16 research outputs found

    Data-driven modelling and monitoring of industrial processes with applications in nuclear waste vitrification

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    PhD ThesisProcess models are critical for process monitoring, control, and optimisation. With the increasing amount of process data and advancements in computational hardware, data-driven models are a good alternative to mechanistic models, which often have inaccuracies or are too costly to develop. One problem with data-driven models is the difficulty in ensuring that the models perform well on new data and produce accurate predictions in complex situations, which are frequently encountered in the process industry. Within this context, part of this thesis explores developing better data-driven models through using a latent variable technique, known as slow feature analysis, as a pre-processing step to regression. Slow feature analysis extracts slow varying features that contain underlying trends in the data, which can improve model performance through providing more meaningful information to regression, reducing noise, and reducing dimensionality. Firstly, the effectiveness of combining linear slow feature analysis with a neural network is demonstrated on two industrial case studies of soft sensor development and is compared with conventional techniques, such as neural networks and integration of principal component analysis with a neural network. It is shown that integration of slow feature analysis with neural networks can significantly improve model performance. However, linear slow feature analysis can fail to extract the driving forces behind data in nonlinear situations such as batch processes. Therefore, using kernel slow feature analysis with a neural network is proposed to further enhance process model performance. A numerical example was used to demonstrate the effective extraction of driving forces in a nonlinear case where linear slow feature analysis cannot. Model generalisation performance was improved using the proposed method on both this numerical example, and an industrial penicillin process case study. Dealing with radioactive nuclear waste is an important obstacle in nuclear energy. Sellafield Ltd have a nuclear waste vitrification plant which converts high-level nuclear waste into a more stable, lower volume glass form, which is more appropriate for long term storage in sealed containers. This thesis presents three applications of data-driven modelling to this nuclear waste vitrification process. A predictive model of the pour rate of processed nuclear waste into containers, an early detection system for blockages in the dust scrubber, and a model of the long-term chemical durability of the stored glass waste. These applications use the previously developed slow feature analysis methods, as well as other data-driven techniques such as extreme learning machine and bootstrap aggregation, for enhancing the model performance.Engineering and Physical Sciences Research Council (EPSRC) and Sellafield Lt

    Pertanika Journal of Science & Technology

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    Design and Management of Manufacturing Systems

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    Although the design and management of manufacturing systems have been explored in the literature for many years now, they still remain topical problems in the current scientific research. The changing market trends, globalization, the constant pressure to reduce production costs, and technical and technological progress make it necessary to search for new manufacturing methods and ways of organizing them, and to modify manufacturing system design paradigms. This book presents current research in different areas connected with the design and management of manufacturing systems and covers such subject areas as: methods supporting the design of manufacturing systems, methods of improving maintenance processes in companies, the design and improvement of manufacturing processes, the control of production processes in modern manufacturing systems production methods and techniques used in modern manufacturing systems and environmental aspects of production and their impact on the design and management of manufacturing systems. The wide range of research findings reported in this book confirms that the design of manufacturing systems is a complex problem and that the achievement of goals set for modern manufacturing systems requires interdisciplinary knowledge and the simultaneous design of the product, process and system, as well as the knowledge of modern manufacturing and organizational methods and techniques

    The use of statistics in understanding pharmaceutical manufacturing processes

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    D.Eng.Industrial manufacturing processes for pharmaceutical products require a high level of understanding and control to demonstrate that the final product will be of the required quality to be taken by the patient. A large amount of data is typically collected throughout manufacture from sensors located around reaction vessels. This data has the potential to provide a significant amount of information about the variation inherent within the process and how it impacts on product quality. However to make use of the data, appropriate statistical methods are required to extract the information that is contained. Industrial process data presents a number of challenges, including large quantities, variable sampling rates, process noise and non-linear relationships. The aim of this thesis is to investigate, develop and apply statistical methodologies to data collected from the manufacture of active pharmaceutical ingredients (API), to increase the level of process and product understanding and to identify potential areas for improvement. Individual case studies are presented of investigations into API manufacture. The first considers prediction methods to estimate the drying times of a batch process using data collected early in the process. Good predictions were achieved by selecting a small number of variables as inputs, rather than data collected throughout the process. A further study considers the particle size distribution (PSD) of a product. Multivariate analysis techniques proved efficient at summarising the PSD data, to provide an understanding of the sources of variation and highlight the difference between two processing plants. Process capability indices (PCIs) are an informative tool to estimate the risk of a process failing a specification limit. PCIs are assessed and developed to be applied to data that does not follow a standard normal distribution. Calculating the capability from the percentiles of the data or the proportion of data outside of the specification limits has the potential to generate information about the capability of the process. Finally, the application of Bayesian statistical methods in pharmaceutical process development are investigated, including experimental design, process validation and process capability. A novel Bayesian method is developed to sequentially calculate the process capability when data is collected in blocks over time, thereby reducing the level of noise caused by small sample sizes

    Pameran Reka Cipta, Penyelidikan dan Inovasi (PRPI) 2009

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    PRPI 2009 kini telah memasuki tahun penganjurannya yang ke-7. Pameran penyelidikan di UPM telah bermula sejak tahun 1997 semasa Exhibition & Seminar Harnessing for Industry Advantage. Pada tahun 2002, Pameran Reka Cipta dan Penyelidikan (PRP) buat pertama kali telah diadakan dengan menggunakan konsep pertandingan hasil projek penyelidikan yang telah dijalankan oleh para penyelidik UPM. Kejayaan penganjuran PRP 2002 telah merintis usaha untuk menjadikannya sebagai aktiviti tahunan UPM dan ianya terus berkembang sejajar dengan nama baharunya yang ditukar kepada Pameran Reka Cipta, Penyelidikan dan Inovasi yang bermula penganjurannya pada tahun 2005. Sebagai kesinambungan daripada kejayaan penganjuran PRPI 2006, 2007 dan 2008 yang lalu dan status UPM sebagai salah sebuah Universiti Penyelidikan, PRPI 2009 kali ini yang merupakan pameran penyelidikan yang terbesar di UPM terus dilaksanakan dengan aspirasi dan semangat yang lebih jitu. Pameran ini juga menjadi pelantar kepada para penyelidik untuk mengenengahkan hasil penyelidikan yang dijalankan dan penemuan baharu kepada umum. Di samping itu ianya juga menjadi penanda aras terhadap kualiti sesuatu projek penyelidikan bagi melayakkan para penyelidik UPM untuk menyertai pameran di peringkat kebangsaan dan seterusnya antarabangsa. Adalah diharapkan pelaksanaan PRPI 2009 ini akan dapat menyemarakkan budaya penyelidikan di kalangan staf dan juga pelajar UPM sekaligus menjadikan UPM sebagai Universiti Penyelidikan yang cemerlang di negara ini

    ESSE 2017. Proceedings of the International Conference on Environmental Science and Sustainable Energy

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    Environmental science is an interdisciplinary academic field that integrates physical-, biological-, and information sciences to study and solve environmental problems. ESSE - The International Conference on Environmental Science and Sustainable Energy provides a platform for experts, professionals, and researchers to share updated information and stimulate the communication with each other. In 2017 it was held in Suzhou, China June 23-25, 2017
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