Modular plants emerged as the key solution for reducing time-to-market and increasing flexibility in the process industry by combining different modules known as Process Equipment Assemblies (PEAs). While PEA automation is standardised through the Module Type Package (MTP), comparable tools for their documentation remain absent. This work presents the Semantic PEA Datasheet (SPEAD) ontology, which represents PEA documentation as a machine-readable knowledge graph that adheres to the FAIR principles. SPEAD integrates established standards such as DEXPI and the VDI 2776 guidelines and ensures data quality through comprehensive annotations and constraint-based validation. The ontology was evaluated against twelve competency questions derived from a representative use case as well as competency questions from the literature using a continuous stirred-tank reactor PEA as well as a dosing PEA as example systems. SPEAD successfully covers operational and design parameters as well as interface... [more]

This talk presents possible strategies for the calibration of crystallization models aimed at predicting particle size distributions (PSDs) of active pharmaceutical ingredients (APIs) when using industrial datasets, which are limited in terms of number or information for the modeling exercise. Industrial data concerning a seeded batch cooling recrystallization of an API in an organic solvent are used as a case study, representing an example of the issues to be faced with real-world experimental datasets. The results are discussed showing how the model performances can be deemed satisfactory, at least from the industrial perspective, and how this can be useful to enhance process understanding and to guide process development and scale-up.