Energy Savings in Foundries

118th Metalcasting Congress
April 8-11, 2014 – Schaumburg, IL USA
Computerization in the Foundry Industry
(Panel 14-132)
Energy Savings and Productivity
Improvements using Computer Controlled,
Remotely-Monitored, Smart Sensors
• James Wiczer, Sensor Synergy Inc., Barrington, IL
• Brad Tidd, North Vernon Industry Corp., North Vernon, IN
• Michael Wiczer, Sensor Synergy Inc., Barrington, IL
• 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

Today’s Talk
• Background on Energy Savings &
Monitoring
• Project 1 – V-Process Pumps
• Project 2 – Shakeout Table & Dust
Collector
• Project 3 – Induction Furnaces
• Analysis Tools
• Conclusions

What we want to talk about
• Real-world application of Technology
to Foundry Issues
• Millions of Measurements
• Data shared in the Cloud & the Foundry
• Application of High-Tech Software
Tools to Analyze Lots of Data
• Create Actionable & Meaningful
Recommendations

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

Measure Electric Power
Consumed by 3 Connected
Motors –
Pouring Floor Vacuum
Pressure Monitor

Unexpected Motor Failure During
Measurements Revealed Excess Capacity

Vacuum System Energy Savings
Opportunities
Dates
Hours “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
Expenditure
s 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

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

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

Project #2 Conclusions
• Improved Synchronization of
Shakeout Table and Dedicated Dust
Collector Drive Motor can Save
– $38,000/year in Electricity

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

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

Equipment Installation

PC Data Viewer located in Foundry

Web Browser Data Viewer on the Internet

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.

Cost of Electricity/Heat per Ton
$0.00
$20.00
$40.00
$60.00
$80.00
$100.00
$120.00
$140.00
$160.00
12:00 AM 4:48 AM 9:36 AM 2:24 PM 7:12 PM 12:00 AM 4:48 AM
Electricity Costs/Heat per 2,000 LB --- Costs
Measured for Duration from Tap to Tap time)
Cost per 2,000…
Electricity Cost per Ton per Heat
Average $53.15
Std. Dev $19.29
Coefficient 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

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

Costs with Worst 25%
$0.00
$50.00
$100.00
$150.00
$200.00
$250.00
$300.00
$350.00
$400.00
9/4/13 12:00 AM 9/4/13 12:00 PM 9/5/13 12:00 AM 9/5/13 12:00 PM 9/6/13 12:00 AM 9/6/13 12:00 PM 9/7/13 12:00 AM 9/7/13 12:00 PM
Tap-to-TapCosts($'s)
Date and Time of Day
Tap to Tap Costs/Heat ($'s) for
3 Day Sample / Orange - Lower 75% / Lt. Blue = Upper 25%
Tap to Tap Costs
Average
Costs
including
Lt. Blue
Values
Average Costs
Excluding Lt.
Blue Values

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

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
estimate 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 window
Power 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 scores
Power 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”
events
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)
AveragePower(KW)

Exploratory Analysis
Tap to Ready Time (min)
EnergyConsumed(KWHr)

Improved Metrics
Mean = 42.1 min
σ = 12.4 min
Mean 11.7 min
σ = 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%

Conclusions
• These Projects Improved Energy Costs and
Operational Efficiencies by Applying High-
Tech Monitoring Hardware & Software
• Foundry Operations Experts & Energy
Analysts Exploit these Tools to Identify and
Quantify Issues
• In each Case, Monitoring & Analysis
Generated Actionable Recommendations

For additional information:
James (Jamie) Wiczer, PhD
Sensor Synergy, Inc. – Barrington, IL
Jwiczer@SensorSynergy.com / 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
Michael Wiczer, Senior Software Engineer
Sensor Synergy, Inc. – Barrington, IL
Mike@SensorSynergy.com / Ph. 847-226-0687

Energy Savings in Foundries

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