction optimisation and lipid-lowering activity of Auricularia heimuer polysaccharides

Assessments of molecular weight distribution and activity/efficacy of Auricularia heimuer polysaccharides (AAP) are of substantial significance for its extraction process optimisation. In the present study, single-factor orthogonal test and response surface methodology were employed to optimise extraction conditions of AAP. Furthermore, a rat hyperlipidaemia model was established to compare the lipid-lowering activity of polysaccharides obtained by three extraction methods. Conditions for enzymatic hydrolysis were optimised as pH 5.0, 1% cellulase, 2.5% substrate concentration and enzymolysis time of 1.5 h, leading to an up to 31.8% polysaccharide yield and 89.13% of polysaccharides within the molecular weight range of 5 000 Da to 10 000 Da. The results of animal experiments showed that the lipid-lowering activity of enzymolysis-extracted polysaccharides was significantly higher than that of water- and ultrasonic-extracted ones (P < 0.01). So the present study revealed that enzymatic hydrolysis-extracted polysaccharides showed the strongest hypolipidaemia activity, providing a basis for the development of A. heimuer-based functional foods and drugs.

CycleStyleGAN-Based Knowledge Transfer for a Machining Digital Twin

Digitalisation of manufacturing is a crucial component of the Industry 4.0 transformation. The digital twin is an important tool for enabling real-time digital access to precise information about physical systems and for supporting process optimisation via the translation of the associated big data into actionable insights. Although a variety of frameworks and conceptual models addressing the requirements and advantages of digital twins has been suggested in the academic literature, their implementation has received less attention. The work presented in this paper aims to make a proposition that considers the novel challenges introduced for data analysis in the presence of heterogeneous and dynamic cyber-physical systems in Industry 4.0. The proposed approach defines a digital twin simulation tool that captures the dynamics of a machining vibration signal from a source model and adapts them to a given target environment. This constitutes a flexible approach to knowledge extraction from the existing manufacturing simulation models, as information from both physics-based and data-driven solutions can be elicited this way. Therefore, an opportunity to reuse the costly established systems is made available to the manufacturing businesses, and the paper presents a process optimisation framework for such use case. The proposed approach is implemented as a domain adaptation algorithm based on the generative adversarial network model. The novel CycleStyleGAN architecture extends the CycleGAN model with a style-based signal encoding. The implemented model is validated in an experimental scenario that aims to replicate a real-world manufacturing knowledge transfer problem. The experiment shows that the transferred information enables the reduction of the required target domain data by one order of magnitude.