Sustainable Reaction Engineering

Alexei Lapkin's group @ University of Cambridge

A sustainable society will use manufacturing processes that do not deplete resources and do not harm the environment.

In order to understand implications of a given technology (or a manufacturing process) on resources, society and the environment, we need to place it within the context of a wider system. Thus, if we are considering a single chemical reaction step, we need to understand also the complete process, including any separation requirements and sources/destinations of feedstocks and products/byproducts. If we are considering whether a given chemical product is better sourced from a renewable bio-feedstock, or from carbon dioxide, then we need to understand the supply chain and life cycle environmental impacts of such a substitution.

Sustainable Reaction Engineering group is developing clean, intensive processes for manufacture of molecules, formulations or functional materials. We are working on methods of modelling chemical processes, starting from molecular modelling methods and extending to multi-objective process optimisation and life cycle assessment. We have pioneered methods of machine learning for automated process optimisation, and are exploring methods of Big Data in application to chemical reaction networks. We are working with many industry sectors, from inorganic materials and formulations, to platform chemicals and pharmaceuticals, and have an international network of collaborations.

RECENT PUBLICATIONS

Z. Hao, M.H. Barecka, A.A. Lapkin, Accelerating net zero from the perspective of optimising a carbon capture and utilisation system, Energy Environ. Sci., 15 (2022) 2139-2153. doi: 10.1039/D1EE 03923G 03923G

J.M. Weber, Z. Guo, A.A. Lapkin, Discovering circular process solutions through automated reaction network optimisation, ACS Engineering Au, 2 (2022) 333-349. doi: 10.1021/acsengineeringau.2c00002

N.A. Jose, M. Kovalev, A.A. Lapkin, Double-hydroxide superstructures for high-rate supercapacitor cathodes, Energy Technologies., (2022) 2200633, doi: 10.1002/ente.202200633

H. Ren, M. Kovalev, Z. Weng, M.Z. Muhamad, Y. Sheng, L. Sun, J. Wang, S. Rihm, H. Ma, W. Yang, A.A. Lapkin, J.W. Ager, Operando Proton Transfer Reaction-Time of Flight-Mass Spectrometry of Carbon Dioxide Reduction Electrocatalysis, Nature Catal., 5 (2022) 1169-1179 doi: 10.1038/s41929-022-00891-3

L. Cao, D. Russo, E. Matthews, A. Lapkin, D. Woods, Computer-aided design of formulated products: a bridge-design of experiments for ingredient selection, Comp. Chem. Engng. 169 (2023) 108083 doi: 10.1016/j.compchemeng.2022.108083

A. Pomberger, N. Jose, D. Walz, J. Meissner, C. Holze, P. Muller-Bischof, A.A. Lapkin, Automated pH adjustment driven by robotic workflows and active machine learning, Chem. Eng. J., 451 (2022) 139099, doi: 10.1016/j.cej.2022.139099

A.A. Khan, A.A. Lapkin, Designing the process designer: hierarchical reinforcement learning for optimisation-based process design, Chem. Eng. Process. Process Intensification, (2022) 108885. doi: 10.1016/j.cep.2022.108885

J. Raphael Seidenberg, A. Khan, A.A. Lapkin, Boosting autonomous process design and intensification with formalised domain knowledge, Comp. Chem. Engng. 169 (2023) 108097 doi: 10.1016/j.compchemeng.2022.108097