1,644 research outputs found
Hybrid Mixed-Integer Non-Linear Programming Approach for Directional Over-Current Relay Coordination
Design of small-scale hybrid energy systems taking into account generation and demand uncertainties
The adoption of energy systems powered by renewable sources requires substantial economic investments. Hence, selecting system components of an appropriate size becomes a critical step, which is significantly influenced by their distinct characteristics. Furthermore, the availability of renewable energy varies over time, and estimating this availability introduces considerable uncertainty. In this paper, we present a technique for the optimal design of hybrid energy systems that accounts for the uncertainty associated with resource estimation. Our method is based on stochastic programming theory and employs a surrogate model to estimate battery lifespan using a feedforward neural network (FFNN). The optimization analysis for system design was conducted using a genetic algorithm (GA) and the poplar optimization algorithm (POA). We assessed the effectiveness of the proposed technique through a hypothetical case study. The introduction of a surrogate model, based on an FFNN, resulted in an approximation error of 9.6 % for cost estimation and 20.6 % for battery lifespan estimation. The probabilistic design indicates an energy system cost that is 25.7 % higher than that obtained using a deterministic approach. Both the GA and POA achieved solutions that likely represent the global optimum
Lost in optimisation of water distribution systems? A literature review of system design
This is the final version of the article. Available from MDPI via the DOI in this record.Optimisation of water distribution system design is a well-established research field, which has been extremely productive since the end of the 1980s. Its primary focus is to minimise the cost of a proposed pipe network infrastructure. This paper reviews in a systematic manner articles published over the past three decades, which are relevant to the design of new water distribution systems, and the strengthening, expansion and rehabilitation of existing water distribution systems, inclusive of design timing, parameter uncertainty, water quality, and operational considerations. It identifies trends and limits in the field, and provides future research directions. Exclusively, this review paper also contains comprehensive information from over one hundred and twenty publications in a tabular form, including optimisation model formulations, solution methodologies used, and other important details
Design of a second life product family from the perspective of the remanufacturing agent
This thesis presents a method of solving a newly posed Second Life Product Family Design problem. This
is unique in that the architecture of the product is not speci ed to be identical to one of the recaptured
products, rather it is determined through optimization. The problem is framed using Conjoint Analysis
and the Multi Nomial Logit Model, formatted with respect to components available for inclusion in the
nal products and then solved using an implementation of Genetic Algorithms. The solution method is also
encapsulated in a software module which can be disseminated to industrial users without a background in
optimization or familiarity with Genetic Algorithms.
A case study is performed to determine the e ectiveness of the proposed solution method, and analyze
the in
uences di erent market conditions and component similarities can have on the optimal design. It is
concluded that the proposed method converges to an optimal Second Life Product Family Design
Optimisation of multiplier-less FIR filter design techniques
This thesis is concerned with the design of multiplier-less (ML) finite impulse response (FIR) digital filters. The use of multiplier-less digital filters results in simplified filtering structures, better throughput rates and higher speed. These characteristics are very desirable in many DSP systems. This thesis concentrates on the design of digital filters with power-of-two coefficients that result in simplified filtering structures. Two distinct classesof ML FIR filter design algorithms are developed and compared
with traditional techniques. The first class is based on the sensitivity of filter coefficients to rounding to power-of-two. Novel elements include extending of the algorithm for multiple-bands filters and introducing mean square error as the sensitivity criterion. This improves the performance of the algorithm and reduces the complexity of resulting filtering structures. The second class of filter design algorithms is based on evolutionary techniques, primarily genetic algorithms. Three different algorithms based on genetic algorithm kernel are developed. They include simple genetic algorithm, knowledge-based genetic algorithm and hybrid of genetic algorithm and simulated annealing. Inclusion of the additional knowledge has been found very useful when re-designing filters or refining previous designs. Hybrid techniques are useful when exploring large, N-dimensional searching spaces. Here, the genetic algorithm is used to explore searching space rapidly, followed by fine search using simulated annealing. This approach has been found beneficial for design of high-order filters. Finally, a formula for estimation of the filter length from its specification and complementing both
classes of design algorithms, has been evolved using techniques of symbolic regression and genetic programming. Although the evolved formula is very complex and not easily understandable, statistical analysis has shown that it produces
more
accurate results than traditional Kaiser's formula.
In summary, several novel algorithms for the design of multiplier-less digital filters
have been developed. They outperform traditional techniques that are used for the
design of ML FIR filters and hence contributed to the knowledge in the field of ML
FIR filter design
A method for the architectural design of distributed control systems for large, civil jet engines: a systems engineering approach
The design of distributed control systems (DCSs) for large, civil gas turbine engines is a complex
architectural challenge. To date, the majority of research into DCSs has focused on the contributing
technologies and high temperature electronics rather than the architecture of the system itself. This
thesis proposes a method for the architectural design of distributed systems using a genetic algorithm to
generate, evaluate and refine designs. The proposed designs are analysed for their architectural quality,
lifecycle value and commercial benefit. The method is presented along with results proving the concept.
Whilst the method described here is applied exclusively to Distributed Control System (DCS) for jet
engines, the principles and methods could be adapted for a broad range of complex systems
PROPOSED METHODOLOGY FOR OPTIMIZING THE TRAINING PARAMETERS OF A MULTILAYER FEED-FORWARD ARTIFICIAL NEURAL NETWORKS USING A GENETIC ALGORITHM
An artificial neural network (ANN), or shortly "neural network" (NN), is a powerful
mathematical or computational model that is inspired by the structure and/or
functional characteristics of biological neural networks. Despite the fact that ANN has
been developing rapidly for many years, there are still some challenges concerning
the development of an ANN model that performs effectively for the problem at hand.
ANN can be categorized into three main types: single layer, recurrent network and
multilayer feed-forward network. In multilayer feed-forward ANN, the actual
performance is highly dependent on the selection of architecture and training
parameters. However, a systematic method for optimizing these parameters is still an
active research area. This work focuses on multilayer feed-forward ANNs due to their
generalization capability, simplicity from the viewpoint of structure, and ease of
mathematical analysis. Even though, several rules for the optimization of multilayer
feed-forward ANN parameters are available in the literature, most networks are still
calibrated via a trial-and-error procedure, which depends mainly on the type of
problem, and past experience and intuition of the expert. To overcome these
limitations, there have been attempts to use genetic algorithm (GA) to optimize some
of these parameters. However most, if not all, of the existing approaches are focused
partially on the part of architecture and training parameters. On the contrary, the GAANN
approach presented here has covered most aspects of multilayer feed-forward
ANN in a more comprehensive way. This research focuses on the use of binaryencoded
genetic algorithm (GA) to implement efficient search strategies for the
optimal architecture and training parameters of a multilayer feed-forward ANN.
Particularly, GA is utilized to determine the optimal number of hidden layers, number
of neurons in each hidden layer, type of training algorithm, type of activation function
of hidden and output neurons, initial weight, learning rate, momentum term, and
epoch size of a multilayer feed-forward ANN. In this thesis, the approach has been
analyzed and algorithms that simulate the new approach have been mapped out
The exploitation of parallelism on shared memory multiprocessors
PhD ThesisWith the arrival of many general purpose shared memory multiple processor
(multiprocessor) computers into the commercial arena during the mid-1980's, a
rift has opened between the raw processing power offered by the emerging
hardware and the relative inability of its operating software to effectively deliver
this power to potential users. This rift stems from the fact that, currently, no
computational model with the capability to elegantly express parallel activity is
mature enough to be universally accepted, and used as the basis for programming
languages to exploit the parallelism that multiprocessors offer. To add to this,
there is a lack of software tools to assist programmers in the processes of designing
and debugging parallel programs.
Although much research has been done in the field of programming languages,
no undisputed candidate for the most appropriate language for programming
shared memory multiprocessors has yet been found. This thesis examines why this
state of affairs has arisen and proposes programming language constructs,
together with a programming methodology and environment, to close the ever
widening hardware to software gap.
The novel programming constructs described in this thesis are intended for use
in imperative languages even though they make use of the synchronisation
inherent in the dataflow model by using the semantics of single assignment when
operating on shared data, so giving rise to the term shared values. As there are
several distinct parallel programming paradigms, matching flavours of shared
value are developed to permit the concise expression of these paradigms.The Science and Engineering Research Council
Paths forward for sustainable maritime transport : A techno-economic optimization framework for next generation vessels
Climate change is omnipresent in our society. It is known that climate change is occurring, and that additional warming is unavoidable. Therefore, the decarbonization of industrial sectors has gained increased importance in the last years. The maritime transport sector is one of the most targeted industries as it contributes to approximately 3% of global GHG emissions. Nevertheless, maritime transport accounts for up to 80% of the global trade volume, underlying its importance for the world economy. A technical feasible and reliable solution is, thus, essential for the shipping industry to reach the ambitious climate goals established by the Paris Agreement. In the past, the maritim sector has been highly reliant on fossil fuels, using heavy fuel oil as the major energy input. Heavy fuel oil has been the most dominant fuel in the industry due to its cost advantage and high energy density. Recent developments in the maritime industry promote the emergence of dual fuel engines (e.g. LNG and HFO). Even though increased efficiencies and low carbon fuels can reduce maritime pollution, they cannot achieve carbon neutrality. In the long-term, it will be necessary to implement zero emission fuels including green hydrogen, ammonia, methanol, and LNG. The implementation of new sustainable technologies and fuels in the maritime sector will however depend on their economic competitiveness compared to alternative solutions. Therefore, the following research question arises: When can sustainable maritime transport achieve cost parity compared to conventional technologies? The master thesis investigates the break-even point of sustainable shipping technologies in order to achieve climate targets. Thereby, the focus is set on the life cycle costs of different maritime technologies. A techno-economic framework is necessary to decide on the most suitable options for the industry in prospective years. The framework should be able to analyze current as well as prospective technologies, and guide during the technological decision-making process. Therefore, the definition of key performance indicators (KPI) is essential to set a standard for further assessments. The KPIs will be the main value to compare technologies from an economic perspective. In order to answer the research question a case study is developed. The case study is formed by an extensive literature review on current and next-generation sustainable energy systems for vessels. A priority lies on potential carbon neutral technologies and engines such as fuel cells and battery systems based on a predetermined shipping route and shipping class. In a first step, a simulation model for the developed case is established. The output of the simulation model will then be used in the techno-economic framework, connecting components of the system through thermodynamic and physical properties. In a last step, cost functions translate the systems behavior into economic behavior. Once the case study is analyzed, a statistical model is applied on the results in order to evaluate the system under varying boundary conditions. This sensitivity approach is further necessary to underline the impact of the aforementioned KPIs. By that, the robustness of the framework is tested and secured. Finally, the results of the analysis are explained and interpreted with regard to the research question. A conclusion is drawn regarding the potential economic benefits of sustainable maritime transport technologies within the light of potential market access.The results of the thesis are to be documented in a scientifically appropriate manner and discussed within the context of existing literature and regulatory targets for the industry
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