Each year, thousands of academic papers are published on catalyst design, while there are only around 300 commercial catalysts available currently. To a certain extent this is due to the traditional approach of catalyst design, which starts with identifying a useful candidate catalyst for the desired chemical transformation, then iteratively synthesizing, modifying, and testing to improve the activity, selectivity and breadth of application. If the product is known, a catalyst(s) with the required attributes could be designed to manufacture it. Even the “best” catalyst might never find its way to industrial practice, as the processes that use it can employ solvents, reagents, and conditions that may be partially or fully incompatible. A change in the catalyst might alter the design of separation units completely. This work aims to develop a digital methodology, which is founded on the integration of catalyst and process design, to find optimal ranges of catalyst attributes (e.g. activity, selectivity, operating temperature) and optimal process selection (reaction, separation, purification, telescoping) to guide the chemist more directly to a commercially viable catalyst. As a proof of concept, we simulated a reaction being evaluated in another Catalysis Hub project4: the gas-phase dehydration of bio n-butanol to butenes, producing dibutyl ether as co-product. Considering two catalysts from the literature5,6, our results have shown that a lower reactor temperature is not the most economic option, as the process with a Zn-Mn-Co modified γ-Al2O3 catalyst with 96% selectivity to 1-butene (at 400 °C) outperforms GdPO4 that has a lower selectivity (85%) despite its lower energy demand (300 °C) for the same 1000 kg/h butanol feed and 97% molar purity of 1-butene as the product. This demonstrates the need to consider not only safe operating conditions, but also reactant feed, catalyst attributes, product price and purity, and a whole process view to select a suitable catalyst.
Reza Abbasi (UCL)