DIAGNOSTIC ASSESSMENT AND ADVANCEMENT OF MULTI-OBJECTIVE RESERVOIR CONTROL UNDER UNCERTAINTY

This dissertation contributes to the assessment of new scientific developments for multi-objective decision support to improve multi-purpose river basin management. The main insights of this work highlight opportunities to improve modeling of complex multi-purpose water reservoir systems and opportunities to flexibly incorporate emerging demands and hydro-climatic uncertainty. Additionally, algorithm diagnostics contributed in this work enable the water resources field to better capitalize on the rapid growth in computational power. This opens new opportunities to increase the scope of the problems that can be solved and contribute to the robustness and sustainability of water systems management worldwide. This dissertation focuses on a multi-purpose reservoir system that captures the contextual and mathematical difficulties confronted in a broad range of global multi-purpose systems challenged by multiple competing demands and uncertainty. The first study demonstrates that advances in state of the art multiobjective evolutionary optimization enables to reliably and effectively find control policies that balance conflicting tradeoffs for multi-purpose reservoir control. Multiobjective evolutionary optimization techniques coupled with direct policy search can reliably and flexibly find suitable control policies that adapt to multi-sectorial water needs and to hydro-climatic uncertainty. The second study demonstrates the benefits of cooperative parallel MOEA architectures to reliably and effectively find many objective control policies when the system is subject to uncertainty and computational constraints. The more advanced cooperative, co-evolutionary parallel search expands the scope of problem difficulty that can be reliably addressed while facilitating the discovery of high quality approximations for optimal river basin tradeoffs. The insights from this chapter should enable water resources analysts to devote computational efforts towards representing reservoir systems more accurately by capturing uncertainty and multiple demands when properly using parallel coordinated search. The third study extended multi- purpose reservoir control to better capture flood protection. A risk-averse formulation contributed to the discovery of control policies that improve operations during hydrologic extremes. Overall this dissertation has carefully evaluated and advanced the Evolutionary Multiobjective Direct Policy Search (EMODPS) framework to support multi-objective and robust management of conflicting demands in complex reservoir systems

What Lies beyond the Pareto Front? A Survey on Decision-Support Methods for Multi-Objective Optimization

We present a review that unifies decision-support methods for exploring the solutions produced by multi-objective optimization (MOO) algorithms. As MOO is applied to solve diverse problems, approaches for analyzing the trade-offs offered by MOO algorithms are scattered across fields. We provide an overview of the advances on this topic, including methods for visualization, mining the solution set, and uncertainty exploration as well as emerging research directions, including interactivity, explainability, and ethics. We synthesize these methods drawing from different fields of research to build a unified approach, independent of the application. Our goals are to reduce the entry barrier for researchers and practitioners on using MOO algorithms and to provide novel research directions.Comment: IJCAI 2023 Conference Paper, Survey Trac

Optimization of sludge dewatering process at bensberg municipal wastewater treatment plant

El objetivo principal de este trabajo de investigación fue mejorar el proceso de deshidratado de lodos para la planta de tratamiento de aguas residuales municipales Beningsfeld, localizada en la municipalidad de Bergisch Gladbach, Alemania. Se quiere optimizer el proceso de deshidratado con el propósito de aumentar la concentración de sólidos en el lodo. Para este fin, se realizaron varias pruebas a nivel laboratorio y a escala industrial. Se probó un polímero basado en poliacrilamida (NALCO) y se optimizó la dosis a pequeña escala. Se obtuvieron los mejores valores de CST en el rango de 7 a 8 mL de polímero por cada 100 mL de lodo digerido (7-8% V/V), obteniendo valores de CST de 8.96 y 9.94 segundos respectivamente; sin embargo, cuando este rango fue probado en escala real, no se observaron mejorías. Adicionalmente, se probó el proceso Kemicond (tratamiento con ácido sulfúrico y peroxido de hidrógeno) para el acondicionamiento del lodo. Para muestras de lodo digerido de 500 mL, los mejores resultados se obtuvieron cuando el pH se disminuyó hasta 6 mediante H2SO4 y fue tratado posteriormente con 1 ml de H2O2; sin embargo, es un tratamiento muy agresivo y require de maquinaria especial y a la larga se podría generar mayor contaminación por lo que no es factible su aplicación a gran escala. En general los resultados de sólidos totales de la torta filtrada no fueron mejoraros; sin embargo, se encontraron otros parámetros de importancia para la deshidratación mecánica, los cuales fueron: tiempo de llenado de la prensa, tiempo de compresión, edad y tipo de membranas, así como la calidad del lodo de entrada

MultiObjPolicy-RBF Analysis Kit: Exploring Radial Basis Functions for Multiobjective Policy Optimization

<p>Code to perform the analysis and explore different radial basis functions as global approximators when using evolutionary multiobjective direct policy search. This analysis toolkit is explored in two different case studies, the Susquehanna River Basin to find optimal reservoir release policies for 6 objectives, and for the Lake Problem, to find an optimal emissions policy optimized for 4 objectives.</p&gt

Robust Infrastructure Sequencing and Management for Growing Food Energy and Water Demands in the Zambezi River Basin

Fast population growth and economic development in several African countries is driving large infrastructure investments for growing energy, food and water demands which will likely strain existing ecosystem services. To minimize negative impacts and guarantee long-term success and sustainability of these investments, careful management and temporal planning of existing and new infrastructure is required. Our study focuses on the Zambezi River Basin (ZRB), a transboundary system supporting key economic growth and poverty reduction across its multiple riparian countries, while sustaining essential ecosystem services. The ZRB currently encompasses five hydropower dams, with three additional dams planned. The goal of this study is to generate efficient pathways that allow the temporal sequencing of planned dam projects along with robust management strategies that balance food, energy and environmental demands. A participatory approach is adopted at an early stage by running a Negotiation Simulation Lab (NSL) to elicit stakeholders’ preferences and concerns supporting both model development and formulation of the optimization problem. Specifically, the pathway design is structured in three stages: first, optimal control policies are generated using Evolutionary Multi-objective Direct Policy Search for all possible combinations of dams projects; the time of construction is subsequently optimized, including the update of the system operation when a new dam is built, by balancing the benefits and the costs of additional infrastructure investments which are activated by projections of population growth triggering higher water and energy demands, finally promising policies are tested under a broad set of irrigation demand and streamflow scenarios. Our analysis shows that the rising demands cause all the planned dams to be built within the planning horizon from 2020-2060. The study also indicates that the operational preferences are key since they dictate the system’s performance across multiple objectives and this behavior prevails under a larger suite of plausible future scenarios. Overall, our study provides a novel approach that integrates infrastructure investment planning that can be coupled with cooperative operations to meet growing regional demands while involving stakeholders in crucial stages of the decision making process

Diagnostic Assessment of the Difficulty using Direct Policy Search in Many-Objective Water Reservoir Control

Globally reservoir operations provide fundamental services to water supply, energy generation, recreation, and ecosystems. The pressures of expanding populations, climate change, and increased energy demands are motivating a significant investment in re-operationalizing existing reservoirs or defining operations for new reservoirs. Recent work has highlighted the potential benefits of exploiting recent advances in many-objective optimization and direct policy search (DPS) to aid in addressing these systems’ multi-sector demand tradeoffs. This study contributes to a comprehensive diagnostic assessment of multi-objective evolutionary optimization algorithms (MOEAs) efficiency, effectiveness, reliability, and controllability when supporting DPS for the Conowingo dam in the Lower Susquehanna River Basin. The Lower Susquehanna River is an interstate water body that has been subject to intensive water management efforts due to the system’s competing demands from urban water supply, atomic power plant cooling, hydropower production, and federally regulated environmental flows. Seven benchmark and state-of–the-art MOEAs are tested on deterministic and stochastic instances of the Susquehanna test case. In the deterministic formulation, the operating objectives are evaluated over the historical realization of the hydroclimatic variables (i.e., inflows and evaporation rates). In the stochastic formulation, the same objectives are instead evaluated over an ensemble of stochastic inflows and evaporation rates realizations. The algorithms are evaluated in their ability to support DPS in discovering reservoir operations that compose the tradeoffs for six multi-sector performance objectives with thirty-two decision variables. Our diagnostic results highlight that many-objective DPS is very challenging for modern MOEAs and that epsilon dominance is critical for attaining high levels of performance. Epsilon dominance algorithms epsilon-MOEA, epsilon-NSGAII and the auto adaptive Borg MOEA, are statistically superior for the six-objective Susquehanna instance of this important class of problems. Additionally, shifting from deterministic history-based DPS to stochastic DPS significantly increases the difficulty of the problem

Balancing exploration, uncertainty and computational demands in many objective reservoir optimization

Reservoir operations are central to our ability to manage river basin systems serving conflicting multi-sectoral demands under increasingly uncertain futures. These challenges motivate the need for new solution strategies capable of effectively and efficiently discovering the multi-sectoral tradeoffs that are inherent to alternative reservoir operation policies. Evolutionary many-objective direct policy search (EMODPS) is gaining importance in this context due to its capability of addressing multiple objectives and its flexibility in incorporating multiple sources of uncertainties. This simulation-optimization framework has high potential for addressing the complexities of water resources management, and it can benefit from current advances in parallel computing and meta-heuristics. This study contributes a diagnostic assessment of state-of-the-art parallel strategies for the auto-adaptive Borg Multi Objective Evolutionary Algorithm (MOEA) to support EMODPS. Our analysis focuses on the Lower Susquehanna River Basin (LSRB) system where multiple sectoral demands from hydropower production, urban water supply, recreation and environmental flows need to be balanced. Using EMODPS with different parallel configurations of the Borg MOEA, we optimize operating policies over different size ensembles of synthetic streamflows and evaporation rates. As we increase the ensemble size, we increase the statistical fidelity of our objective function evaluations at the cost of higher computational demands. This study demonstrates how to overcome the mathematical and computational barriers associated with capturing uncertainties in stochastic multiobjective reservoir control optimization, where parallel algorithmic search serves to reduce the wall-clock time in discovering high quality representations of key operational tradeoffs. Our results show that emerging self-adaptive parallelization schemes exploiting cooperative search populations are crucial. Such strategies provide a promising new set of tools for effectively balancing exploration, uncertainty, and computational demands when using EMODPS