Advances in Low Grade Iron Ore Beneficiation

ByBy

Kamal Kant Jain

Ravindra Kumar Verma

Khalid Razi

Presentation Layout

1. Background

2. Low grade Iron Ore Beneficiation Techniques

3. Current Iron Ore Beneficiation Practices in India

4. Process Control and Automation4. Process Control and Automation

5. Advance Control Tools and Applications

6. New Developments in Iron Ore Beneficiation

7. Conclusion

8. References

1. BACKGROUND

High grade reserves of Haematite are depleting & theIndian iron ore mining scenario is changing.

In order to maximise the ore reserve utilization and meetstringent product quality required by end users industry,

rigorous beneficiation techniques are employed.rigorous beneficiation techniques are employed.

If desired quality is not met then after Crushing,Screening & Classification any one or in combination ofgravity concentration, magnetic separation, flotation,selective flocculation and pelletisation techniques areadopted to achieve desired quality

Now there is trend of integrating geology, mineralogy,mineral processing and metallurgy to build a spatially-based model for production management

( GEO-METALLURGY)

Improved technologies for increasing production Improved technologies for increasing productionefficiency while further reducing water, raw materials andenergy usage is prerequisites for balanced andsustainable development

Now Iron Ore Beneficiation Plants designed withadvance level of automation and applicationsoftwares

2. LOW GRADE IRON ORE

BENEFICIATION TECHNIQUESBENEFICIATION TECHNIQUES

Following principal process technologies/equipments are available for iron ore beneficiation:

Scrubbers (Attrition & Drum) and Log Washers Heavy Media Separation & Jig Teeter Bed Separators (like Flotex density separator, All

flux Separator etc.) Teeter Bed Separators (like Flotex density separator, All

flux Separator etc.)

Centrifugal Concentrator, Spirals & Reichert cone Magnetic Separation (LIMS, MIMS, WHIMS, HGMS &

VPHGMS)

Floatation (Conventional & Column) & SelectiveFlocculation

Pelletisation and Roasting

3. CURRENT TRENDS IN INDIA3. CURRENT TRENDS IN INDIA

Major High Capacity Iron Ore Beneficiation in

India Plant are :

Kudremukh, KIOCL (plant now stopped)

(SAG & BALL MILL, LIMS,SPIRALS, FLOTEX DENSITY SEPARATOR,FLOTATION-(SAG & BALL MILL, LIMS,SPIRALS, FLOTEX DENSITY SEPARATOR,FLOTATION-CONV. & COL.)

Barsua, SAIL(DRUM SCRUBBER,JIG)

Kirandul ,ESSAR(BALL MILL,SPIRALS,LIMS,HGMS)

Toranagallu, JSW(BALL MILL,ATTRITION SCRUBBER,LIMS,SLON/HGMS)

Barbil ,BRPL

(ROD & BALL MILL,ALLFLUX,WHIMS)

Rengali, Bhushan

(DRUM SCRUBBER,JIG,BALL MILL,SPIRALS,LIMS,HGMS)

4. PROCESS CONTROL

&&

AUTOMATION

CHANGE IN OBJECTIVE OF AUTOMATION

CONCENTRATE TONNAGE

&

TONNAGE, QUALITY &CONCENTRATE

TONNAGE MAXIMISATION

& QUALITY AS PER

REQUIREMENT

&PEAK ECONOMICPERFORMANCE

Hierarchy in Process Control

PLANT

OPTIMISATION

CONTROL

PROCESS OPTIMISATION

CONTROL

OPTIMISATION

PROCESS

DCS/PLC

INSTRUMENTATION

STABLISATION

Overall Process Unit Control

CROSS

COUPLED

MULTI

VARIABLE

TECHNIQUE

Knowledge

Based

Expert

Control

Production cost

Lower

FEED FORWARD

& CASCADE

COUPLED

Lower HigherFeed Back

Higher

5. ADVANCE CONTROL TOOLS

&

APPLICATIONS

OBJECTIVE

The commonly used present system is the Distributed Control System (DCS).

It is made up of three main components, the data highway, the operatorstation and the microprocessor based controllers.

Shift from maintaining quality to peak performance (often) requires something Shift from maintaining quality to peak performance (often) requires something

more than a DCS / PLC: an optimizing control system

Objective for advanced process control - is to establish a dynamic

mathematical model, monitor the deviation from the model and finally restorethe original optimized conditions of operation.

The process of controlling a dynamic system is complicated especially in ironore processing systems where a number of variables are involved

simultaneously

SYSTEM

Intelligent control, including ES and fuzzy logic

Model predictive control, using linear or non-linear models

originating in phenomenological or empirical models

adjusted on the basis of operating dataadjusted on the basis of operating data

Attempts have been made to combine them into a single

integrated solution (Hybrid) , with the algorithms known as

fuzzy model predictive control

Intelligent Control

Expert systems (ES) integrate the knowledge of one or moreprocess specialists into a set of rules or a knowledge basethat defines the actions of an expert controller who actssimilarly to a proportional (P), proportionalintegral (PI) orproportionalintegralderivative (PID) automatic controlalgorithmalgorithm

One of the most frequently adopted alternatives for improvingthe robustness of expert control systems in handlinguncertainties and errors is fuzzy logic.

The most commonly used membership functions aretriangular, trapezoidal or Gaussian

Expert systems that incorporate fuzzy logic into processingrules are known as fuzzy ES.

Intelligent Control..contd.

Notable among the non-linear models are neuralnetworks, which are used to numerically approximate ahighly complex non-linear function by interconnectingsimpler processing elements such as adders, multipliersand sigmoid functions.and sigmoid functions.

As with linear time series models, the neural model mustbe calibrated by adjusting its parameters to the operatingdata, a task generally performed by a back propagationgradient algorithm.

Genetic Algorithm is also one alternative in Intelligentcontrol

Model Predictive Control

Model predictive control (MPC) embraces a complete family ofcontrollers whose basic concepts are: Use of an explicit dynamic model (predicts process outputs at

discrete future time instants over a prediction horizon) Computation of a sequence of future control actions through the

optimization of an objective function with given operatingconstraints and desired reference trajectories for process outputsconstraints and desired reference trajectories for process outputs

Repetition of the optimization process at each sampling instantand application of the first value of the calculated controlsequence (receding horizon strategy)

Above three characteristics allow MPC to handle multivariable,non-minimum phase, open-loop unstable and non-linearprocesses with a long time delay and the inclusion, if necessary,of constraints for manipulated and/or controlled variables.

MPC SUPPLIERS

ABB -Expert Optimizer Andritz- BrainWave6 Emerson Process Management- Delta V Honeywell -Profit7 Suite Invensys -Connoisseur Metso Minerals -Optimizing Control System Mintek -StarCS Rockwell -Pavilion Technologies SGS -MinnovEX Expert Technology

6. NEW DEVELOPMENTS IN

IRON ORE BENEFICIATION IRON ORE BENEFICIATION

New developments and products are in in the followingareas:

Visual sensors with greater accuracy and robustness.

On-line hardness and mineralogy analyzers

New sensors for the measurement of grinding, New sensors for the measurement of grinding,classification and flotation variables

Additional new tools for advanced control that combineexpert system (ES) with model-based control andcontinuous with discrete control (hybrid systems)

Dynamic optimization applications for integratedprocesses and plant interconnection

7. CONCLUSION

Use of latest beneficiation techniques in iron oreindustry in India has immense scope to cater theburgeoning demand of steel industry.

Region specific integrated approach is to bedeveloped to prepare the ore characterizationdeveloped to prepare the ore characterizationdatabase and standardization of beneficiationtechnology.

Robust Design and model based optimizedmineral beneficiation techniques would be keyenlightener for decision making to choose theright path for beneficiation of low grade iron ore.

The optimization based on models predictive control(MPC) is widely applicable state of art feature ofadvance beneficiation technology. It is economicallyviable & getting importance globally as well in India

Combined expert system (ES) with model-based control Combined expert system (ES) with model-based controland discrete control (hybrid systems) hold good future inLow Grade Iron ore beneficiation plants

Training of professionals and technicians charged withdesign, supervising and operating mineral processingplant and automation equipments is essential elementfor robust design and optimsed operation of iron orebeneficiation plant especially for low grade ore utilization.

8. REFERENCES

Web site www.steel.gov.in Burt, R.O. & Mills, C. (1984), Gravity Concentration Technology, Advances in

Mineral Processing Series, Volume 5, Elsevier, and Amsterdam J. Lynch (January 1977), Mineral Crushing and Grinding Circuits: Their

Simulation, Optimization, Design and Control, Elsevier Scientific Napier-Munn TJ,Morrell S,Morrison RD, Kolovic T (1996),Mineral Comminution

Circuits:Their Operation and Optimisation. JKMRC, University of Queensland,BrisbaneBrisbane

Mular AL, Barratt DJ, Halbe DN (2002) Mineral Processing Plant Design,Practice, and Control (2-volume set). Society for Mining Metallurgy &Exploration, New York

Barry A. Wills, Tim Napier-Munn,(2006), An Introduction to the Practical Aspectsof Ore Treatment and Mineral Recovery, Elsevier Science & Technology Books

A.Gupta and D.S.Yan, (2006), Introduction to Mineral Processing Design andOperation

Daniel Sbrbaro Ren del Villar (2010), Advanced Control and Supervision ofMineral Processing Plants, Springer-Verlag London Limited

THANKSTHANKS

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slides

AUTOMATION BENEFITS

Increased production

Process stability improvements

Better use of raw materials

Reduced maintenance and improved safety Reduced maintenance and improved safety

Improved process knowledge

LEVEL OF AUTOMATION

Level 1- Basic block

Level 2-Supervision block

Level 3-High level block Level 3-High level block

Level 4- "Watch dog"

AUTOMATION LEVEL

Level 1This is the regulatory level where basic controls loops like, P+I control loopsinclude control of feed tonnages from bins, conveyors, manipulating of bins,water addition loop (in milling circuit) pump speed and sump level controls,thickener overflow density control etc are involved (depending on the processcircuit).

Level 2This is a supervisory control stage that includes process stabilization andThis is a supervisory control stage that includes process stabilization andoptimizing, usually using cascade loop and ratio loops. For example, in a ballmill circuit the ratio loop controls the ball mill water while the cascade loopcontrols the particle size of product by manipulating the tonnage set point.

Level 3Controls at this level include maximizing circuit throughput, limiting circulatingload (where applicable).

Level 4This is a higher degree of supervisory controls of various operations includingplant shut downs for maintenance or emergency. It has been referred to level 4controls as "watchdog" control.

LIST OF VARIABLES

Variety of process variables are measured by Sensors :

Feeder frequency, conveyor load and crusher chute level(crushing)

Tonnage, water flow rate, mill speed, pulp level, pump speed,pulp volumetric Flow rate, pulp density, cyclone and millpressure for Screens, pumps and cyclones (grinding), thepulp volumetric Flow rate, pulp density, cyclone and millpressure for Screens, pumps and cyclones (grinding), thepower draw of mills

Pulp flow rate, cell and column pulp levels, air flow rate,reagent flow rate, wash Water flow rate and pH (flotation).

Pulp particle size distribution sensors in grinding and gradeanalyzers in flotation/Gravity/Magnetic Separators

Mathematical Models Models can be used to improve efficiency and

sustainability of mineral processing in many ways. They

can be used, for example, in process research and

development, design, optimization and control.

If the model is time-dependent, it is dynamic while static

(or steady-state) models do not depend on time.

Dynamic models are typically represented as differential Dynamic models are typically represented as differential

(or difference) equations.

Mechanistic models are based on the actual or

assumed mechanisms of studied phenomenon while

empirical models are based on observations.

Simulations based on either mechanistic or data-based

models operating in steady-state or dynamic conditions

have also been used commonly in mineral industry.

Model Predictive Control..contd.

The control sequence is obtained by optimizing an objectivefunction that describes the goals the control strategy isintended to achieve.

In classical MPC, an objective function minimizes the errorbetween predicted outputs and the set-points during theprediction horizon as well as the control effort during thecontrol horizon.control horizon.

The function may be expressed as The optimization processmay involve hard or soft constraints.

For linear unconstrained systems this optimization problem istractable and convex and can be solved analytically, but ingeneral applications it is common to take into accountconstraints or non-linearities in the process, and in suchcases the optimization problem must be solved using iterativenumerical methods.

Model Predictive Control..contd.

A fundamental element in MPC is the model used to

characterize the dynamic behavior of the process.

The origins and formulations of such models are diverse, but

may be classified as follows:

Phenomenological or first principle models, in the vast Phenomenological or first principle models, in the vast

majority of cases nonlinear and continuous time

Models obtained through numerical adjustments based on

operating data using discrete time series, either linear or

non-linear

Model parameters are obtained mainly by two methods :

Regression

Curve fitting method

MPC APPLICATION In recent years, the application of MPC to hybrid

dynamic systems has emerged as a significant area ofresearch.

In these systems, continuous dynamic sub processesinteract with discrete event detection elements andstart/stop commands .start/stop commands .

Characterizing this type of system involves combiningcontinuous with discrete variables and differential ordifference equations with finite state automata orswitching theory.

Although this approach increases the complexity of themodel, its potential for accurately capturing the dynamicof an industrial process is much greater.