Suchergebnisse

To define a holistic and systematic approach to municipal waste management, an integrated municipal solid waste management (IMSWM) system is proposed. This system includes functional elements of waste generation, source handling, and processing, waste collection, waste processing at facilities, transfer, and disposal. Multi-objective optimization algorithms are used to develop an optimum IMSWM that can satisfy all main pillars of sustainable development, aiming to minimize the total cost of the system (economic), and minimize the total greenhouse gas emissions (environmental), while maximizing the total social suitability of the system (social). For the social objective, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is used to identify the main parameters that affect the social suitability of the system. This research focuses on developing an optimized holistic model that considers all four main components of a modern IMSWM namely transfer, recycling, treatment, and disposal. The model is formulated as a mixed-integer linear programming (MILP) problem and solved using the epsilon constraint handling method. A metaheuristic method is developed using non dominated sorting genetic algorithm (NSGA) to deal with larger problems. A solution repair function is developed to handle several equality constraints included in the proposed IMSWM model. Sensitivity analyses are conducted to identify the effect of changes in parameters on the objective functions. Based on the results, the proposed metaheuristic algorithm based on NSGA-II performed better than other algorithms. The interval-parameter programming (IPP) methods are used to consider various uncertainties that exist in the system. The model is applied to the case study of the Australian capital territory (ACT). The data is gathered from several resources including Australian national waste reports, and ACT government transport Canberra and city services (TCCS). Based on the waste characteristic and city map several feasible ...

Urban water supply planning has changed greatly in recent decades, and has generally become a much more technically serious endeavor. (Urban water supply has always been a politically serious endeavor, with abundant sources of uncertainty (Lund, 1988a, b).) Yet for all the serious and fine technical work and research on urban water supply engineering and economics, it often seems that such work has not provided a clear unified approach for combining the many technical measures available for water supply system planning and management. This report seeks to provide such a unified analytical approach, addressing the integrated economical use of yield enhancement, water transfer, and demand management measures in a context of risk and uncertainty from many hydrologic and institutional sources.

Trabajo presentado al 20th IFAC (International Federation of Automatic Control) World Congress, celebrado en Toulouse (Francia) del 9 al 14 de julio de 2017. ; Solving the problem of energy dispatch in a heterogeneous complex system is not a trivial task. The problem becomes even more complex considering uncertainties in demands and energy prices. This paper discusses the development of several Economic Model Predictive Control (EMPC) based strategies for solving an energy dispatch problem in a smart micro-grid. The smart grid components are described using control-oriented model approach. Considering uncertainty of load demands and energy prices simultaneously, and using an economic objective function, leads to a non-linear non-convex problem. The technique of using an affine dependent controller is used to convexify the problem. The goal of this research is the development of a controller based on EMPC strategies that tackles both endogenous and exogenous uncertainties, in order to minimize economic costs and guarantee service reliability of the system. The developed strategies have been applied to a hybrid system comprising some photovoltaic (PV) panels, a wind generator, a hydroelectric generator, a diesel generator, and some storage devices interconnected via a DC Bus. Additionally, a comparison between the standard EMPC, and its combination with MPC tracking in single-layer and two-layer approaches was also carried out based on the daily cost of energy production. ; This work was funded by the Ministerio de Economía, Industria y Competitividad (MEICOMP) of the Spanish Government and FEDER through the project HARCRICS (ref. DPI2014-58104-R) and the grant IJCI-2014-20801. ; Peer Reviewed

Motor vehicles have been identified as a growing contributor to air pollution, such that analyzing the traffic policies on energy and environment systems (EES) has become a main concern for governments. This study developed a dual robust stochastic fuzzy optimization - energy and environmental systems (DRSFO-EES) model for sustainable planning EES, while considering the traffic sector through integrating two-stage stochastic programming, robust two-stage stochastic optimization, fuzzy possibilistic programming, and robust fuzzy possibilistic programming methods into a framework, which can be used to effectively tackle fuzzy and stochastic uncertainties as well as their combinations, capture the associated risks from fuzzy and stochastic uncertainties, and thoroughly analyze the trade-offs between system costs and reliability. The proposed model can: (i) generate robust optimized solutions for energy allocation, coking processing, oil refining, heat processing, electricity generation, electricity power expansion, electricity importation, energy production, as well as emission mitigation under multiple uncertainties ; (ii) explore the impacts of different vehicle policies on vehicular emission mitigation ; (iii) identify the study of regional atmospheric pollution contributions of different energy activities. The proposed DRSFO-EES model was applied to the EES of the Beijing-Tianjin-Hebei (BTH) region in China. Results generated from the proposed model disclose that: (i) limitation of the number of light-duty passenger vehicles and heavy-duty trucks can effectively reduce vehicular emissions ; (ii) an electric cars&rsquo ; policy is enhanced by increasing the ratio of its power generated from renewable sources ; and (iii) the air-pollutant emissions in the BTH region are expected to peak around 2030, because the energy mix of the study region would be transformed from one dominated by coal to one with a cleaner pattern. The DRSFO-EES model can not only provide scientific support for the sustainable managing of EES by cost-effective ways, but also analyze the desired policies for mitigating pollutant emissions impacts with a risk adverse attitude under multiple uncertainties.