Introductory Remarks

Modular process intensification aims to develop more efficient, more environmentally friendly, and more compact technologies by combining multiple process tasks into a single equipment, maximizing driving forces, and maximizing heat and mass transfer rates. A key research question is how to systematically generate innovative process designs. To this purpose, phenomena-based process synthesis approaches have been proposed which offer the potential to re-invent unit operations by finding optimized process solutions without pre-postulation of equipment/flowsheet alternatives. Herein, phenomena are fundamental chemical process functions such as heat transfer and mass transfer, which can serve as building blocks to represent unit operations from a lower aggregated level. A major challenge in applying phenomena-based synthesis is the computational complexity due to the large-scale optimization problem and the nonlinearities introduced by modeling the first principles physical phenomena.

To address this challenge, we propose a data-driven optimization approach for phenomena-based process intensification synthesis using the Generalized Modular Representation Framework (GMF). In GMF, two types of phenomenological building blocks are used: pure heat exchange modules (HE) and mass/heat exchange modules (M/H). The mass transfer feasibility in a M/H module is characterized by the Gibbs free energy-based driving force constraints, which contribute to the key GMF representation capability to identify multifunctional separation and/or reaction tasks while also render the major mathematical complexities with logarithmic terms and multivariable polynomials. In view of this, the rectified linear units (ReLU) approach is applied to generate a data-driven model to correlate driving forces with key process variables (e.g., temperature, molar fractions) in a mixed-integer linear formulation. This offers the advantages to: (i) reduce the computational strain to identify optimal solutions, and (ii) extract simplified mathematical expressions to spotlight the synergistic relations of multifunctional phenomena. The data-driven model is then integrated with other GMF modeling constraints on mass balances, energy balances, and superstructure combination rules to synthesize modular and intensified process systems. The proposed hybrid data-driven/mechanistic GMF synthesis approach will be showcased on two case studies: (i) reactive distillation optimization to demonstrate the solution optimality and computational efficiency with respect to continuous optimization, and (ii) modular reactor network optimization for decision making with mixed-integer variables. The automation and integration of this strategy as a software toolkit to the SYNOPSIS prototype platform will also be presented.