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Machine learning helps achieve a five-fold boost in formaldehyde yield

By Gerald Ondrey |

Chemistry professor Oliver Trapp and his colleagues at the Ludwig Maximilians-Universität München (LUM; Germany; www.en.uni-muenchen.de) have developed a new workflow for the production of formaldehyde, which is based on an algorithm constructed with the aid of machine learning (ML), optimization and design of experiments (DoE). The new procedure increases yields of the compound by a factor of five, as the team recently reported in the journal Chemical Science. Industrial synthesis of formaldehyde begins with synthesis gas (syngas), to which methanol is added before being oxidized with the help of a catalyst. However, the production of syngas itself requires high temperatures and fossil fuels such as natural gas or coal. In a previous study, the LMU researchers described the development of a reaction scheme that allowed dimethoxymethane (DMM) — a formaldehyde derivative that can be hydrolyzed into formaldehyde and methanol — to be synthesized in a single step from syngas in the presence of a ruthenium-based catalyst, under moderate conditions of temperature and pressure. The strategy has a number of advantages over the conventional procedure. First, it allows CO2 to be utilized. “In addition, the whole process requires far…
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