Biochemical Methane Potential tests

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Table of contents

I. BETTER INPUT CHARACTERIZATION FOR BETTER ANAEROBIC DIGESTION MODELS: TOWARDS NEW METHODOLOGIES FOR MUNICIPAL SLUDGE CHARACTERIZATION
I.1. Statement in methane production prediction from municipal wastewater sludge
I.1.1. Municipal wastewater treatment sludge: definition and composition
I.1.2. Predicting methane production: the analytical way
I.1.3. Predicting methane production: predicting tools
I.1.3.1. Static models
I.1.3.2. Dynamic models and evolution of substrate complexity
I.1.3.3. ADM1 and influent characterization
I.2. Critical review
I.3. Advanced techniques for organic matter characterization
I.3.1. Near infrared reflectance spectroscopy (NIRS)
I.3.2. 3D Excitation Emission fluorescence spectroscopy
I.4. Conclusions and perspectives
I.5. Problematic definition and scientific strategy
II. MATERIAL AND METHODS
II.1. Sludge characterization: analytical methods
II.1.1. Total organic matter analysis
II.1.1.1. Total solids and volatile solids
II.1.1.2. Chemical Oxygen Demand
II.1.1.3. Total carbon analysis
II.1.1.4. Nitrogen analysis
II.1.2. Biochemical characterization
II.2. Biodegradability and bioaccessibility : definition of quantitative variables
II.2.1. Biochemical Methane Potential tests
II.2.2. Interpretation and calculation of BMP
II.2.3. Interpretation and calculation of XRC/XSC
II.3. Sludge samples
II.4. Chemical sequential extraction protocol
II.4.1. Definitions
II.4.2. Sequential extraction Protocol
II.4.2.1. Laboratory material
II.4.2.2. DOM
II.4.2.3. S-EPS
II.4.2.4. REPS
II.4.2.5. HSL
II.4.2.6. COD mass balance and organic matter extraction yield calculations
II.5. 3D-EEM fluorescence spectroscopy
II.5.1. Fluorescence Spectrometer
II.5.2. Fluorescence Spectra
II.5.3. Dilution and linearity for quantification
II.5.4. Spectra interpretation
II.6. Anaerobic digestion laboratory scale reactors
II.7. Mathematical modeling : modified ADM1 and statistical methods
II.7.1. Modified ADM1
II.7.2. Input variables modifications
II.7.3. Kinetic modifications
II.7.4. Liquid/Gas transfer modification
II.7.5. Modified ADM1 input implementation
II.8. Statistical tools
II.8.1. Partial Least Square Regression
II.8.1.1. Definition
II.8.1.2. Interpretation
II.8.2. Other statistical tests
II.9. Conclusion
III. BIOACCESSIBILITY AND CHEMICAL ACCESSIBILITY CORRELATION INVESTIGATION
III.1. Preliminary results : Biochemical methane potential (BMP) test and sequential extractions
III.1.1. Biochemical Methane Potential: S/X ratio investigation
III.1.2. Sequential extraction and sludge profile
III.1.2.1. Validation extractions number
III.1.2.2. Fractions extraction repartition and sludge type
III.1.2.3. Effect of size particle distribution on chemical extractions protocol
III.1.3. Biochemical nature of sludge and extracted organic matter
III.1.3.1. Non-fractionated sludge characterization
III.1.3.2. Fractionated sludge characterization
III.1.4. Sludge fractionation conclusion
III.2. Correlation between chemical and biochemical accessibility investigation
III.2.1. Material flow investigation : anaerobic stabilization test
III.2.2. Biodegradability and bioaccessibility investigation of sequential extraction fractions
III.2.3. Methane production curve and correlation of fractions extracted
III.3. Conclusions
IV. BIODEGRADABILITY AND BIOACCESSIBILITY INDICATORS INVESTIGATION FROM SEQUENTIAL EXTRACTIONS COUPLED WITH 3D-EEM FLUORESCENCE SPECTROSCOPY
IV.1.1. Fluorescence spectroscopy and organic matter complexity
IV.1.1.1. Sequential extractions fractions fluorescence
IV.1.1.2. Evolution of fractions during anaerobic treatment
IV.1.1.3. Thermally treated sludge
IV.2. Definition of indicators from sequential extractions coupled with 3D-EEM liquid phase fluorescence spectroscopy (3D-SE-LPF) results
IV.2.1. General complexity indicator
IV.2.2. Zone-specific biodegradability indicator
IV.3. Correlations between 3D-SE-LPF indicators and biodegradability
IV.3.1. Exploratory PLS regressions
IV.3.2. PLS regression model set up for biodegradability prediction
IV.3.2.1. Calibration and validation datasets
IV.3.2.2. Validation results of PLS regression
IV.4. Correlations between 3D-SE-LPF indicators and bioaccessibility
IV.4.1. Exploratory PLS regressions
IV.4.2. Validation PLS regression for XRC prediction
IV.5. Identification of recalcitrant molecules to biodegradation: sensitivity analysis
IV.5.1. Sensitivity analysis of PLS models: definition
IV.5.1.1. Sensitivity analysis of PLS models: fractionation variables
IV.5.1.2. Sensitivity analysis of PLS models: fluorescence zones variables
IV.5.1.3. Sensitivity analysis of PLS models: scenario analysis
IV.6. Conclusions
V. METHODOLOGY GLOBAL VALIDATION : MODIFIED ADM1 MODELLING AND DIGESTERS DESIGN IMPACT
V.1. Continuous lab pilots performances
V.1.1. Reference period
V.1.2. Disturbing period
V.2. Modified ADM1 modeling
V.2.1. Modeling procedure
V.2.2. Input implementation and parameters
V.2.3. Model calibration
V.2.3.1. Steady state calibration
V.2.3.2. Dynamic state calibration
V.3. Model validation
V.3.1. Dynamic validation with disturbing data
V.3.1.1. Input data during both references and disturbing period
V.3.1.2. Dynamic validation
V.3.1.3. Sensitivity analysis
V.4. Bioaccessibility variables and impact on hydraulic retention time
V.4.1. Scenarii analysis
V.4.2. Correlations found between bioaccessibility variables and HRT
V.5. Conclusions and discussion