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Waste Paper Sorting System for Efficient Recycling
Mechatronic Design and Control of a
PI: Richard A. Venditti and M. K. (Ram) Ramasubramanian
Industry Partners: Weyerhaeuser (Tom Friberg) MSS (Michael Grubbs)
Technology Description
The primary challenge in the recycling of paper is to obtain raw material with the highest purity. • Highly sorted paper stream will facilitate high quality end
product, and save processing chemicals and energy. • Current manual sorting techniques are not effective in
reducing landfill waste.
The project goal is to develop sensors for sorting grades of paper and board from a mixed stream automatically at high speed for more efficient recycling.
Project Strategy� Key technical barriers. Development of stiffness measurement in
real-time on free, non-oriented samples, and inferring the type of paper based on this information is a challenging problem.
� Combination of sensing technologies in real-time, namely, lignin, stiffness, color, and adhesives to come up with the sorting scheme.
� Describe your project’s strategy for overcoming these barriers � Investigating alternative stiffness measurement methods � Developing improvements to the current stiffness
measurements � Evaluating other sensing techniques, gloss, color, IR
temperature sensor for stickies identification � Criteria for go/no-go decisions using Neural Networks.
� Sensor must be fast � Sensor must provide info on paper type � Sensor must be economical to implement � Sensor must be rugged
Project Partners
MSS, Inc., Michael Grubbs, General Manager� Provides access to pilot facilities
� Provides feedback on progress reports
� Communicates needs of the industry
� Designs, manufactures and sells sorting equipment
Weyerhaeuser, Tom Friberg, Researcher� Provides feedback on progress reports � Communicates needs of the industry � Provides paper recycling perspective on research direction
and progress
Commercialization� Potential Market: � Any recycling facility involved in the sorting
and/or disposal of waste paper. � Commercialization:
� As we develop prototype sensors we are testing them in acommercial environment with the industrial partner
� The review of commercial trials guide further work � We have taken this approach successfully with the lignin
sensor and have it commercialized. � We have done similar trials with the stiffness sensors and
have identified areas of improvement. � Currently working on the stiffness sensor and sensor
integration as two achievable milestones in the coming year.
Commercialization Status� A lignin sensor has been commercialized by the industrial
partner.� MultiWave Sensor with Lignin Sensor operating at IMS Recycling
in San Diego, CA for removal of OCC, Carrier-Board, plastics and trash from newspaper.
� The Lignin Sensor set-up for Carrier-Board identification in SanDiego, CA is crucial for properly identifying the targeted materials.
� Unit shipping February 2006 to VISSER Waste Management in Udenhout, Holland.
� Two units that are being fabricated and will ship March 2006 - to Stora Enso in Cologne, Germany and to Cougle Recycling in Hamburg, PA.
� It is projected that the stiffness sensor will be commercialized in 2008.
Energy Savings� Electricity: 10 million kWh for the US Industry. � A 5% decrease in rejected recycled pulp may occur
by recycling all sorted recovered paper rather than mixed.
� Up to 1% of the total amount of all paper and paperboard produced is rejected due to quality problems with recycled fibers.
� The rejected paper is typically re-pulped, blended at low level with fresh paper stock material and fed back to the paper machine.
� The use of higher quality pulp from recycled sorted recovered paper may decrease the 1% reject level.
Other Benefits to using sorted paper in recycling
� Makes recycling more cost effective and efficient promotes increased recycling rates � reduce the need for virgin fibers � reduce paper waste sent to landfills
� Utilization of sorted papers in recycling processes will decrease the amount of sludge and rejects generated in recycling
� Utilization of sorted papers in recycling processes will decrease the amount of water needed to produce recycled paper
Automated Paper Sorting SystemLignin sensor
Stiffness sensor
Gloss sensor Decision making Actuating mechanismalgorithmColor tracking
sensor
Stickies Sensor
Lignin Sensor
� The sensor measures ligninfluorescence when excited in the visible region.
� Newsprint samples, typically containing high lignin,produce high intensity.
� Ledger printing and writinggrades with low lignin content produce low-fluorescence intensity.
� Gives normalized lignincontent in paper
� Sensor output can be usedas an input for the controlalgorithm
Lignin sensor-Dynamic performance
� The sensor is able to identify papers moving at high speeds and is quite robust for sorting applications.
� The sensor can be successfully used as a part of a multi-sensor system to sort mixed office waste for more efficient recycling.
Stiffness Sensor� Can be used for differentiating different grades of
paper based on their relative bending stiffness values
� Can work together with Lignin and Gloss detection sensors for better sorting
� Is more useful for sorting cardboard from mixed paper feed when compared to other sensors
Stiffness Sensor Design Constraints
� Should be non-contact in nature � Short response time � Should be compatible with the existing conveyor
systems
Current Techniques for Stiffness Measurement Contact Methods:� Contact transducers generate ultrasonic waves on
the surface of the paper � Excessive noise due to mechanical vibrations is a
problem � Finer grades and paper boards are difficult to
identify using this method
Current Techniques for Stiffness Measurement Non Contact Methods:� Air coupled piezoelectric transducers, air coupled
capacitive transducers � Poor coupling of energy between the transducer and
the paper surface � Hard to implement online � Laser ultrasonic measurement technique is an
exception
Why need different sensor design?� All the previously mentioned techniques are for
testing paper webs of almost constant thickness � These methods are aimed at calculating the exact
elastic constants � Equipment is complex � For sorting there is no need to find the elastic
constants � Unlike paper webs, the thickness of paper on a
sorting conveyor varies widely from one sample to another.
Stiffness sensor setup
Distance sensor� Non-contact in nature � High resolution � Output is linearly proportional to the distance � Output is not affected by target’s optical properties
Distance sensor performance
Microcontroller� Controls the solenoid valve timing � A/D conversion of distance sensor output � Varies the load by varying the load timing � Runs the control algorithm � Identifies the samples based on the output of the
algorithm
Parameters which influence the deflection� Orientation of the sample with respect to the
conveyor belt (machine direction, cross machine direction)
� Thickness of the sample � Basis weight � Modulus of elasticity � Distance between the supports � Intensity of the loading � Conveyor speed � Coefficient of friction of the conveyor belt
Static stiffness sensor
� Paper samples sitting on fixed supports are loaded pneumatically
� Samples with various elastic properties are studied � Deflection values are obtained for these samples at
a given load � Variation of the deflection with respect to various
parameters is studied
Nozzle pressure profile
Pressure profile of the nozzle that was used for static testing of paper samples
Static testing results
Static testing results
Pilot plant trials of stiffness sensor
� To better understand the problems involved during the operation of the sensor, the stiffness sensor was tested on a high speed moving conveyor
� The dynamic response of the stiffness sensor was evaluated on a moving conveyor at the MSS Inc, Nashville, TN research/manufacturing site
� Load on top of the sample was applied by the air jet from flat fan nozzle
Pilot plant trials of stiffness sensorStatic Test Dynamic Test
Distance Sensor
Flat fan nozzle
Conveyor speed = 280 ft/min
Dynamic test results
0
5
10
15
20
25
30
YellRul
Fil
l
l
l
Copy Paper
ow ed
Paper
ter Paper
Medium Card
Stock
Heavy Card Stock
Speciality Card Stock
Card board
Defe
ctio
n, m
m
Nozz e height=1"
Nozz e height =7"
Nozzle inlet pressure = 10 psi, Samples were loaded in MD
Stiffness sensor characterizationStep 1:� Identifying different grades of paper which are
commonly found in the recovered paper � Testing the selected grades of paper to find the
mechanical properties Step 2: � Building an FEA ( Finite Element Analysis) model of
the system � Simulating the original loading and boundary
conditions of the system � Using the simulation output for decision making
Paper samples material dataFour samples of different grades are picked and their mechanical properties are investigated in order to build the FEA model
Material test data for 105µm thick sample
Paper samples test data
Copy
Medium
Stock
( 105 206 229 234
2) 75 145 200 175
l i i )
l)
Paper grade paper
card stock
Heavy card
Specialty card stock
Thickness µm)
Grammage (g/m
Modulus of e astic ty in machine d rection (GPa 3.98 1.6898 1.7935 1.6103
Modu us of elasticity in cross machine direction (Gpa
1.27 1.1143 1.090 1.1123
FEA model� An FEA model of the system is constructed � Paper samples are modeled as orthotropic shell
elements � Material test data is used to create the material
model � Large displacement formulation is used for the
elements � The conveyor supports are modeled as rigid
bodies� The material properties of the actual samples are
used in the model � Actual Loading and boundary conditions are
simulated
FEA model
Conveyor-2
Conveyor-1
Paper sample
Gap = 40mm
Paper Orientations on Conveyor
Finite Element SimulationsConveyor Speed Orientation Nozzle Pressure
MD
300 ft/min
CD
MD-30Degrees
MD-60Degrees
10psi
20psi
25psi
30psi
10psi
20psi
25psi
30psi
10psi
20psi
25psi
30psi10psi
20psi
25psi
30psi
Conveyor Speed = 300 ft/min, MD
Response of 105µm paper sample to applied load; conveyor speed =300 ft/min, load = 20psi
Conveyor Speed = 300 ft/min, MD
Response of 229µm paper sample to applied load; conveyor speed =300 ft/min, load = 20psi
Conveyor Speed= 300 ft/min, MD
Conveyor Speed = 300 ft/min, MD 30
Response of 105µm paper sample to applied load; conveyor speed =300 ft/min, load = 20psi
Conveyor Speed = 300 ft/min, MD 30
Response of 229µm paper sample to applied load; conveyor speed =300 ft/min, load = 20psi
Conveyor Speed = 300 ft/min, MD 30
Conveyor Speed = 300 ft/min, CD
Response of 105µm paper sample to applied load; conveyor speed =300 ft/min, load = 10psi
Conveyor Speed = 300 ft/min, CD
Response of 229µm paper sample to applied load; conveyor speed =300 ft/min, load = 10psi
Conveyor Speed = 300 ft/min, CD
Time Response Curves, MD-300ft/min
Conveyor Speed = 1200 ft/min, MD
Response of 105µm paper sample to applied load; conveyor speed =1200 ft/min, load = 10psi
Conveyor Speed = 1200 ft/min, MD
Response of 229µm paper sample to applied load; conveyor speed =1200 ft/min, load = 10psi
Conveyor Speed = 1200 ft/min, MD
Damping caused by the surrounding air
Response of the paper when there is no viscous pressure acting on top of it
Damping caused by the surrounding air
Response of the paper when there is viscous pressure acting on top of it
Response of the sample to pneumatic load
Applied pneumatic load is equal to the load applied by the cylindrical nozzle operating at 5 psi and held 1” above the conveyor surface, conveyor speed=1200ft/min
Time Response Curves, MD-1200ft/min
Comparison of Response Curves
20psi-MD-300 20psi-MD-1200
Future Work� Stiffness sensor development completion� Use of RF sensors for fast response and
higher speed sorting � Commercialization of stiffness sensor to
identify carrier boards and other stiffmaterials
� IR imaging based sensors for stickiesidentification
� Neural network control algorithmimplementation
Flutter of paper
Flutter can be defined as the dynamic instability of an elastic body in an air stream
� The vibration modes of the samples subjected to lateral load depend on the elastic constants of the samples
� For a given value of tangential load, stiff samples vibrate at a much lower frequency whereas flexible thin samples vibrate with larger amplitudes and higher frequencies
Flutter based sorting setup
Tangential load
Stiffness sensor performance enhancement
� The use of frequency domain analysis (web flutter in a fluid flow) to compliment the results obtained from the deflection data.
� Results from the classic “Flag Flutter” problem show that the flutter frequency is related to the bending stiffness as shown.
� This method also eliminates the requirement for the paper samples to be at a constant height from the sensor, thereby making it more robust
Stiffness Sensor - Performance Enhancements� Potential Sensors for frequency analysis
� Laser Distance Sensor from LMI Technologies (USA), Inc � Resolutions down to 0.001mm � Standardized with optical filters to reduce the influence
of ambient light � High speed, Analog outputs (V), up to 100 kHz � Optional modulated version (-M) to exclude any
influence from external light � Fast laser intensity control for object color changes
� LK-G series from Keyence (USA), Inc � Resolutions down to 0.01micrometer � High speed, Analog outputs (V), up to 50 kHz � Resistant to ambient lighting conditions
� Flutter frequency measurements which can then be used to correlate to the stiffness measured from the displacement data.
Future Needs� Development of the stiffness sensor can be
completed by June 2007 and commercialization can be accomplished by January 2008.
� Exploration and development of the IR sensor is very useful and can be a new project for potential support.
� Funding runs out end of 2006.� Additional support for one year can significantly
influence the outcome of this research.
Acknowledgement
This research was supported by the U.S. Department of Energy under the Industries for the Future Program, Forest Products Industry Agenda 2020; project number DE-FC07-00ID13880