Applying Advanced Sensor Techniques to Biomass Measurement

Transcription

1 Applying Advanced Sensor Techniques to Biomass Measurement Sensors & Instrumentation KTN Luke Hares 23 February 2009 S3937-CONF-015 v1.0

2 Optional Document Title (short form) Agenda: Introduction Understanding the requirements Assessing the techniques Evaluating complete systems Implementing a successful system 2 23 February 2009 S3937-CONF-015 v1.0

3 Background to Cambridge Consultants Contract design & development, IP creation & licensing, innovation consulting Formed in 1962 Part of Altran Group 300 staff Cambridge, UK Boston, US >200 Projects per year >50% from 20 Clients We also manufacture and supply specialist radar and sensor systems 3 23 February 2009 S3937-CONF-015 v1.0

4 Background to Cambridge Consultants Our customers range from established blue-chips to dynamic startups 4 23 February 2009 S3937-CONF-015 v1.0

5 In our core Product/System development activities we sell and carry out our work through a number of pro-active campaigns Medical Technology & Life Sciences Wireless Products and Systems Drug delivery Clinical diagnostics Wireless medical Medical & surgical Wireless medical Professional wireless Monitoring and control Broadband wireless Mobile communications Cleantech Transport Defence & security Missile scoring Industrial & commercial Semiconductor Consumer products 5 23 February 2009 S3937-CONF-015 v1.0

6 Our work in Cleantech Our Cleantech Campaign provides hi-tech solutions for the renewable energy and Cleantech sectors Our Primary focus is in the Renewable Energy Sector Innovation and Sustainable Design Transport, Industrial, Consumer Transaction Support and Market Entry 6 23 February 2009 S3937-CONF-015 v1.0

7 Optional Document Title (short form) Agenda: Introduction Understanding the requirements Assessing the techniques Evaluating complete systems Implementing a successful system 7 23 February 2009 S3937-CONF-015 v1.0

8 Biomass is not a simple classification, it relates to origins rather substance Most biomass in MSW falls into three rough categories Carbohydrates, mainly cellulose and derivates (cellophane) Lipids Proteins These materials have similar bulk mechanical properties (density, colour, shape) to non-biomass derived materials. Sensors need to search for properties that indicate a biological origin Complex organic compounds Decay products Traces that are characteristic of common chemical bonds 8 23 February 2009 S3937-CONF-015 v1.0

9 The measurement requirement needs careful definition Regulatory and commercial significance The figure must be arrived at in a rigorous fashion if it is going to be used to qualify for ROCs. Accuracy of the system must be demonstrable to the appropriate authorities. ROCs are granted for power generated, not biomass content What is the timescale for the measurement? Can readings over three months be combined? Or six? Does the reading get more accurate if you do? Process input Detailed information about what is being fed into the plant is likely to have be valuable for controlling the plant. Other considerations Robustness, environment will have contamination, grime, noise etc Power consumption 9 23 February 2009 S3937-CONF-015 v1.0

10 The size of the market Cost? How valuable would a biomass measuring system be: Currently the UK has approximately 330MWe of installed MSW capacity split between ~120 plants (UK energy statistics Digest, 2008) This gives 2700 GWh/year from MSW (assuming 95% utilisation). Best estimates state that 62.5% of the power is generated from biomass the MSW stream or 1700 GWh/year (UK energy statistics Digest, 2008) Value of electricity of a ROC is per MWh, biomass qualifies for ROC band of 0.5 Total ROC value is therefore 1,700,000*0.5* = 28M In the UK about 30MT of household and similar waste is put in landfills, enough to generate 20,000 GWh/year. This would have a ROC value of 210M However, this is distributed across many plants and processes February 2009 S3937-CONF-015 v1.0

11 Optional Document Title (short form) Agenda: Introduction Understanding the requirements Assessing the techniques Evaluating complete systems Implementing a successful system February 2009 S3937-CONF-015 v1.0

12 Inspection process Online measurement of all MSW Simple from a sampling perspective High throughput system, limits technologies that can be used Sampled systems Take samples from the input stream and process on line Take samples from the input stream and process off line May be spot sampling of the waste stream Sample shipments as they arrive All sampling systems have to cope with the inhomogeneous nature of MSW. Is the sampling system biased? How does it affect the measurement February 2009 S3937-CONF-015 v1.0

13 Optical Techniques Imaging techniques and sophisticated processing allows for a large inspection area, useful for inline inspection Without sample processing material properties may be obscured by surface grime Environment poses challenges Candidates include Raman spectroscopy Hyperspectral imaging Fluorimetry Robustness can be added with processing as well as design New absolute optical linear encoder, designed to be dirt tolerant Sensor head optics, code and processing were developed in parallel for optimum system design Bayesian Inference and Shannon Information theory used for optimum performance and robustness February 2009 S3937-CONF-015 v1.0

14 Other EM techniques RF techniques can sample the bulk of the material an provide information about Bulk, density, water content T-wave TDS techniques may be able to detect specific chemicals, but this technology is comparatively undeveloped. X-Ray and multi-spectral X-Ray Gamma ray emission neutron bombardment Nuclear Magnetic resonance? y cm x-y x cm February 2009 S3937-CONF-015 v1.0

15 Chemical analysis techniques Wet chemical testing using indicators Decompose a sample to simplify the analysis Mass spectroscopy, chromatography Artificial nose chemical specific sensors Gas sniffers Most techniques are developed for small batch sampling Cambridge Consultants has developed high through put versions of laboratory process Fluorescence measurements Charge measurement System for the high throughput investigation of single airborne particles High Throughput Fluorescence Confocal Imaging February 2009 S3937-CONF-015 v1.0

16 Optional Document Title (short form) Agenda: Introduction Understanding the requirements Assessing the techniques Evaluating complete systems Implementing a successful system February 2009 S3937-CONF-015 v1.0

17 No single sensing technology provides enough information to measure the amount of biomass in solid-fuel. A system that combines multiple technologies is necessary Sensors will have different sensitivity to different biomass constituents. Some will also be sensitive to non-biomass materials How much information is contained in each information stream? How are the sensor outputs correlated how much should they be correlated? Which technologies complement each other? How does sampling and bulk material properties effect the readings? bulky materials will read differently in an optical sensor Rigorous system design techniques are need to answer all these questions February 2009 S3937-CONF-015 v1.0

18 Information about the biomass content comes from detecting many different biomass sensors. How can we process this information to get a single, reliable, figure? Bio-materials will have different signatures -cardboard box vs wood chips Model the signals from different combinations of material and optimise the processing techniques to handle them Noise models are very unlikely to be simple Gaussian distributions so more advanced techniques will be needed To be credible the final measurement must be derived in a statistically rigorous fashion A Bayesian approach may well be applicable BayesLab tm tool kit February 2009 S3937-CONF-015 v1.0

19 Optional Document Title (short form) Agenda: Introduction Understanding the requirements Assessing the techniques Evaluating complete systems Implementing a successful system February 2009 S3937-CONF-015 v1.0

20 No reason to say measuring the percentage of biomass in MSW is impossible, but the system will pose significant technical challenges A final product will require Detailed understanding of a wide range of sensing technologies Knowledge of the commercial requirements of such a system Complex information processing to be confident of the final answer The system must also meet other requirements Cost Power consumption Robustness February 2009 S3937-CONF-015 v1.0

21 Sophisticated processing can increase the capability of simple sensors. Sensor for condition-monitoring of hydraulic fluid First sensor able to characterise particles directly in largebore pipe no sampling stream Novel low-cost optical sensor: Product of CCL in-house IP development Bayesian signal processing for optimum extraction of information DSP implementation February 2009 S3937-CONF-015 v1.0

22 How it works Propose different signal processing schemes (digests) For each suggested digest, we calculate the content of information about what we want to know. We choose that with most. We then consider another suggested digest, and calculate the content of further information about what we want to know. and repeat until satisfied. We then apply Bayes to the resulting digests, using the information in them optimally February 2009 S3937-CONF-015 v1.0

23 Sensor Fusion We have demonstrated the potential of sensor fusion by taking advantage of the strengths of radar and image processing. Sensor fusion speeds the image processing up by x100 Radar Accurate determination of object position and velocity Video Ability to determine object extent and potential to classify into object type Sensor Fusion Radar directs the image processing algorithm to process a subset of the image Processing and Control February 2009 S3937-CONF-015 v1.0

24 Contact details: Cambridge Consultants Ltd Cambridge Consultants Inc Science Park, Milton Road 101 Main Street Cambridge, CB4 0DW Cambridge MA England USA Tel: +44(0) Tel: Fax: +44(0) Fax: Registered No England Cambridge Consultants Ltd, Cambridge Consultants Inc. All rights reserved February 2009 S3937-CONF-015 v1.0