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