Bregua de la Sotilla, Marta

Plósz, Benedek G.

Smets, Barth F.

Sæbø, M.

Valverde Perez, Borja

Van Wagenen, Jonathan

Wágner, Dorottya Sarolta

Online Research Database In Technology

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Downloaded from orbit.dtu.dk on: Dec 21, 2017Modeling green microalgal growth, nutrient uptake and storage in the ASM frameworkWágner, Dorottya Sarolta; Valverde Pérez, Borja; Sæbø, M.; Bregua de la Sotilla, Marta; van Wagenen,Jonathan Myerson; Smets, Barth F.; Plósz, Benedek G.Publication date:2015Document VersionPeer reviewed versionLink back to DTU OrbitCitation (APA):Wágner, D. S., Valverde Perez, B., Sæbø, M., Bregua de la Sotilla, M., Van Wagenen, J., Smets, B. F., & Plósz,B. G. (2015). Modeling green microalgal growth, nutrient uptake and storage in the ASM framework[Sound/Visual production (digital)]. 9th IWA Symposium on Systems Analysis and Integrated Assessment(Watermatex 2015), Gold Coast, Queensland, Australia, 14/06/2015Modeling green microalgal growth, nutrient uptake and storage in the ASM framework Dorottya S. Wágner, Borja Valverde-Pérez, Mariann Sæbø,Marta Bregua de la Sotilla, Jonathan van Wagenen, Barth F. Smets, Benedek Gy. Plósz Motivation Microalgae photobioreactors can be used for wastewater treatment as: Tertiary treatment step for nutrient removal  Nutrient recovery technology due to the phosphorus and nitrogen internal storage   Valverde-Pérez et al. (2015) Motivation Available models are good, however the applicability may be limited: By the number of considered variables By the model structure Factors affecting microalgal growth:  Carbon, both organic and inorganic Nutrients: phosphorus and nitrogen pH Light Micronutrients (e.g. iron)   Agenda Model development Targeted experiments for parameter estimation: Green microalgae identification and equipment Microbatch and 1-L batch experiments Open pond experiments Model evaluation   Model development (ASM-A) Mechanistic description of biokinetic processes: multiple-substrate kinetic process rate equations based on literature       D: Droop M: Monod H: Haldane Model development (ASM-A) Developed as an extension of ASM-2d, so compatible with activated sludge models Units and nomenclature expressed according to the ASM (activated sludge modelling) framework       Microalgal nutrient uptake and storage Rieger et al. (2001) Rhee (1973)  Ambrose et al. (2006) Processes 1&2: uptake and storage of nitrogen using ammonia and nitrate as nitrogen source     Process 3: uptake and storage of phosphate Microalgal growth and decay Droop (1974) Broekhuizen et al. (2012) Rieger et al. (2001)  Ambrose et al. (2006) Chen and Johns (1994) Processes 4: autotrophic growth rate   Process 5: heterotrophic growth rate   Process 6: decay Agenda Model development Targeted experiments for parameter estimation: Green microalgae identification and equipment Microbatch and 1-L batch experiments Open pond experiments Model evaluation  Microalgal culture Mixed green microalgal culture consists mainly of Chlorella sp. (C. sorokiniana) and Scenedesmus sp.       Microscope image of the mixed green microalgae culture in the batch experiment.  Reactors 24 L open airlift PhBR  2 mL microbatches  1-L batches  Microbath experiments: light intensity effect µmax = 3.6 d-1 Assessing the specific growth rate under different light intensities  Steele I (mol m-2 s-1)0 200 400 600 800 1000(d-1)012345Steele equation: Is = 758 µmol m-2 s-1 R2=0.995 0 0,005 0,01 0,015 0,02 0,025 0,03 1,00 2,00 3,00 4,00 5,00 6,00 7,00 8,00 Concentration [gP/gbiomass] Time [days] Controll PO4- limited  X Alg,PPmin = 0.002 gP/gBiomass X Alg,PPmax = 0.025 gP/gBiomass KPO4 = 0.71 mgPO4-P/L Nutrient uptake and storage: P-uptake and storage  Iav=100 µmol m-2s-1     Parameter estimation using Simplex 0 0,5 1 1,5 2 2,5 3 3,5 0 10 20 30 40 50 PO4 (mg-P/L) Time (t) N-uptake and storage Cycle Initial N conc.  (g N/m3) 1 20 2 10 3 5 4 1 5 0.5     Parameter estimation using Simplex time (days)SN (mg N L-1)0510152025Descending cycles12345ASM-A calibration Model calibration using data from cycle 2 Parameter Values Parameter Microplate 1-L batch 24-L batch Literature Value Unit µA,max 3.6 ± 0.04a 3.3 ± 0.55e  4.19±1.19g 4.7 30; 0.1-11 36; 1.4 70; 0.15 71; 2.64 67;    1.47 67; 3.26 72 d-1 µH,max 0.75 ± 0.1b 3.23 ± 1.1f - 14 37; 3.48 73;        2.35-2.64 67; 0.96-1 67 d-1 Is 758.2 ± 22.9a - - - µmol m-2 s-1 XAlg,PPmin - 0.002±0.0008e - - g P∙g-1COD XAlg,Nmin - 0.014±0.0015 e - 0.023 37; 0.02 72 g N∙ g-1COD XAlg,PPmax - 0.02±0.008e  - - g P∙ g-1COD XAlg,Nmax - 0.9±0.5 e - 0.0683 37; 0.067 72 g N∙ g-1COD KNO,Alg - 3.39 ± 1.89e 8.22±0.97 g 0.0602 37; 0.3 71 g N∙m-3 KNH4,Alg - 2.4 ± 0.42e 3.91±0.88 g 5 34; 0.1-31.5 71 g N∙m-3 KPO4,Alg - 0.25 ± 0.18e 0.36±0.046 g 0.046-10.5 71 g P∙m-3 kNH4,Alg - 2.97 ± 2.42  0.22±0.01 g - g N∙g-1COD∙d-1 kNO,Alg - 2.97 ± 2.42  0.018±0.012 g 0.49 37; 0.43 72 g N∙g-1COD∙d-1 kPO4,Alg - 0.066 ± 0.042 0.028±0.021 g - g P∙g-1COD∙d-1 KAlk - - - 3 34; 0.035-0.29 71 g C∙m-3 KA 11.87 ± 5.2b 89 ± 28f - - gCOD∙m-3 Ki,A 550.58 ± 232.1b - - - gCOD∙m-3 KI 878.6±75d - - - µmol m-2s-1 KO 2 (20% of  DOsat) 56 gCOD m-3 bXalg - - 0.091±0.0 g 0.072 (2%*µA,max) 33; 3.7% µA,max30;     0.003-0.1 71 d-1 YAlk - - - 2.33; 1.06 71 g COD∙g-1C YAc - 0.39f - 0.34 45 g COD∙g-1COD iPXalg - - 0.0006731, 34  g P ∙g-1COD iNXalg - - 0.0049 31; 0.0534 34 g N∙ g-1COD fXI - - 0.1 g COD∙g-1COD NXAlgD 0.00441 g N∙g-1COD PXAlgD 0.000603 g P∙g-1COD Agenda Model development Targeted experiments for parameter estimation: Green microalgae identification and equipment Microbatch and 1-L batch experiments Open pond experiments Model evaluation  Model evaluation: assessment of parameter variability impact Research questions: What is the influence of culture history and/or substrate availability on parameter estimates? Can we use a default parameter set? Can we explain the discrepancy as a result of parameter variability? Cycle Initial N conc.  (g N/m3) 1 and 9 20 2 and 8 10 3 and 7 5 4 and 6 1 5 0.5 Model evaluation: experimental design time (days)SN (mg N L-1)0510152025Ascending cyclesDescending cycles1 9823 74 65time (days)SN (mg N L-1)0510152025Ascending cyclesDescending cycles1 982374 65Model evaluation: two evaluation steps Does culture history affect parameter values? Parameter sets obtained through the descending cycles confronted with data from ascending cycles Janus coefficient Cycle RMSE calibration RMSE evaluation Janus coefficient 1-9 0.24 23.9 99.58 3-7 0.26 3.85 14.81 4-6 0.23 9.35 40.65 Model evaluation: two evaluation steps • J~1 calibrated model prediction is good • J>> calibrated model prediction fails Nitrate prediction in the other cycles: Model evaluation: experimental results • The nitrate uptake after starvation conditions is enhanced Pink dots: before starvation Yellow dots : after starvation Model evaluation: experimental results • After the N quota is replenished there is a temporary enhanced N storage Pink dots: before starvation Green dots: after starvation Model evaluation: parameter variability SN (mg N L-1)0 5 10 15 20 25kNO (g N g-1COD d-1)Ascending cyclesDescending cycles 19823746Model evaluation: two evaluation steps Can we use an average parameter set? Can we explain the discrepancy as a result of parameter variability? Monte Carlo simulations run on the 4 ascending cycles Parameter values: mean values of the estimated parameters through descending cycles. Probability range: standard deviation of the mean values through descending cycles. time (days)SN (mg N L-1)0510152025Ascending cyclesDescending cycles1 982374 65Calibration Evaluation Model evaluation: second steps Algal biomass & ammonia: discrepancies can be explained by parameter variability Model evaluation: two evaluation steps Soluble and stored phosphate: discrepancies can be explained by parameter variability Model evaluation: two evaluation steps  Soluble nitrate & stored nitrogen: the model prediction is compromised by Culture history for nitrate  Substrate availability for nitrogen storage   Concluding remarks A novel process model in the ASM framework for predicting algal behavior in PBR has been identified, calibrated and critically evaluated Different scale lab experiments have been used to estimate different parameter sets. The model can predict algal biomass, ammonia, phosphate and internal PP quota using a mean parameter set Maximum nitrate uptake rate depends on the history of the culture  Future perspectives Model extensions including physic-chemical processes: Mass transfer pH Light attenuation Hydrodynamics Model extensions relevant to other end-uses: lipid accumulation for biodiesel Model calibration and evaluation using other microalgae species  Aknowlegement Thank you for your attention! Dorottya S. Wágner, Mariann Sæbø,Marta Bregua de la Sotilla, Jonathan van Wagenen, Barth F. Smets, Benedek Gy. Plósz 

Modeling green microalgal growth, nutrient uptake and storage in the ASM framework

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Modeling green microalgal growth, nutrient uptake and storage in the ASM framework

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