TY - BOOK

T1 - Databased Modeling and Control of Dry Grinding / Classification-Circuits

AU - Kirchner, Rupert

N1 - no embargo

PY - 1800

Y1 - 1800

N2 - The diverse and increasingly stringent demands on the mineral processing industry ¿ such as energy efficiency, CO2 neutrality, and product quality ¿ require innovative control solutions to ensure that the processing methods and techniques used can continue to be operated economically in the future. This thesis introduces a machine learning-based approach, specifically utilizing online reinforcement learning, to offer a dynamic and customizable control system. Focusing on key operational variables, a data-based digital twin of the grinding circuit was developed as an accurate simulation tool for operational changes. This allows for the testing of new setpoints without negatively impacting the real-world operation of the plant. A specialized training environment for reinforcement learning algorithms was also established, reflecting the actual characteristics of the grinding circuit and enabling iterative adaptation. The research conducted as part of this dissertation combines simulation and algorithmic modeling, demonstrating the potential of machine learning for improved and industrially viable control approaches. The developed control framework was rigorously tested in a semi-industrial environment, utilizing a digital twin to validate its effectiveness and readiness for real-world industrial applications. A corresponding industrial layout, designed for edge-computing deployment, adds robustness and user-friendliness to the system. Further contributions were made in exploring the Human-in-the-Loop (HITL) concept, addressing the themes of safety, legal considerations, and system scalability. The study lays a solid foundation for future academic research and offers a ready-to-implement solution for industrial applications. It brings a new dimension to grinding circuit operations by leveraging machine learning's efficiency and flexibility, thereby contributing substantively to the existing body of knowledge in the area of mineral processing.

AB - The diverse and increasingly stringent demands on the mineral processing industry ¿ such as energy efficiency, CO2 neutrality, and product quality ¿ require innovative control solutions to ensure that the processing methods and techniques used can continue to be operated economically in the future. This thesis introduces a machine learning-based approach, specifically utilizing online reinforcement learning, to offer a dynamic and customizable control system. Focusing on key operational variables, a data-based digital twin of the grinding circuit was developed as an accurate simulation tool for operational changes. This allows for the testing of new setpoints without negatively impacting the real-world operation of the plant. A specialized training environment for reinforcement learning algorithms was also established, reflecting the actual characteristics of the grinding circuit and enabling iterative adaptation. The research conducted as part of this dissertation combines simulation and algorithmic modeling, demonstrating the potential of machine learning for improved and industrially viable control approaches. The developed control framework was rigorously tested in a semi-industrial environment, utilizing a digital twin to validate its effectiveness and readiness for real-world industrial applications. A corresponding industrial layout, designed for edge-computing deployment, adds robustness and user-friendliness to the system. Further contributions were made in exploring the Human-in-the-Loop (HITL) concept, addressing the themes of safety, legal considerations, and system scalability. The study lays a solid foundation for future academic research and offers a ready-to-implement solution for industrial applications. It brings a new dimension to grinding circuit operations by leveraging machine learning's efficiency and flexibility, thereby contributing substantively to the existing body of knowledge in the area of mineral processing.

KW - Maschinelles Lernen in der Rohstoffverarbeitung

KW - Reinforcement Learning für Mahlkreisläufe

KW - Digitaler Zwilling in der Industrie

KW - Regelungssysteme für Trockenmahlprozesse

KW - Energieeffiziente Mahlverfahren

KW - Adaptives Regelungssystem

KW - Simulation von Betriebszustandsänderungen

KW - Edge-Computing in industriellen Anwendungen

KW - Industrielle Anwendungen von maschinellem Lernen

KW - Human-in-the-Loop Regelung

KW - Skalierbare Regelungsansätze

KW - Machine Learning in Mineral Processing

KW - Reinforcement Learning for Grinding Circuits

KW - Industrial Digital Twin

KW - Control Systems for Dry Grinding Processes

KW - Energy-Efficient Grinding Methods

KW - Adaptive Control System

KW - Simulation of Operational Changes

KW - Edge Computing in Industrial Applications

KW - Industrial Applications of Machine Learning

KW - Human-in-the-Loop Control

KW - Scalable Control Approaches

M3 - Doctoral Thesis

ER -