Environmental impact. Improve resource efficiency. Quality secondary raw materials. Pollution. CO 2 emission. Biodiversity. Policy and regulations

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Blake Leo Cooper
5 years ago
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2 Improve resource efficiency Environmental impact Quality secondary raw materials Pollution CO 2 emission Policy and regulations Biodiversity Waste Resources

3 Overall view of Innovation EOL Building 0-22 mm EOL Concrete Smart demolition Crushed concrete Screening/ Sorting Cement HAS 0-4 mm ADR 0-12 mm mm 4-12 mm Mortar aggregate LIBS Mortar aggregate

4 Laser Induced Breakdown Spectroscopy (LIBS) Laser interaction with matter Plasma formation Plasma cools Element specific emission spectrum Identification and characterization of target materials

Portable Non-contact stand-off In-situ, real

5 Laser Induced Breakdown Spectroscopy (LIBS) Almost nondestructive No sample preparation Fast, quantitative measurement Universal material sampling Environment independent Portable Non-contact stand-off In-situ, real time

online, in-situ diagnostic for on-site assessment Efficient detection of

6 Concrete and demolition waste Detection of contaminants in the waste flow Qualitative and quantitative analysis for process control Fast, online, in-situ diagnostic for on-site assessment Efficient detection of lightweight contaminants ½ m per sec with 30 tons per hour 10k measurements/ton

Online sensor for quality control Laser: 7 ns

7 Online sensor for quality control Laser: 7 nm, 25 mj 100 Hz, Diode pumped, Q-switched (Quantel - Centurion) Spectrometer: Czerny-turner, nm, 0.3 nm resolution (Avantes)

8 LIBS timeline Continuum background Atomic / ionic / molecular emission Trigger Laser pulse Gating of CCD t delay t integration

9 LIBS spectra Emission depends on: Time of acquisition Species Degree of ionization Unique spectra for each elements Matrix effect Self absorption

Classification methodology Statistical analysis of the spectra to determine unknown samples PLS-DA (Partial Least Square Discriminate Analysis) Training data set (8 elements, 72 averaged spectra

10 Classification methodology Statistical analysis of the spectra to determine unknown samples PLS-DA (Partial Least Square Discriminate Analysis) Training data set (8 elements, 72 averaged spectra each) Predictor matrix : X ( ) and Y (576 8) PLS regression Number of Principal Components Correlation co-efficient matrix multiply Unknown test spectrum PCA (Partial Component Analysis) + Adaptive Boosting binary classification Class of the particle Neural network

11 Towards online application Laboratory demonstration Belt speed 35 cm/s On-site demonstration

12 What is quality for a high-end application? Assuming average 8 mm diameter ball particles with the same grain size distribution <300 brick particle in >3000 aggregate particles <8 glass particle in >3000 aggregate particles <1 wood particle in >3000 aggregate particles

13 From Lab-scale to Production-scale Real life challenges: Problem of dust and moisture Signal level decrease Laser ablation cleaning Compressed air Heat treatment Lens to sample surface distance Problem of focus, focal plane Shape of particles Particle grain size distribution Smart way of flattening the surface + Triangulation sensor

14 From Lab-scale to Production-scale Location of the measurement Cross sectional area for best representation of the components Conveyor belt vibration Real time data acquisition and classification Dedicated data acquisition system Increasing accuracy of identification Robustness of the classification methodology

15 From Lab-scale to Production-scale Upgrade experimental setup Powerful laser system Cost effective Works well in harsh environment Long life for continuous operation Minimum maintenance Efficient fast detectors Optimization of the focusing and light collection optics

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Effortless Emulsion Evaluation for Everyone

Effortless Emulsion Evaluation for Everyone Jeffrey Bodycomb, Ph.D. HORIBA Scientific www.horiba.com/us/particle 2013HORIBA, Ltd. All rights reserved. Outline What is an emulsion? Measuring size->laser