Plantwide simulation and monitoring of offshore oil and gas production facility

Monitoring is one of the major concerns in offshore oil and gas production platform since the access to the offshore facilities is difficult. Also, it is quite challenging to extract oil and gas safely in such a harsh environment, and any abnormalities may lead to a catastrophic event. The process data, including all possible faulty scenarios, is required to build an appropriate monitoring system. Since the plant wide process data is not available in the literature, a dynamic model and simulation of an offshore oil and gas production platform is developed by using Aspen HYSYS. Modeling and simulations are handy tools for designing and predicting the accurate behavior of a production plant. The model was built based on the gas processing plant at the North Sea platform reported in Voldsund et al. (2013). Several common faults from different fault categories were simulated in the dynamic system, and their impacts on the overall hydrocarbon production were analyzed. The simulated data are then used to build a monitoring system for each of the faulty states. A new monitoring method has been proposed by combining Principal Component Analysis (PCA) and Dynamic PCA (DPCA) with Artificial Neural Network (ANN). The application of ANN to process systems is quite difficult as it involves a very large number of input neurons to model the system. Training of such large scale network is time-consuming and provides poor accuracy with a high error rate. In PCA-ANN and DPCA-ANN monitoring system, PCA and DPCA are used to reduce the dimension of the training data set and extract the main features of measured variables. Subsequently ANN uses this lower-dimensional score vectors to build a training model and classify the abnormalities. It is found that the proposed approach reduces the time to train ANN and successfully diagnose, detects and classifies the faults with a high accuracy rate

Manutenção baseada no estado de condição. Uma abordagem utilizando cartas de controlo modificadas

Dissertação para obtenção do Grau de Doutor em Engenharia IndustrialNa Marinha, como na indústria, a manutenção condicionada é hoje em dia, a política de manutenção tendencialmente mais usada. Vários fatores contribuem para esse efeito, nomeadamente a necessidade imperiosa de evitar gastar recursos inutilmente, aplicando-os apenas e quando a manutenção é necessária para garantir as altas taxas de disponibilidade e segurança exigidas. Trata-se, pois, de uma forma de manutenção baseada essencialmente, em informação e, mais especificamente, no conhecimento preciso do estado dos equipamentos em cada instante. Isto significa a recolha contínua de dados acerca dos equipamentos e o seu tratamento em tempo real (online) por técnicas estatísticas adequadas aos processos de decisão nesta área. Só o atual estado de desenvolvimento tecnológico permite concretizar essas políticas com sucesso, o que explica que, sendo conhecidas há décadas, só agora se assista ao seu uso generalizado. Especificamente, a monitorização online, em detrimento da offline, pode contribuir significativamente para uma melhoria do conhecimento do estado ou condição dos equipamentos e contribuir para a definição do timing correto das intervenções de acordo com os fatores externos associados e exigidos pelos gestores de projeto das organizações. São assim minimizadas as intervenções associadas a falsos alarmes ou determinadas por planos desfasados da realidade. Podem, também, ser evitadas eventuais avarias catastróficas que conduzam a uma perda total do equipamento. A tese fundamental deste trabalho é a de que as Cartas de Controlo usadas para Controlo Estatístico de Processos (SPC) mas aplicadas agora a dados de funcionamento de equipamentos, podem ser um instrumento fundamental no diagnóstico e predição de avarias mecânicas num contexto de manutenção condicionada. Nesta investigação não só foi desenvolvida uma metodologia que incluiu a adaptação e desenvolvimento de algumas cartas de controlo neste domínio, como a respetiva validação experimental usando principalmente – mas não só – dados de vibração de equipamentos mecânicos recolhidos em condições experimentais (eletrobomba montada numa oficina da Escola Naval) como dados observacionais reais recolhidos de máquinas propulsoras Navais (turbinas a gás e motores diesel). Pretendeu-se criar um modelo que admita adaptações de acordo com as exigências de normativos em vigor, as definições do fabricante e os requisitos do utilizador, podendo assim haver ajustamento nas regras que impõem uma intervenção. Neste sentido, para amostras grandes, consideram-se duas fases na aplicação das cartas de controlo. Na primeira fase é testada a independência e normalidade das variáveis. Caso a independência, eventualmente, não se verifique, os dados podem ser modelados usando um modelo da família ARIMA (p, d, q), sendo calculados os resíduos do modelo ajustado, definindo assim os parâmetros de funcionamento dos equipamentos. Na segunda fase, calculam-se os erros de previsão do modelo ajustado de forma a permitir a monitorização online através de cartas especiais modificadas univariadas, e cartas tradicionais e especiais multivariadas. Para amostras pequenas - caso dos equipamentos propulsores - aplicam-se as cartas de controlo Short Run com uma só fase e em que a monitorização é efetuada a partir da quarta observação. O estudo experimental evidenciou resultados encorajadores - nomeadamente aplicabilidade, flexibilidade e adaptabilidade - que levam a admitir que a aplicação futura da metodologia proposta pode contribuir para uma manutenção mais eficaz e eficiente

Applications and Experiences of Quality Control

The rich palette of topics set out in this book provides a sufficiently broad overview of the developments in the field of quality control. By providing detailed information on various aspects of quality control, this book can serve as a basis for starting interdisciplinary cooperation, which has increasingly become an integral part of scientific and applied research

PhD students´day FMST 2023

The authors gave oral presentations of their work online as part of a Doctoral Students’ Day held on 15 June 2023, and they reflect the challenging work done by the students and their supervisors in the fields of metallurgy, materials engineering and management. There are 82 contributions in total, covering a range of areas – metallurgical technology, thermal engineering and fuels in industry, chemical metallurgy, nanotechnology, materials science and engineering, and industrial systems management. This represents a cross-section of the diverse topics investigated by doctoral students at the faculty, and it will provide a guide for Master’s graduates in these or similar disciplines who are interested in pursuing their scientific careers further, whether they are from the faculty here in Ostrava or engineering faculties elsewhere in the Czech Republic. The quality of the contributions varies: some are of average quality, but many reach a standard comparable with research articles published in established journals focusing on disciplines of materials technology. The diversity of topics, and in some cases the excellence of the contributions, with logical structure and clearly formulated conclusions, reflect the high standard of the doctoral programme at the faculty.Ostrav

Semi-automatic liquid filling system using NodeMCU as an integrated Iot Learning tool

Computer programming and IoT are the key skills required in Industrial Revolution 4.0 (IR4.0). The industry demand is very high and therefore related students in this field should grasp adequate knowledge and skill in college or university prior to employment. However, learning technology related subject without applying it to an actual hardware can pose difficulty to relate the theoretical knowledge to problems in real application. It is proven that learning through hands-on activities is more effective and promotes deeper understanding of the subject matter (He et al. in Integrating Internet of Things (IoT) into STEM undergraduate education: Case study of a modern technology infused courseware for embedded system course. Erie, PA, USA, pp 1–9 (2016)). Thus, to fulfill the learning requirement, an integrated learning tool that combines learning of computer programming and IoT control for an industrial liquid filling system model is developed and tested. The integrated learning tool uses NodeMCU, Blynk app and smartphone to enable the IoT application. The system set-up is pre-designed for semi-automation liquid filling process to enhance hands-on learning experience but can be easily programmed for full automation. Overall, it is a user and cost friendly learning tool that can be developed by academic staff to aid learning of IoT and computer programming in related education levels and field

Quantitative Risk Analysis using Real-time Data and Change-point Analysis for Data-informed Risk Prediction

Incidents in highly hazardous process industries (HHPI) are a major concern for various stakeholders due to the impact on human lives, environment, and potentially huge financial losses. Because process activities, location and products are unique, risk analysis techniques applied in the HHPI has evolved over the years. Unfortunately, some limitations of the various quantitative risk analysis (QRA) method currently employed means alternative or more improved methods are required. This research has obtained one such method called Big Data QRA Method. This method relies entirely on big data techniques and real-time process data to identify the point at which process risk is imminent and provide the extent of contribution of other components interacting up to the time index of the risk. Unlike the existing QRA methods which are static and based on unvalidated assumptions and data from single case studies, the big data method is dynamic and can be applied to most process systems. This alternative method is my original contribution to science and the practice of risk analysis The detailed procedure which has been provided in Chapter 9 of this thesis applies multiple change-point analysis and other big data techniques like, (a) time series analysis, (b) data exploration and compression techniques, (c) decision tree modelling, (d) linear regression modelling. Since the distributional properties of process data can change over time, the big data approach was found to be more appropriate. Considering the unique conditions, activities and the process systems use within the HHPI, the dust fire and explosion incidents at the Imperial Sugar Factory and the New England Wood Pellet LLC both of which occurred in the USA were found to be suitable case histories to use as a guide for evaluation of data in this research. Data analysis was performed using open source software packages in R Studio. Based on the investigation, multiple-change-point analysis packages strucchange and changepoint were found to be successful at detecting early signs of deteriorating conditions of component in process equipment and the main process risk. One such process component is a bearing which was suspected as the source of ignition which led to the dust fire and explosion at the Imperial Sugar Factory. As a result, this this research applies the big data QRA method procedure to bearing vibration data to predict early deterioration of bearings and final period when the bearing’s performance begins the final phase of deterioration to failure. Model-based identification of these periods provides an indication of whether the conditions of a mechanical part in process equipment at a particular moment represent an unacceptable risk. The procedure starts with selection of process operation data based on the findings of an incident investigation report on the case history of a known process incident. As the defining components of risk, both the frequency and consequences associated with the risk were obtained from the incident investigation reports. Acceptance criteria for the risk can be applied to the periods between the risks detected by the two change-point packages. The method was validated with two case study datasets to demonstrate its applicability as procedure for QRA. The procedure was then tested with two other case study datasets as examples of its application as a QRA method. The insight obtained from the validation and the applied examples led to the conclusion that big data techniques can be applied to real-time process data for risk assessment in the HHPI

University Catalog 1994-1996

This catalog is published for students and other persons who want to know more about the University of Northern Iowa. Its purpose is to communicate as objectively and completely as possible what the university is and what it does. The catalog is presented in sections to give a general view of the university as well as the detailed information required for informed decision making.https://scholarworks.uni.edu/uni_catalogs/1014/thumbnail.jp

University Catalog 1994-1996

This catalog is published for students and other persons who want to know more about the University of Northern Iowa. Its purpose is to communicate as objectively and completely as possible what the university is and what it does. The catalog is presented in sections to give a general view of the university as well as the detailed information required for informed decision making.https://scholarworks.uni.edu/uni_catalogs/1014/thumbnail.jp