Optimization Framework for Crushing Plants
Licentiatavhandling, 2019

Optimization is a decision-making process to utilize available resources efficiently. The use of optimization methods provide opportunities for continuous improvements, increasing competitiveness, trade-off analysis and as a support tool for the decision-making process in industrial applications. One of such industrial applications where optimization methods are needed is coarse comminution and classification processes for aggregates and minerals processing industries. The coarse comminution and classification process, consisting of crushing and screening, is a heavy industrial process characterized by continuous operations. The processes handle large material volumes, are energy intensive, and suffer large variabilities during process operations.

To understand the complexity and to replicate the process performance of the coarse comminution and classification processes, process simulation models have been under development for the past few decades. There are two types of process simulation models: steady-state simulation and dynamic simulation. The steady-state simulation models are based on instantaneous mass balancing while the dynamic simulation models are capable of capturing the process change over time due to non-ideal operating conditions. Both simulation types are capable of capturing the process performance, although the dynamic process simulations have been proven to have a higher fidelity for industrial applications. Both the steady-state and dynamic simulation models lack the capability of optimization methods which can potentially increase the utilization of the developed process simulation models. The optimization capabilities can further increase the functionality of the process simulation models and provide decision-making support.

The thesis presents a modular optimization framework for carrying out process optimization and process improvements in a coarse comminution and classification process using process simulation models. The thesis describes the results of explorative studies carried out for developing the application of optimization methods and key performance indicators for the coarse comminution and classification process. The application of the optimization methods can generate new insights about the process performance with respect to the operating parameters, and non-intuitive results. The application of the key performance indicators can be used to carry out process diagnostics and process improvement activities. As a conclusion, a conceptual framework for carrying out optimization procedure within the coarse comminution and classification process is presented. The development of the optimization system and performance measuring system can be useful for process optimization and process improvements for industrial applications.

Minerals Processing

Industry 4.0

Multi-Objective Optimization (MOO)

Screening

Process Optimization

Process Improvement

Modelling

Comminution

Key Performance Indicators (KPIs)

Classification

Multi-Disciplinary Optimization (MDO)

Crushing

Dynamic Simulations

Virtual Development Laboratory (VDL), Chalmers Tvärgata 4C
Opponent: Dr. Mats Lindqvist, FLSmidth & Co. A/S

Författare

Kanishk Bhadani

Chalmers, Industri- och materialvetenskap, Produktutveckling

State of the Art in Application of Optimization Theory in Minerals Processing

European Symposium on Comminution and Classification, Izmir, Turkey,; (2017)

Konferensbidrag (offentliggjort, men ej förlagsutgivet)

Application of multi-disciplinary optimization architectures in mineral processing simulations

Minerals Engineering,; Vol. 128(2018)p. 27-35

Artikel i vetenskaplig tidskrift

Comparative Study of Optimization Schemes in Mineral Processing Simulations

IMPC 2018 - 29th International Mineral Processing Congress,; Vol. 2019(2018)p. 464-473

Paper i proceeding

Ämneskategorier

Mineral- och gruvteknik

Maskinteknik

Produktionsteknik, arbetsvetenskap och ergonomi

Drivkrafter

Hållbar utveckling

Styrkeområden

Produktion

Utgivare

Chalmers tekniska högskola

Virtual Development Laboratory (VDL), Chalmers Tvärgata 4C

Opponent: Dr. Mats Lindqvist, FLSmidth & Co. A/S

Mer information

Senast uppdaterat

2019-05-20