Data-efficient and interpretable machine learning for iridium-catalyzed methanol carbonylation: Ru-enabled operating windows and process-ready guidance

Zhou Xiaokun; Yang Tao; Qin Yao; Li Yue; Wei Qiyue; Xie Jinhui; Wu Yanxun; Liu Yuchang; Wei Zengxi; Zhao Shuangliang

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Data-efficient and interpretable machine learning for iridium-catalyzed methanol carbonylation: Ru-enabled operating windows and process-ready guidance

Updated：2026-08-24

- Data-efficient and interpretable machine learning for iridium-catalyzed methanol carbonylation: Ru-enabled operating windows and process-ready guidance
- Chinese Journal of Chemical Engineering Vol. 94, Issue 6, Pages: 147-155(2026)
- Affiliations：
  
  University Engineering Research Center of Green Chemical New Materials, School of Chemistry and Chemical Engineering, Guangxi University, Nanning 530004, China
- Author bio：
  
  Corresponding authors. E-mail addresses: zengxiwei@gxu.edu.cn(Z. Wei)
  szhao@gxu.edu.cn(S. Zhao)
- Funds：
- DOI：
  CLC：
- Received：07 November 2025，
  
  Revised：2026-01-06，
  
  Accepted：06 January 2026，
  
  Online First：26 February 2026，
  
  Published：2026-06
- Accepted：
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Zhou Xiaokun, Yang Tao, Qin Yao, et al. Data-efficient and interpretable machine learning for iridium-catalyzed methanol carbonylation: Ru-enabled operating windows and process-ready guidance[J]. Chinese Journal of Chemical Engineering, 2026, 94(6): 147-155.
DOI：

Zhou Xiaokun, Yang Tao, Qin Yao, et al. Data-efficient and interpretable machine learning for iridium-catalyzed methanol carbonylation: Ru-enabled operating windows and process-ready guidance[J]. Chinese Journal of Chemical Engineering, 2026, 94(6): 147-155. DOI：

摘要

Abstract

Methanol carbonylation is the primary method for producing acetic acid

but the formulation screening and process control for homogeneous iridium systems are hindered by limited and heterogeneous datasets. We compile literature and patent records and construct stratified models based on the presence of a ruthenium cocatalyst

integrating data augmentation with Bayesian model selection to achieve high-accuracy

interpretable predictors of formation rate. Multi-scale interpretation

(including rank correlations and SHAP analysis

reveals that the addition of ruthenium (Ru) alters rate control: the

insitu

water level emerges as the primary driver through its influence on acidity/iodide activity and methyl iodide (MeI) regeneration. Consequently

the marginal effectiveness of iridium (Ir) increases

whereas the independent impact of initial acetic acid (AcOH)/MeI is diminished due to the swift establishment of solvent and MeI pools. Guided by these findings

we define Ru-enabled operating windows for Ru-containing systems

prioritizing

in-situ

concentration management by keeping H

O at a relatively low but non-zero level and maintaining MeOAc in a MeOAc-rich region where the SHAP curves begin to flatten

indicating diminishing returns. On this basis

fine-tune Ir loading and pressure in small i

ncrements to enhance per-Ir effective turnover without disturbing the established

in-situ

balance.

关键词

Keywords

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