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Data-driven models can fail in real-time optimization, research finds

By PulseAugur Editorial · [1 sources] ·

A new research paper explores the limitations of data-driven models in real-time optimization (RTO) for industrial processes. While these models can accurately fit historical data, they may fail to identify the true economic optimum, instead presenting numerous "phantom optima." The study highlights that even with perfect data and initialization, the training process itself can introduce errors, leading to suboptimal RTO solutions. The findings suggest that models used for RTO should be rigorously tested on decision-oriented benchmarks before deployment. AI

IMPACT Highlights potential pitfalls in applying machine learning to critical industrial optimization tasks, suggesting a need for more robust validation methods.

RANK_REASON The cluster contains a research paper detailing a new finding about the limitations of data-driven models in a specific application. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Data-driven models can fail in real-time optimization, research finds

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The cluster contains a research paper detailing a new finding about the limitations of data-driven models in a specific application. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Prithvi Dake, Rahul Bindlish, James B. Rawlings ·

    A tale of perfect fit and phantom optima: how data-driven models can fail in real-time optimization

    arXiv:2608.23885v1 Announce Type: cross Abstract: Real-time optimization (RTO) relies on process models to locate economically optimal operating conditions. Because developing first-principles models requires significant process knowledge, data-driven alternatives are increasingl…

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