The Internet of Things: past the hype, what does it propose to the aggregates industry?

Transcription

1 The Internet of Things: past the hype, what does it propose to the aggregates industry? Peter Radziszewski*, Janne Kytökari, Sudarshan Martins * presenter 1

2 Introduction Need a photo Peter Radziszewski, VP Research, Technology Development & Engineering Previously to joining Metso in 2012, Peter spent 20 years in academia working on topics from wear to grinding to sensors development to lunar mobility. Currently, Peter is with the Grinding Media Solutions team addressing the challenges related to wear. Janne Kytökari, Global Director, Monitoring & Control Engineering Janne leads product development efforts implementing advanced measurement technologies, equipment connectivity and automated data collection and analysis. Sudarshan Martins, Industrial Post-Doctoral Researcher Sudarshan has supported a number of innovation and sensor development concepts in support of machine intelligence development. Introduction A leading process performance provider, with customers in the mining, oil and gas, and aggregates industries, Metso has its corporate headquarters in Helsinki Finland, offices in 50 countries and manufacturing facilities on five continents. Metso s services and solutions improve the availability and reliability in minerals processing and flow control, providing sustainable process improvements and profitability. 2

3 Objectives take away the hype? How much data is enough and how do you select the important vs. the nice to have? What does this mean for aggregate customers and OEMs? Where are the benefits and what comes next. Objectives take away the hype? How much data is enough and how do you select the important vs. the nice to have? What does this mean for aggregate customers and OEMs? Where are the benefits and what comes next. 3

4 A quick Google search of the Internet of Things (IoT) would indicate some 725 million results (Nov 24, 2015) one result for every 7 people! as one tech consultant exclaimed: IoT fever is off the chart! Consider two concepts: (i) The TALK - Gartner Hype cycle (ii) The WALK - Technology adoption lifecycle 4

5 Consider two concepts: (i) The TALK - Gartner Hype cycle (ii) The WALK - Technology adoption lifecycle The Gartner hype cycle Gartner Inc. developed a now branded methodology to understand and interpret technology hype in graphical format. Although this hype curve methodology has its detractors, the curve nevertheless generalises the evolution of hype around a given technology over time. 5

6 2015 IoT fever is off the chart Sam Whitmore, consultant to tech-industry publicists Gartner Hype Cycle new technologies Consider two concepts: (i) The TALK - Gartner Hype cycle (ii) The WALK - Technology adoption lifecycle 6

7 Technology adoption lifecycle (or development) is typically described with an S curve where the change in a performance metric is illustrated as a function of time Machine to Machine (M2M) communication sliding into the trough (Gartner, 2015) Gartner Hype Cycle new technologies 7

8 appropriate KPI The TALK IoT IoT Platform The WALK M2M communication time All of these players express how they see IoT differently ARM There are a number of players in the IoT field. These include but are not limited to: ARM, Atmel, Bosch, Cisco, Ericsson, GE, Google, IBM, Intel, MicroSoft, Oracle, Siemens,... and us. Cisco Oracle GE Mining 8

9 However, fundamentally, IoT breaks down to three things: 1. Things embedded (connected) with sensors, 2. Networks to connect them, 3. Systems to process the data. Which becomes screens In our world, things cover the mineral and aggregate processing space and include such things as crushers, screens, etc. crushers 9

10 ore However, things also includes the ore or the aggregate. Consider the mine-to-mill case. Mine-to-Mill Furthermore, the whole thing could be greater than the sum of the things plant control & optimisation 10

11 Or one can support the development of a proprietary IoT platform developed in order to guarantee customers that the data is secure and only known people can view it. There are a number of IoT platforms on which to develop. These include but are not limited to: Zatar, Yaler, Thinfspeak, SeeControl, Kaa, HP Cense, Carriots, Bugswarm, Or one can support the development of a proprietary IoT platform developed in order to guarantee customers that the data is secure and only known people can view it. 11

12 The interface to this network is accomplished by a Quake Global communications system. Difference between TALK and WALK could be due to perceived need for sensors and actual need for sensors 12

13 Objectives take away the hype? How much data is enough and how do you select the important vs. the nice to have? What does this mean for aggregate customers and OEMs? Where are the benefits and what comes next. How much data is enough and how do you select the important vs. the nice to have? The curse of big data Many people when they are asked what data will they want from the equipment will say: Everything available. Doable: Yes Sensible: Perhaps not Consider: Most IoT data are not used currently. For example, only 1 percent of data from an oil rig with 30,000 sensors is examined. The data that are used today are mostly for anomaly detection and control, not optimization and prediction, which provide the greatest value. McKinsey (2015), IoT: mapping the value beyond the hype 13

14 How much data is enough and how do you select the important vs. the nice to have? The curse of bag data Many people when they are asked what data will they want from the equipment will say: Everything available. Doable: Yes Sensible: Perhaps not It s a common mistake to think that by collecting all the data we can do big data analytics, find out cause-effect patterns and detect problems in advance. A lot of the data will be left unused while occupying disc space. It s more sensible to create KPIs, cumulative values etc. at the machine level and carefully select the important raw values for remote data collection. Consider the following two cases How much data is enough: CASE: Fleet Management Using a satellite connection one can collect alarms and machine hours that are used to plan maintenance, estimate the need of spares and wears, and to derive a utilization KPI. Together with fuel consumption and production tonnage information the customers are able to get a good understanding of the state of the machine and process. Amount of data per day: ~ 0,1 Mbits (adjacent picture of a Lokotrack is 3,8 Mbits) Cost of communication / month: 30-40$ 14

15 Measuring the right values CASE: Screen monitoring What is measured? Simple vibration measurements only from all corners to find out: Stress to the screen body Correct operating angle Correct operating speed Length of the stroke As well as housing temperature and bearing wear. Provides a lot of data in raw format most of which is not interesting. Measuring the right values CASE: Screen monitoring Only after analysis we find out what the condition of the screen actually looks like. Although such data is valuable, it has little value to the operator. 15

16 Measuring the right values CASE: Screen monitoring On the other hand, the processed data can be linked to plant automation system, collected for history and reported in different formats such as a visual format. Measuring the right values CASE: Screen monitoring Ultimately, through continuous monitoring and analysis of the screen s vibration, one can be alerted to improper motion caused by the following events: Flapping media Uneven feed Broken springs Natural frequency Insufficient structural support Improper counterweight settings Structural interference Excessive material buildup Incorrect run speed Worn bearings 16

17 How much data is enough and how do you select the important vs. the nice to have? Can you spare the cost of data transfer and data storing for the next five years without any issues? Can you utilize the data already? Fuel consumption, alarms, operator identifier are all very useful information as such Oil pressure, temperature are not very useful Oil pressure over limit X counter, max temperature during the last hour, temperature less than Y alarm can be all very useful. If not, do you have an idea where the data would be useful in the future? Did you answer no, no and no? Most likely you should not start collecting. Objectives take away the hype? How much data is enough and how do you select the important vs. the nice to have? What does this mean for aggregate customers and OEMs? Where are the benefits and what comes next. 17

18 What does this mean for aggregate customers and OEMs? Change is inevitable. Progress is optional. Tony Robbins It is all about decisions and supporting those decisions with knowledge and information of both your market and the state of your capabilities. If the next guy feels that they can get a competitive advantage over you by gathering quickly such knowledge and information, well then Brazilian client: What does this mean for aggregate customers and OEMs? Information is power --Humfrey, Piers Anthony For OEMs, there is technically no option. If we do not develop IoT capabilities to our equipment, somebody else will do it without us. And we will start to lose power over our own equipment, its development and improvement. Consider where we are going Fleet management 18

19 Objectives take away the hype? How much data is enough and how do you select the important vs. the nice to have? What does this mean for aggregate customers and OEMs? Where are the benefits and what comes next. Where are the benefits and what comes next. 19

20 Where are the benefits and what comes next. Excessive Wear Alarms Root Cause and Reliability Analysis Track Feed Quantity Monitor Energy Consumption Analyze process control variables & set-point estimation Monitor Crusher Performance and Wear Where are the benefits and what comes next. Plan logistics and inventory Recommend & Schedule Wears and Spares Compare with machine type average and maximum Advanced Screen Monitoring Conveyor Power, Wear PRISMA Data Assurance Monitor Operator Performance Analyze Energy Consumption Conveyor Power, Wear Plan & Schedule Maintenance, Service Monitor Crusher Performance and Wear Track and Analyze Maintenance Costs Client Offices Track Machine and Capacity Utilization Track Equipment & Process parameter changes Conveyor Power, Wear PRISMA The Internet of Metso Things (IoMT) Monitor Alarms Order spares Track Inventory Quantity Monitor Process Performance & Quality Track Inventory Quantity Manage Maintenance & Inspection Track Inventory Quantity 20

21 The process view Where are the benefits and what comes next. Improve control loop design Identify process bottlenecks Track quality Track energy consumption Track overall utilization and effectiveness Identify the need for parts Track maintenance Track timing of deliveries and work orders Help in decision making process Monitor operator performance Root Cause and Reliability Analysis Monitor Alarms Excessive Wear Alarms Track Feed Quantity Monitor Energy Consumption Analyze process control variables & set-point estimation Monitor Crusher Performance and Wear Where are the benefits and what comes next. Plan logistics and inventory Recommend & Schedule Wears and Spares Compare with machine type average and maximum Advanced Screen Monitoring Conveyor Power, Wear PRISMA Order spares Track Inventory Quantity Data Assurance Monitor Operator Performance Analyze Energy Consumption Conveyor Power, Wear Plan & Schedule Maintenance, Service Monitor Crusher Performance and Wear Monitor Process Performance & Quality Track and Analyze Maintenance Costs Client Offices Track Inventory Quantity Track Machine and Capacity Utilization Track Equipment & Process parameter changes Conveyor Power, Wear Manage Maintenance & Inspection Track Inventory Quantity PRISMA The Internet of Metso Things (IoMT) including, in the future, process control and optimisation 21

22 Take Aways To be completed by the participant Take Aways How much data is enough and how do you select the important vs. the nice to have? To be completed by the participant 22

23 Take Aways What does this mean for aggregate customers and OEMs? To be completed by the participant Take Aways Where are the benefits and what comes next? To be completed by the participant 23

24 Acknowledgements The authors would like to acknowledge the support and encouragement in the preparation of this presentation of: Ms Caroline Lecomte - Global Business Development Manager, Aggregates Life Cycle Services, Mr Rick Richardson - Director of Aggregates Services, USA and Canada, Michelle Branzolewski - Product Specialist, ExperTune. Nota Bene: PRISMA is a fictional name for a new product line that will be officially launched in April at the Bauma 2016 trade fair in Munich Germany. Thank you! Crusher Particle Size Distribution (PSD) monitoring 24

25 Crusher PSD monitoring Online particle size distribution (PSD) measurement for dry material have been around for a number of years. PSD measurement systems are designed to be meet the requirement of the aggregate industry. The PSD value indicates the quality of crushing. The particle size should remain as set but at the same time there should not be too much fines. Crusher PSD monitoring Knowing the PSD value is but the first step in understanding the reasons of good and bad quality. With it we know the time when certain quality occured. With analytics we can combine information from multiple sources such as: PSD measurement Crusher automation system Service history Weather condition data Weather conditions like heavy rain, snow, ice can impact the process and affect the quality. 25

26 Ore Tracking from Mine to Mill Ore Tracking from Mine to Mill The Problem: devastating problem with throughput pockets of extremely hard ore but we just didn t know when for throughput to suddenly drop The Alternative: $24 million crushing plant The Solution: SmartTAG TM and blast design Phu Kham case (Lopez-Pacheco (2015), Confronting complexity, CIM Magazine, v.10, n.7, Phu Kham open pit copper-gold mine in Laos 26

27 Ore Tracking from Mine to Mill The ore tracking system employs robust passive radio frequency (RFID) tags which are placed with the ore in the mine and the starting location of each unique tag is stored in a database. The tags survive blasting and travel with the ore through the process where antennas detect them at critical points in the process ahead of the milling circuit. They make it possible to link the physical ore properties associated with the ore in the mine to the time-based performance data of the plant. Ore Tracking from Mine to Mill For geometallurgical modelling to be effective it is necessary to have a high degree of confidence in the data collected, and to link the plant performance with the ore properties. Such an ore tracking system can automatically update block models and mine plans with actual plant performance data in real time. 27

28 Ore Tracking from Pit-to-Port Pit-to-Port Product tracking can use the same system to track product through the complete supply chain. Properties such as grade, shape, texture, moisture, ash, sulphur, phosphorus, energy content, etc can be tagged and tracked which facilitate optimisation of plant operation, sorting, blending and homogenisation to maximise the value of the final product Plant wide optimisation 28

29 Plant wide optimisation Plant wide optimisation Optimize the performance of the entire plant: Save Energy Reduce Maintenance Costs Increase Production 29

30 Plant wide optimisation Four Lafarge - Holcim Plants Brazil (2007) Brazilian Aggregate Client 30

31 Brazilian Aggregate Client Customers start to be more demanding about getting the data from their equipment. In one such case, a big Brazilian customer wanted to get data from their Metso manufactured portable crushing plant. They use a REST API provided to collect the data to their own system and they can also use a web interface to view the data from anywhere in the world. Brazilian Aggregate Client Brazilian aggregate client In stead of Metso specifying what data to collect the customer identified the data points and KPIs they were interested in. Some examples: Maximum value for temperature within a three minute period > follow up on maximum value trend Counter for pressure value over limit X within a day Level values send once per minute Equipment on/off value. Every change to be recorded. Safety switch. Every activation recorded. 1.The frequency of measurement depends on the speed of change of that value 2.Digital values need to be recorded only as they change 3.With some values the number of abnormal events is more important than the magnitude. 31

32 Fleet Management System Fleet Management System Objective: Fleet Management installed in 300 Lokotracks by the end of Currently (24/02/16): over 100 LokoTracks tracked 32

33 Fleet Management System 33