Risk-Priced Radio Resources for In-Line Spectroscopy and Reaction Monitoring in Chemical Plants
Abstract
In-line spectroscopy now carries the chemical plant’s quality decisions over shared industrial wireless links, yet the schedulers distributing those links optimize throughput, fairness or delay and are blind to chemistry: an MIR quantification stream survives compression, while a runaway precursor confined to a narrow wavenumber window disappears with it. We present ChemRAN, a chemistry-aware orchestration framework for wireless process analytical technology networks. ChemQoI predicts how an allocation changes a stream’s analytical error, detection sensitivity and measurement-failure probability, and converts them into an expected chemical risk reduction using a sparse spectral sketch and a process cost matrix. SCARF allocates each window by marginal risk reduction under a hard safety floor for hazard streams, and RECAP couples allocation with offloading through a shared adaptation tensor and asynchronous updates conditioned on chemistry descriptors. On the Tennessee Eastman process, Buchwald–Hartwig and Suzuki–Miyaura screens and in-line MIR soft sensing, ChemRAN attains 0.9755 F1, 0.966 yield 𝑅2 and 1.08 g L−1 𝜅-casein RMSECV, cuts the risk-weighted chemical loss by 19% and the hazard miss rate from 2.31% to 0.88%, and retains over 90% of its performance on unseen plants and campaigns.
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BibTeX
@misc{pan2026riskpriced,
title = {{Risk-Priced Radio Resources for In-Line Spectroscopy and Reaction Monitoring in Chemical Plants}},
author = {Zeyu Pan and Yating Qin and Yihang Wu and Chenyi Zhang and Zixuan Peng Zhongnan},
howpublished = {ChemRxiv},
year = {2026},
month = oct,
doi = {10.26434/chemrxiv.15010836/v1},
url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010836/v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Risk-Priced Radio Resources for In-Line Spectroscopy and Reaction Monitoring in Chemical Plants
AU - Zeyu Pan
AU - Yating Qin
AU - Yihang Wu
AU - Chenyi Zhang
AU - Zixuan Peng Zhongnan
PY - 2026
DA - 2026/10/22
PB - ChemRxiv
DO - 10.26434/chemrxiv.15010836/v1
UR - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010836/v1
AB - In-line spectroscopy now carries the chemical plant’s quality decisions over shared industrial wireless links, yet the schedulers distributing those links optimize throughput, fairness or delay and are blind to chemistry: an MIR quantification stream survives compression, while a runaway precursor confined to a narrow wavenumber window disappears with it. We present ChemRAN, a chemistry-aware orchestration framework for wireless process analytical technology networks. ChemQoI predicts how an allocation changes a stream’s analytical error, detection sensitivity and measurement-failure probability, and converts them into an expected chemical risk reduction using a sparse spectral sketch and a process cost matrix. SCARF allocates each window by marginal risk reduction under a hard safety floor for hazard streams, and RECAP couples allocation with offloading through a shared adaptation tensor and asynchronous updates conditioned on chemistry descriptors. On the Tennessee Eastman process, Buchwald–Hartwig and Suzuki–Miyaura screens and in-line MIR soft sensing, ChemRAN attains 0.9755 F1, 0.966 yield 𝑅2 and 1.08 g L−1 𝜅-casein RMSECV, cuts the risk-weighted chemical loss by 19% and the hazard miss rate from 2.31% to 0.88%, and retains over 90% of its performance on unseen plants and campaigns.
ER -