Browsing by Author "Makatsoris, Charalampos (Harris)"

Now showing 1 - 3 of 3
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    Advanced flow technologies for the controlled & continuous manufacture of nanoscale materials
    (Cranfield University, 2019) Isaev, Svetlin; Makatsoris, Charalampos (Harris)
    Batch processes have been successfully used in the process industry over two centuries. However, changing customer demands and discovery of novel products have led the scientists and engineers to develop new manufacturing methods for the process industry. High-value products such as nanomaterials, smart and functional materials require precise process control for the entire product. Controlling of particle size and shape becomes more difficult in the large scale batch processes. Therefore, over the past few decades, there has been an increasing interest in the flow processing techniques due to their inherent benefits, such as better heat and mass transfer and small control volumes. Continuous Oscillatory Baffled Reactor (COBR) is a novel type of flow reactor. COBR combines oscillatory motion and periodically placed baffled flow channels to generate plug flow conditions, providing better mixing control similar to microreactors. Plug flow conditions can be achieved with the combination of optimum net flow, oscillatory amplitude and frequency using COBRs. With this new reactor and mixing concept, high-value products can be manufactured more efficiently using uniform mixing conditions and better temperature control. This will decrease the reaction time and production cost of novel products, use less energy, and increase time-to-market of novel products. The aim of this research is to develop a scalable and continuous manufacturing platform using continuous oscillatory baffled reactors to produce high-value products in low cost. The focus of this study includes developing modular oscillatory baffled reactors, characterisation of modular oscillatory baffled reactors using experimental methods, developing scale-up methodology from laboratory scale to industrial production size and demonstration of nanomaterial synthesis using modular oscillatory flow reactor...[cont.]
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    A hybrid machine learning and text-mining approach for the automated generation of early warnings in construction project management.
    (2017-05) Alsubaey, Mohammed Hajer; Makatsoris, Charalampos (Harris)
    The thesis develops an early warning prediction methodology for project failure prediction by analysing unstructured project documentation. Project management documents contain certain subtle aspects that directly affect or contribute to various Key Performance Indicators (KPIs). Extracting actionable outcomes as early warnings (EWs) from management documents (e.g. minutes and project reports) to prevent or minimise discontinuities such as delays, shortages or amendments is a challenging process. These EWs, if modelled properly, may inform the project planners and managers in advance of any impending risks. At presents, there are no suitable machine learning techniques to benchmark the identification of such EWs in construction management documents. Extraction of semantically crucial information is a challenging task which is reflected substantially as teams communicate via various project management documents. Realisation of various hidden signals from these documents in without a human interpreter is a challenging task due to the highly ambiguous nature of language used and can in turn be used to provide decision support to optimise a project’s goals by pre-emptively warning teams. Following up on the research gap, this work develops a “weak signal” classification methodology from management documents via a two-tier machine learning model. The first-tier model exploits the capability of a probabilistic Naïve Bayes classifier to extract early warnings from construction management text data. In the first step, a database corpus is prepared via a qualitative analysis of expertly-fed questionnaire responses that indicate relationships between various words and their mappings to EW classes. The second-tier model uses a Hybrid Naïve Bayes classifier which evaluates real-world construction management documents to identify the probabilistic relationship of various words used against certain EW classes and compare them with the KPIs. The work also reports on a supervised K-Nearest-Neighbour (KNN) TF-IDF methodology to cluster and model various “weak signals” based on their impact on the KPIs. The Hybrid Naïve Bayes classifier was trained on a set of documents labelled based on expertly-guided and indicated keyword categories. The overall accuracy obtained via a 5-fold cross-validation test was 68.5% which improved to 71.5% for a class-reduced (6-class) KNN-analysis. The Weak Signal analysis of the same dataset generated an overall accuracy of 64%. The results were further analysed with Jack-Knife resembling and showed consistent accuracies of 65.15%, 71.42% and 64.1% respectively.
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    Novel bioprocessing technologies for the cultivation of microalgae.
    (Cranfield University, 2019-07) Alissandratos, Ioannis; Makatsoris, Charalampos (Harris)
    Microalgae are single cell photosynthetic organisms which have the potential to be game changers in industrial biotechnology. In spite of their many reported benefits, their technological advancement and industrial adoption rate has fallen behind expectation. As reported by numerous influential publications in the past decade, this can be traced to a lack of communication between engineering and science, leading to the development of technology (photobioreactors) which systematically underestimate algal growth parameters at scale; suggesting that that there is a need for considerable redesign of the photobioreactor technology. Therefore, in this work the development of a novel photobioreactor based on continuous flow technologies is introduced. Using the work carried out by the Makatsoris Group in the field of oscillatory baffled flow reactors as a foundation, the development of an enabling platform in the shape of a continuous oscillatory baffled flow photobioreactor ensued. This platform aimed to facilitate scalability, increase cost effectiveness and intensify the cultivation of microalgae; carried out via the implementation continuous plug flow mixing and novel light utilisation techniques. This resulted in a technology which in combination with a novel cost- effective nutrient mix tailored to C.Vulgaris, the model strain, achieved three key results. First accelerated the growth rate of microalgae. Second, it reduced the cost of media from 0.04 £/l to 0.0046 £/l. Third it systematically produced high biomass yields in the range of 1.65 and 2.8 g/l in 8-10 days, at a price per unit biomass of approximately 2.1£/kg; for both laboratory (<100ml) and pilot scale (>10L). The success of this work led to the creation of a spinout commercial entity called Centillion Technology Ltd, which operates the technology at ramped up volumes, at the Cranfield University pilot plant.