Work

From Process Simulation to Digital Twin: Custom Gasifier Modelling, Complex Kinetics and Optimisation in AVEVA Process Simulation

Year: 2025

Type: article

Abstract: ABSTRACT This study demonstrates the potential of AVEVA Process Simulation (APS) as a platform for developing process engineering digital twins, integrating customised process models, simulation, and optimisation into a unified environment. A custom gasifier model was built within APS, incorporating thermodynamic equilibrium and empirical corrections to enhance predictive accuracy, and validated against experimental data. Various gasification scenarios were simulated to evaluate the influence of equivalence ratio, biomass moisture, and temperature on syngas composition and process performance. APS's flexibility in handling complex kinetic systems was showcased through the implementation of a fugacity‐based kinetic model for methanol synthesis, which deviates from traditional Langmuir‐‐Hinshelwood reaction rates. Notably, APS accepts reaction rates in any mathematical form without the need for adaptation to predefined formats, enabling the seamless implementation of unconventional kinetic expressions. Systematic scenario analysis using APS's case study feature explored the effects of temperature, pressure, and feed composition on methanol yield, identifying optimal ratios and the adverse impact of excess . Finally, multi‐variable optimisation was performed to maximise methanol production by simultaneously tuning key operating variables. The results highlight APS's capability not only as an advanced process simulator but as a foundational tool for creating predictive adaptable digital twins that support process design, optimisation, and decision‐making across the process lifecycle.

Source: Digital twins and applications.

DOI: 10.1049/dgt2.70009

Authors: Geisiane Rodrigues Pereira, Heleno Bispo, Thiago Vaz da Costa, Sérgio Mauro da Silva Neiro

Institutions: Universidade Federal de Uberlândia, Universidade Federal de Campina Grande

Language: English

Topic: Process Optimization and Integration

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure, Climate action

Open Access status: diamond

Funder: Coordenação de Aperfeiçoamento de Pessoal de Nível Superior