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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 less energy than alternative routes of synthesis, as it occurs at lower temperatures and involves fewer steps,” says Trapp.

The group has now optimized its procedure by varying seven parameters that affect the yield of formaldehyde synthesis in their system, and using ML to identify the parameter combinations that give the best results. By appropriately tuning the input parameters in a new reaction setup, they were able to test the efficacy of the algorithm directly. “The new reaction scheme increased the efficiency of synthesis by 500% relative to that of the conventional mode of formaldehyde production,” says Trapp.

The authors are confident that their results will motivate chemical engineers to adopt the process and implement it on a technical scale. “BASF, our partner in the project, is already engaged in assessing the industrial relevance of the process,” says Trapp.

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