Energy Savings in Foundries - AFS R&D Project Summary

118th Metalcasting Congress April 8-11, 2014 – Schaumburg, IL USA

Identify Energy Savings Opportunities at Foundries through Energy-Use

Monitoring & Analysis*

♦ Principal Investigator – James (Jamie) Wiczer, PhD ♦

Sensor Synergy, Inc. – Barrington, IL - 847-353-8200

♦ Co-Investigator – Brad Tidd, Senior Kaizen Manager ♦ North Vernon Industry Corporation, North Vernon, IN

• Case studies described in this presentation were funded by the American Foundry Society under R&D project #12-13#03 and by the Collaborating Foundries including NVIC & CA Lawton


Knowledge is PowerSir Francis Bacon -- 1597


Knowledge is Power SavingSensor Synergy Staff -- 2014

$

$

$$

$ $


"If you can not measure it, you can not improve it.".

- - Lord Kelvin 1883


Overview

• Program Goals (JW)

• 3 Projects within Main Program – V-Process Vacuum Pumps (JW / BT)

– Shakeout Table & Dust Collector (JW)

– Induction Furnaces (JW/BT)

• Overall Conclusions (JW/BT)


Project #1 - Vacuum Pumps for V-Process Molds – Energy Saving Opportunities?

• Up to 4 Large Electric Motors Driving Vacuum Pumps to Supply Vacuum to the Pouring Floor and Shakeout area– Motors Include 250hp, 250hp, 250hp & 200hp

• Monitor Power Used by Motors and Vacuum Pressure Measured at Pouring Floor and Shakeout Floor

Lawanda

Brad Tidd slide.1 3 ea 250 HP 1ea 200 HP Vacuum Pump Motors2. Pouring Floor and Shake Out


Simultaneous Measurements of Electric Power Consumed and Vacuum Pressure

• Monitoring in Plant and Across the Internet Enabled Off-Site Review and Analysis of Data During Measurements

• Primary Goal: Observe correlation of Vacuum Pressure Changes with Power Consumption Changes.

• Secondary Goal: Identify Opportunities for Saving Power by Monitoring Pump Turn- On / Turn-Off Time

Lawanda

Brad slide1. Cloud Monitoring of the Vacuum Pump MotorsIdentified a Vacuum Pump Motor FailureAdditional Findings that we had additional capacity2. Primary Goal to corrolate Vacuum Pressure changes with power consumption3. Secondary Goal Identify Opportunities for savings. Turn on / Turn Off Time.


Electric Power Consumption by 3 Connected Motors – 250hp, 250hp & 200hp.

Sensor Synergy Staff

JW Slide


Shake-Out Area Vacuum Pressure Monitor


JW Slide


Pouring Floor Vacuum Pressure Monitor


JW Slide


Unexpected Motor Failure During Measurements Revealed Excess Capacity


JW Slide


Vacuum System Energy Savings Opportunities

DatesHours “On” Past

Ideal Turn Off time

Approx. Extra kW-hr usage

Potential Savings if turn-

off time optimum

Total Potential Savings from 6 weekends

10,280 kW-hr $1028

Total Potential Savings from

1 year (50 wks) if these 6 weekends are

typical

85,570 kW-hr $8,570

Potential Savings by Reducing

Extra Vacuum

Capacity

No Capital Expenditure

Expenditures per

Building ($12,000)

Estimated Annual Failure Costs Due to

Reduced BackUp Vacuum Capacity

Measured in Bldg. 2 $80,000 $100,000 to

$130,000 $0 / $7,500

Estimated Total for both Bldg. 1 & 2

$160,000 $200,000 to $260,000 $0 / $15,000


JW Slide


Results from Vacuum Pump Monitoring

• Excess Capacity for “Design Margin” Can Be Costly in the long term

• Identified $50,000/Yr to $90,000/Yr at Each Facility Extra Capacity Energy Costs

• Effort Needed to Identify Extra Capacity (if any) and to Achieve Savings

• Cost vs. Savings Trade-Off


JW Slide


Additional Conclusions from Vacuum Pump Study

• Measurements of Vacuum Pressure During Vacuum Pump Failure Highlighted Extra Capacity in System

• Weekly Measurements Highlighted “Turn-Off” Time Inconsistencies

• 24/7 Vacuum Pressure Measurements Indicate Low Fluctuations Due to Pump System Extra Capacity

Lawanda

Brad Tidd Slide1. Vacuum Pump Failure2 Turn On / Turn Off Times3 Extra Capacity in the Syastem


Project #2 – Monitor Dust Collector and Shakeout Table

• Monitor the Synchronization of the Shakeout Table and a Dedicated Dust Collector

• Opportunities for Savings with Better Synchronization

• Use 2-second Measurements to Monitor Operations of these two Power Hogs


JW Slide



JW Slide


Total Hours of Operation

Power Consumed On / Off Cycles

Electricity Costs for Operations @$ 0.1/kW-Hr

Overlap Hours of Operation

Shake Out Table

17.9 hrs 513 kW-Hr 77 On-Off $51.3 17.9 hrs

North Dust Collector

40.43 hrs 6808.1 kW-Hr 8 On-Off $681.81 17.9 hrs

Potential Savings by Turning Off Dust Collector

22.5 hrs

Excess Operational Time

3788.4 kW-Hr

Excess Electrical Power Consumed

$ 378.84

Excess Costs for Operations while Dust Collector is OFF

(No Overlap 22.5 hrs)

Results from 2-Day Power-Use Study of North Dust Collector and Shake-Out Table


JW Slide


Project #2 Conclusions

• Improved Synchronization of Shakeout Table and Dedicated Dust Collector Drive Motor can Save– $38,000/year in Electricity


JW Slide


Project #3 – Induction Furnaces #4 & #5 @ NVIC

• Install Power-Use Measurement Equipment on 2, 4MW induction furnaces ---- #4 and #5

• Make 1-Measurement/second, 24/7 for the duration of the project

• Send Data to Cloud Server to Share Data with all Interested Stake-Holders

• Correlate Power-Use Measurements with Activities at Foundry

Lawanda

Brad1.Worked with Furnace Manufacturer to install Power Monitoring Equipment.2. 1 measurement / Second 24/73. Data sent to the cloud4. Correlate Power use to process


Key Features of Induction Furnace Monitoring Project

• Over 16 Million Power Measurements during the first 3-months on 2 Furnaces

• Correlated Weight of Metal Heated During Each Furnace Heat with the Amount of Electricity Used for a Portion of the 3-month Study Period

• Correlated Run Sheet “Time of Day” Information with Power Measurements Time of Day Data

Lawanda

Brad1. Over 16 million power measurements2. Correlate Heat weight to Electricity used. Variation for operator to operator3. Correlated Run Sheet with power measurements. Variation from operator to operator.


Additional Factoids about this Project

• A short 3-Day period was selected for intense detailed analysis.– This Case Study Focused on 3 days Sept. 4 – Sept. 6,

2013

• Whatever Issues or Problems that Operators Noted on Run Sheets were also Noted in our study to correlate “real in-foundry” events with measured data for the corresponding heat

Lawanda

Brad1. 3 day period for detail analysis. minimal downtime2. Used operator data to correlate measured data for each heat. Operator data was not always reported correctly


Equipment Installation


JW Slide


Induction Furnace Power Supply

Remote, Real-Time AC Power Usage Monitor

Internet-based Cloud ServerCollects Data from Sensor Computer

at Foundry and Provides Data to Web Browsers for Authorized

Stake-Holders

Wireless Router

Data Acquisition Unit Converts Analog AC

Power Sensor Signals to Digital Info for

Network

Notebook Computer at FoundryFactory Loaded with All

Monitoring/Logging & Display Software

Web Browser Data Viewer – Anywhere, Anytime - Requires

Internet Connection

mini-LAN for SensorsWireless data

signals within foundry to deliver power-use data

to staff desktop


PC Data Viewer located in Foundry


JW Slide

Web Browser Data Viewer on the Internet


JW Slide


Sample of Raw Data

30 Row sample of the 150,000 Rows for #4 Furnace Used During 3-Day Analysis Period !


JW Slide


Finally …. Data!• First - How Much Electricity is

Used by All 4 Induction Furnaces at Foundry based on 8 weeks of Detailed Measurements

Approx. $3.0 - $4.0 Million/yr


JW Slide


Important to be able to Support Data

– Power Measurements from July 21, 2013 to Sept. 14, 2013 – Data Measured Every Second on Each Furnace

– Furn. #4 + Furn. #5 Used 3.4 million kW-Hrs during 8 weeks

– At this rate, annually All 4 Furnaces use 42.4 Million kw-Hr during a 50 week year

– If Costs at $0.10/kW-Hr = $4.240 M/year for Electricity


JW Slide


Power measurements for the same time duration during normal operations and during problem operations

Red area shows when

furnace is using

electrical power

White area shows when

furnace is not using electrical

power

Furnace Electrical Power

Usage is as expected – Start at ½ power then go to full power and finally off while tapping

Furnace Electrical Power Usage Profile is

unexpected – Long periods of “Off Time” while

equipment is repaired and other issues.

Lawanda

bradNormal furnace operation 50% - 100%Upper graph would recognize close to normal operation.Lower graph indicates process variation due to equipment downtime.


Electricity Costs/Heat – Tap to Tap

12:00 AM 4:48 AM 9:36 AM 2:24 PM 7:12 PM 12:00 AM 4:48 AM$0

$50

$100

$150

$200

$250

$300

$350

$400

Electricity Costs/Heat ---- Costs Measured from Tap to Tap duration (with electric cost

@ $.1/kW-h)

Cost @ $.1/k...

Electricity Cost per HeatAverage $240.33Std. Dev $52.51Coefficient of Variation 0.219

In this Data, there is No correction for the amount of metal added to the furnace after the previous furnace tap. Power Measurement showed a wide spread in the amount of electricity used per heat during our 3-day measurement period.

Average Cost/Heat

$240.33without correction

for weight

Lawanda

Brad SlideTap to Tap Electrical cost Average $240.33Wide range identifies need for training


Cost of Electricity/Heat per Ton

12:00 AM 4:48 AM 9:36 AM 2:24 PM 7:12 PM 12:00 AM 4:48 AM0

20

40

60

80

100

120

140

160

Electricity Costs/Heat per 2,000 LB --- Costs Measured for Duration from Tap to Tap time)

Cost per 2,000 LB

Electricity Cost per Ton per HeatAverage $53.15Std. Dev $19.29Coefficient of Variation 0.363

Associating the amount of electricity used (costs) for a single heat with the amount of metal heated does not result in a strong correlation. In fact the Coefficient of Variation is Greater when we tried to compensate for the different amount of metal being heated during each furnace run.

Average Cost/Ton

$53.15

Lawanda

Brad SlideAbility to look at cost per ton Avg $53.15.Variation due to operator and equipment issues


Range of Costs for Different Heats Measured During 3–day Tests

$0/T

on -

$10/

Ton

$10/

Ton -

$20/

Ton

$20/

Ton -

$30/

Ton

$30/

Ton -

$40/

Ton

$40/

Ton -

$50/

Ton

$50/

Ton -

$60/

Ton

$60/

Ton -

$70/

Ton

$70/

Ton -

$80/

Ton

$80/

Ton -

$90/

Ton

$90/

Ton -

$100

/Ton

>$100

/Ton

Elec

tricit

y0

5

10

15

20

25Electricity Costs $/Ton per Heat

Electricity Costs $/Ton

# Heats in Cost Range

Lawanda

bradWide range of heat cost per ton.Throw out the High and the lowTrain to achieve the middle


Tap-to-Ready Duration vs Weight

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00

100.00

0 2000 4000 6000 8000 10000 12000

Len

gth

of

Tim

e T

ap t

o R

ead

y (m

inu

tes)

Weight of Metal Added for Melt

Tap to Ready Duration (min)

Tap to Ready …

Associating the amount of time required to complete a single heat with the amount of metal heated also does not result in a strong correlation.

Average Time Required from Tap-to-Ready 46.44 minutes

Expected Relationship

between Required Heat

Time and Weight of Heat

Lawanda

bradAverage tap to ready time was with-in desired specified time.Based on heat weight we did not recognize a reduced time.Based on the other measurements taken actual tap time was over the desired time. Slagging of Furnace or furnace hold time.


Monitoring Can Provide Operational InsightsHow Much Power Is Used During Cold-Start?

Date Time Function Total Cold-Start Power

8/11/2013 8:45 PM Begin Cold-Start 8/12/2013 12:55 AM End Cold-Start 2155 kW-Hr





Average Power Used 2,167 kW-Hrfor Both Furnaces #4 & #5 on Sunday Night during Start-Up

Lawanda

brad & jwRecognized that our start-up power for cold start vs holding the furnace overnight.Trade off was the ability to run production quicker while holding the furnace.


How Much Power Is Used During Cold-Start?Average Power Used 2,167 kW-Hr for Both Furnaces #4 & #5 on Sunday Night

Hold Power /Furnace about 100 KW. Calculations indicate both furnaces consume about 4,800 kW-Hr during Sunday before production begins again.

Measurements show 6,106 kW-Hr used during Sunday day for both furnaces.

Lawanda

JW and/or BradMeasurements show that more power was consumed than should have been required to hold both furnaces.


Observations from Project #3

• Power Data is a High Resolution Indicator of Induction Furnace Activity

• Knowing the Costs of “Issues” may help a foundry determine which ones to Remedy – Equipment in need of Frequent Repair– Shift Change Issues– Hold Power Usage

• Power Setting During Hold Times• What Events Cause a Shift to Hold Power• Total Energy Expenditures after Ready Time

Lawanda

bradPower measurements are indicative of our furnace operation.Power measurements allow management to ask real time questions. Placing a dollar figure to our shortcomings.Allow management to prioritze training for proper furnace operations.


Using Data & Observations Explore the Up-Side --

Do Some What If Analyses• “What If” observations can indicate what

savings might be achieved

• For example, “What If” some of the worst case heats could be eliminated from a typical week.– From the Measurements Perspective, this is NOT about Making

Recommendations to Change the Details of a Process – Those Changes are the Foundry’s Expertise!

– This IS about, IF some process related Issues can be Improved, what would be the Benefit.

– Now the Foundry Staff Needs to Explore what it costs to make these changes


JW Slide


What IF ?• Can You Get Rid of Your Worst 25%

Performing Heating– What if the most expensive 25% of the

induction furnace heats could be eliminated and replaced with a heat that performed the same function but used an amount of electricity and time similar to the lower 75% of these measurements?


JW Slide


Costs with Worst 25%

9/4/13 12:00 AM 9/4/13 12:00 PM 9/5/13 12:00 AM 9/5/13 12:00 PM 9/6/13 12:00 AM 9/6/13 12:00 PM 9/7/13 12:00 AM 9/7/13 12:00 PM$0.00

$50.00

$100.00

$150.00

$200.00

$250.00

$300.00

$350.00

$400.00

Tap to Tap Costs/Heat ($'s) for 3 Day Sample / Orange - Lower 75% / Lt. Blue = Upper 25%

Tap to Tap CostsUpper 25% of Tap to Tap Costs

Date and Time of Day

Tap

-to

-Tap

Co

sts

($'s

)

Average Costs

including Lt. Blue Values

Average Costs Excluding Lt. Blue Values


JW Slide


If we could do this, then ….

• The average electricity costs/heat for operating the induction furnaces would decrease from

•$200.18 … to … $178.11

• This would create and Annual Electricity Savings of …

•$466,400


JW Slide


Other What If

• What if the Electricity Expenses after the Induction Furnace is Ready to Tap could be Eliminated ?– 6.1% of the Electricity Cost/Heat Could be

Eliminated– for an Annual Savings of …

•$254,400/year


JW Slide


And ….

• The average duration for a complete Tap –to – Tap Cycle Would be Reduced from

•61.26 minutes …to… 53.49 minutes

• Annually, this 12.7% reduction in Induction Furnace Operations Time Would Save …

•874 Hours/Year of Induction Furnace Operations


JW and/or Brad Slide


Enhanced Operator Training Can Remedy Many Issues

• Energy Measurements Identify Operational Issues that, in some cases, Can be Remedied with Additional Operator Training

• Specific Examples ----

Lawanda

bradPower measurements offer Training Oportunities by identifying operational short comings.Charging the furnaceFlux and tap temperatureShut down procedures (Elephants foot)


Other Conclusions

• Real-Time Power-Use Monitoring Can Support Process Improvement Studies to Reduce Energy Consumption and Reduce Induction Furnace Operations Time

• The Addition of Real-Time Text Message Alerts Can Help Make Improvements and Identify Issues Using Automation - independent of staff actions/communications.

Lawanda

bradReal time power use monitoring Supports process improvementsAdditional real time text message alerts can help make improvements and identify operational issues using automation.


4 Metrics for Induction Furnace Energy Efficiency

• 1) Measure the Fraction of Time and Energy Spent on

OFF / HOLD / MEDIUM / FULL Power

• 2) Measure & Continuously Monitor the Energy Cost/Ton for Charge to Tap Energy Input


JW Slide


More -- 4 Metrics for Induction Furnace Efficiency

• 3) Measure & Track the Tap-to-Tap Cycle Tap for Different Alloys

• 4) Measure & Compare Your Energy Cost/Ton to Industry “standard” values.


JW Slide


Continuous Function of Power Levels During Prior Week Furnace Operations

1/kW


JW Slide


JW Slide


Aggregate Weekly OperationsPercent of Week at 4 Power Levels

Percent of Time During Week Week Furnace # Off Mode Hold Mode Medium Power Full Power Total Time (Hrs)

1/13 --- 1/18 1 16.79% 31.11% 12.37% 39.72% 1281/13 --- 1/18 2 15.44% 33.36% 14.24% 36.96% 1281/19 --- 1/25 1 18.70% 32.92% 9.72% 38.66% 1681/19 --- 1/25 2 20.32% 31.61% 16.57% 31.50% 1681/26 --- 2/1 1 12.81% 35.53% 11.65% 40.01% 1681/26 --- 2/1 2 15.56% 37.59% 15.61% 31.23% 1682/2 --- 2/8 1 19.10% 29.41% 10.55% 40.93% 1682/2 --- 2/8 2 16.26% 32.35% 20.04% 31.34% 168

Average Percent Time 16.87% 32.98% 13.85% 36.30%

Furnace Power Use ModesPower Levels Defined for this Analysis

OFF Hold Medium Full TotalsOff = <10 KW 10 KW < Hold < 160 KW 160K < Medium <275 KW Full > 275 KW All Power Levels

Off Low & High Levels (kW) Hold Low & High Levels (kW) Medium Low & High Levels (kW) Full Low & High Levels (kW)

0 10 160 275 010 160 275 450 450


JW Slide


Aggregate Weekly OperationsPercent of Total Power Usage at Each Level

Percent of Power Used During Week in Different Modes Week Furnace # Off Mode Hold Mode Medium Power Full Power Total Power (kWH)

1/13 --- 1/18 1 0.06% 13.90% 12.36% 73.68% 26,396.661/13 --- 1/18 2 0.10% 16.47% 14.39% 69.03% 26,209.261/19 --- 1/25 1 0.07% 14.90% 10.20% 74.83% 33,320.921/19 --- 1/25 2 0.14% 16.45% 18.57% 64.84% 31,524.751/26 --- 2/1 1 0.05% 15.61% 11.46% 72.88% 35,204.071/26 --- 2/1 2 0.12% 18.96% 17.19% 63.72% 32,173.292/2 --- 2/8 1 0.07% 13.69% 10.27% 75.97% 34,710.042/2 --- 2/8 2 0.12% 15.67% 22.23% 61.98% 32,704.04

Average Percent Power 0.09% 15.71% 14.58% 69.62% 33,272.85


JW Slide



JW Slide



JW Slide


Finally… Program Conclusions

• Time Resolved, Energy-Use Measurements Can Provide Unique Insights about the Operations of Power-Intense Equipment – Including Pump Motors, Dust Collectors, Air

Compressors, & Induction Furnaces

• Energy Savings Can Often Be Achieved with Simple Administrative Solutions

• Energy Savings Should Pay NOT Cost• Energy Savings Are Often Accompanied by

Productivity Gains• Energy Measurements Are Necessary to Identify

“Engineering Margin” Energy Costs


JW Slide


Putting the Data to Use

• Real Time Monitoring• Operator Training• Downtime Notification• Equipment Design• Peace of Mind

Lawanda

Brad Slidereal time monitoringOperator TrainingDowntime notificationEquipment designPeace of mind


For additional information:

James (Jamie) Wiczer, PhD Sensor Synergy, Inc. – Barrington, IL

[email protected] / Ph. 847-353-8200

Brad Tidd, Senior Kaizen Manager North Vernon Industry Corporation, North Vernon, IN

Brad.Tidd@NVIC-CWT / 1-812-346-8772 x 1288


Extra MaterialPreview of Thursday’s

Presentation

• More complete descriptions of this work can be found at the Metalcasting Congress

Panel: Computerization in the Foundry Industry

• Thursday 3:45 PM. 14-132


Data Analysis Tools

• Build tools to study data specifically for foundry applications

• Goal: Extract the best information for those most knowledgeable about the operations & demands of a particular foundry– Experts + data can learn the most about

improving their operations


Generating Furnace Operation Logs

• Let’s focus on one example of a data analysis tool we’re working on– Problem inspired by this project with

NVIC• In this project, we focused on

correlating energy with operations.– This required accurate logging that is

difficult to achieve over extended periods of time


Generating Furnace Operation Logs (cont.)

• Challenges to getting accurate operations logs / run sheets:1. Manual logging is time-consuming

• Inaccuracies may result from the many tasks performed by furnace operators at tap time

2. Additional instrumentation for electronic detection of melt start/end times is often complex and costly


• Use the power data we collect to guess when operation events (e.g. tap time, melt ready time, etc) occur

• But first…

Furnace Operation Logs – A Third Option


Some preparation for a little bit of computer talk

• Algorithm: A calculation or procedure a computer program uses

• Parse: Splitting up data into pieces• Score: The algorithm’s evaluation of

some data.• Sigma (σ): Standard deviation. The

“spread” of some data.


Software identification of melt start & stop events

• Computer prediction can sacrifice some accuracy in favor of ease-of-implementation– Algorithmic approach– Get lots of data and treat them

statistically– Enable operators to focus on core

duties


How the algorithm works• Manually log times during

“training” period– We used 3 days & 82 melts for this

example• Learning based on Principle

Component Analysis (PCA)– Most notably used in face

recognition algorithm “eigenfaces”– Think of a 15 min window around

each event as a “face”(Photo: AT&T Labs Cambridge)


Sliding windowPower measurements

Prediction score

Window (prediction input)

Evaluate a window and predict how likely start of tap event is at the center.

15 Minutes


Sliding window scoresPower measurements

Prediction score

The power curve gets a corresponding curve for likelihood of tap start.


Algorithm processes scores and generates melt start times

Power measurements

Predicted event times

Take the highest scores in a neighborhood and get discrete tap time predictions.


Repeat for melt ready times

Apply the same algorithm to melt ready times and combine the results.

Power measurements

Predicted start times

Predicted ready times


Exploratory Analysis

Tap to Ready Time (min)

Ave

rage

Pow

er (

KW

)


Exploratory Analysis

Tap to Ready Time (min)

Ene

rgy

Con

sum

ed (

KW

Hr)


Improved Metrics

Mean = 42.1 minσ = 12.4 min

Mean 11.7 minσ = 4.3 min


Improved Metrics

• Analyze furnace usage from “Ready time” to “End-of-Tap time”:– Average energy: 24 KW Hr– % of Tap-to-Tap energy: 2.4%– Average time: 11.8 min– % of Tap-to-Tap time: 23.7%

Date post:	18-Nov-2014
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Energy Savings in Foundries - AFS R&D Project Summary

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