rodrigo andai vp enterprise software - abb mineoptimize

MineOptimizeRodrigo Andai – VP Enterprise Software - ABB

Total mining performance, delivered

© ABB Group

September 7, 2016 | Slide 2

ABB in mining today

1200+ Distributed control system

600+ Mine hoist solutions

720+ Km of belt conveyor system

250+ Bucket-wheel excavators

200+ patents into mining industry

125+ Gearless mill drives systems

80+ Turnkey electrification & automation

50+ Countries references

ABB in Mining today Fostering a one-system approach

800xA based integrated sub systemsMain process and power control

Grinding

drive systemsAutomatic stockpile

loading, unloading,

transport

Mine Hoist

ECS, ISA-95, OPC…

Extended operator workplace

Wireless communicationHigh integrity control

safety shutdownfire and gasAC800 M

process controller

Operation, engineering and maintenance

Process optimization

Production management

HeadquarterERP and business systems

Firewall router

Sub-system controllerAC800 M

Corporate network

Automation system networkMobile / remote operation

engineering and maintenance

© ABB Group


MineOptimizeIntegrated value chain from pit to port to customer

Energy

Management

Wireless

Solutions

Hoist, Ventilation,

Dewatering, APC

Dispatching &

Scheduling

Central Control

Room

Grinding, GMD,

RMD, APC

Conveyor Belt

Control

Electrification

Solutions

Condition

Monitoring

Ship Load &

Unload Control

Flotation, APC,

Instrumentation

Stockyard

Control

Substation

Control

Drives

Solutions

High levels of visibility across operations

© ABB Group


The mining industry today The main challenge is productivity improvement

• Standardization of processes

• Mechanization means dramatic shifts

in production capabilities

• Operation of equipment still requires

human interaction

• Integrated modeling and planning for

higher quality yield

• Greater visibility into parts of the value

chain

• More detailed information coming

from equipment and plant to enable

remote mining

• Reduce total ownership cost

• More responsive demand and supply

• Higher level of automation driven by

labor shortages and remote locations

• Limiting bottlenecks by adopting more

continuous processes

• High levels of visibility across the

value chain and between operations

Mechanization Automation Optimization

Key

mining industry

requirements

• Productivity

• Safety

• Sustainability

• Reliability

© ABB Group


The mining industry future Attractive changes moving forward

Reduce cost, increase productivity, and safety by

remote monitoring, diagnostics and interventions

Move automation and electricity to where the

ore is extracted, minimize haulage, and transport

… people further away from processes … equipment closer to processes

Key features of future mining operations

Limited human presence in production area

Continuous production and mechanical excavation

Central control room

Continuous availability of ore, people, and equipment

The traditional way Remote monitoring of equipment, preventive maintenance

The traditional way Underground electricalsand autonomous equipment

… enabled by integrated operations from pit to port, fully automated, and remotely controlled

Operator Environment Supporting ProductivityAll built based on with ergonomics in focus

Probably the best operator environment available

Offering a truly attractive and ergonomic Collaboration

Environment

Based on standard Products

© ABB Group


MineOptimize

Study

Plan

Design

BuildOperate

Integrated value chain from planning to operation

Planning partnerSystem and solution

providerOperational partner

© ABB Group


MineOptimizeTotal mining performance, delivered

Collaborative Production Mgt.

•Scheduling and dispatching

•Condition Monitoring

• Information Management

•Energy management

Project Mgt. & System Engineering

•Feasibility studies

•Conceptual Design

•Tendering

•Erection and commissioning

Study, Plan, Design, Build & Operate

Electrification & Automation

• Infrastructure, lighting, cabling

• Integrated electrical products, drive and motor systems

• Integrated automation, network, central control room

Power & Process Control

•Power management, load sharing & shedding

•Process operation, conveying, reclaiming, grinding, stacking, shipping

•Grinding, flotation advanced process control

Information challenges and the evolution of analytics

Source: Gartner (February 2015)

© ABB Group


Some facts about maintenance activities

© ABB Group


of preventive

maintenance costs are

spent on assets with

negligible effect on

uptime1

1 T.A. Cook, Maintenance Efficiency Report 2013, August 2013.

http://uk.tacook.com/fileadmin/files/3_Studies/Studies/2013/T.A._Cook_Maintenance_Efficiency_Report_2013_En.pdf?tracked=12 Oniqua Enterprise Analytics, Reducing the Cost of Preventative Maintenance, http://www.plant-maintenance.com/articles/PMCostReduction.pdf

Is maintenance necessary?What type of maintenance is

reasonable and justified?

Is there a better way?

Asset condition

40%of preventive

maintenance

activities are carried

out too frequently2

30%of all

maintenance

efforts are

ineffective2

45%

Condition Based

Maintenance offers

several benefits

addressing:

Is Condition Based Maintenance (CBM) better?

© ABB Group


1 http://www.slideshare.net/sunk818/utilities-performance-management-asset-health-risk

Reduction in

maintenance

costs1

25%

Maintenance

costs

Elimination

of

breakdowns1

70%

Breakdowns

Reduction

of

maintenanc

e costs1

50%

Uptime

and downtime

Reduction

of

unplanned

outages1

50%

Capital

investment

Reduction

of

scheduled

repair work1

12%

Is there an even

better way? Asset risk

Risk-based asset

management

Reduction

of capital

investment1

3-5%

© ABB Group


MineOptimizeIntegrated Portfolio

Collaborative Production Mgt.

Electrification & Automation

Power & Process Control

Product Mgt. & System Engineering

© ABB Group


MineOptimize Scheduling & DispatchingJust-in-time optimal process management

Personnel, mobile and

fix equipment

integrated into control

system

Operations team

working in the

optimum level

High degree of automation

and information access

enables safety and

production as per plan

Mine operators

schedule, dispatch

and track operations

in real time

• No information about the

location and status of

mobile/fixed equipment

• Cannot prioritize works plans

and loading sequences

The MineOptimize way

The traditional way

New task plans and

loading sequences

are calculated and

executed

Production analyses

and statistics can be

retrieved on-line

Mobile and fixed

equipment report local

conditions, task status

and location

MineOptimize

© ABB Group


MineOptimize Condition MonitoringReact to asset condition in real time

Asset management

integrated into control

system

Reduces losses due

to equipment failure

Reduce maintenance costs

while improve equipment

reliability and reduce

unplanned shut-downs

Asset monitor detects

condition

• Reactive maintenance

• High operating costs

• Unexpected breakdown of

critical assets

• Catastrophic impact on

production targets

The MineOptimize way

The traditional way

Work order with site

maintenance crew is

raised automatically

Maintenance and

operation responds

ahead to reduce

failure risk

Predictive

maintenance alarm

is triggered

MineOptimize

Chile-based R&D

Integration of Stockpile Modelling and Ore Processing

Ore Transportation and Ore Processing System Lack of integration

Lack of integrated information results in lower efficiency

Changes in plant

feed

characteristics

require

operational

adjustments

Static set points

limit operational

agility

Higher overall

energy use and

cost per ton

Little to no

information on

incoming feed

material

Plant operates

sub-optimally

Goal: integration of ore transportation, storage and processing

Better feed prediction improves and automates operational decision making

Short term feed

characteristic

schedule

integrated with

Advanced

Process Control

APC responds

and adjusts

automatically in

anticipation of

feed

characteristics

Process more

material with

reduced

downtime and

increased

efficiency

Material tracking

provides detailed

visibility of feed

characteristics

Integration

ensures plant

operates

optimally

Ore Transportation and Grinding SystemSolution Overview

Feed rates are automatically adjusted, resulting in less energy consumption

and higher production

Detailed material trackingOptimal processing with advanced

process control

Stacking and Reclaiming Models

Proposal: Integrated Transportation and Ore Processing

Features

Online Identification of the given Conveyor characteristics

Exploitation of degrees of freedom to increase production and reduce energy consumption during

transport

Material flow tracked in 3D stockpiles

Uses material composition information to optimize the ore processing performance

Real time data

Targets for grade and hardness at processing plants

Plant real time data (hardness, grade, flows)

Benefits

optimal production rate for each location

Time and direction in which material flows

Optimal operation of ore processing sites

Technology & Business ModelsInternet of Things, Services and People (IoTSP)

The Internet of ThingsABB things

Smart, communicating devices by ABB

The Internet of Things (IoT) is the network of

physical objects or "things" embedded with

electronics, software, sensors, and network

connectivity, which enables these objects to

collect and exchange data [1]

The Internet of Things allows objects to be

sensed and controlled remotely across

existing network infrastructure [2]

Things Robots Motors

Switchgear Controllers

[1] "Internet of Things Global Standards Initiative“, [2] https://hbr.org/resources/pdfs/comm/verizon/18980_HBR_Verizon_IoT_Nov_14.pdf

Device health and performance is derived from the analysis of the devices

diagnostic data collected

Health or performance can also be observed in measurements from devices

along mechanical, electrical, or control connections

Package monitoringMonitoring and diagnostic potential

Drive Motor Gearbox Compressor

M=

~

~

= 0 0.1 0.2 0.3 0.4 0.5 0.61

1.05

1.1

1.15

1.2

1.25

1.3

1.35

1.4

Mass Flow Rate [kg s-1

]

Pre

ssure

Ratio

1675.5

161 R

PM

1884.9

556 R

PM

2094.3

951 R

PM

2303.8

346 R

PM

2617.9939 RPM

2932.1531 RPM

Surg

e L

ine

SC

L

Integrating monitoring data from all sources in the plant

including electrical and control systems provide thorough information

Fleet managementPredictive maintenance potential

Good statistical knowledge important for accurate predictive maintenance

Time to react increased with improved predictive methods

Failure patterns observed in the fleet can be identified early in measurements

Failure initiated

First indications in

measurements Audible and

thermal indications

Ancillary damage

Catastrophic failure

Statistical spreadCon

ditio

n

First alarms

Integrating and analyzing monitoring data from a variety of installations of the same device type

throughout the industry is essential

Optimizing maintenance and operationsCombining plant view with fleet view

Unique combination of asset focused maintenance optimization vs. plant focused operations optimization

Fleet view

Data analysis across a fleet of devices

installed in different sites

Data analytics potential:

- Predictive maintenance

- Benchmark

- Asset lifecycle optimization

- Usage-driven product improvement

Focus on maintenance optimization

Plant view

Data analysis across a site or a number

of similar sites

Data analytics potential:

- Data engineering

- Process performance optimization

- Energy efficiency optimization

- Operational excellence

Focus on operations optimization

© ABB Group


ABB

Load

EventsTrxf. 1

Trxf. 2

Trxf. 3

…

Trxf. n

Top Oil

T. amb

Tap pos.

Problem Identification

Recommend Actions

Update Risk of Failure

Enterprise

… … …

DTMP TOOL

Enterprise

Headquarters

Online Sensors

ROF, %

Unit Im

port

an

ce, %

Asset Health CenterRisk Of Failure vs. Importance (Criticality Index)

TX3456TX3321

TXA450

TX6778

TX5966

© ABB Group


Complexity...

No, it is not moving

Nothing moving here!!!Loading guide Expert System

1. The tool s automated hence the “eyes” of the expert are not there…

2. Need to identify “normal” and “abnormal” from historical data (stats description)

3. Need to identify trends and their importance (95% C.I. of slope)

4. Need to compare things (example: is the gas evolution related to load increase?)

5. Need to provide user with visual and comparative statistics

6. Need to avoid excessive number of alarm messages/false alarms

7. Need several engineering performance models to automatically

interpret offline data and data coming from online sensors

8. Need to be prepared to solve conflict between online and offline data

9. Need to be able to interpret data from multiple types of sensor such as

gas in oil, moisture, bushing, PD, etc. and some with multiple types of output

10. Need a good thermal model capable of identifying loss of life, aging acceleration

factor and hot-spot temperature forecast (load guide)

11. Need Expert System to put it all together and make recommendations

© ABB Group


AlgorithmsLook for abnormalities

Expertise is importantMust emulate human expert’s reasoning

Recommend Actions

Flag Issues & Identify

potential failure modes

Follow levels/trends

EXPERT SYSTEM

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