To achieve sustainable development goals and the requirements of a circular economy, a new class of intelligent computer-aided methods and tools is needed. Artificial intelligence (AI) techniques have been gaining much attention due to their ability to provide options to tackle the challenges we are currently facing. However, the successful application of AI to solve problems of current interest requires AI to be integrated with associated process systems engineering methods and tools that are already available or being developed. In this perspective paper, we highlight the use of a collection of process systems engineering methods and tools augmented by AI techniques to solve problems related to process manufacturing, with a focus on chemical product design, process synthesis and design, process control, and process safety and hazards.

Process flowsheet synthesis, design, and simulation require integrated approaches that combine domain knowledge and data-driven methods for fast, efficient, and reliable solutions. However, due to the recent surge in data and machine learning capabilities, there has been a shift towards building purely data-driven systems for process flowsheet synthesis and related problems. Such approaches have certain drawbacks. Here, we present a hybrid method that combines data-driven approaches with domain knowledge to represent process flowsheets and solve problems related to process synthesis, design, and simulation. We present an extended SFILES (or eSFILES) representation, a multi-level hierarchical flowsheet representation with varying degrees of process knowledge. At level 0, flow diagrams are represented as purely text-based SFILES strings. At level 1, the SFILES grammar, along with inferencing algorithms, is used to construct a flowsheet hypergraph explicitly representing flow diagram connectivity. At level 2, specifications needed for material and energy balance calculations are introduced, and, after simulation, the results are also added using annotated flowsheet hypergraphs. Finally, at level 3, a process ontology is connected with the annotated flowsheet hypergraph to include design and operation parameters as well as the detailed simulation results. We discuss this hierarchical framework using several case studies.

Process flowsheet synthesis and design involves simultaneously solving several problems, including determining the unit operations and their sequence, underlying reactions and reaction stoichiometry, downstream separation design and operation parameters, sustainability factors, and many more. Naturally, this results in a large amount of data being associated with a given process flowsheet that captures the relevant process context and should be readily accessible. This data is useful for solving related problems both using data-driven and process knowledge-based methods. A hierarchical framework, called the extended SFILES (or eSFILES), proposed recently stores this information using a combination of text-based, graph-based, and ontology-based representations. Here, we provide details on a prototype software for automated flowsheet representation and generation across various levels in the eSFILES framework. The underlying methods include a novel flowsheet grammar, a set of inferencing algorithms, and interfacing with a commercial process simulator facilitating rigorous flowsheet simulation.