Hybrid Digital Twin for process industry using Apros simulation environment

TL;DR

This paper proposes a hybrid Digital Twin approach for process plants, combining first-principles models with data-driven machine learning models to improve accuracy and adaptability over the plant's lifecycle.

Contribution

It introduces a step-by-step method for updating and creating hybrid Digital Twins of process plants using Apros simulation and machine learning, especially for brownfield systems.

Findings

Enhanced accuracy of Digital Twins through data integration

Continuous improvement of models with process history data

Discussion of challenges in generating as-built hybrid Digital Twins

Abstract

Making an updated and as-built model plays an important role in the life-cycle of a process plant. In particular, Digital Twin models must be precise to guarantee the efficiency and reliability of the systems. Data-driven models can simulate the latest behavior of the sub-systems by considering uncertainties and life-cycle related changes. This paper presents a step-by-step concept for hybrid Digital Twin models of process plants using an early implemented prototype as an example. It will detail the steps for updating the first-principles model and Digital Twin of a brownfield process system using data-driven models of the process equipment. The challenges for generation of an as-built hybrid Digital Twin will also be discussed. With the help of process history data to teach Machine Learning models, the implemented Digital Twin can be continually improved over time and this work in progress can be further optimized.