Recent Submissions

  • Steam consumption minimization using genetic algorithm optimization method: an industrial case study

    Alabdulkarem, A.; Rahmanian, Nejat (2020)
    Condensate stabilization is a process where hydrocarbon condensate recovered from natural gas reservoirs is processed to meet the required storage, transportation, and export specifications. The process involves stabilizing of hydrocarbon liquid by separation of light hydrocarbon such as methane from the heavier hydrocarbon constituents such as propane. An industrial scale back-up condensate stabilization unit was simulated using Aspen HYSYS software and validated with the plant data. The separation process consumes significant amount of energy in form of steam. The objectives of the paper are to find the minimum steam consumption of the process and conduct sensitivity and exergy analyses on the process. The minimum steam consumption was found using genetic algorithm optimization method for both winter and summer conditions. The optimization was carried out using MATLAB software coupled with Aspen HYSYS software. The optimization involves six design variables and four constraints, such that realistic results are achieved. The results of the optimization show that savings in steam consumption is 34% as compared to the baseline process while maintaining the desired specifications. The effect of natural gas feed temperature has been investigated. The results show that steam consumption is reduced by 46% when the natural gas feed temperature changes from 17.7 to 32.7°C. Exergy analysis shows that exergy destruction of the optimized process is 37% less than the baseline process.
  • Equivalence classes of coherent projectors in a Hilbert space with prime dimension: Q functions and their Gini index

    Vourdas, Apostolos (2020-05)
    Coherent subspaces spanned by a finite number of coherent states are introduced, in a quantum system with Hilbert space that has odd prime dimension d. The set of all coherent subspaces is partitioned into equivalence classes, with d 2 subspaces in each class. The corresponding coherent projectors within an equivalence class, have the 'closure under displacements property' and also resolve the identity. Different equivalence classes provide different granularisation of the Hilbert space, and they form a partial order 'coarser' (and 'finer'). In the case of a two-dimensional coherent subspace spanned by two coherent states, the corresponding projector (of rank 2) is different than the sum of the two projectors to the subspaces related to each of the two coherent states. We quantify this with 'non-addditivity operators' which are a measure of quantum interference in phase space, and also of the non-commutativity of the projectors. Generalized Q and P functions of density matrices, which are based on coherent projectors in a given equivalence class, are introduced. Analogues of the Lorenz values and the Gini index (which are popular quantities in mathematical economics) are used here to quantify the inequality in the distribution of the Q function of a quantum state, within the granular structure of the Hilbert space. A comparison is made between Lorenz values and the Gini index for the cases of coarse and also fine granularisation of the Hilbert space. Lorenz values require an ordering of the d 2 values of the Q function of a density matrix, and this leads to the ranking permutation of a density matrix, and to comonotonic density matrices (which have the same ranking permutation). The Lorenz values are a superadditive function and the Gini index is a subadditive function (they are both additive quantities for comonotonic density matrices). Various examples demonstrate these ideas.
  • Lotus-leaf inspired surfaces: hydrophobicity evolution of replicas due to mechanical cleaning and mold wear

    Romano, J-M.; Garcia-Giron, A.; Penchev, P.; Gulcur, Mert; Whiteside, Benjamin R.; Dimov, S. (2020-03)
    Inspired from the low wetting properties of Lotus leaves, the fabrication of dual micro/nano-scale topographies is of interest to many applications. In this research, superhydrophobic surfaces are fabricated by a process chain combining ultrashort pulsed laser texturing of steel inserts and injection moulding to produce textured polypropylene parts. This manufacturing route is very promising and could be economically viable for mass production of polymeric parts with superhydrophobic properties. However, surface damages, such as wear and abrasion phenomena, can be detrimental to the attractive wetting properties of replicated textured surfaces. Therefore, the final product lifespan is investigated by employing mechanical cleaning of textured polypropylene surfaces with multipurpose cloths following the ASTM D3450 standard. Secondly, the surface damage of replication masters after 350 injection moulding cycles with glass-fiber reinforced polypropylene, especially to intensify mould wear, was investigated. In both cases, the degradation of the dual-scale surface textures had a clear impact on surface topography of the replicas and thus on their wetting properties, too.
  • Mathematical modelling of performance and wear prediction of PDC drill bits: impact of bit profile, bit hydraulic, and rock strength

    Mazen, Ahmed Z.; Mujtaba, Iqbal M.; Hassanpour, A.; Rahmanian, Nejat (2020-05)
    The estimation of Polycrystalline Diamond Compact (PDC) cutters wear has been an area of concern for the drilling industry for years now. The cutter's wear has been measured practically by pulling the bit out for evaluation at the surface. It is important to find the right time for tripping out as this helps to avoid the fishing job and reduces the operational cost significantly. The prediction of the drilling performance is based on the interaction of cutter and rock. Several authors focused on the cutter-rock interface but only a few researchers tried to model the wear of the PDC bit cutters. The aim of this research is to understand the relationships between the rate of penetration (ROP) and the drilling variables per each foot, and then determine the overall bit efficiency for the whole drilling operation. A new mathematical model is derived to predict the PDC bit performance by considering the factors that were already not taken into account. These factors include rock strength, bit design, and bit hydraulic. The model investigates the effect of these parameters to estimate the abrasive cutters wear on the inner and the outer bit cones by deriving modified equations to calculate the mechanical specific energy (MSE), torque, and depth of cut (DOC) as a function of effective blades (EB). The model is used to forecast the bit cutters wear conditions in four wells in the oil fields located in Libya, which were drilled with three different PDC's sizes. The model enables the results to be compared to the actual bit cutters wear measured for inner and outer cones. The results are found that are well in agreement with the actual field data obtained in bit records.
  • Flow structures in wake of a pile-supported horizontal axis tidal stream turbine

    Zhang, J.; Lin, X.; Wang, R.; Guo, Yakun; Zhang, C.; Zhang, Y. (2020-03)
    This study presents results from laboratory experiments to investigate the wake structure in the lee side of a scaled three-bladed horizontal axis tidal stream turbine with a mono-pile support structure. Experiments are conducted for a range of approaching flow velocity and installation height of rotor. Analysis of the results shows that bed shear stress increases with the increase of approaching velocity and decrease of installation height within 2D (D is the diameter of the rotor) downstream of the rotor. The flow field within 2D downstream of the rotor is greatly influenced by the presence of nacelle and mono-pile. Low stream-wise flow velocity and large turbulence intensity level is detected along the flume center right behind the nacelle and mono-pile from 1D to 2D downstream of the rotor. Stream-wise velocity at the blade tip height lower than the nacelle increases sharply from 1D to 2D and gradually grows afterwards. Correspondingly, the turbulence intensity decreases quickly from 1D to 2D and slowly afterwards. Large bed shear stress is measured from 1D to 2D, which is closely related to turbulence induced by the mono-pile. It is also found that the presence of the mono-pile might make the flow field more ‘disc-shaped’.
  • A review of modelling and verification approaches for computational biology

    Konur, Savas (2020)
    This paper reviews most frequently used computational modelling approaches and formal verification techniques in computational biology. The paper also compares a number of model checking tools and software suits used in analysing biological systems and biochemical networks and verifiying a wide range of biological properties.
  • A review on hydrodynamics of free surface flows in emergent vegetated channels

    Maji, S.; Hanmaiahgari, P.R.; Balachandar, R.; Pu, Jaan H.; Ricardo, A.M.; Ferreira, R.M.L. (MDPI, 2020-04)
    This review paper addresses the structure of the mean flow and key turbulence quantities in free-surface flows with emergent vegetation. Emergent vegetation in open channel flow affects turbulence, flow patterns, flow resistance, sediment transport, and morphological changes. The last 15 years have witnessed significant advances in field, laboratory, and numerical investigations of turbulent flows within reaches of different types of emergent vegetation, such as rigid stems, flexible stems, with foliage or without foliage, and combinations of these. The influence of stem diameter, volume fraction, frontal area of stems, staggered and non-staggered arrangements of stems, and arrangement of stems in patches on mean flow and turbulence has been quantified in different research contexts using different instrumentation and numerical strategies. In this paper, a summary of key findings on emergent vegetation flows is offered, with particular emphasis on: (1) vertical structure of flow field, (2) velocity distribution, 2nd order moments, and distribution of turbulent kinetic energy (TKE) in horizontal plane, (3) horizontal structures which includes wake and shear flows and, (4) drag effect of emergent vegetation on the flow. It can be concluded that the drag coefficient of an emergent vegetation patch is proportional to the solid volume fraction and average drag of an individual vegetation stem is a linear function of the stem Reynolds number. The distribution of TKE in a horizontal plane demonstrates that the production of TKE is mostly associated with vortex shedding from individual stems. Production and dissipation of TKE are not in equilibrium, resulting in strong fluxes of TKE directed outward the near wake of each stem. In addition to Kelvin–Helmholtz and von Kármán vortices, the ejections and sweeps have profound influence on sediment dynamics in the emergent vegetated flows.
  • An EKF-Based Performance Enhancement Scheme for Stochastic Nonlinear Systems by Dynamic Set-Point Adjustment

    Tang, X.; Zhang, Qichun; Hu, L. (2020-04-01)
    In this paper, a performance enhancement scheme has been investigated for a class of stochastic nonlinear systems via set-point adjustment. Considering the practical industrial processes, the multi-layer systematic structure has been adopted to achieve the control design requirements subjected to random noise. The basic loop control is given by PID design while the parameters have been fixed after the design phase. Alternatively, we can consider that there exists an unadjustable loop control. Then, the additional loop is designed for performance enhancement in terms of the tracking accuracy. In particular, a novel approach has been presented to dynamically adjust the set-points using the estimated states of the systems through extended Kalman filter (EKF). Minimising the entropy criterion, the parameters of the set-point adjustment controller can be optimised which will enhance the performance of the entire closed-loop systems. Based upon the presented scheme, the stochastic stability analysis has been given to demonstrate that the closed-loop tracking errors are bounded in probability one. To indicate the effectiveness of the presented control scheme, the numerical examples have been given and the simulation results imply that the designed systems are bounded and the tracking performance can be enhanced simultaneously. In summary, a new framework for system performance enhancement has been presented even if the loop control is unadjustable which forms the main contribution of this paper.
  • The use of multiple mobile sinks in wireless sensor networks for large scale areas

    Al-Behadili, H.; AlWane, S.; Al-Yasir, Yasir; Ojaroudi Parchin, Naser; Olley, Peter; Abd-Alhameed, Raed A. (IET Digital Library, 2020)
    Sensing coverage and network connectivity are two of the most fundamental issues to ensure that there are effective environmental sensing and robust data communication in a WSN application. Random positioning of nodes in a WSN may result in random connectivity, which can cause a large variety of key parameters within the WSN. For example, data latency and battery lifetime can lead to the isolation of nodes, which causes a disconnection between nodes within the network. These problems can be avoided by using mobile data sinks, which travel between nodes that have connection problems. This research aims to design, test and optimise a data collection system that addresses the isolated node problem, as well as to improve the connectivity between sensor nodes and base station, and to reduce the energy consumption simultaneously. In addition, this system will help to solve several problems such as the imbalance of delay and hotspot problems. The effort in this paper is focussed on the feasibility of using the proposed methodology in different applications. More ongoing experimental work will aim to provide a detailed study for advanced applications e.g. transport systems for civil purposes.
  • The versatile biomedical applications of bismuth-based nanoparticles and composites: therapeutic, diagnostic, biosensing, and regenerative properties

    Shahbazi, M-A.; Faghfouri, L.; Ferreira, M.P.A.; Figueiredo, P.; Maleki, H.; Sefat, Farshid; Hirvonen, J.; Santos, H.A. (2020-02)
    Studies of nanosized forms of bismuth (Bi)-containing materials have recently expanded from optical, chemical, electronic, and engineering fields towards biomedicine, as a result of their safety, cost-effective fabrication processes, large surface area, high stability, and high versatility in terms of shape, size, and porosity. Bi, as a nontoxic and inexpensive diamagnetic heavy metal, has been used for the fabrication of various nanoparticles (NPs) with unique structural, physicochemical, and compositional features to combine various properties, such as a favourably high X-ray attenuation coefficient and near-infrared (NIR) absorbance, excellent light-to-heat conversion efficiency, and a long circulation half-life. These features have rendered bismuth-containing nanoparticles (BiNPs) with desirable performance for combined cancer therapy, photothermal and radiation therapy (RT), multimodal imaging, theranostics, drug delivery, biosensing, and tissue engineering. Bismuth oxyhalides (BiOx, where X is Cl, Br or I) and bismuth chalcogenides, including bismuth oxide, bismuth sulfide, bismuth selenide, and bismuth telluride, have been heavily investigated for therapeutic purposes. The pharmacokinetics of these BiNPs can be easily improved via the facile modification of their surfaces with biocompatible polymers and proteins, resulting in enhanced colloidal stability, extended blood circulation, and reduced toxicity. Desirable antibacterial effects, bone regeneration potential, and tumor growth suppression under NIR laser radiation are the main biomedical research areas involving BiNPs that have opened up a new paradigm for their future clinical translation. This review emphasizes the synthesis and state-of-the-art progress related to the biomedical applications of BiNPs with different structures, sizes, and compositions. Furthermore, a comprehensive discussion focusing on challenges and future opportunities is presented.
  • An ontology-based system for discovering landslide-induced emergencies in electrical grid

    Phengsuwan, J.; Shah, T.; Sun, R.; James, P.; Thakker, Dhaval; Ranjan, R. (2020)
    Early warning systems (EWS) for electrical grid infrastructure have played a significant role in the efficient management of electricity supply in natural hazard prone areas. Modern EWS rely on scientific methods to analyze a variety of Earth Observation and ancillary data provided by multiple and heterogeneous data sources for the monitoring of electrical grid infrastructure. Furthermore, through cooperation, EWS for natural hazards contribute to monitoring by reporting hazard events that are associated with a particular electrical grid network. Additionally, sophisticated domain knowledge of natural hazards and electrical grid is also required to enable dynamic and timely decision‐making about the management of electrical grid infrastructure in serious hazards. In this paper, we propose a data integration and analytics system that enables an interaction between natural hazard EWS and electrical grid EWS to contribute to electrical grid network monitoring and support decision‐making for electrical grid infrastructure management. We prototype the system using landslides as an example natural hazard for the grid infrastructure monitoring. Essentially, the system consists of background knowledge about landslides as well as information about data sources to facilitate the process of data integration and analysis. Using the knowledge modeled, the prototype system can report the occurrence of landslides and suggest potential data sources for the electrical grid network monitoring.
  • An IoT-enabled Decision Support System for Circular Economy Business Model

    Mboli, Julius S.; Thakker, Dhaval; Mishra, J. (Wiley, 2020)
    The traditional linear economy using a take‐make‐dispose model is resource intensive and has adverse environmental impacts. Circular economy (CE) which is regenerative and restorative by design is recommended as the business model for resource efficiency. While there is a need for businesses and organisations to switch from linear to CE, there are several challenges that needs addressing such as business models and the criticism of CE projects often being small scale. Technology can be an enabler toward scaling up CE; however, the prime challenge is to identify technologies that can allow predicting, tracking and proactively monitoring product's residual value to motivate businesses to pursue circularity decisions. In this paper, we propose an IoT‐enabled decision support system (DSS) for CE business model that effectively allows tracking, monitoring, and analysing products in real time with the focus on residual value. The business model is implemented using an ontological model. This model is complemented by a semantic decision support system. The semantic ontological model, first of its kind, is evaluated for technical compliance. We applied DSS and the ontological model in a real‐world use case and demonstrate viability and applicability of our approach.
  • An in-core grid index for transferring finite element data across dissimilar meshes

    Scrimieri, Daniele; Afazov, S.M.; Ratchev, S.M. (2015)
    The simulation of a manufacturing process chain with the finite element method requires the selection of an appropriate finite element solver, element type and mesh density for each process of the chain. When the simulation results of one step are needed in a subsequent one, they have to be interpolated and transferred to another model. This paper presents an in-core grid index that can be created on a mesh represented by a list of nodes/elements. Finite element data can thus be transferred across different models in a process chain by mapping nodes or elements in indexed meshes. For each nodal or integration point of the target mesh, the index on the source mesh is searched for a specific node or element satisfying certain conditions, based on the mapping method. The underlying space of an indexed mesh is decomposed into a grid of variable-sized cells. The index allows local searches to be performed in a small subset of the cells, instead of linear searches in the entire mesh which are computationally expensive. This work focuses on the implementation and computational efficiency of indexing, searching and mapping. An experimental evaluation on medium-sized meshes suggests that the combination of index creation and mapping using the index is much faster than mapping through sequential searches.
  • MotiVar: Motivating Weight Loss Through A Personalised Avatar

    Ugail, Hassan; Mackevicius, Rokas; Hardy, Maryann L.; Hill, A.; Horne, Maria; Murrells, T.; Holliday, J.; Chinnadorai, R. (IEEE, 2019)
    This work aims to develop a personalised avatar based virtual environment for motivating weight loss and weight management. Obesity is a worldwide epidemic which has not only enormous resource impact for the healthcare systems but also has substantial health as well as a psychological effect among the individuals who are affected. We propose to tackle this issue via the development of a personalised avatar, the form of which can be adjusted to show the present and the future self of the individual. For the avatar design and development phase, we utilise a parametric based mathematical formulation derived from the solutions of a chosen elliptic partial differential equation. This method not only enables us to generate a parameterised avatar model, but it also allows us to quickly and efficiently create various avatar shapes corresponding to different body weights and even to different body postures.
  • A multi-agent architecture for plug and produce on an industrial assembly platform

    Antzoulatos, N.; Castro, E.; Scrimieri, Daniele; Ratchev, S. (2014-12)
    Modern manufacturing companies face increased pressures to adapt to shorter product life cycles and the need to reconfigure more frequently their production systems to offer new product variants. This paper proposes a new multi-agent architecture utilising “plug and produce” principles for configuration and reconfiguration of production systems with minimum human intervention. A new decision-making approach for system reconfiguration based on tasks re-allocation is presented using goal driven methods. The application of the proposed architecture is described with a number of architectural views and its deployment is illustrated using a validation scenario implemented on an industrial assembly platform. The proposed methodology provides an innovative application of a multi-agent control environment and architecture with the objective of significantly reducing the time for deployment and ramp-up of small footprint assembly systems.
  • A k-nearest neighbour technique for experience-based adaptation of assembly stations

    Scrimieri, Daniele; Ratchev, S.M. (2014-01)
    We present a technique for automatically acquiring operational knowledge on how to adapt assembly systems to new production demands or recover from disruptions. Dealing with changes and disruptions affecting an assembly station is a complex process which requires deep knowledge of the assembly process, the product being assembled and the adopted technologies. Shop-floor operators typically perform a series of adjustments by trial and error until the expected results in terms of performance and quality are achieved. With the proposed approach, such adjustments are captured and their effect on the station is measured. Adaptation knowledge is then derived by generalising from individual cases using a variant of the k-nearest neighbour algorithm. The operator is informed about potential adaptations whenever the station enters a state similar to one contained in the experience base, that is, a state on which adaptation information has been captured. A case study is presented, showing how the technique enables to reduce adaptation times. The general system architecture in which the technique has been implemented is described, including the role of the different software components and their interactions.
  • Learning and reuse of engineering ramp-up strategies for modular assembly systems

    Scrimieri, Daniele; Oates, R.F.; Ratchev, S.M. (2015-12)
    We present a decision-support framework for speeding up the ramp-up of modular assembly systems by learning from past experience. Bringing an assembly system to the expected level of productivity requires engineers performing mechanical adjustments and changes to the assembly process to improve the performance. This activity is time-consuming, knowledge-intensive and highly dependent on the skills of the engineers. Learning the ramp-up process has shown to be effective for making progress faster. Our approach consists of automatically capturing information about the changes made by an operator dealing with disturbances, relating them to the modular structure of the machine and evaluating the resulting system state by analysing sensor data. The feedback thus obtained on applied adaptations is used to derive recommendations in similar contexts. Recommendations are generated with a variant of the k-nearest neighbour algorithm through searching in a multidimensional space containing previous system states. Applications of the framework include knowledge transfer among operators and machines with overlapping structure and functionality. The application of our method in a case study is discussed.
  • Fast mapping of finite element field variables between meshes with different densities and element types

    Scrimieri, Daniele; Afazov, S.M.; Becker, A.A.; Ratchev, S.M. (2014-01)
    In the simulation of a chain of manufacturing processes, several finite element packages can be employed and for each process or package a different mesh density or element type may be the most suitable. Therefore, there is a need for transferring finite element analysis (FEA) data among packages and mapping it between meshes. This paper presents efficient algorithms for mapping FEA data between meshes with different densities and element types. An in-core spatial index is created on the mesh from which FEA data is transferred. The index is represented by a dynamic grid partitioning the underlying space from which nodes and elements are drawn into equal-sized cells. Buckets containing references to the nodes indexed are associated with the cells in a many-to-one correspondence. Such an index makes nearest neighbour searches of nodes and elements much faster than sequential scans. An experimental evaluation of the mapping techniques using the index is conducted. The algorithms have been implemented in the open source finite element data exchange system FEDES.
  • Automated experience-based learning for plug and produce assembly systems

    Scrimieri, Daniele; Antzoulatos, N.; Castro, E.; Ratchev, S.M. (2017-07)
    This paper presents a self-learning technique for adapting modular automated assembly systems. The technique consists of automatically analysing sensor data and acquiring experience on the changes made on an assembly system to cope with new production requirements or to recover from disruptions. Experience is generalised into operational knowledge that is used to aid engineers in future adaptations by guiding them throughout the process. At each step, applicable changes are presented and ranked based on: (1) similarity between the current context and those in the experience base; (2) estimate of the impact on system performance. The experience model and the self-learning technique reflect the modular structure of the assembly machine and are particularly suitable for plug and produce systems, which are designed to offer high levels of self-organisation and adaptability. Adaptations can be performed and evaluated at different levels: from the smallest pluggable unit to the whole assembly system. Knowledge on individual modules can be reused when modules are plugged into other systems. An experimental evaluation has been conducted on an industrial case study and the results show that, with experience-based learning, adaptations of plug and produce systems can be performed in a shorter time.
  • Analyzing Crowd-Sourced Information and Social Media for Crisis Management

    Andrews, S.; Day, T.; Domdouzis, K.; Hirsch, L.; Lefticaru, Raluca; Orphanides, C. (Springer International Publishing, 2017-03)
    The analysis of potentially large volumes of crowd-sourced and social media data is central to meeting the requirements of the ATHENA project. Here, we discuss the various stages of the pipeline process we have developed, including acquisition of the data, analysis, aggregation, filtering, and structuring. We highlight the challenges involved when working with unstructured, noisy data from sources such as Twitter, and describe the crisis taxonomies that have been developed to support the tasks and enable concept extraction. State-of-the-art techniques such as formal concept analysis and machine learning are used to create a range of capabilities including concept drill down, sentiment analysis, credibility assessment, and assignment of priority. We ground many of these techniques using results obtained from a set of tweets which emerged from the Colorado wildfires of 2012 in order to demonstrate the applicability of our work to real crisis scenarios.

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