A PEI-Oriented Information Modeling Method for Process Manufacturing

The Process–Equipment–In-Process State (PEI) architecture provides a conceptual framework for organizing process manufacturing systems using Process Structures (P), Equipment Entities (E), and In-Process States (I). However, practical application of the PEI architecture requires source engineering information to be systematically transformed into a consistent PEI representation, while explicit object-boundary, mapping, ownership, and structural rules for this transformation have not been established. To address this issue, this paper proposes a PEI-oriented information modeling method for process manufacturing. The proposed method consists of three stages: engineering information organization, PEI structure encoding, and PEI structure construction. Formal definitions, object-boundary criteria, cardinality constraints, and engineering-to-PEI mapping rules are introduced for Process Structures, Equipment Entities, In-Process States, and industrial variables. The encoded information is subsequently assembled into a machine-readable PEI structure through complementary relationship and object-oriented machine-readable representations. The Tennessee Eastman Process (TEP) is employed as a representative benchmark to demonstrate the proposed construction procedure and structurally verify the resulting PEI representation. All 12 identified manufacturing operations, 5 explicitly defined equipment objects, and 53 industrial variables were successfully mapped to valid PEI representations. In addition, all 53 variables satisfied the defined ownership rules, all 188 structural consistency checks were passed, and all 15 source topology relationships were preserved. For the investigated TEP case, these results show that the constructed PEI representation achieved complete coverage of the organized reference information, conformed to the predefined PEI structural rules, and preserved the organized process topology. The proposed method provides a structured information-modeling basis for subsequent PEI-based model construction, software implementation, and future automated PEI structure generation.