Application of Computational Fluid Dynamics to the simulation and optimization of multi-environment bioreactors for wastewater treatment

RESUMEN: Los reactores multi-ambiente representan una alternativa innovadora para simplificar los trenes de tratamiento convencionales de Eliminación Biológica de Nutrientes (EBN), ya que son más compactos y pueden adaptarse a los requerimientos de calidad existentes. En concreto, el reactor AnoxAn es capaz de integrar las zonas anaerobia y anóxica del proceso convencional de EBN en un único reactor de flujo ascendente. Sin embargo, su zonificación multi-ambiental y la configuración de elementos singulares dan lugar a un comportamiento hidrodinámico complejo que interfiere en el funcionamiento óptimo del reactor. En por ello que, en la presente tesis doctoral, se realiza un análisis exhaustivo de la hidrodinámica de AnoxAn, así como un estudio la influencia de la misma en la eficiencia biológica del proceso. Para ello, se desarrolla una herramienta numérica basada en Dinámica de Fluidos Computacional (CFD) con el software de código abierto OpenFOAM®, y se propone una metodología para la optimización hidrodinámica de reactores multi-ambiente. Los resultados obtenidos en este trabajo han contribuido al desarrollo tecnológico y operacional de AnoxAn.ABSTRACT: Multi-environment reactors are an innovative alternative to simplify conventional Biological Nutrient Removal (BNR) treatment trains as they are more compact and can adapt to existing quality requirements. Concretely, AnoxAn unifies the anaerobic and anoxic zones of conventional BNR processes in a continuous upflow sludge blanket reactor. However, the multi environmental zoning and singular elements configuration give rise to a complex hydrodynamic behaviour that interferes in the desired biological operation of the reactor. Therefore, in this thesis, a comprehensive hydrodynamic assessment of AnoxAn is carried out, and the influence of the hydraulic behaviour on the biological efficiency of the process is evaluated. For that purpose, a Computational Fluid Dynamics (CFD) based numerical tool is developed with the open source toolbox OpenFOAM®, and a hydrodynamic optimization methodology for multi-environment reactors is proposed. The results obtained in this work have contributed to the development of technological and operational improvements of AnoxAn.A la Universidad de Cantabria primero (PRE03, CVE-2016-11670), y al Ministerio de Educación, Cultura y Deporte del Gobierno de España después (FPU16-05036), por proporcionarme mediante beca la financiación necesaria para la consecución de esta tesis doctoral

Optimal Carbon Taxes for Emissions Targets in the Electricity Sector

The most dangerous effects of anthropogenic climate change can be mitigated by using emissions taxes or other regulatory interventions to reduce greenhouse gas (GHG) emissions. This paper takes a regulatory viewpoint and describes the Weighted Sum Bisection method to determine the lowest emission tax rate that can reduce the anticipated emissions of the power sector below a prescribed, regulatorily-defined target. This bi-level method accounts for a variety of operating conditions via stochastic programming and remains computationally tractable for realistically large planning test systems, even when binary commitment decisions and multi-period constraints on conventional generators are considered. Case studies on a modified ISO New England test system demonstrate that this method reliably finds the minimum tax rate that meets emissions targets. In addition, it investigates the relationship between system investments and the tax-setting process. Introducing GHG emissions taxes increases the value proposition for investment in new cleaner generation, transmission, and energy efficiency; conversely, investing in these technologies reduces the tax rate required to reach a given emissions target

Klein-Gordon equation from Maxwell-Lorentz dynamics

We consider Maxwell-Lorentz dynamics: that is to say, Newton's law under the action of a Lorentz's force which obeys the Maxwell equations. A natural class of solutions are those given by the Lagrangian submanifolds of the phase space when it is endowed with the symplectic structure modified by the electromagnetic field. We have found that the existence of this type of solution leads us directly to the Klein-Gordon equation as a compatibility condition. Therefore, surprisingly, quite natural assumptions on the classical theory involve a quantum condition without any process of limit. This result could be a partial response to the inquiries of Dirac.Comment: 8 pages; some misprints corrected; added some coments in the abstract and also explaining the relationship with the work of P.A.M. Dirac; also added 2 reference

Fairness in maximal covering location problems

Acknowledgments The authors thank the anonymous reviewers and the guest editors of this issue for their detailed comments on this paper, which provided significant insights for improving the previous versions of this manuscript. This research has been partially supported by Spanish Ministerio de Ciencia e Innovación, AEI/FEDER grant number PID2020-114594GB C21, AEI grant number RED2022-134149-T (Thematic Network: Location Science and Related Problems), Junta de Andalucía projects P18- FR-1422/2369 and projects FEDERUS-1256951, B-FQM-322-UGR20, CEI-3-FQM331 and NetmeetData (Fundación BBVA 2019). The first author was also partially supported by the IMAG-Maria de Maeztu grant CEX2020-001105-M /AEI /10.13039/501100011033 and UENextGenerationEU (ayudas de movilidad para la recualificación del profesorado universitario. The second author was also partially supported by the Research Program for Young Talented Researchers of the University of Málaga under Project B1-2022_37, Spanish Ministry of Education and Science grant number PEJ2018-002962-A, and the PhD Program in Mathematics at the Universidad de Granada.This paper provides a mathematical optimization framework to incorporate fairness measures from the facilities’ perspective to discrete and continuous maximal covering location problems. The main ingredients to construct a function measuring fairness in this problem are the use of (1) ordered weighted averaging operators, a popular family of aggregation criteria for solving multiobjective combinatorial optimization problems; and (2) -fairness operators which allow generalizing most of the equity measures. A general mathematical optimization model is derived which captures the notion of fairness in maximal covering location problems. The models are first formulated as mixed integer non-linear optimization problems for both the discrete and the continuous location spaces. Suitable mixed integer second order cone optimization reformulations are derived using geometric properties of the problem. Finally, the paper concludes with the results obtained from an extensive battery of computational experiments on real datasets. The obtained results support the convenience of the proposed approach.Spanish Ministerio de Ciencia e InnovaciónAEI/FEDER grant number PID2020-114594GB C21AEI grant number RED2022-134149-T (Thematic Network: Location Science and Related Problems)Junta de Andalucía projects P18- FR-1422/2369FEDERUS-1256951B-FQM-322-UGR20CEI-3-FQM331NetmeetData (Fundación BBVA 2019)IMAG-Maria de Maeztu grant CEX2020-001105-M /AEI /10.13039/501100011033UE NextGenerationEUResearch Program for Young Talented Researchers of the University of Málaga under Project B1-2022_37Spanish Ministry of Education and Science grant number PEJ2018-002962-

Mathematical optimization models for reallocating and sharing health equipment in pandemic situations

In this paper we provide a mathematical programming based decision tool to optimally reallocate and share equipment between different units to efficiently equip hospitals in pandemic emergency situations under lack of resources. The approach is motivated by the COVID-19 pandemic in which many Heath National Systems were not able to satisfy the demand of ventilators, sanitary individual protection equipment or different human resources. Our tool is based in two main principles: (1) Part of the stock of equipment at a unit that is not needed (in near future) could be shared to other units; and (2) extra stock to be shared among the units in a region can be efficiently distributed taking into account the demand of the units. The decisions are taken with the aim of minimizing certain measures of the non-covered demand in a region where units are structured in a given network. The mathematical programming models that we provide are stochastic and multiperiod with different robust objective functions. Since the proposed models are computationally hard to solve, we provide a divide-et-conquer math-heuristic approach. We report the results of applying our approach to the COVID-19 case in different regions of Spain, highlighting some interesting conclusions of our analysis, such as the great increase of treated patients if the proposed redistribution tool is applied.Spanish Ministerio de Ciencia e Innovacion, Agencia Estatal de Investigacion/FEDER PID2020-114594GB-C21Junta de Andalucia SEJ-584 FQM-331 P18-FR-1422 US-1256951 P18-FR-2369Spanish Government PEJ2018-002962-ADoctoral Program in Mathematics at the Universidad of GranadaProyect NetMeetData (Fundacion BBVA - Big Data)IMAG-Maria de Maeztu grant CEX2020-001105-M/AEICenter for Forestry Research & Experimentation (CIEF)European Commission CIGE/2021/16