“Dynamic model adaptation to an anaerobic digestion reactor of a water resource recovery facility”
Authors: Shadi Attar and Finn Haugen,Affiliation: University of South-Eastern Norway
Reference: 2019, Vol 40, No 3, pp. 143-160.
Keywords: Anaerobic digestion, Fisher Information Matrix, identifiability analysis, mathematical model adaptation, sensitivity analysis, sloppiness, water resource recovery facility
Abstract: This study deals with model adaptation of the AM2 model to an anaerobic digestion reactor of a water resource recovery facility, namely a 6000m3 reactor at VEAS WWRF, the largest of Norway. The model is based on the mass balance with six states including acidogens, methanoges, alkalinity, organic substrate, volatile fatty acid and inorganic carbon. The model adaptation is applied firstly to simulated reactor data for testing the algorithms, and then to experimental data. The experimental data are collected from laboratory analysis and online measurements from January to October 2017. The data of the first 100 days are used for model identification, and the remaining data for model validation. Identification analysis is based on the Fisher Information Matrix and the Hessian matrix. Also, a sensitivity analysis of the parameter estimates is accomplished.
PDF (2119 Kb)
DOI: 10.4173/mic.2019.3.2DOI forward links to this article:
| [1] Juan G. Rueda-Escobedo, Mihaela Sbarciog, Jaime A. Moreno, Jan Van Impe and Alain Vande Wouwer (2022), doi:10.1016/j.jprocont.2022.07.014 |
| [2] Niloofar Raeyatdoost, Michael Bongards, Thomas Back and Christian Wolf (2023), doi:10.1016/j.jprocont.2022.11.013 |
| [3] Xiaojun Liu, Arnaud Coutu, Stephane Mottelet, Andre Pauss and Thierry Ribeiro (2023), doi:10.3390/en16031108 |
| [4] Hemlata Karne, Aayush Bendre and Amartya Singh (2023), doi:10.1016/j.matpr.2023.01.303 |
| [5] Abdelouahab Zaatri (2024), doi:10.29333/ejosdr/14637 |
[1] Batstone, D.J., Keller, J., Angelidaki, I., Kalyuzhnyi, S., Pavlostathis, S., Rozzi, A., Sanders, W., Siegrist, H., and Vavilin, V. (2000). Batstone, D, J., Keller, J., Angelidaki, I., Kalyuzhnyi, S., Pavlostathis, S., Rozzi, A., Sanders, W., Siegrist, H., and Vavilin, V. Anaerobic digestion model no.1. Technical report, IWA. .
[2] Bergland, W.H. and Bakke, R. (2016). Bergland, W, H. and Bakke, R. Modelling anaerobic digestion during temperature and load variations. International Journal of Energy Production and Management. 2016. Vol. 1. Iss. 4. 1(4):393--402. doi:10.2495/EQ-V1-N4-393-402
[3] Bernard, O., Hadj-Sadok, Z., Dochain, D., Genovesi, A., and Steyer, J.-P. (2001). Bernard, O, , Hadj-Sadok, Z., Dochain, D., Genovesi, A., and Steyer, J.-P. Dynamical model development and parameter identification for an anaerobic wastewater treatment process. Biotechnology and bioengineering. 75(4):424--438. doi:10.1002/bit.10036
[4] Boubaker, F. and Ridha, B.C. (2008). Boubaker, F, and Ridha, B.C. Modelling of the mesophilic anaerobic co-digestion of olive mill wastewater with olive mill solid waste using anaerobic digestion model no. 1 (adm1). Bioresource technology. 99(14):6565--6577. doi:10.1016/j.biortech.2007.11.035
[5] Brown, K.S. and Sethna, J.P. (2003). Brown, K, S. and Sethna, J.P. Statistical mechanical approaches to models with many poorly known parameters. Physical review E. 68(2):021904. doi:10.1103/PhysRevE.68.021904
[6] Cintron-Arias, A., Banks, H., Capaldi, A., and Lloyd, A.L. (2009). Cintron-Arias, A, , Banks, H., Capaldi, A., and Lloyd, A.L. A sensitivity matrix methodology for inverse problem formulation. Journal of Inverse and Ill-posed Problems. page 545. doi:10.1515 / JIIP.2009.002
[7] Dochain, D. (2013). Dochain, D, Automatic control of bioprocesses. John Wiley & Sons. .
[8] Dochain, D. and Vanrolleghem, P. (2001). Dochain, D, and Vanrolleghem, P. Dynamical Modelling and Estimation in Wastewater Treatment Processes. IWA Publishing, London, UK.. .
[9] Drosg, B. (2013). Drosg, B, Process monitoring in biogas plants. IEA Bioenergy Paris, France. .
[10] Eddy, M.., Tchobanoglous, G., Burton, F.L., and Stensel, D.H. (2003). Eddy, M, ., Tchobanoglous, G., Burton, F.L., and Stensel, D.H. Wastewater Engineering treatment and reuse. McGraw-Hill Higher Education. .
[11] Guidi, F.H. (2008). Guidi, F, H. Open and closed loop model identification and validation. Ph.D. thesis, University of Pretoria. .
[12] Hajji, A., Rhachi, M., Garoum, M., and Laaroussi, N. (2016). Hajji, A, , Rhachi, M., Garoum, M., and Laaroussi, N. The effects of ph, temperature and agitation on biogas production under mesophilic regime. In 2016 3rd International Conference on Renewable Energies for Developing Countries (REDEC). IEEE, pages 1--4. doi:10.1109/REDEC.2016.7577510
[13] Hashimoto, G.A., Chen, Y.R., and Varel, V.H. (1995). Hashimoto, G, A., Chen, Y.R., and Varel, V.H. Theoretical aspect of methane production: State-of-the-art. Livestock wastes: American society of agriculture engineering, 1995. pages 86--91. .
[14] Haugen, F., Bakke, R., and Lie, B. (2013). Haugen, F, , Bakke, R., and Lie, B. Adapting dynamic mathematical models to a pilot anaerobic digestion reactor. 2013. doi:10.4173/mic.2013.2.1
[15] Hill, D. and Barth, C. (1977). Hill, D, and Barth, C. A dynamic model for simulation of animal waste digestion. Journal (Water Pollution Control Federation). pages 2129--2143. .
[16] Jayaraj, S., Deepanraj, B., and Sivasubramanian, V. (2014). Jayaraj, S, , Deepanraj, B., and Sivasubramanian, V. Study on the effect of ph on biogas production from food waste by anaerobic digestion. In 9th Annual Green Energy Conference in Tianjin China (IGEC-IX). 2014. .
[17] Kheiredine, B., Derbal, K., and Bencheikh-Lehocine, M. (2014). Kheiredine, B, , Derbal, K., and Bencheikh-Lehocine, M. Effect of starting ph on the produced methane from dairy wastewater in thermophilic phase. CHEMICAL ENGINEERING. 38:511--516. doi:10.15242/IICBE.C0615001
[18] Koch, K., Lubken, M., Gehring, T., Wichern, M., and Horn, H. (2010). Koch, K, , Lubken, M., Gehring, T., Wichern, M., and Horn, H. Biogas from grass silage--measurements and modeling with adm1. Bioresource Technology. 101(21):8158--8165. doi:10.1016/j.biortech.2010.06.009
[19] Komorowski, M., Costa, M.J., Rand, D.A., and Stumpf, M.P. (2011). Komorowski, M, , Costa, M.J., Rand, D.A., and Stumpf, M.P. Sensitivity, robustness, and identifiability in stochastic chemical kinetics models. Proceedings of the National Academy of Sciences. 108(21):8645--8650. doi:10.1073/pnas.1015814108
[20] Lee, D.-J., Lee, S.-Y., Bae, J.-S., Kang, J.-G., Kim, K.-H., Rhee, S.-S., Park, J.-H., Cho, J.-S., Chung, J., and Seo, D.-C. (2015). Lee, D, -J., Lee, S.-Y., Bae, J.-S., Kang, J.-G., Kim, K.-H., Rhee, S.-S., Park, J.-H., Cho, J.-S., Chung, J., and Seo, D.-C. Effect of volatile fatty acid concentration on anaerobic degradation rate from field anaerobic digestion facilities treating food waste leachate in south korea. Journal of Chemistry. 2015. doi:10.1155/2015/640717
[21] Li, Z., Lu, P., Zhang, D., and Zhang, T. (2018). Li, Z, , Lu, P., Zhang, D., and Zhang, T. Practical identifiability analysis and optimal experimental design for the parameter estimation of the asm2d-based ebpr anaerobic submodel. Mathematical Problems in Engineering. 2018. doi:10.1155/2018/9201085
[22] Little, M.P., Heidenreich, W.F., and Li, G. (2010). Little, M, P., Heidenreich, W.F., and Li, G. Parameter identifiability and redundancy: theoretical considerations. PloS one. 5(1):e8915. doi:10.1371/journal.pone.0008915
[23] Ljung, L. and Chen, T. (2013). Ljung, L, and Chen, T. Convexity issues in system identification. In 2013 10th IEEE International Conference on Control and Automation (ICCA). IEEE, pages 1--9. doi:10.1109/ICCA.2013.6565206
[24] Muroi, H. and Adachi, S. (2015). Muroi, H, and Adachi, S. Model validation criteria for system identification in time domain. IFAC-PapersOnLine. 48(28):86--91. doi:10.1016/j.ifacol.2015.12.105
[25] Petersen, B., Gernaey, K., and Vanrolleghem, P. (2001). Petersen, B, , Gernaey, K., and Vanrolleghem, P. Practical identifiability of model parameters by combined respirometric-titrimetric measurements. Water Science and Technology. 43(7):347--355. doi:10.2166/wst.2001.0444
[26] Rothenberg, T.J. etal. (1971). Rothenberg, T, J. etal. Identification in parametric models. Econometrica. 39(3):577--591. doi:10.2307/1913267
[27] Saccomani, M.P. (2013). Saccomani, M, P. Structural vs practical identifiability in system biology. In IWBBIO. pages 305--313. .
[28] Shang, Y., Johnson, B., and Sieger, R. (2005). Shang, Y, , Johnson, B., and Sieger, R. Application of the iwa anaerobic digestion model (adm1) for simulating full-scale anaerobic sewage sludge digestion. Water Science and Technology. 52(1-2):487--492. doi:10.2166/wst.2005.0557
[29] Shang, Y., Johnson, B., Sieger, R., and Forbes, R. (2004). Shang, Y, , Johnson, B., Sieger, R., and Forbes, R. Evaluation of the iwa anaerobic digestion model (adm1) for simulating full-scale anaerobic sewage sludge digestion. Proceedings of the Water Environment Federation. 2004(10):530--541. doi:10.2175/193864704784131563
[30] Stare, A., Hvala, N., and Vrecko, D. (2006). Stare, A, , Hvala, N., and Vrecko, D. Modeling, identification, and validation of models for predictive ammonia control in a wastewater treatment plant - a case study. ISA transactions. 45(2):159--174. doi:10.1016/S0019-0578(07)60187-6
[31] Strang, G., Strang, G., Strang, G., and Strang, G. (1993). Strang, G, , Strang, G., Strang, G., and Strang, G. Introduction to linear algebra, volume3. Wellesley-Cambridge Press Wellesley, MA. .
[32] Villaverde, A.F. (2019). Villaverde, A, F. Observability and structural identifiability of nonlinear biological systems. Complexity. 2019. doi:10.1155/2019/8497093
[33] Zhang, T., Zhang, D., Li, Z., and Cai, Q. (2010). Zhang, T, , Zhang, D., Li, Z., and Cai, Q. Evaluating the structural identifiability of the parameters of the ebpr sub-model in asm2d by the differential algebra method. Water research. 44(9):2815--2822. doi:10.1016/j.watres.2010.02.027
BibTeX:
@article{MIC-2019-3-2,
title={{Dynamic model adaptation to an anaerobic digestion reactor of a water resource recovery facility}},
author={Attar, Shadi and Haugen, Finn},
journal={Modeling, Identification and Control},
volume={40},
number={3},
pages={143--160},
year={2019},
doi={10.4173/mic.2019.3.2},
publisher={Norwegian Society of Automatic Control}
};