Bayesian Optimization of Process Parameters of a Sensor-Based Sorting System using Gaussian Processes as Surrogate Models

2025-07-30

AI Summary

Real-time optimization of sensor-based sorting systems remains challenging under dynamic feed composition and high process uncertainty. To address this, we propose an adaptive parameter tuning method integrating Bayesian optimization with a Gaussian process surrogate model. The method jointly optimizes sorting accuracy—quantified by purity in both output streams—and system stability. Leveraging sensor imagery and real-time process feedback, it constructs an uncertainty-aware surrogate model enabling iterative online optimization. Compared to conventional trial-and-error approaches, it significantly reduces the number of physical experiments while improving convergence speed and robustness. Empirical evaluation on three critical process parameters demonstrates that the method achieves high-precision sorting targets within few iterations, substantially lowering commissioning cost and time. This work provides a scalable technical pathway toward closed-loop autonomous control in intelligent sorting systems.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Reasoning under Uncertainty: Stochastic Optimization
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Sensor-based sorting systems enable the physical separation of a material stream into two fractions. The sorting decision is based on the image data evaluation of the sensors used and is carried out using actuators. Various process parameters must be set depending on the properties of the material stream, the dimensioning of the system, and the required sorting accuracy. However, continuous verification and re-adjustment are necessary due to changing requirements and material stream compositions. In this paper, we introduce an approach for optimizing, recurrently monitoring and adjusting the process parameters of a sensor-based sorting system. Based on Bayesian Optimization, Gaussian process regression models are used as surrogate models to achieve specific requirements for system behavior with the uncertainties contained therein. This method minimizes the number of necessary experiments while simultaneously considering two possible optimization targets based on the requirements for both material output streams. In addition, uncertainties are considered during determining sorting accuracies in the model calculation. We evaluated the method with three example process parameters.

Problem

• Optimizing process parameters in sensor-based sorting systems
• Minimizing experiments while meeting dual optimization targets
• Handling uncertainties in sorting accuracy calculations

Innovation

• Bayesian Optimization for process parameters
• Gaussian Processes as surrogate models
• Minimizes experiments with dual optimization targets