Abstract
The increasing demand for energy, water, and chemical products signals the need for more sustainable and efficient process design methodologies. Traditional methods for conceptual process design constrains the exploration of novel and intensified process alternatives, as they rely on prior knowledge in defining the design space. Previous studies employing bottom-up approaches, such as phenomena building blocks (PBBs), suggest that the synthesis of complex bottom-up flowsheets remains computationally challenging and is thus limited to the synthesis of individual units of operation. This work proposes a bottom-up, data-driven framework for process synthesis and intensification based on phenomena building blocks (PBBs), in which process flowsheets are constructed from their underlying physical and chemical phenomena rather than conventional units of operation. The proposed framework introduces a phenomena-based text representation and data collection module. Furthermore, a sequence training and generation module is developed, which learns patterns governing PBB interactions and placement within flowsheets. A case study on ethylene glycol production is presented, in which nine fundamental phenomena yield 49 distinct PBB tokens. Results show that the trained model successfully reproduces established flowsheet structures and generates flowsheets using novel PBB combinations, highlighting its potential to support sustainable and intensified process designs.