812 research outputs found

    Optimising heating and cooling of smart buildings

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    This thesis is concerned with optimization techniques to improve the efficiency of heating and cooling of both existing and new buildings. We focus on the thermal demand-side and we make novel contributions to the optimality of both design and operational questions. We demonstrate that our four novel contributions can reduce operations cost and consumption, optimize retrofit and estimate relevant parameters of the built environment. The ultimate objective of this work is to provide affordable and cost-effective solutions that take advantage of local existing resources. This work addresses four gaps in the state-of-the-art. First, we contribute to current building practice that is mostly based on human experience and simulations, which often leads to oversized heating systems and low efficiency. The results in this thesis show the advantages of using optimization approaches for thermal aspects in buildings. We propose models that seek optimal decisions for one specific design day, as well as an approach that optimizes multiple day-scenarios to more accurately represent a whole year. Second, we study the full potential of buildings’ thermal mass and design. This has not been fully explored due to two factors: the complexity of the mathematics involved, and the fast developing and variety of emerging technologies and approaches. We tackle the mathematical challenge by solving non-linear non-convex models with integer decisions and by estimating building’s thermal mass. We support rapid architectural development by studying flexible models able to adapt to the latest building technologies such as passive house design, smart façades, and dynamic shadings. Third, we consider flexibility provision to significantly reduce total energy costs. Flexibility studies often only focus on flexible building loads but do not consider heating, which is often the largest load of a building and is less flexible. Because of that, we study and model a building’s heating demand and we propose optimization techniques to support greater flexibility of heating loads, allowing buildings to participate more efficiently in providing demand response. Fourth, we consider a building as an integrated system, unlike many other modelling approaches that focus on single aspects. We model a building as a complex system comprising the building’s structure, weather conditions and users’ requirements. Furthermore, we account for design decisions and for new and emerging technologies, such as heat pumps and thermal storage. Optimal decisions come from the joint analysis of all these interconnected factors. The thesis is structured in three parts: the introduction, the main body and the conclusions. The main body is made by five chapters, each of which focuses on one research project and has the following structure: overview, introduction, literature review, mathematical framework description, application and results section, conclusion and future works. The first two chapters discuss the optimization of operational aspects. The first focuses on a single thermal zone and the second in two connected ones. The third chapter is a continuation of the first two, and presents an approach to optimize both operations and design of buildings in a heat community. This approach integrates the use of an energy software already in the market. The fourth chapter discusses an approach to find the optimal refurbishment of an existing building at minimum cost. The fifth chapter shows an inferring model to represent a house of a building stock. We study the common case where the house’s data is lacking or inaccurate, and we present a model that is able to estimate the required thermal parameters for modelling the house using only heating demand

    Enhancing the Structural Stability of α-phase Hybrid Perovskite Films through Defect Engineering Approaches under Ambient Conditions

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    This thesis investigates methods whereby perovskite solar cell power conversion efficiency and material stability may be improved. Hybrid perovskites have gained increased attention for optoelectronic applications due to favourable properties such as strong absorption, facile processing, and changeable band-gap. Despite excellent improvements in power conversion efficiency of devices, perovskite films are unstable, degrading with relative ease in the presence of moisture, oxygen, light, heat, and electric fields. The focus of this thesis is on ambient atmosphere stability, concerned with the influence of moisture in particular on perovskite film fabrication, degradation, and device functionality. In order to shed light on the impact of ambient atmosphere on perovskite films, experiments are designed to investigate films during fabrication and degradation. The influences firstly of stoichiometry during ambient fabrication, and then ionic substitution (with caesium and formadinium) upon moisture-induced degradation are investigated. Finally, films and devices with a novel composition incorporating Zn are fabricated under ambient conditions to investigate the effect of Zn addition on perovskite film stability

    Procedural Constraint-based Generation for Game Development

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    Demand Response in Smart Grids

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    The Special Issue “Demand Response in Smart Grids” includes 11 papers on a variety of topics. The success of this Special Issue demonstrates the relevance of demand response programs and events in the operation of power and energy systems at both the distribution level and at the wide power system level. This reprint addresses the design, implementation, and operation of demand response programs, with focus on methods and techniques to achieve an optimized operation as well as on the electricity consumer

    Energy-aware coordination of machine scheduling and support device recharging in production systems

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    Electricity generation from renewable energy sources is crucial for achieving climate targets, including greenhouse gas neutrality. Germany has made significant progress in increasing renewable energy generation. However, feed-in management actions have led to losses of renewable electricity in the past years, primarily from wind energy. These actions aim to maintain grid stability but result in excess renewable energy that goes unused. The lost electricity could have powered a multitude of households and saved CO2 emissions. Moreover, feed-in management actions incurred compensation claims of around 807 million Euros in 2021. Wind-abundant regions like Schleswig-Holstein are particularly affected by these actions, resulting in substantial losses of renewable electricity production. Expanding the power grid infrastructure is a costly and time-consuming solution to avoid feed-in management actions. An alternative approach is to increase local electricity consumption during peak renewable generation periods, which can help balance electricity supply and demand and reduce feed-in management actions. The dissertation focuses on energy-aware manufacturing decision-making, exploring ways to counteract feed-in management actions by increasing local industrial consumption during renewable generation peaks. The research proposes to guide production management decisions, synchronizing a company's energy consumption profile with renewable energy availability for more environmentally friendly production and improved grid stability

    LIPIcs, Volume 261, ICALP 2023, Complete Volume

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    LIPIcs, Volume 261, ICALP 2023, Complete Volum

    Intelligent robotic disassembly optimisation for sustainability using the bees algorithm

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    Robotic disassembly plays a pivotal role in achieving efficient and sustainable product lifecycle management, with a focus on resource conservation and waste reduction. This thesis discusses robotic disassembly sequence planning (RDSP) and robotic disassembly line balancing (RDLB), with a specific emphasis on optimising sustainability models. The overarching goal was to enhance the efficiency and effectiveness of disassembly processes through intelligent robotic disassembly optimisation techniques. At the heart of this research lies the application of the Bees Algorithm (BA), a metaheuristic optimisation algorithm inspired by the foraging behaviour of honeybees. By harnessing the power of the BA, this research aims to address the challenges associated with RDSP and RDLB, ultimately facilitating sustainable disassembly practices. The thesis gives an extensive literature review of RDSP and RDLB to gain deeper insight into the current research landscape. The challenges of the RDSP problem were addressed in this work by introducing a sustainability model and various scenarios to enhance disassembly processes. The sustainability model considers three objectives: profit, energy savings, and environmental impact reduction. The four explored scenarios were recovery (REC), remanufacture (REM), reuse (REU), and an automatic recovery scenario (ARS). Two novel tools were developed for assessing algorithm performance: the statistical performance metric (SPM) and the performance evaluation index (PEI). To validate the proposed approach, a case study involving the disassembly of gear pumps was used. To optimise the RDSP, single-objective (SO), multiobjective (MO) aggregate, and multiobjective nondominated (MO-ND) approaches were adopted. Three optimisation algorithms were employed — Multiobjective Nondominated Bees Algorithm (MOBA), Nondominated Sorting Genetic Algorithm - II (NSGA-II), and Pareto Envelope-based Selection Algorithm - II (PESA-II), and their results were compared using SPM and PEI. The findings indicate that MO-ND is more suitable for this problem, highlighting the importance of considering conflicting objectives in RDSP. It was shown that recycling should be considered the last-resort recovery option, advocating for the exploration of alternative recovery strategies prior to recycling. Moreover, MOBA outperformed other algorithms, demonstrating its effectiveness in achieving a more efficient and sustainable RDSP. The problem of sequence-dependent robotic disassembly line balancing (RDLBSD) was next investigated by considering the interconnection between disassembly sequence planning and line balancing. Both aspects were optimised simultaneously, leading to a balanced and optimal disassembly process considering profitability, energy savings, environmental impact, and line balance using the MO-ND approach. The findings further support the notion that recycling should be considered the last option for recovery. Again, MOBA outperformed other algorithms, showcasing its capability to handle more complex problems. The final part of the thesis explains the mechanism of a new enhanced BA, named the Fibonacci Bees Algorithm (BAF). BAF draws inspiration from the Fibonacci sequence observed in the drone ancestry. This adoption of the Fibonacci-sequence-based pattern reduces the number of algorithm parameters to four, streamlining parameter setting and simplifying the algorithm’s steps. The study conducted on the RDSP problem demonstrates BAF’s performance over the basic BA, particularly in handling more complex problems. The thesis concludes by summarising the key contributions of the work, including the enhancements made to the BA and the introduction of novel evaluation tools, and the implications of the research, especially the importance of exploring alternative recovery strategies for end-of-life (EoL) products to align with Circular Economy principles

    Digital process design to define and deliver pharmaceutical particle attributes

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    A digital-first approach to produce quality particles of an active pharmaceutical ingredient across crystallisation, washing and drying is presented, minimising material requirements and experimental burden during development. To demonstrate current predictive modelling capabilities, the production of two particle sizes (D90 = 42 and 120”m) via crystallisation was targeted to deliver a predicted, measurable difference in in vitro dissolution performance. A parameterised population balance model considering primary nucleation, secondary nucleation, and crystal growth was used to select the modes of production for the different particle size batches. Solubility prediction aided solvent selection steps which also considered manufacturability and safety selection criteria. A wet milling model was parameterised and used to successfully produce a 90g product batch with a particle size D90 of 49.3”m, which was then used as the seeds for cooling crystallisation. A rigorous approach to minimising physical phenomena observed experimentally was implemented, and successfully predicted the required conditions to produce material satisfying the particle size design objective of D90 of 120”m in a seeded cooling crystallisation using a 5-stage MSMPR cascade. Product material was isolated using the filtration and washing processes designed, producing 71.2g of agglomerated product with a primary particle D90 of 128”m. Based on experimental observations, the population balance model was reparametrised to increase accuracy by inclusion of an agglomeration terms for the continuous cooling crystallisation. The dissolution performance for the two crystallised products is also demonstrated, and after 45minutes 104.0mg of the D90 of 49.3”m material had dissolved, compared with 90.5mg of the agglomerated material with D90 of 128”m. Overall, 1513g of the model compound was used to develop and demonstrate two laboratory scale manufacturing processes with specific particle size targets. This work highlights the challenges associated with a digital-first approach and limitations in current first-principles models are discussed that include dealing ab initio with encrustation, fouling or factors that affect dissolution other than particle size
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