4,888 research outputs found

    Deep learning analysis of plasma emissions: A potential system for monitoring methane and hydrogen in the pyrolysis processes

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    The estimation of methane and hydrogen production as output from a pyrolysis reaction is paramount to monitor the process and optimize its parameters. In this study, we propose a novel experimental approach for monitoring methane pyrolysis reactions aimed at hydrogen production by quantifying methane and hydrogen output from the system. While we appreciate the complexity of molecular outputs from methane hydrolysis process, our primary approach is a simplified model considering detection of hydrogen and methane only which involves three steps: continuous gas sampling, feeding of the sample into an argon plasma, and employing deep learning model to estimate of the methane and hydrogen concentration from the plasma spectral emission. While our model exhibits promising performance, there is still significant room for improvement in accuracy, especially regarding hydrogen quantification in the presence of methane and other hydrogen bearing molecules. These findings present exciting prospects, and we will discuss future steps necessary to advance this concept, which is currently in its early stages of development

    ON A DEFUZZIFICATION PROCESS OF FUZZY CONTROLLERS

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    In this paper, innovations in the field of automatic control systems with fuzzy controllers have been considered. After a short introduction on fuzzy controllers, four different ways of a defuzzification process were introduced, and verified on the simulation of nuclear reactor fuzzy controller. The default Matlab fuzzy toolbox solution is timely most demanding, while two solutions based on the defuzzification on trapezoidal fuzzy numbers have the advantage in the process of crisp numbers calculation. Also, a solution based on the determination of the line dividing the obtained polygon into two parts of equal areas is presented.

    Deep neural network for prediction and control of permeability decline in single pass tangential flow ultrafiltration in continuous processing of monoclonal antibodies

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    Single-pass tangential flow filtration (SPTFF) is a crucial technology enabling the continuous manufacturing of monoclonal antibodies (mAbs). By significantly increasing the membrane area utilized in the process, SPTFF allows the mAb process stream to be concentrated up to the desired final target in a single pass across the membrane surface without the need for recirculation. However, a key challenge in SPTFF is compensating for flux decline across the membrane due to concentration polarization and surface fouling phenomena. In continuous downstream processing, flux decline immediately impacts the continuous process flowrates. It reduces the concentration factor achievable in a single pass, thereby reducing the final concentration attained at the outlet of the SPTFF module. In this work, we develop a deep neural network model to predict the NWP in real-time without the need to conduct actual NWP measurements. The developed model incorporates process parameters such as pressure and feed concentrations through inline sensors and a spectroscopy-coupled data model (NIR-PLS model). The model determines the optimal timing for membrane cleaning steps when the normalized water permeability (NWP) falls below 60%. Using SCADA and PLC, a distributed control system was developed to integrate the monitoring sensors and control elements, such as the NIRS sensor for concentration monitoring, the DNN model for NWP prediction, weighing balances, pressure sensors, pumps, and valves. The model was tested in real-time, and the NWP was predicted within <5% error in three independent test cases, successfully enabling control of the SPTFF step in line with the Quality by Design paradigm

    Development of improvements in UAS for difficult access environments

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    The objective of this document is to study and verify the development and improvements in Unmanned Aircraft Systems (UAS) for difficult access environments since this matter is a critical area of research and innovation. As the use of UAS in various applications continues to expand, the need for these systems to operate in challenging environments such as mountainous terrain, dense forests, or urban areas with high-rise structures is increasing. The main motivation to start developing this project was the challenge exposed in the Xprize Rainforest Competition. The $10M XPRIZE Rainforest is a five-year competition to enhance the understanding of the rainforest ecosystem. I am part of the semifinalist team, Providence Plus, a multidisciplinary team composed by scientists from UPC, CSIC, MIT, and TUDelf. The purpose of this challenge is to obtain the maximum amount of information on biodiversity in the rainforest, using drone technology in this type of environment, with all the difficulties inherent in this environment that must be overcome and that are also the subject of analysis in this work, to propose and compare the different solutions and technologies to achieve the objectives of said challenge. As resources for competing in Xprize Challenge are limited and the final solution shall be scalable, the technologies evaluated must be cost efficient and practical. The first difficulty in this kind of environments is the signal strength and signal quality, not only for the drone commands but for the video and telemetry data. In this work, different solutions will be compared since analogic to digital technology. The second difficulty is autonomy, in terms of energetic supply. Taking into account the Rainforest environment and environmental policies, the most suitable technology available is batteries. There are several types of batteries that are suitable for drones, depending on the size, weight, and specifications of the drone. There will be a comparison between the most popular ones. Apart from that, an analysis of different propulsion configurations (ideal motors and propellers) will be carried out in order to achieve an optimal flight time without compromising the structural integrity of the drone. The third difficulty is reducing noise levels, in order to avoid disturbing the wildlife and with the goal in mind of having the best images possible, a study of different propellers will be carried out. Finally, durability and weather resistance: Rainforests are characterized by high humidity, heavy rainfall, and extreme heat. Drones used in this environment must be built to withstand these conditions and be weather-resistant. This may involve using materials that can withstand moisture, designing waterproof housing for sensitive components, and installing heat dissipation systems to prevent overheating.Objectius de Desenvolupament Sostenible::15 - Vida d'Ecosistemes TerrestresObjectius de Desenvolupament Sostenible::13 - Acció per al Clim

    Next-generation cell line selection methodology leveraging data lakes, natural language generation and advanced data analytics

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    Cell line development is an essential stage in biopharmaceutical development that often lies on the critical path. Failure to fully characterise the lead clone during initial screening can lead to lengthy project delays during scale-up, which can potentially compromise commercial manufacturing success. In this study, we propose a novel cell line development methodology, referenced as CLD4, which involves four steps enabling autonomous data-driven selection of the lead clone. The first step involves the digitalisation of the process and storage of all available information within a structured data lake. The second step calculates a new metric referenced as the cell line manufacturability index (MICL) quantifying the performance of each clone by considering the selection criteria relevant to productivity, growth and product quality. The third step implements machine learning (ML) to identify any potential risks associated with process operation and relevant critical quality attributes (CQAs). The final step of CLD4 takes into account the available metadata and summaries all relevant statistics generated in steps 1–3 in an automated report utilising a natural language generation (NLG) algorithm. The CLD4 methodology was implemented to select the lead clone of a recombinant Chinese hamster ovary (CHO) cell line producing high levels of an antibody-peptide fusion with a known product quality issue related to end-point trisulfide bond (TSB) concentration. CLD4 identified sub-optimal process conditions leading to increased levels of trisulfide bond that would not be identified through conventional cell line development methodologies. CLD4 embodies the core principles of Industry 4.0 and demonstrates the benefits of increased digitalisation, data lake integration, predictive analytics and autonomous report generation to enable more informed decision making

    Modern meat: the next generation of meat from cells

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    Modern Meat is the first textbook on cultivated meat, with contributions from over 100 experts within the cultivated meat community. The Sections of Modern Meat comprise 5 broad categories of cultivated meat: Context, Impact, Science, Society, and World. The 19 chapters of Modern Meat, spread across these 5 sections, provide detailed entries on cultivated meat. They extensively tour a range of topics including the impact of cultivated meat on humans and animals, the bioprocess of cultivated meat production, how cultivated meat may become a food option in Space and on Mars, and how cultivated meat may impact the economy, culture, and tradition of Asia

    INTELLIGENT MODELLING OF GRADIENT FLEXIBLE PLATE STRUCTURE UTILISING HYBRID EVOLUTIONARY ALGORITHM

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    The gradient flexible plate structure has been widely used in engineering industries. However, the gradient flexible plate is susceptible to vibrational disturbances and affecting its durability and performance over time. Hence, the unwanted vibration needs to be controlled and can be accomplished by developing an accurate model. Despite that, the accurate model is hard to be obtained especially in estimating the model parameters. Thus, the research presents the development of dynamic modelling for gradient flexible plate structure (GFPS). A slanted GFPS with orientation angle of 30° and all edges clamped was developed and fabricated to represent the actual dynamics of the system. Then, data acquisition and instrumentation system were integrated to the rig to collect the input-output vibration data. The research utilised parametric system identification based on autoregressive with exogenous input (ARX) model structure. First, evolutionary algorithms, namely particle swarm optimisation (PSO) and grey wolf optimisation (GWO) were used in developing GFPS dynamic model and their performances were compared. It was discovered that GWO model outperformed PSO model. However, the computational time of GWO is slower compared to PSO. Thus, a hybrid of grey wolf and particle swarm optimisation (GWO-PSO) were proposed to further improve the system modelling. It was found out that the hybrid GWO-PSO model outperformed PSO and GWO models by achieving the lowest mean squared error, correlation up to 95 % confidence level, and good stability. The obtained GWO-PSO models which is model order 2 and model order 4 were verified by using proportional-integral-derivative (PID) based controller. Their performances were measured in terms of model robustness based on vibration suppression. The final result confirmed that model order 2 of GWO-PSO is the optimum model to represent GFPS system modelling with 71.08% vibration attenuation

    Inmovilización de enzimas en MOFs: diseño y aplicaciones

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    Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Facultad de Ciencias, Departamento de Química Física Aplicada. Fecha de Lectura: 24-02-202
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