Stochastic Constraint Programming with And-Or Branch-and-Bound

Complex multi-stage decision making problems often involve uncertainty, for example, regarding demand or processing times. Stochastic constraint programming was proposed as a way to formulate and solve such decision problems, involving arbitrary constraints over both decision and random variables. What stochastic constraint programming currently lacks is support for the use of factorized probabilistic models that are popular in the graphical model community. We show how a state-ofthe-art probabilistic inference engine can be integrated into standard constraint solvers. The resulting approach searches over the And-Or search tree directly, and we investigate tight bounds on the expected utility objective. This significantly improves search efficiency and outperforms scenario-based methods that ground out the possible worlds.status: publishe

First IJCAI International Workshop on Graph Structures for Knowledge Representation and Reasoning (GKR@IJCAI'09)

International audienceThe development of effective techniques for knowledge representation and reasoning (KRR) is a crucial aspect of successful intelligent systems. Different representation paradigms, as well as their use in dedicated reasoning systems, have been extensively studied in the past. Nevertheless, new challenges, problems, and issues have emerged in the context of knowledge representation in Artificial Intelligence (AI), involving the logical manipulation of increasingly large information sets (see for example Semantic Web, BioInformatics and so on). Improvements in storage capacity and performance of computing infrastructure have also affected the nature of KRR systems, shifting their focus towards representational power and execution performance. Therefore, KRR research is faced with a challenge of developing knowledge representation structures optimized for large scale reasoning. This new generation of KRR systems includes graph-based knowledge representation formalisms such as Bayesian Networks (BNs), Semantic Networks (SNs), Conceptual Graphs (CGs), Formal Concept Analysis (FCA), CPnets, GAI-nets, all of which have been successfully used in a number of applications. The goal of this workshop is to bring together the researchers involved in the development and application of graph-based knowledge representation formalisms and reasoning techniques

Modelling framework of solar assisted dehumidification system to generate freshwater from "Thin air"

Freshwater scarcity is a major obstacle of growth and prosperity for many nations in the world. Conventional centralised freshwater supply options in general are depleting and the unanticipated social and environmental costs of alternative solutions are emerging. Similar to energy, water sector may also need to explore renewable decentralised freshwater alternatives such as atmospheric moisture as discussed in this thesis. For hot and humid regions, condensed water is unwillingly discharged out of air-conditioning systems and the energy consumed for condensation to full humidity comfort level is wasted. Only a few limited small-scale experimental studies and no systematic modelling have been found in the literature on atmospheric water capture. This thesis works to fill some of this gap by developing an understanding of the fundamental factors that have and continue to challenge the development of technologies for atmospheric water capture. In this thesis, a framework is developed encompassing several modelling elements for assessment of feasibilities of moist air dehumidification technologies for atmospheric water capture. This framework integrates technical, meteorological and economic modelling elements. In the technosphere, detailed models of thermoelectric and absorption cooling are developed as potential dehumidification technologies. These models are interfaced to renewable energy input algorithms, namely solar photo-voltaic (PV) and solar-thermal. Solar energy collection technologies are also part of this framework which includes models of solar PV systems and evacuated tube collectors (ETCs). Studies of such integration of solar-assisted dehumidification and associated analysis for atmospheric water capture are limited in the literature. Fundamental solar energy input models are developed and interfaced to meteorological data to provide geographical location specific analysis. In this way the model framework is generic and applicable to any location on Earth where meteorological data is available. Finally, an economic modelling component completes the framework to provide comprehensive techno-economic assessments of different technologies for atmospheric water capture. This framework therefore provides a tool to support decision making related to feasibilities of different technologies associated with water capture from atmosphere. Along the way to developing the modelling framework, a detailed categorisation of dehumidification systems is established and a model to estimate condensation rates based on local climate data is built. The hurdle of condensation energy requirement is highlighted through simulation results. To alleviate this energy burden, an assessment of renewable solar energy input is then made. Techno-economic challenges for two different climates, Sydney and Abu Dhabi are examined and compared throughout this thesis providing comparisons for water and energy profiles. Several modelling components are developed and presented f or this purpose, requiring implementations in different modelling environments including Matlab, Trnsys, Homer and VBA. Based on the operation principles, dehumidification techniques are categorised into three categories in this thesis (Fig. 2.2). Gas separation membrane technologies were modelled but are not included in this thesis presentation because initial analysis showed they suffer from several key technical drawbacks primarily associated with the sensitivity to fluctuations in feed air temperature and humidity. Technologies in the cooling surfaces category in general use electrical or mechanical power to circulate and compress a refrigerant and cooling down conductive surfaces or coils. This process aims to decrease the temperature of moist air stream below dew point where water vapour molecules start to bond and settle forming the condensation stream. Amongst a wide range of cooling surface techniques, thermoelectric cooler (TEC) devices are attachable to cooling surfaces without using a refrigerant medium. A conceptual TEC dehumidification system is modelled in this thesis targeted at moist air streams with ambient temperature ranges (10-50) C and relative humidity ranges (10-100) %. For large-scale water production, the energy cost is calculated and found to be the major factor contributing to more than 95% of the total cost of generated water. This model is implemented for Sydney and Abu Dhabi case studies by using their annual typical meteorological weather data. This shows the generic nature of the applicability of the model and in this specific comparison confirms the influence of energy consumption over the cost of generated water in those two very different regions. However, lower local utility rates and favourable climatic conditions for dehumidification in Abu Dhabi show significant differentiation in water cost over Sydney. To confront excessive energy demands for atmospheric water capture, the idea of facilitating solar energy via PV panels is examined in this thesis. A comprehensive solar algorithm is developed and implemented to optimise solar collector positioning and for calculating solar penetration ratios for Sydney and Abu Dhabi. As far as the author is aware, this is the first time such optimal position calculation for Sydney and Abu Dhabi is done. It is found that optimal surface tilt angles for Sydney and Abu Dhabi are 32 and 22 respectively, while optimal surface azimuth angles for Sydney and Abu Dhabi are 195 and 16 respectively. This algorithm is generic in its structure allowing such calculation to be executed for any city in the world and is later used in this thesis for calculations associated with a new ETC diffuse at reflector (DFR) model. This thesis also presents a detailed economic model for prediction of utility costs with consideration for CAPEX, OPEX, subsidies and carbon taxation. It is found that investing a $338,000 on a PV array of 100 kW at current utility rates can meet 53$15 /kL. If this system is projected to work during the day only, solar penetration ratio will substantially increase and could meet the entire diurnal load for dehumidification in Abu Dhabi. If the capital cost of developing such system is affordable, absorption model can be further optimised to specifically match local conditions in respect to solar radiation and energy sources where the cost of generated water can economically compete with other conventional sources. In regions such as Abu Dhabi, the idea of having small-scale dehumidification system where the energy demand is mostly met by solar radiation and the volume of generated water is freely controlled and managed by household seems appealing

Multi-objective optimization in graphical models

Many real-life optimization problems are combinatorial, i.e. they concern a choice of the best solution from a finite but exponentially large set of alternatives. Besides, the solution quality of many of these problems can often be evaluated from several points of view (a.k.a. criteria). In that case, each criterion may be described by a different objective function. Some important and well-known multicriteria scenarios are: · In investment optimization one wants to minimize risk and maximize benefits. · In travel scheduling one wants to minimize time and cost. · In circuit design one wants to minimize circuit area, energy consumption and maximize speed. · In knapsack problems one wants to minimize load weight and/or volume and maximize its economical value. The previous examples illustrate that, in many cases, these multiple criteria are incommensurate (i.e., it is difficult or impossible to combine them into a single criterion) and conflicting (i.e., solutions that are good with respect one criterion are likely to be bad with respect to another). Taking into account simultaneously the different criteria is not trivial and several notions of optimality have been proposed. Independently of the chosen notion of optimality, computing optimal solutions represents an important current research challenge. Graphical models are a knowledge representation tool widely used in the Artificial Intelligence field. They seem to be specially suitable for combinatorial problems. Roughly, graphical models are graphs in which nodes represent variables and the (lack of) arcs represent conditional independence assumptions. In addition to the graph structure, it is necessary to specify its micro-structure which tells how particular combinations of instantiations of interdependent variables interact. The graphical model framework provides a unifying way to model a broad spectrum of systems and a collection of general algorithms to efficiently solve them. In this Thesis we integrate multi-objective optimization problems into the graphical model paradigm and study how algorithmic techniques developed in the graphical model context can be extended to multi-objective optimization problems. As we show, multiobjective optimization problems can be formalized as a particular case of graphical models using the semiring-based framework. It is, to the best of our knowledge, the first time that graphical models in general, and semiring-based problems in particular are used to model an optimization problem in which the objective function is partially ordered. Moreover, we show that most of the solving techniques for mono-objective optimization problems can be naturally extended to the multi-objective context. The result of our work is the mathematical formalization of multi-objective optimization problems and the development of a set of multiobjective solving algorithms that have been proved to be efficient in a number of benchmarks.Muchos problemas reales de optimización son combinatorios, es decir, requieren de la elección de la mejor solución (o solución óptima) dentro de un conjunto finito pero exponencialmente grande de alternativas. Además, la mejor solución de muchos de estos problemas es, a menudo, evaluada desde varios puntos de vista (también llamados criterios). Es este caso, cada criterio puede ser descrito por una función objetivo. Algunos escenarios multi-objetivo importantes y bien conocidos son los siguientes: · En optimización de inversiones se pretende minimizar los riesgos y maximizar los beneficios. · En la programación de viajes se quiere reducir el tiempo de viaje y los costes. · En el diseño de circuitos se quiere reducir al mínimo la zona ocupada del circuito, el consumo de energía y maximizar la velocidad. · En los problemas de la mochila se quiere minimizar el peso de la carga y/o el volumen y maximizar su valor económico. Los ejemplos anteriores muestran que, en muchos casos, estos criterios son inconmensurables (es decir, es difícil o imposible combinar todos ellos en un único criterio) y están en conflicto (es decir, soluciones que son buenas con respecto a un criterio es probable que sean malas con respecto a otra). Tener en cuenta de forma simultánea todos estos criterios no es trivial y para ello se han propuesto diferentes nociones de optimalidad. Independientemente del concepto de optimalidad elegido, el cómputo de soluciones óptimas representa un importante desafío para la investigación actual. Los modelos gráficos son una herramienta para la represetanción del conocimiento ampliamente utilizados en el campo de la Inteligencia Artificial que parecen especialmente indicados en problemas combinatorios. A grandes rasgos, los modelos gráficos son grafos en los que los nodos representan variables y la (falta de) arcos representa la interdepencia entre variables. Además de la estructura gráfica, es necesario especificar su (micro-estructura) que indica cómo interactúan instanciaciones concretas de variables interdependientes. Los modelos gráficos proporcionan un marco capaz de unificar el modelado de un espectro amplio de sistemas y un conjunto de algoritmos generales capaces de resolverlos eficientemente. En esta tesis integramos problemas de optimización multi-objetivo en el contexto de los modelos gráficos y estudiamos cómo diversas técnicas algorítmicas desarrolladas dentro del marco de los modelos gráficos se pueden extender a problemas de optimización multi-objetivo. Como mostramos, este tipo de problemas se pueden formalizar como un caso particular de modelo gráfico usando el paradigma basado en semi-anillos (SCSP). Desde nuestro conocimiento, ésta es la primera vez que los modelos gráficos en general, y el paradigma basado en semi-anillos en particular, se usan para modelar un problema de optimización cuya función objetivo está parcialmente ordenada. Además, mostramos que la mayoría de técnicas para resolver problemas monoobjetivo se pueden extender de forma natural al contexto multi-objetivo. El resultado de nuestro trabajo es la formalización matemática de problemas de optimización multi-objetivo y el desarrollo de un conjunto de algoritmos capaces de resolver este tipo de problemas. Además, demostramos que estos algoritmos son eficientes en un conjunto determinado de benchmarks

Methodologies for simultaneous optimization of heat, mass, and power in industrial processes

Efficient consumption of energy and material resources, including water, is the primary focus for process industries to reduce their environmental impact. The Conference of Parties in Paris (COP21) highlighted the prominent role of industrial energy efficiency in combatting climate change by reducing greenhouse gas (GHG) emissions. Consumption of energy and material resources, especially water, are strongly interconnected; and therefore, must be treated simultaneously using a holistic approach to identify optimal solutions for efficient processing. Such approaches must consider energy and water recovery within a comprehensive process integration framework which includes options such as organic Rankine cycles for electricity generation from low to medium temperature heat. This thesis addresses the issue of how to efficiently manage energy and water in industrial processes by presenting two systematic methodologies for the simultaneous optimization of heat and mass and combined heat and power production. A novel iterative sequential solution strategy is proposed for optimizing heat-integrated water allocation networks through decomposing the overall problem into three sub-problems using mathematical programming techniques. The approach is capable of proposing a set of potential energy and water reduction opportunities that should be further evaluated for technical, economical, physical, and environmental feasibilities. A novel and comprehensive superstructure optimization methodology is proposed for organic Rankine cycle (ORC) integration in industrial processes including architectural features, such as turbine-bleeding, reheating, and transcritical cycles. Meta-heuristic optimization (via a genetic algorithm) is combined with deterministic techniques to solve the problem: by addressing fluid selection, operating condition determination, and equipment sizing. This thesis further addresses the importance of holistic approaches by applying the proposed methodologies on a kraft pulp mill. In doing so, freshwater consumption is reduced by more than 60%, while net power output is increased by a factor of six. The results exhibit that interactions among these elements are complex and therefore underline the necessity of such methods to explore their optimal integration with industrial processes. The potential implications of this work are broad, extending from total site integration to industrial symbiosis