Adnan KhanAdnanProduct & AI
San Francisco Bay Area
Get in touch

Source note2 min read

Explaining Process Control Optimisation Recommendations via GradientSHAP and Implicit Differentiation

Source

arXiv

arxiv.org/abs/2607.14970v1

Read the source

Source

Plain-Language Brief

A new approach is presented to explain optimization recommendations in industrial processes using GradientSHAP, Implicit Function Theorem (IFT), and Large Language Models (LLM). The method provides efficient and accurate SHAP attributions for an industrial High Pressure Grinding Roll (HPGR) control optimisation problem, leading to real-time natural language explanations. The approach was validated on industrial scenarios and received positive feedback from domain experts.

What the Document Says

  • The authors present an approach that integrates Implicit Function Theorem (IFT) based sensitivity analysis with SHAP attribution and narrative generation via Large Language Models (LLM).
  • The method computes exact parameter sensitivities from the optimality conditions using IFT, enabling efficient GradientSHAP computation.
  • For an industrial High Pressure Grinding Roll (HPGR) control optimisation problem with 22 features, the approach achieves equivalent SHAP attributions with over 40x speedup compared to KernelSHAP.
  • The method enables real-time natural language explanations.
  • The approach was validated on industrial scenarios and received positive feedback from domain experts.

Key Concepts Mentioned

  • Automated optimization
  • Explainable AI
  • SHAP
  • Implicit Function Theorem (IFT)
  • GradientSHAP
  • High Pressure Grinding Roll (HPGR)
  • Natural Language Processing (NLP)

Claims Made by the Document

  • The proposed approach improves the efficiency of SHAP attributions for industrial optimization problems.
  • The method achieves equivalent SHAP attributions with over 40x speedup compared to KernelSHAP for an industrial HPGR control optimisation problem.
  • Real-time natural language explanations are possible using the presented approach.

Evidence Used

  • The text describes the integration of IFT and GradientSHAP for efficient computation of SHAP attributions.
  • The text mentions a speedup of over 40x compared to KernelSHAP for an industrial HPGR problem.
  • The text states that real-time natural language explanations are enabled by the presented approach.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

Automated optimisation is increasingly adopted in industrial processes, yet a trust gap persists between engineers who design these algorithms and operators who must act on their recommendations. Explainable AI methods like SHAP (SHapley Additive exPlanations) have transformed interpretability for machine learning predictions; optimisation outputs could benefit from similar techniques. We present an approach that integrates Implicit Function Theorem (IFT) based sensitivity analysis with SHAP attribution and narrative generation via Large Language Models (LLM), producing explanations tailored for operators. Our approach leverages IFT to compute exact parameter sensitivities $\partial p^*/\partial x$ from the optimality conditions, enabling efficient GradientSHAP computation. For an industrial High Pressure Grinding Roll (HPGR) control optimisation problem with 22 features, we achieve equivalent SHAP attributions (correlation $>$0.99 with KernelSHAP) with over 40$\times$ speedup, enabling real-time natural language explanations. We validate on industrial scenarios and present feedback from domain experts on generated explanations.