Beyond Point Solutions: Learning Infrastructure for the Circular Economy

Circular economy deployment in molecular recycling (chemical/feedstock/advanced recycling), biomass valorisation (“biomass mining”), carbon capture and utilisation (CCU), and broader material recovery is often executed through a growing set of single-purpose (“point”) technologies. While point solutions can be effective within narrow operating envelopes, they are structurally challenged by heterogeneous feedstocks, evolving specifications and regulations, and the need for rapid process adaptation. This paper argues that circular systems increasingly require non-point architectures: modular transformation platforms that combine programmable actuation, continuous sensing, and codified process knowledge—enabling learning across deployments. We emphasise energy disposition (programmable deposition of electromagnetic energy) as a rare actuator relative to enzymes, catalysts, and solvents, and we connect this to Process Analytical Technology (PAT) and cyber-physical systems (CPS) best practices for real-time sensing and control. We conclude with design principles for “energy-matter nervous systems” in circular economies—distributed transformation assets that continuously improve process recipes and control policies based on operational data.