Professor Mohsen Yahyaei

Professorial Research Fellow and Ce

Sustainable Minerals Institute
m.yahyaei@uq.edu.au
+61 7 334 65989

Overview

Mohsen Yahyaei is an expert in modelling, optimising, and controlling Mineral Processing circuits using novel approaches and tools. He is currently the Director of the Julius Kruttschnitt Mineral Research Centre (JKMRC) and Program Leader for Future Autonomous Systems & Technologies (FAST) at the University of Queensland’s Sustainable Minerals Institute.

Mohsen completed his undergraduate study in Mine Exploration, and in 2002, he completed his Master’s degree in Mineral Processing. He worked on applying column flotation in Sarcheshmeh Copper Complex (The largest copper mine in the Middle East) as his Master’s thesis. After completing his Master’s degree, he worked with the R&D centre of Zarand coal washing plant in Iran for two years before moving to an operational role as plant manager of a Coal washing plant in Zarand. In 2007, he returned to the University of Kerman to do his PhD, investigating the effect of liner wear in charge motion and power draw of SAG mills. He completed his PhD in 2010, and after working on several industry-funded projects in Iran, he joined JKMRC in 2011.

Mohsen has extensive experience conducting applied research, and over the past 15 years, he has successfully delivered several industry-funded projects. He is a comminution specialist who strongly desires to implement fundamental understandings in his research to offer solutions to the minerals industry and educate engineers and researchers with problem-solving skills for tackling future resource industry challenges.

Research Interests

  • Process Autonomy
    Enabling tools and technologies to enable trusted autonomous systems and technologies for mineral processing plants.
  • Dynamic modelling for process control and optimisation
    New approach in implementing dynamic modelling of mineral processing circuits for developing process control strategies and accessing process performance
  • Study surface breakage of rock particles
    Measuring surface breakage of rocks under different loading mechanisms to inform mechanistic breakage models
  • Mechanistic approach in liner wear modelling
    Incorporating factors affecting liner wear in the structure of a mechanistic wear model

Research Impacts

With a successful background in industry-based work, my contributions to the development of soft sensors for comminution and classification circuits have delivered significant value to the industry. One such contribution is the JK Mill FIT, a soft sensor for real-time monitoring of tumbling mill content. This soft sensor has been installed in more than 20 operations globally and has had a significant impact on process stability, enhancing the utilization of grinding mills and reducing operational costs. Another solution I have been involved in developing is the JK Dynamic Stockpile/Bin model. This unique model offers significant opportunities for real-time process prediction and automation of mineral processing circuits. It enables ore tracking from the ground to the processing plant, enhancing Mine to Mill optimization and geometallurgical modelling. This solution has been validated with industrial data and implemented in several industrial applications. The range of new soft sensors and dynamic models that are in the pipeline of my research will transform the future of process control in mineral processing circuits.

As director of JKMRC, a world-leading research centre in mineral processing, I have contributed to the research direction of the centre, pivoting it towards developing and delivering transformational processing solutions to the resources industry. Our applied research and practical solutions have significantly contributed to the sustainable development of society through the supply of transitional mineral resources with minimal environmental and social impacts. Our research focuses on aspects of the process such as water utilization, mine waste reduction or transformation into by-products, energy efficiency, enabling the transition to green energy sources, and minimizing process waste, all of which are linked to sustainable development goals.

Qualifications

  • Masters (Coursework) of Business Administration, The University of Queensland
  • Doctoral Diploma of Engineering, Shahid Bahonar University of Kerman

Publications

View all Publications

Supervision

View all Supervision

Available Projects

  • Project Description

    A wide range of process control strategies have been developed for stabilising grinding circuits. Various degrees of control technology are applied ranging from simple PID feedback loops to advanced process control systems including expert systems, machine learning and model predictive controllers. The difference with respect to plant production performance can be substantial. However, advanced controllers are usually installed as a control system upgrade and due to their complexity, their performance can be unreliable. It is all too frequent that advanced control systems in grinding circuits are switched off to revert to conventional controllers. This PhD project aims to investigate this problem by analysing industrial case studies of advanced controllers and their effectiveness. This includes simulating the grinding circuits in Matlab/Simulink and developing a set of metrics that allow the effectiveness of the advanced control systems to be evaluated.

    Project Objectives

    This HDR project aims to develop a framework for assessing the effectiveness of different advanced process control strategies and tools to understand how to select process control strategies in grinding circuit applications.

  • Project Description

    For the past two decades, large mining companies have made major investments in “digitisation” projects to integrate sensor technologies and data flows across their operations with process control and management systems using sophisticated data analytics and upgraded IT infrastructure. The explosion of new advances in this area has seen the recent availability of low-cost hardware based on open standards and high-quality open source software toolsets that can be applied digitisation projects at mine sites. The proposed PhD project will review which digitisation strategies have been successful in the minerals industry and which strategies already used in larger operations can be translated to smaller scale mining and processing operations.

    Project Objectives

    This HDR project is focused at mining companies that have not yet implemented large digitisation/data analytics/big data projects, nor installed centralised process control centres. The project aims to identify specific opportunities and data analytics work flow with the aim of demonstrating the value of digitisation technologies. It is intended that the study will work closely with a small-medium mining company and result in a work-flow and tool set framework for real-time data analytics and case-study implementation of ideas generated during the study.

  • Project Description

    Due to the complexity of the process dynamics in most SAG mill circuits, their industrial control systems typically comprise cascaded control loops, and often use some form of expert system or advanced control. This HDR project aims to be a case study for the application of the Model-informed Process Control (MiPC) concept to grinding circuits. The MiPC methodology incorporates dynamic models of processing units into a process control layer linked to process sensor data. The unit models for the grinding and classification units in the circuit are to be based on the latest theoretical phenomenological models developed at SMI-JKMRC. Unlike standard process control loops based on feedback, using mathematical models which are mathematical analogues of the actual process allows the future process state to be predicted. The methodology therefore aims to forward-predict effects of disturbances and respond accordingly, and to infer process conditions that cannot be easily measured with instrumentation such as changes in ore hardness. The project will require the researcher to travel to a mine site to help design an appropriate control strategy, obtain operating data, and to implement process control modifications.

    Project Objectives

    This HDR project aims to demonstrate the effectiveness of Model-informed Process Control in stabilising and operating industrial grinding circuits. The project will also investigate opportunities to collect additional sensor data for interacting with the models. It is intended that the study will begin with laboratory or bench-scale developments which will then be extended to an implementation within an industrial milling circuit.

View all Available Projects

Publications

Book Chapter

  • Yahyaei, Mohsen, Hilden, Marko, Shi, Fengnian, Liu, Lian X., Ballantyne, Grant and Palaniandy, Sam (2016). Comminution. Production, Handling and Characterization of Particulate Materials. (pp. 157-199) edited by Henk G. Merkus and Babriel M.H. Meesters. Switzerland: Springer International Publishing. doi: 10.1007/978-3-319-20949-4

Journal Article

Conference Publication

Other Outputs

Grants (Administered at UQ)

PhD and MPhil Supervision

Current Supervision

  • Doctor Philosophy — Principal Advisor

  • Doctor Philosophy — Principal Advisor

  • Doctor Philosophy — Principal Advisor

  • Doctor Philosophy — Principal Advisor

  • Doctor Philosophy — Associate Advisor

  • Doctor Philosophy — Associate Advisor

  • Doctor Philosophy — Associate Advisor

  • Doctor Philosophy — Associate Advisor

  • Doctor Philosophy — Associate Advisor

  • Doctor Philosophy — Associate Advisor

    Other advisors:

Completed Supervision

Possible Research Projects

Note for students: The possible research projects listed on this page may not be comprehensive or up to date. Always feel free to contact the staff for more information, and also with your own research ideas.

  • Project Description

    A wide range of process control strategies have been developed for stabilising grinding circuits. Various degrees of control technology are applied ranging from simple PID feedback loops to advanced process control systems including expert systems, machine learning and model predictive controllers. The difference with respect to plant production performance can be substantial. However, advanced controllers are usually installed as a control system upgrade and due to their complexity, their performance can be unreliable. It is all too frequent that advanced control systems in grinding circuits are switched off to revert to conventional controllers. This PhD project aims to investigate this problem by analysing industrial case studies of advanced controllers and their effectiveness. This includes simulating the grinding circuits in Matlab/Simulink and developing a set of metrics that allow the effectiveness of the advanced control systems to be evaluated.

    Project Objectives

    This HDR project aims to develop a framework for assessing the effectiveness of different advanced process control strategies and tools to understand how to select process control strategies in grinding circuit applications.

  • Project Description

    For the past two decades, large mining companies have made major investments in “digitisation” projects to integrate sensor technologies and data flows across their operations with process control and management systems using sophisticated data analytics and upgraded IT infrastructure. The explosion of new advances in this area has seen the recent availability of low-cost hardware based on open standards and high-quality open source software toolsets that can be applied digitisation projects at mine sites. The proposed PhD project will review which digitisation strategies have been successful in the minerals industry and which strategies already used in larger operations can be translated to smaller scale mining and processing operations.

    Project Objectives

    This HDR project is focused at mining companies that have not yet implemented large digitisation/data analytics/big data projects, nor installed centralised process control centres. The project aims to identify specific opportunities and data analytics work flow with the aim of demonstrating the value of digitisation technologies. It is intended that the study will work closely with a small-medium mining company and result in a work-flow and tool set framework for real-time data analytics and case-study implementation of ideas generated during the study.

  • Project Description

    Due to the complexity of the process dynamics in most SAG mill circuits, their industrial control systems typically comprise cascaded control loops, and often use some form of expert system or advanced control. This HDR project aims to be a case study for the application of the Model-informed Process Control (MiPC) concept to grinding circuits. The MiPC methodology incorporates dynamic models of processing units into a process control layer linked to process sensor data. The unit models for the grinding and classification units in the circuit are to be based on the latest theoretical phenomenological models developed at SMI-JKMRC. Unlike standard process control loops based on feedback, using mathematical models which are mathematical analogues of the actual process allows the future process state to be predicted. The methodology therefore aims to forward-predict effects of disturbances and respond accordingly, and to infer process conditions that cannot be easily measured with instrumentation such as changes in ore hardness. The project will require the researcher to travel to a mine site to help design an appropriate control strategy, obtain operating data, and to implement process control modifications.

    Project Objectives

    This HDR project aims to demonstrate the effectiveness of Model-informed Process Control in stabilising and operating industrial grinding circuits. The project will also investigate opportunities to collect additional sensor data for interacting with the models. It is intended that the study will begin with laboratory or bench-scale developments which will then be extended to an implementation within an industrial milling circuit.

  • Project Description

    Successful optimisation of current comminution circuits and the ability to model and simulate novel and complex circuits is likely to become essential to our need to improve the processing efficiency of ore deposits dramatically. However, our ability to understand and simulate the interactions between ore characteristics and operating factors with process efficiency is still limited to empirical models. A fundamental understanding of the under-pinning mechanisms of size reduction is vital for developing a mechanistic model of comminution. To enable this, an appropriate approach in ore characterisation is critical to experimentally test the breakage under conditions as close as possible to those occurring in the size reduction processes. The drop weight test and JKRBT developed in the Julius Kruttschnitt Mineral Research Centre (JKMRC) are well established for characterising the behaviour of ores in impact and incremental breakage. However, there is no such robust methodology for characterising the surface damage behaviour of rocks. Low energy surface damage, even though it occurs at the lower end of the energy spectrum, has a high frequency of occurrence and plays a significant part during any size reduction process. This project aims to investigate the mechanics of surface damage of various materials under different ranges of stress levels and mechanisms to provide a fundamental understanding of surface damage. Specifically, it aims to study characteristics of the surface damage progeny and develop a methodology to classify ores based on their surface damage behaviour in comminution. The project also aims to apply the understanding of superficial breakage mechanisms to develop a mechanical abrasion model, which is applicable within the UCM’s (Unified Comminution Model) framework.

  • More projects are available on Comminution and Classification, Process modelling, Dynamic Modelling, Advanced Process Control and Digitalisation. For details please conact me,

  • Project Description

    Most AG and SAG mills use a pulp lifter to remove slurry from the mill. Slurry and fine particles (but not the grinding media) pass through grate apertures at the discharge end of the mill to enter a series of radial compartments, then as the mill rotates with the slurry inside and the compartment is upended, the slurry pours out of the mill. Reducing the efficiency of discharge are: 1) flowback, which occurs when slurry pours via the apertures back into the mill before able to be discharged and 2) carryover, which occurs when some of the slurry remains within the compartment often due to the centrifugal effect.

    Various pulp lifter designs are available. In addition to the commonly used radial design, various curved designs offer higher capacity. Furthermore, various chamber designs such as the turbo pulp lifter improve discharge efficiency by reducing flowback and carryover.

    The pulp lifter efficiency influences the slurry level for a given throughput, and therefore the grinding performance. There is therefore a link between the lifter design and metallurgical performance of a mill. Unfortunately, models of pulp lifters are inadequate for design and more work needs to be done to understand how various aspects of pulp lifter design impact on the discharge capacity. Moreover, the link between discharge capacity and the grinding performance also needs to be quantified.

    Project Objectives

    Areas of possible research objectives related to the pulp lifter discharge include:

    1. Quantify the relationship between mill holdup (filling) and discharge using scale experiments. Limitations of previous experimental work in this area include 1) use of spherical media/water mixtures 2) use of flat-ended designs (cone angle = 0 deg) 3) test mills lacking geometric scaling to industrial mills. 3D printing technology, for example, would enable more realistic scale models to be constructed quickly and cheaply. Data can be used for developing mathematical models of pulp lifter discharge.
    2. Gain insights into pulp lifter performance including the effect of grate wear on rates of flowback and discharge using numerical methods (DEM/CFD/SPH), and investigate to what extent grate and pulp lifter design can be used to influence this. Data can be used for developing mathematical models or for comparing or developing improved lifter designs.
    3. Measure SAG mill grinding performance under different slurry discharge rates. For example, a high discharge capacity will result in a lower slurry filling, but a low discharge capacity would result in a higher slurry filling and potentially a slurry pool inside the mill for a given mill filling. Its effect on grinding rates can be studied in a pilot scale mill and/or by analysis of industrial-scale mills surveyed at different times. The outcomes could be used for SAG Mill modelling and for optimising the grate relining and mill speed control strategies employed on mine sites.
    4. Develop a mathematical model of the discharge capacity. This should include the sub-processes of flowback and carryover. The model would be useful for process design and optimisation.
    5. Develop methods to monitor pulp lifter performance, such as through the application of sensors. The methodologies developed can be used for process optimisation and improving SAG mill control strategies.
  • Project Description

    Neither the SAG mill nor Ball Mill model contain a mechanistic sub-model of mill transport; and the empirical models used presently encompass other discharge mechanisms such as the pulp lifter and flowback in SAG mills and the flow through the trunnion in ball mills. The transport rate determines the ability of particles and slurry to flow through the mill charge and the axial diffusion rate of particles, and is dependent in particular on charge porosity / tortuosity, slurry rheology, and the mill breakage environment. This HDR project proposes to extend both the AG/SAG mill model and the ball mill model with the inclusion of a new transport sub-model based on the theoretical transport equations. A proposed route for modelling is the application of the model being developed by Prof Indresan Govender at UKZN. Govender’s modelling datasets have used bead media therefore the model needs to be tested on real ore slurries and charges containing coarse particles.

    Project Objectives

    The project objectives are to develop experimental test equipment and procedures to measure transport rate, while controlling slurry viscosity; experimentally and numerically validate the Govender mathematical model of mill charge transport for mineral ore slurries and charge containing balls and coarse particles; and incorporate this model into the AG/SAG and ball mill models.