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HIT-Norge kursus Laboratorie Ingeniører Future Climate ? Det CO2-neutrale samfund !

HIT-Norge kursus Laboratorie Ingeniører Future Climate ? Det CO2-neutrale samfund ! Hvordan skaber vi en bæredygtig udvikling? Bioenergi er en alsidig og ikke helt uvæsentligt spiller i det internationale energi mix i fremtiden! Jens Bo Holm-Nielsen

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HIT-Norge kursus Laboratorie Ingeniører Future Climate ? Det CO2-neutrale samfund !

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  1. HIT-Norge kursus Laboratorie Ingeniører Future Climate ? Det CO2-neutrale samfund ! Hvordan skaber vi en bæredygtig udvikling? Bioenergi er en alsidig og ikke helt uvæsentligt spiller i det internationale energi mix i fremtiden! Jens Bo Holm-Nielsen Ph.D., Center for Bioenergi og Green Engineering Institute for Energy Technology Aalborg University, Esbjerg Institute of Technology Niels Bohrs vej 8, 6700 Esbjerg Cell; +45 2166 2511 E-mail: jhn@et.aau.dk www.acabs.dk; www.iet.aau.dk;

  2. Process Analytical Technologies for Anaerobic Digestion Systems - Robust Biomass Characterisation, Process Analytical Chemometrics, and Process Optimisation Jens Bo Holm-Nielsen, Ph.D. Head of Center of Bioenergy and Green Engineering ACABS Research Group: Applied Chemometrics, Analytical Chemistry, Applied Biotechnology & Bioenergy, and Sampling Research Group (ACABS) , Esbjerg Institute of Technology, Aalborg University

  3. Biogas for a sustainable clean environment and renewable energy production LIGHT PHOTOSYNTHESIS O2 CO2 ANIMAL MANURE H2O BIOFERTILISER CHP-GENERATION BIOGAS AS VEHICLE FUEL BIOGAS PLANT ORGANIC WASTE Source: JBHN/TAS

  4. Estimated amounts of animal manure in EU-27 (based on Faostat, 2003)

  5. Energy potential of pig and cattle manure in EU-27 Methane heat of combustion: 40.3 MJ/m3; 1 Mtoe = 44.8 PJ Assumed methane content in biogas: 65% Actual 2008 production of biogas in EU 27: 7 Mtoe 2012-2015 EU forecast 15 Mtoe Manure potentials 18.5-20 Mtoe Organic waste and byproducts 15-20 Mtoe Crops and crop residuals 20-30 Mtoe Total long term forcast Biogas 60 Mtoe Biogas can cover 1/3 of EU’s total RES 20% demands year 2020 Biogas Production & Forecast:

  6. - Manure • - Food waste • Organic by-products • Crops AD Co-digestion - heterogeneous feedstock’s

  7. Fig. 4.3: The four principal process steps in the general anaerobic digestion – biogas production process. The dashed boxes indicate important intermediate compounds (Holm-Nielsen et al 2008, in. prep).

  8. Different PAT/PAC laboratory approaches and strategies • Off-line; • At-line; • On-line; • (McLennan 1995) Comparison of analytical strategies for process monitoring (Mortensen 2006) Time consuming versus on-line real time measurements!

  9. Fundamental disciplines of Process Analytical Chemistry (PAC) (Mortensen, 2006) Numerous technologies can be applied in a PAT measuring programs for process understanding and controlling. The technologies can be categorized in four major areas: 1. Technologies that imply use of Process Analytical Technology or Process Analytical Chemistry 2. Technologies for monitoring and control of the process and end products 3. Technologies for continuous improvement of gained process knowledge 4. Technologies for acquisition and analysis of multivariate data ( FDA - PAT Guidance, 2005)

  10. Ribe Biogas Plant; a full scale test facility for several R&D projects. Sampling points for feedstock’s and inoculums

  11. Paper 1; Introduction of TOS correct sampling & on-line PAT measurements in full scale applications Composite sampling !!! Schematic illustration of primary sampling at the full-scale biogas plant in which a two-step composite sampling approach was used. Eight 10L primary increments were individually mass-reduced to 1L, before being compounded. Mechanical agitation is essential to keep the bio-slurries in a state of maximum homogenization while being sub-sampled. This compound sampling scheme is in full accordance with the principles of TOS.

  12. Sampling unit operations: TOS-correct sampling • Structurally correct sampling is the only safeguard • against sampling bias • 2. Heterogeneity characterization of 0-D lots • 3. Homogenization, mixing, blending • 4. Composite sampling • 5. Representative mass reduction • 6. Particle size reduction (comminution or crushing) • 7. Lot dimensionality transformation (3D or 2D → 1-D or 0-D) • (Petersen et al., 2005)

  13. Transflexive embedded NIR recurrent loop measurement system; developed by CAU, Kiel, D Figure 4. TENIRS stand-alone prototype, view from the front and back, from Andree et al. (2005).8 The TENIRS system consists of: (1) ZEISS CORONA 45 NIR spectrophotometer; (2) measuring cell; (3) pump; (4) multi-way valve; (5) sample holder with 1 L container; (6) frequency converter; (7) Control PC.

  14. Primary sampling point developed for on-line laboratory and meso-scale R&D projects (KAU-AAUE) Fig. 7.8. Prototype sampling device. 10 increments each of 10 ml were sampled during a period of 10 minutes – an effective composite sample for chemical reference analysis. (Holm-Nielsen et al. 2007)

  15. Flow-through measuring cell, TENIRS Remarks: Tested path length 3 mm and 6 mm. Learning process during these trials to change from transflection towards pure reflection measurements in the heterogenious bio-slurries and other brown-black liquid medias.

  16. Figure 5: Raw NIR spectra of all original 63 samples, spectra were used as log (1/R). Because of the low resolution nature of the TENIRS spectra, expressed as very broad, continuous peaks and valleys, there was found no need for more specific pre-treatments, e.g. derivatives or MSC, see text. Note one gross - and several minor outliers.

  17. Where: E is the energy, h Planck’s constant, c the speed of the light, and λ the wavelength.

  18. Figure 7.9 Principles behind creation of a multivariate calibration models

  19. Figure 7.10. Steps in PSL-regression. During step 1 - a multivariate calibration model is generated. In step 2 the calibration model from step 1 is applied on new X-data in order to predict the corresponding unknown y-data, (Petersen, 2005). Important: A 3. step for real full scale implementation and process controlling: Y predicted tested versus Y test-set validation – needed routine validation in on-line Food, Fuels, Pharma industries etc

  20. Paper 2; Off-line & simulated at-line PAT studies of important intermediates of the AD process. Co-digestion of manure and energycrops/maize silage. A At-line testing of 4 anaerobic digestion fermenters – key analytes The Reidling biogas plant. Fermentor 1 is the flat roofed fermentor to the left. This fermentor have been in full operation a year. The fermentor in the center part of the photo is fermentor number 2, which was started in February 2005, photo JBHN, 03/05

  21. ”Unfortunately, we had to ….” Fermentor no. 2 at the Strem biogas plant. Insulation was not completed due to the fact that the mesofilic fermentation process generated surplus of heat for keeping the process temperature and even slightly increase in temperature was registrated. Inoculum at this biogas plant was cow manure, but after start up the feedstock was almost maize silage. Photo JBHN, 03/05.

  22. Examples of incorrect sampling points in fermentation systems in biotech. - Food-feed-fuels-pharma industries from: Esbensen & Mortensen: ”TOS – the missing link in PAT” in: Bakeev (Ed.) ”PAT” (Blackwell, 2009)

  23. ACABS’ recurrent loop Generalised on-line PAT measurement and simultaneous TOS-sampling concept - necessary requirement for optimal multivariate calibration – i.e. lowest RMSEP

  24. Figure 6: PCA score plot of fermentor data sets from both the Reidling (R) and Strem (S) locations based on TENIRS spectra. One outlier described in the text has been excluded (Strem58). [S1, S2] and [R1, R2] signifies the number 1 and 2 reactors of either locations respectively (see text).

  25. Figure 7: PLS1 prediction model, Y = volatile solids content (VS); Two segment cross-validation. Two PLS-components. TENIRS data. One outlier removed (Strem 58). Figure 8. PLS1 pred. model. Y = Ammonium-N; 2-segment cross-validation. 4 PLS-components. One outlier removed (Strem 58). Figure 9. PLS1 prediction model. Y = Acetic acid; 2-segment cross-validation. 6 PLS-components. 14 minor outliers removed.

  26. Data from Austrian biogas plants 2005 Co-digestion AD plants Very heterogeneous Many analytes influencing the signals There will always be an uncertanty in the rage of 10-20 % 2xRMSEP: 20% Reference: Holm-Nielsen, J.B., Andree, H., Lindorfer, H., Esbensen, K.H. (2007). Transflexive embedded near infrared monitoring for key process intermediates in anaerobic digestion/biogas production, Journal of Near Infrared Spectroscopy, vol. 15, pp. 123-135, DOI: 10.1255/jnirs.719

  27. Paper 3: Glycerol spiking trials in 3 parallel fermentors, full on-line PAT lab-trials 2006. Figure 4. Glycerol development during the AD process trials, shown for each individual fermenter. Concentration measured in the fermenter effluent.

  28. Paper 3: Glycerol spiking trials in 3 parallel fermentors, full on-line PAT lab-trials 2006. Figure 5. Volatile fatty acids contents. Development for each fermenter.

  29. Figure 7. Glycerol PLS-1 model; number of required PLS components = 2; One outlier was removed; Test set validation was made from data obtained in fermenter no.2 and tested against data from fermenter no.1 and no.3; Measures of precisions r2 = 0.96 and slope 1.04

  30. Figure 10. Total VFA PLS-1 model; number of required PLS-components = 3; Two outliers were removed; Test set validation where data from the fermenter no.2 were tested against calibration data from fermenter no.1 and no.3; measures of precision: r2 = 0.98, and accuracy; slope = 1.03

  31. Paper 4: On-line PAT monitoring – meso-scale anaerobic digestion trials. ACABS/AAUE research group and Research Center Bygholm(DJF)/AU Trial plan: 1. Increased loading rate of pig manure and 2. Sudden changes – increase in temperature; for developing critical data spanning; total trail period 37 days.

  32. Sampling facility. Primary sampling – 10 increments. Secondary sampling for wet chemistry 5 increments for each vial

  33. Spanning the analytes; min. and max. values measured in the lab.

  34. Concentration level of acetate and propanoic acid and corrected biogas production during the trial period.

  35. Model for probionic acid; MSC corrected, test set validation

  36. Statistics from best calibration models; MSC corrected, test set validation

  37. Linkogas full scale trials, 2007 - 2008 - on-line PAT monitoring fermenter 3, 2400 m3

  38. Linkogas trials: Experimental planning Illustration of the sampling procedure, incremental and fractional shovelling sampling

  39. Linkogas full scale trials, 2007 - on-line PAT monitoring fermenter 3, 2400 m3 Figure 7.12.Volatile fatty acids concentration v.s. biogas production (Conc. mg/L, Prod. m³) Biogas production measures to the left hand side and VFA acid concentration levels to the right.

  40. Linkogas full scale trials, 2007 - on-line PAT monitoring fermenter 3, 2400 m3 VFA - PLS-1 model Figure 7.13. Measured vs. Predicted plot for the total VFA model. The black line (the diagonal) indicates the target line, while the blue line is the regression line. The VFA PLS-1 model; number of required PLS-components = 10; Two-segment cross validation was used due to low concentration dataset, and low spanning. Measures of precision: r2 = 0.83, and accuracy; slope = 0.92 (Holm-Nielsen et al. 2008).

  41. Thesis conclusions • and some reflections • It is possible to make satisfactory models of important AD-intermediates like VFA’s and ammonium, documented by several Bioenergy/ACABS-group studies • Biogas – AD production is a complex biological process, which can be monitored and controlled much more advanced and robust/simple in the future. It is one of the most suitable processes for regulation of supply integration in the energy sector of the RES sources. • The AD process is a key technology in biorefinering. It shows a huge potential in the future energy supply chain: real sustainable energy production • These studies have demonstrated the direct potential for a fast track to full process monitoring, regulation and control • Next step will be to implement PAT technologies also in the biorefinery sector, including developing process test platforms – there is an urgent need for more test facilities implemented in full scale operations • Contribution to on-going R&D&D co-operations between universities and bioenergy industries - regionally, nationally and internationally • Bioenergy potentials in sustainable combination with other RES systems (wind-power, solar-power, hydro-and wave-power) will be able to supply ~50-100% of the entire world-wide energy supply in the medium to long term!

  42. Full-scale R & D Test facilities PAT - platform in biorefinery projects Biorefinery test facilities (complete process control loops and facilities). Process platform means here main process steps;( process steps 1,...n.), (Njoku & Holm-Nielsen, 2008)

  43. Biorefineries for the future 1-2-3 generations! Challange fast replacements of oil refineries Figure.8.1 Biorefinery concept. Biorefineries are complex production clusters where the sources (raw materials) are e.g. biomass from agriculture forestry and society, resulting in a broad range of processed products, for example foods / feeds / fuels / fibres and fertilizers. The figure illustrates typical stages /treatments in integrated biomass utilization systems (Nielsen C. et al. 2005). Pre-treatment of ligno-cellulosic materials is crucial for future biorefining.

  44. Thank you for fruitful cooperation to all partners: Danish and International Companies, ILV-University of Kiel, D, - IFA-Tulln, BOKU, A, DJF – Aarhus University, - Risoe/DTU, Danish Biogas Plant Owners – Linkogas, Ribe Biogas, Thorsoe Students and staff at the Bioenergy and Chemistry laboratories; Esbjerg Institute of Technology, Aalborg University Special thank you to Kim H. Esbensen (the thesis supervisor), & Torben Rosenørn and Lars Døvling Andersen at the INS-Faculty at AAU, to make this R&D&D work possible. Thank you for your attention!

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