Explaining Process Control Optimisation Recommendations via GradientSHAP and Implicit Differentiation

2026-07-16

AI Summary

This study addresses the challenge of gaining operator trust in industrial process optimization recommendations, which often suffer from insufficient interpretability. The authors propose an efficient attribution method that integrates sensitivity analysis based on the implicit function theorem with GradientSHAP—a novel combination for explaining optimization outputs—and leverages a large language model to generate natural-language explanations tailored for plant operators. Evaluated on a high-pressure grinding roll (HPGR) control optimization task involving 22 input features, the proposed approach achieves a correlation exceeding 0.99 with KernelSHAP attributions while accelerating computation by over 40×, thereby enabling real-time interpretability. The method received positive assessments from domain experts for its clarity and practical utility.

Abstract

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.

Problem

• Explainable AI
• Process Control Optimisation
• Trust Gap
• SHAP
• Industrial Automation

Innovation

• Implicit Function Theorem
• GradientSHAP
• Explainable AI
• Process Control Optimization
• Large Language Models