299 research outputs found

    Development of monitoring and control systems for biotechnological processes

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    The field of biotechnology represents an important research area that has gained increasing success in recent times. Characterized by the involvement of biological organisms in manufacturing processes, its areas of application are broad and include the pharmaceuticals, agri-food, energy, and even waste treatment. The implication of living microorganisms represents the common element in all bioprocesses. Cell cultivations is undoubtedly the key step that requires maintaining environmental conditions in precise and defined ranges, having a significant impact on the process yield and thus on the desired product quality. The apparatus in which this process occurs is the bioreactor. Unfortunately, monitoring and controlling these processes can be a challenging task because of the complexity of the cell growth phenomenon and the limited number of variables can be monitored in real-time. The thesis presented here focuses on the monitoring and control of biotechnological processes, more specifically in the production of bioethanol by fermentation of sugars using yeasts. The study conducted addresses several issues related to the monitoring and control of the bioreactor, in which the fermentation takes place. First, the topic concerning the lack of proper sensors capable of providing online measurements of key variables (biomass, substrate, product) is investigated. For this purpose, nonlinear estimation techniques are analyzed to reconstruct unmeasurable states. In particular, the geometric observer approach is applied to select the best estimation structure and then a comparison with the extended Kalman filter is reported. Both estimators proposed demonstrate good estimation capabilities as input model parameters vary. Guaranteeing the achievement of the desired ethanol composition is the main goal of bioreactor control. To this end, different control strategies, evaluated for three different scenarios, are analzyed. The results show that the MIMO system, together with an estimator for ethanol composition, ensure the compliance with product quality. After analyzing these difficulties through numeric simulations, this research work shifts to testing a specific biotechnological process such as manufacturing bioethanol from brewery’s spent grain (BSG) as renewable waste biomass. Both acid pre-treatment, which is necessary to release sugars, and fermentation are optimized. Results show that a glucose yield of 18.12 per 100 g of dried biomass is obtained when the pre-treatment step is performed under optimized conditions (0.37 M H2SO4, 10% S-L ratio). Regarding the fermentation, T=25°C, pH=4.5, and inoculum volume equal to 12.25% v/v are selected as the best condition, at which an ethanol yield of 82.67% evaluated with respect to theoretical one is obtained. As a final step, the use of Raman spectroscopy combined with chemometric techniques such as Partial Least Square (PLS) analysis is evaluated to develop an online sensor for fermentation process monitoring. The results show that the biomass type involved significantly affects the acquired spectra, making them noisy and difficult to interpret. This represents a nontrivial limitation of the applied methodology, for which more experimental data and more robust statistical techniques could be helpful

    Monitoring and control for NGL recovery plant

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    The thesis explores the production of natural gas liquids (NGL) and the challenge of monitoring and controlling the fractionation process. NGLs are the C2+ hydrocarbon fraction contained in natural gas, which includes useful feedstocks for industrial production processes. Since NGLs have greater economic value compared to natural gas, their recovery has become increasingly economically significant, leading to a need for efficient fractionation. This energy-intensive process is typically conducted in separation trains that include cryogenic distillation columns. Given the high cost of composition analyzers and the related significant delays, this work proposes the use of only indirect composition control strategies, as well as data-driven control strategies to achieve the desired product quality and optimize the plant energy consumption under typical disturbances. Feedforward neural networks (FFNs) were used for the development of soft sensors used in data-driven control schemes. Given the multitude of data made available by the process simulator, this work aims to develop a demethanizer digital twin that can approximate the column dynamics with reduced computation time. Long Short-Term Memory neural networks (LSTM), along with physical knowledge, were used to develop different neural network architectures compared to select the most suitable for the surrogate model development. Realistic measurement noises were considered to accurately reflect the measurements of real industrial plants and only easy-to-measure variables were used as input data for the developed neural model. Overall, the research presents an energy-efficient NGL recovery offering a cost-effective and efficient alternative to traditional measuring instruments. Moreover, the study illustrates a novel application of LSTM for distillation columns digital twins realization, providing a useful tool for optimization, monitoring and control by employing available plant measurements

    Inferential Composition Control of a Distillation Column Using Active Disturbance Rejection Control with Soft Sensors

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    This paper presents the integration of active disturbance rejection control (ADRC) with soft sensors for enhancing the composition control performance in a distillation column. Static and dynamic soft sensors are developed to estimate the top and bottom product compositions using multiple tray temperatures. In order to cope with the collinearity issues in tray temperature measurements, static and dynamic principal component regression is used in developing the soft sensors. The soft sensor outputs are introduced as the feedback signals to ADRC. This control scheme is termed as “inferential ADRC control”. Static control offsets are eliminated through mean updating in the soft-sensor models. The effectiveness of the proposed control scheme is demonstrated on a benchmark simulated methanol-water distillation column

    Modelling, Monitoring, Control and Optimization for Complex Industrial Processes

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    This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors

    Models, Simulations, and the Reduction of Complexity

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    Modern science is a model-building activity. But how are models contructed? How are they related to theories and data? How do they explain complex scientific phenomena, and which role do computer simulations play? To address these questions which are highly relevant to scientists as well as to philosophers of science, 8 leading natural, engineering and social scientists reflect upon their modeling work, and 8 philosophers provide a commentary

    Graduate Catalog of Studies, 2021-2022

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    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

    Play Among Books

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    How does coding change the way we think about architecture? Miro Roman and his AI Alice_ch3n81 develop a playful scenario in which they propose coding as the new literacy of information. They convey knowledge in the form of a project model that links the fields of architecture and information through two interwoven narrative strands in an “infinite flow” of real books
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