Node-Level Allocation-Based Inter-System Hydrogen−Hydrocarbon−Heat Mass−Energy Cross-Feedback Network Optimization

To address the waste of high-value-added by-product resources (HBRs) in independent chemical systems and exploit their inter-system complementarity, this paper proposes a hydrogen−hydrocarbon−heat mass−energy cross-feedback network (3H-MECFN) optimization method. Within the node-level construction, the HBR is broken into mass carriers encompassing hydrogen−hydrocarbon and energy carriers of heat for hierarchical substitution and utilization within systems for both primary and secondary resource conservation. Afterward, substitution and utilization are performed according to heuristic resource cross-feedback matrix (RCM) along different node chains to maximize HBR recovery and utilization. Besides, pressure swing adsorption (PSA)-based gas separation is carried out by Aspen simulations and a surrogate model. As a solution method, the RCM and surrogate model plugged-in mixed-integer nonlinear programming (MINLP) model is formulated for 3H-MECFN integration. Case study results of olefins production show that the scenario with 5 node levels and 9 node chains can reduce raw coal consumption by 20.7%, cooling, heating, and power demand by 79.9, 6.3, and 45.3%, respectively, as well as carbon emissions by 38.4%. This work demonstrates that 3H-MECFN enables primary and secondary resource conservation and improves chemical production systems toward low-carbon processes through inter-system resource cross-feedback, greatly advancing process integration research.

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Publication Details

Journal
ACS Sustainable Chemistry & Engineering
Published
2026-07-23
DOI
https://doi.org/10.1021/acssuschemeng.6c03708
Primary Topic
Process Optimization and Integration
Type
article
Field-Weighted Citation Impact
0.00

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article

Node-Level Allocation-Based Inter-System Hydrogen−Hydrocarbon−Heat Mass−Energy Cross-Feedback Network Optimization

Xiao Feng, Wenjing Li, Qiao Zhang
ACS Sustainable Chemistry & Engineering
Process Optimization and Integration
article

Node-Level Allocation-Based Inter-System Hydrogen−Hydrocarbon−Heat Mass−Energy Cross-Feedback Network Optimization

Xiao Feng, Wenjing Li, Qiao Zhang
article en

Abstract

To address the waste of high-value-added by-product resources (HBRs) in independent chemical systems and exploit their inter-system complementarity, this paper proposes a hydrogen−hydrocarbon−heat mass−energy cross-feedback network (3H-MECFN) optimization method. Within the node-level construction, the HBR is broken into mass carriers encompassing hydrogen−hydrocarbon and energy carriers of heat for hierarchical substitution and utilization within systems for both primary and secondary resource conservation. Afterward, substitution and utilization are performed according to heuristic resource cross-feedback matrix (RCM) along different node chains to maximize HBR recovery and utilization. Besides, pressure swing adsorption (PSA)-based gas separation is carried out by Aspen simulations and a surrogate model. As a solution method, the RCM and surrogate model plugged-in mixed-integer nonlinear programming (MINLP) model is formulated for 3H-MECFN integration. Case study results of olefins production show that the scenario with 5 node levels and 9 node chains can reduce raw coal consumption by 20.7%, cooling, heating, and power demand by 79.9, 6.3, and 45.3%, respectively, as well as carbon emissions by 38.4%. This work demonstrates that 3H-MECFN enables primary and secondary resource conservation and improves chemical production systems toward low-carbon processes through inter-system resource cross-feedback, greatly advancing process integration research.

ACS Sustainable Chemistry & Engineering
Xi'an Jiaotong University (CN)
National Natural Science Foundation of China
Decent work and economic growth
Openalex Percentile: Top 11%
Process Optimization and Integration
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Node-Level Allocation-Based Inter-System Hydrogen−Hydrocarbon−Heat Mass−Energy Cross-Feedback Network Optimization — Xiao Feng, Wenjing Li, et al. · ACS Sustainable Chemistry & Engineering (2026) | TGRS Research Map | TGRS