Production of magnesium carbonates from serpentinites for CO2 mineral sequestration : optimisation towards industrial application

Global warming is one of the most alarming problems of this century. Initial scepticism concerning its validity is currently dwarfed by the intensification of extreme weather events whilst the gradual arising level of anthropogenic CO2 is pointed out as its main driver. Most of the greenhouse gas (GHG) emissions come from large point sources (heat and power production and industrial processes) and the continued use of fossil fuels requires quick and effective measures to meet the world’s energy demand whilst (at least) stabilizing CO2 atmospheric levels. The framework known as Carbon Capture and Storage (CCS) – or Carbon Capture Utilization and Storage (CCUS) – comprises a portfolio of technologies applicable to large‐scale GHG sources for preventing CO2 from entering the atmosphere. Amongst them, CO2 capture and mineralisation (CCM) presents the highest potential for CO2 sequestration as the predicted carbon storage capacity (as mineral carbonates) far exceeds the estimated levels of the worldwide identified fossil fuel reserves. The work presented in this thesis aims at taking a step forward to the deployment of an energy/cost effective process for simultaneous capture and storage of CO2 in the form of thermodynamically stable and environmentally friendly solid carbonates. R&D work on the process considered here began in 2007 at Åbo Akademi University in Finland. It involves the processing of magnesium silicate minerals with recyclable ammonium salts for extraction of magnesium at ambient pressure and 400‐440⁰C, followed by aqueous precipitation of magnesium in the form of hydroxide, Mg(OH)2, and finally Mg(OH)2 carbonation in a pressurised fluidized bed reactor at ~510⁰C and ~20 bar PCO2 to produce high purity MgCO3. Rock material taken from the Hitura nickel mine, Finland, and serpentinite collected from Bragança, Portugal, were tested for magnesium extraction with both ammonium sulphate and bisulphate (AS and ABS) for determination of optimal operation parameters, primarily: reaction time, reactor type and presence of moisture. Typical efficiencies range from 50 to 80% of magnesium extraction at 350‐450⁰C. In general ABS performs better than AS showing comparable efficiencies at lower temperature and reaction times. The best experimental results so far obtained include 80% magnesium extraction with ABS at 450⁰C in a laboratory scale rotary kiln and 70% Mg(OH)2 carbonation in the PFB at 500⁰C, 20 bar CO2 pressure for 15 minutes. The extraction reaction with ammonium salts is not at all selective towards magnesium. Other elements like iron, nickel, chromium, copper, etc., are also co‐extracted. Their separation, recovery and valorisation are addressed as well and found to be of great importance. The assessment of the exergetic performance of the process was carried out using Aspen Plus® software and pinch analysis technology. The choice of fluxing agent and its recovery method have a decisive sway in the performance of the process: AS is recovered by crystallisation and in general the whole process requires more exergy (2.48–5.09 GJ/tCO2sequestered) than ABS (2.48–4.47 GJ/tCO2sequestered) when ABS is recovered by thermal decomposition. However, the corrosive nature of molten ABS and operational problems inherent to thermal regeneration of ABS prohibit this route. Regeneration of ABS through addition of H2SO4 to AS (followed by crystallisation) results in an overall negative exergy balance (mainly at the expense of low grade heat) but will flood the system with sulphates. Although the ÅA route is still energy intensive, its performance is comparable to conventional CO2 capture methods using alkanolamine solvents. An energy‐neutral process is dependent on the availability and quality of nearby waste heat and economic viability might be achieved with: magnesium extraction and carbonation levels ≥ 90%, the processing of CO2‐containing flue gases (eliminating the expensive capture step) and production of marketable products.

Efficient Algorithms for Coastal Geographic Problems

The increasing performance of computers has made it possible to solve algorithmically problems for which manual and possibly inaccurate methods have been previously used. Nevertheless, one must still pay attention to the performance of an algorithm if huge datasets are used or if the problem iscomputationally difficult. Two geographic problems are studied in the articles included in this thesis. In the first problem the goal is to determine distances from points, called study points, to shorelines in predefined directions. Together with other in-formation, mainly related to wind, these distances can be used to estimate wave exposure at different areas. In the second problem the input consists of a set of sites where water quality observations have been made and of the results of the measurements at the different sites. The goal is to select a subset of the observational sites in such a manner that water quality is still measured in a sufficient accuracy when monitoring at the other sites is stopped to reduce economic cost. Most of the thesis concentrates on the first problem, known as the fetch length problem. The main challenge is that the two-dimensional map is represented as a set of polygons with millions of vertices in total and the distances may also be computed for millions of study points in several directions. Efficient algorithms are developed for the problem, one of them approximate and the others exact except for rounding errors. The solutions also differ in that three of them are targeted for serial operation or for a small number of CPU cores whereas one, together with its further developments, is suitable also for parallel machines such as GPUs.

The use of explicit algorithms and episodic context to teach subtraction to students with learning problems in mathematics /

This research attempted to address the question of the role of explicit algorithms and episodic contexts in the acquisition of computational procedures for regrouping in subtraction. Three groups of students having difficulty learning to subtract with regrouping were taught procedures for doing so through either an explicit algorithm, an episodic content or an examples approach. It was hypothesized that the use of an explicit algorithm represented in a flow chart format would facilitate the acquisition and retention of specific procedural steps relative to the other two conditions. On the other hand, the use of paragraph stories to create episodic content was expected to facilitate the retrieval of algorithms, particularly in a mixed presentation format. The subjects were tested on similar, near, and far transfer questions over a four-day period. Near and far transfer algorithms were also introduced on Day Two. The results suggested that both explicit and episodic context facilitate performance on questions requiring subtraction with regrouping. However, the differential effects of these two approaches on near and far transfer questions were not as easy to identify. Explicit algorithms may facilitate the acquisition of specific procedural steps while at the same time inhibiting the application of such steps to transfer questions. Similarly, the value of episodic context in cuing the retrieval of an algorithm may be limited by the ability of a subject to identify and classify a new question as an exemplar of a particular episodically deflned problem type or category. The implications of these findings in relation to the procedures employed in the teaching of Mathematics to students with learning problems are discussed in detail.

Properties and algorithms of the (n, k)-star graphs

The (n, k)-star interconnection network was proposed in 1995 as an attractive alternative to the n-star topology in parallel computation. The (n, k )-star has significant advantages over the n-star which itself was proposed as an attractive alternative to the popular hypercube. The major advantage of the (n, k )-star network is its scalability, which makes it more flexible than the n-star as an interconnection network. In this thesis, we will focus on finding graph theoretical properties of the (n, k )-star as well as developing parallel algorithms that run on this network. The basic topological properties of the (n, k )-star are first studied. These are useful since they can be used to develop efficient algorithms on this network. We then study the (n, k )-star network from algorithmic point of view. Specifically, we will investigate both fundamental and application algorithms for basic communication, prefix computation, and sorting, etc. A literature review of the state-of-the-art in relation to the (n, k )-star network as well as some open problems in this area are also provided.

Decoding algorithms using side-effect machines

Bioinformatics applies computers to problems in molecular biology. Previous research has not addressed edit metric decoders. Decoders for quaternary edit metric codes are finding use in bioinformatics problems with applications to DNA. By using side effect machines we hope to be able to provide efficient decoding algorithms for this open problem. Two ideas for decoding algorithms are presented and examined. Both decoders use Side Effect Machines(SEMs) which are generalizations of finite state automata. Single Classifier Machines(SCMs) use a single side effect machine to classify all words within a code. Locking Side Effect Machines(LSEMs) use multiple side effect machines to create a tree structure of subclassification. The goal is to examine these techniques and provide new decoders for existing codes. Presented are ideas for best practices for the creation of these two types of new edit metric decoders.

Properties and algorithms of the (n, k)-arrangement graphs

The (n, k)-arrangement interconnection topology was first introduced in 1992. The (n, k )-arrangement graph is a class of generalized star graphs. Compared with the well known n-star, the (n, k )-arrangement graph is more flexible in degree and diameter. However, there are few algorithms designed for the (n, k)-arrangement graph up to present. In this thesis, we will focus on finding graph theoretical properties of the (n, k)- arrangement graph and developing parallel algorithms that run on this network. The topological properties of the arrangement graph are first studied. They include the cyclic properties. We then study the problems of communication: broadcasting and routing. Embedding problems are also studied later on. These are very useful to develop efficient algorithms on this network. We then study the (n, k )-arrangement network from the algorithmic point of view. Specifically, we will investigate both fundamental and application algorithms such as prefix sums computation, sorting, merging and basic geometry computation: finding convex hull on the (n, k )-arrangement graph. A literature review of the state-of-the-art in relation to the (n, k)-arrangement network is also provided, as well as some open problems in this area.

Properties and algorithms of the hyper-star graph and its related graphs

The hyper-star interconnection network was proposed in 2002 to overcome the drawbacks of the hypercube and its variations concerning the network cost, which is defined by the product of the degree and the diameter. Some properties of the graph such as connectivity, symmetry properties, embedding properties have been studied by other researchers, routing and broadcasting algorithms have also been designed. This thesis studies the hyper-star graph from both the topological and algorithmic point of view. For the topological properties, we try to establish relationships between hyper-star graphs with other known graphs. We also give a formal equation for the surface area of the graph. Another topological property we are interested in is the Hamiltonicity problem of this graph. For the algorithms, we design an all-port broadcasting algorithm and a single-port neighbourhood broadcasting algorithm for the regular form of the hyper-star graphs. These algorithms are both optimal time-wise. Furthermore, we prove that the folded hyper-star, a variation of the hyper-star, to be maixmally fault-tolerant.

Using genetic algorithms for the single allocation hub location problem

Hub location problem is an NP-hard problem that frequently arises in the design of transportation and distribution systems, postal delivery networks, and airline passenger flow. This work focuses on the Single Allocation Hub Location Problem (SAHLP). Genetic Algorithms (GAs) for the capacitated and uncapacitated variants of the SAHLP based on new chromosome representations and crossover operators are explored. The GAs is tested on two well-known sets of real-world problems with up to 200 nodes. The obtained results are very promising. For most of the test problems the GA obtains improved or best-known solutions and the computational time remains low. The proposed GAs can easily be extended to other variants of location problems arising in network design planning in transportation systems.

Comparison of classification ability of hyperball algorithms to neural network and k-nearest neighbour algorithms

The main focus of this thesis is to evaluate and compare Hyperbalilearning algorithm (HBL) to other learning algorithms. In this work HBL is compared to feed forward artificial neural networks using back propagation learning, K-nearest neighbor and 103 algorithms. In order to evaluate the similarity of these algorithms, we carried out three experiments using nine benchmark data sets from UCI machine learning repository. The first experiment compares HBL to other algorithms when sample size of dataset is changing. The second experiment compares HBL to other algorithms when dimensionality of data changes. The last experiment compares HBL to other algorithms according to the level of agreement to data target values. Our observations in general showed, considering classification accuracy as a measure, HBL is performing as good as most ANn variants. Additionally, we also deduced that HBL.:s classification accuracy outperforms 103's and K-nearest neighbour's for the selected data sets.

Characterisation and optimisation of the flavour of health-promoting, plantderived bitterants in functional beverages.

Flavour is a combination of taste, odour, and chemesthetic sensations. Close associations exist between these sensory modalities, and thus, the overall flavour of a food or beverage product can change when the intensity of one or more of these sensations is altered. Strategies to modify flavour are often utilized by the food industry, and are central to the engineering of new and reformulated products. For functional food and beverages, flavour modification is particularly important, as fortifying agents can elicit high levels of less than desirable sensations, such as bitterness and astringency. The application of various flavour modifying strategies can decrease the perceived intensity of these sensations, and in tum, improve the sensory profile of the product. This collection of studies describes the sensory characteristics of experimental functional beverages fortified with trans-resveratrol, (+)-catechin, and/or caffeine, and examines the impact of novel flavour modifying strategies on the perceived flavour of these beverages. In the first study, results demonstrate that the flavour profile of Cabemet Sauvignon wines fortified with 20 mglL and 200 mg/L of trans-resveratrol is not perceived as different compared to control wine (0 mglL). However, Riesling wine fortified with 200 mg/L is perceived as significantly higher in bitterness compared to 20 mglL and control. For some functional food formulations, alternative strategies for flavour modification are needed. Traditional methods, such as the addition of sucrose and sodium chloride, may decrease the perceived 'healthiness' of a product, and thus, may be sub-optimal. In a second study, high and low concentrations of five different bitter inhibiting compounds - 'bitter blockers' - (B-cyclodextrin, homoeridictyol sodium salt, carboxymethylcellulose - low viscosity, zinc sulfate, magnesium sulfate) were tested for their efficacy towards decreasing the bitterness of high and low concentrations of caffeine and (+)catechin - two health-relevant, plant-derived bitterants. B-cyclodextrin and homoeridictyol sodium salt were the most effective blockers at decreasing (+ )-catechin and caffeine, respectively. In addition to bitter blockers, additional flavour modifying strategies, either alone or in combination - may also be successful in functional food formulations. Both sucrose and rebaudioside A - a plant-derived sweetener - were effective at decreasing the bitterness of (+)catechin. When added to (+)-catechin along with B-cyc1odextrin, both sweeteners provided the most effective decrease in bitterness compared to binary, ternary, or quaternary mixtures of (+)catechin together with bitter blockers, sweeteners, andlor odourants. The perceived intensity of sensations elicited by sweeteners and odourants was not affected by the addition of bitter blockers, and thus, their impact within these complex matrices is minimal. In addition, withinmodal (taste-taste) compared to cross-modal (taste-odour) sensory interactions were more effective at decreasing the bitterness of (+ )-catechin. Overall, results from these studies demonstrate that certain novel, alternative flavour modifying approaches may be successful towards lowering the bitterness and astringency elicited by (+ )-catechin and caffeine in aqueous solutions.

Multi-Objective Genetic Algorithms for the Single Allocation Hub Location Problem

Hub Location Problems play vital economic roles in transportation and telecommunication networks where goods or people must be efficiently transferred from an origin to a destination point whilst direct origin-destination links are impractical. This work investigates the single allocation hub location problem, and proposes a genetic algorithm (GA) approach for it. The effectiveness of using a single-objective criterion measure for the problem is first explored. Next, a multi-objective GA employing various fitness evaluation strategies such as Pareto ranking, sum of ranks, and weighted sum strategies is presented. The effectiveness of the multi-objective GA is shown by comparison with an Integer Programming strategy, the only other multi-objective approach found in the literature for this problem. Lastly, two new crossover operators are proposed and an empirical study is done using small to large problem instances of the Civil Aeronautics Board (CAB) and Australian Post (AP) data sets.

Properties and Algorithms of the KCube Graphs

The KCube interconnection topology was rst introduced in 2010. The KCube graph is a compound graph of a Kautz digraph and hypercubes. Compared with the at- tractive Kautz digraph and well known hypercube graph, the KCube graph could accommodate as many nodes as possible for a given indegree (and outdegree) and the diameter of interconnection networks. However, there are few algorithms designed for the KCube graph. In this thesis, we will concentrate on nding graph theoretical properties of the KCube graph and designing parallel algorithms that run on this network. We will explore several topological properties, such as bipartiteness, Hamiltonianicity, and symmetry property. These properties for the KCube graph are very useful to develop efficient algorithms on this network. We will then study the KCube network from the algorithmic point of view, and will give an improved routing algorithm. In addition, we will present two optimal broadcasting algorithms. They are fundamental algorithms to many applications. A literature review of the state of the art network designs in relation to the KCube network as well as some open problems in this field will also be given.

Characterizing Dynamic Optimization Benchmarks for the Comparison of Multi-Modal Tracking Algorithms

Population-based metaheuristics, such as particle swarm optimization (PSO), have been employed to solve many real-world optimization problems. Although it is of- ten sufficient to find a single solution to these problems, there does exist those cases where identifying multiple, diverse solutions can be beneficial or even required. Some of these problems are further complicated by a change in their objective function over time. This type of optimization is referred to as dynamic, multi-modal optimization. Algorithms which exploit multiple optima in a search space are identified as niching algorithms. Although numerous dynamic, niching algorithms have been developed, their performance is often measured solely on their ability to find a single, global optimum. Furthermore, the comparisons often use synthetic benchmarks whose landscape characteristics are generally limited and unknown. This thesis provides a landscape analysis of the dynamic benchmark functions commonly developed for multi-modal optimization. The benchmark analysis results reveal that the mechanisms responsible for dynamism in the current dynamic bench- marks do not significantly affect landscape features, thus suggesting a lack of representation for problems whose landscape features vary over time. This analysis is used in a comparison of current niching algorithms to identify the effects that specific landscape features have on niching performance. Two performance metrics are proposed to measure both the scalability and accuracy of the niching algorithms. The algorithm comparison results demonstrate the algorithms best suited for a variety of dynamic environments. This comparison also examines each of the algorithms in terms of their niching behaviours and analyzing the range and trade-off between scalability and accuracy when tuning the algorithms respective parameters. These results contribute to the understanding of current niching techniques as well as the problem features that ultimately dictate their success.

Properties and Algorithms of the KCube Interconnection Networks

The KCube interconnection network was first introduced in 2010 in order to exploit the good characteristics of two well-known interconnection networks, the hypercube and the Kautz graph. KCube links up multiple processors in a communication network with high density for a fixed degree. Since the KCube network is newly proposed, much study is required to demonstrate its potential properties and algorithms that can be designed to solve parallel computation problems. In this thesis we introduce a new methodology to construct the KCube graph. Also, with regard to this new approach, we will prove its Hamiltonicity in the general KC(m; k). Moreover, we will find its connectivity followed by an optimal broadcasting scheme in which a source node containing a message is to communicate it with all other processors. In addition to KCube networks, we have studied a version of the routing problem in the traditional hypercube, investigating this problem: whether there exists a shortest path in a Qn between two nodes 0n and 1n, when the network is experiencing failed components. We first conditionally discuss this problem when there is a constraint on the number of faulty nodes, and subsequently introduce an algorithm to tackle the problem without restrictions on the number of nodes.

A Scalability Study and New Algorithms for Large-Scale Many-Objective Optimization

Many real-world optimization problems contain multiple (often conflicting) goals to be optimized concurrently, commonly referred to as multi-objective problems (MOPs). Over the past few decades, a plethora of multi-objective algorithms have been proposed, often tested on MOPs possessing two or three objectives. Unfortunately, when tasked with solving MOPs with four or more objectives, referred to as many-objective problems (MaOPs), a large majority of optimizers experience significant performance degradation. The downfall of these optimizers is that simultaneously maintaining a well-spread set of solutions along with appropriate selection pressure to converge becomes difficult as the number of objectives increase. This difficulty is further compounded for large-scale MaOPs, i.e., MaOPs possessing large amounts of decision variables. In this thesis, we explore the challenges of many-objective optimization and propose three new promising algorithms designed to efficiently solve MaOPs. Experimental results demonstrate the proposed optimizers to perform very well, often outperforming state-of-the-art many-objective algorithms.