(440e) Monitoring and Parameter Estimation in Ecological Models Including Fish Balances for Lake Restoration Planning with Dynamic Optimization Techniques Conference: AIChE Annual MeetingYear: 2015Proceeding: 2015 AIChE Annual MeetingGroup: Environmental DivisionSession: Water Sustainability and Integrated Water Resource Management Time: Tuesday, November 10, 2015 - 4:31pm-4:50pm Authors: Estrada, V., Planta Piloto de Ingenieria Quimica (PLAPIQUI), Universidad Nacional del Sur - CONICET Di Maggio, J. A., PLAPIQUI (CONICET-UNS) Diaz, M. S., Planta Piloto de Ingeniería Química, PLAPIQUI (CONICET-UNS), Universidad Nacional del Sur Grosman, F., Universidad Nacional Centro de la Prov. Buenos Aires Sanzano, P., Universidad Nacional Centro de la Prov. Buenos Aires Crisafulli, M., Hoffmeyer, M., Guerrero, J., Water Authority Province of Buenos Aires Baglivi, J., Water Authority Province of Buenos Aires Fritz, L., Colasurdo, V., Universidad Nacional Centro de la Prov. Buenos Aires Jelinsky, G., Algal blooms constitute one of the most important environmental problems in many lakes and reservoirs. During the last decades, several restoration strategies have been implemented to address eutrophication (Paerl and Otten, 2013; Jeppesen et al., 2012). In this sense, artificial wetlands have been used to decrease external nutrient loading from nonpoint sources and perform bottom up control on phytoplankton growth. However, biogeochemical processes that take place within water bodies delay restoration (phosphorus and nitrogen recycles, as well as nutrient release from sediments). To accelerate restoration, bottom up control strategies are complemented with several inlake strategies, such as chemical treatment, hypolimnetic oxygenation and biomanipulation (Sondergaard et al., 2007). The application of any restoration strategy requires deep knowledge of both the ecological state and the dynamics of the water body, as well as the development of reliable ecological models to plan and predict restoration effects (Estrada et al., 2011, Di Maggio et al., 2015). In this work, we address monitoring, parameter estimation and restoration planning for Paso de las Piedras reservoir (38° 22´ S and 61° 12´ W), a warm polymictic lake which is the drinking water source for two cities (population above 450,000) and the most important petrochemical complex in Argentina. Field data have been collected throughout 2014-2015 and include nutrient, phytoplankton, dissolved oxygen, zooplankton and fish biomass concentrations. Phytoplankton and zooplankton data have been collected weekly and twice a month, respectively, while fish data have been collected every three months during the first year and bimonthly in the second year (Di Maggio et al, 2014). Collected data have been used to calibrate a mechanistic ecological model (Estrada et al., 2011) that includes dynamic mass balances for three phytoplankton groups (cyanobacteria, diatomea, chlorophyta); two zooplankton groups (cladocera, copepoda) and two size classes of local zooplanktivorous fish, as well as dissolved oxygen and main nutrients. Algebraic equations stand for forcing functions profiles, such as temperature, solar radiation, river inflows and concentrations, etc. As a third step, we address the optimal planning of alternative restoration strategies through advanced dynamic optimization techniques. We have implemented some of these strategies as different optimal control problems, by formulating the ecological water model within a dynamic optimization environment. The dynamic optimization problem is formulated within a control vector parameterization framework (PSEnterprise, 2013). Field data show recurrent algal blooms and abundance of zooplanktivorous fish, which are in poor conditions, as well as piscivorous fish. The model is being re-calibrated with these data, providing a reliable tool for restoration planning. Dynamic optimization results include optimal profiles for control variables to plan alternative restoration actions, as well as a quantitative estimation of restoration effects on the water body References Asaeda, T. and T. Van Bon (1997), Modeling the effects of macrophytes on algal blooming in eutrophic shallow lakes, Ecological Modeling, 104, 262-267. Di Maggio, J., Estrada V., M.S. Diaz, Water Resources Management with Dynamic Optimization Strategies and Integrated Models of Lakes and Artificial Wetlands, PSE2015 – ESCAPE25, 12th International Symposium on Process Systems Engineering and 25th European Symposium on Computer Aided Process Engineering , 31 May - =4 June 2015, Copenhagen, Denmark Di Maggio, J.; Estrada, V. ; Guerrero, J.M.; Baglivi, J.C.;Crisafulli, M.; Jelinsky, G.; Diaz, M.S.; Colasurdo, V.; Grosman, F. y Sanzano, P. (2014) Modelo Ecológico del Embalse Paso de las Piedras (Buenos Aires, Argentina), VI CAL, 6to Congreso Argentino de Limnología, Septiembre 14-14, 2014, La Plata, Biología Acuática, 26, 157 Estrada V., J. Di Maggio, M.S. Diaz (2011), Water Sustainability: A Process Systems Engineering approach to the restoration of eutrophic lakes, Computers and Chemical Engineering, 35, 8, 1598-1613 Jeppesen, E., Sondergaard, M., Lauridsen, T., Davidson, T., Liu, Z., Mazzeo, N., Trochine, C., Ozkan, K., Jensen, H., Tolle, D., Starling, F, Lazzaro, X., Johansson, L., Bjerring, R., Liboriussen, L., Larsen, S., Landkildehus, F., Egemose, S. & Meerhoff, M. (2012). Biomanipulation as a Restoration Tool to Combat Eutrophication: Recent Advances and Future Challenges. Advances in Ecological Research, 47. Paerl, H. and Otten, T. (2013) Harmful Cyanobacterial Blooms: Causes, Consequences, and Controls. Environmental microbiology, 65, 995-1010. Process Systems Enterprise (2013), gPROMS, www.psenterprise.com/gproms Sondergaard, M., Jeppesen, E., Lauridsen, T. L., Skov, C., van Nes, E. H., Roijackers, R., Lammens, E. & Portielje, R. (2007). Lake restoration: successes, failures and long-term effects. Journal of Applied Ecology, 44, 1095–1105.