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Robust Nonlinear Soft Sensor for Online Estimation of Product Compositions in Heat-Integrated Distillation Column

Lookup NU author(s): NURA TAHIR, Dr Jie ZhangORCiD, Dr Matthew Armstrong

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

Abstract

© 2025 by the authors.This paper proposes the development of a robust nonlinear soft sensor for online estimation of product compositions in a Heat-Integrated Distillation Column (HIDiC). Traditional composition analyzers, such as gas chromatographs, are costly and suffer from long measurement delays, making them inefficient for real-time monitoring and control. To address this, data-driven soft sensors are developed using tray temperature data obtained from a high-fidelity dynamic HIDiC simulation. The study investigates both linear and nonlinear modeling strategies for composition estimation, including principal component regression (PCR), artificial neural networks (ANNs), and, for the first time in HIDiC modeling, a Bidirectional Long Short-Term Memory (BiLSTM) network. The objective is to evaluate the capability of each method for accurate estimation of product compositions in a HIDiC. The results demonstrate that the BiLSTM-based soft sensor significantly outperforms conventional methods and offers strong potential for enhancing real-time composition estimation and control in HIDiC systems.

Publication metadata

Author(s): Tahir NM, Zhang J, Armstrong M

Publication type: Article

Publication status: Published

Journal: ChemEngineering

Year: 2025

Volume: 9

Issue: 4

Online publication date: 11/08/2025

Acceptance date: 07/08/2025

Date deposited: 09/09/2025

ISSN (electronic): 2305-7084

Publisher: Multidisciplinary Digital Publishing Institute (MDPI)

URL: https://doi.org/10.3390/chemengineering9040087

DOI: 10.3390/chemengineering9040087

Data Access Statement: Data will be made available on request

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