IFAC-PapersOnLine最新文献

The aim of the present study is to formulate a model that describes the dynamics of university students on the basis of continuous time differential equations and Petri Nets. Students are modeled by continuous time functions that represent their ability to deal with theoretical concepts and put them into practice. In addition, the curriculum is seen as a set of activities that students can select according to their willingness. The application of the model to public data of aerospace engineering students will be the subject of future work.

In this paper, a new adaptive extended state observer based data-driven anti-disturbance control (AESO-DDADC) design is proposed for industrial nonlinear systems with unknown dynamics subject to external disturbances. By reformulating such system description into a compact-form dynamic linearization model with a residual term, a new AESO is firstly constructed to estimate the residual term using the partial derivative (PD) estimation from the previous time step, such that the residual term could be proactively counteracted by the feedback control law, in contrast to the existing data-driven ESO where the residual term in the PD estimation is absolutely neglected to facilitate the convergence analysis. Then, the bounded convergence of PD estimation and AESO is jointly analyzed by the Gerschgorin disk theorem, followed by robust convergence analysis of the established closed-loop system. Moreover, another AESO-DDADC scheme is developed using a partial-form dynamic linearization model of the system, along with rigorous robust convergence analysis. Finally, an illustrative example is shown to confirm the efficacy and advantages of the proposed designs.

Over the years, hundreds of applications have proved the effectiveness of constraint-based methods to validate the definition of metabolic networks, determine the robustness of metabolic models, and analyze the flow of metabolites through a network. However, stoichiometric models do not include information on flux capacity via enzymatic activity. Methods combining biological data from genome to metabolome have been developed to obtain improved flux predictions and constrain the range of possible flux distributions. Yet, these models still lack relevant information to design de novo metabolic pathways. Expressing the exogenous enzymes induces a cell burden due to competition for cell resources between the exogenous genes and the endogenous host ones, compromising the performance of the designed pathway. Thus, optimal selection of the expression strength of the pathway enzymes is still a challenge. Host-aware models have been developed to tackle cell burden in the context of designing increasingly complex synthetic genetic circuits in synthetic biology. This paper suggests a method to integrate host-aware gene expression models with constraint-based modeling to maximize the flux through an exogenous pathway by optimizing promoter and ribosome binding site strengths, crucial parameters that define the required transcription and translation strengths of the pathway enzymes’ genes. This study considers the formation of p-coumaric acid, shows promising results, and paves the way for further investigations.

This paper presents a hierarchical MPC-based control framework for a real microgrid including solar panels and batteries, that considers the uncertainty from the point of view of faults and risks (F&R) mitigation. While fault management is applied during plant operation, risk management considers external factors that can change microgrid planning in the medium-long term. Due to their different time-scales, a two-layer control scheme is proposed using Model Predictive Control (MPC) at both levels. At the bottom layer, the fault-tolerant predictive controller optimizes the operation by manipulating inputs to follow microgrid set-points. A reconfiguration strategy is implemented using structured residuals and stochastic thresholds. On the other hand, the upper layer develops an optimal mitigation strategy, also based on MPC, to reduce the effects of risks obtained from external information, i.e., unexpected changes in demands, maintenance costs, or deviations in generation. The decision variables of this layer are the selection of mitigation actions to be undertaken, which minimise a proposed multicriteria objective function. different simulations have been carried out to show the efficacy of this methodology in a F&R scenario from a stochastic point of view.

Developing a precise irrigation control strategy is essential for improving water use efficiency, and this requires accurate soil moisture information. However, certain challenges associated with state estimation must be addressed when dealing with large-scale fields. For instance, a vast farmland may be composed of different types of soil, making it challenging to obtain accurate parameters. Consequently, model mismatch becomes inevitable for agro-hydrological systems. In this study, we focus on addressing the issue of state estimation under such circumstance. A high dimensional nonlinear system is obtained by discretizing a 3D polar Richards equation that characterizes water movement dynamics. The proposed approach represents model mismatch as unknown inputs (UIs) relative to the state equations. To reduce computational complexity, a recursive expectation-maximization (EM) approach is modified from the existing batch EM algorithm to identify the UIs. The extended Kalman filter (EKF) is applied to calculate the posterior expectation of the states. Furthermore, an appropriate set of sensors is chosen to ensure complete observability of the system. The simulation results demonstrate the efficacy of the proposed estimation method.

Soft sensors experience an increasing interest in recent years, as they can replace expensive hardware meters and the required embedded computing hardware has become cheap and powerful. We report results for the implementation of a soft sensor for the flow rate estimation in centrifugal pumps that achieves root mean square errors of about 5%. The proposed soft sensor is based on generic models for the drive and hydraulic part of the pump to ensure widespread applicability. We show the soft sensor and the models it is based on can be parametrized with simple measurements. All theoretical considerations are corroborated with measurements on a real industrial pump in a laboratory setup. The results show that the proposed soft sensor is capable of providing reliable flow rate estimates in spite of plant model mismatch and uncertain hardware components.

Differentiating between various types of faults and classifying them based on their importance is essential for process fault detection and diagnosis. This classification helps operators to prioritize their actions based on the severity of the faults. This paper proposes a reservoir computing-based slow feature analysis (RCSFA) to model complex and nonlinear industrial processes and study its application in fault classification while integrated with a graph neural network (GNN) and majority voting ensemble causality detection. To make the algorithm robust to unseen faults, real-time operator feedback is included by utilizing operator eye tracking. The practical applicability of the proposed method and its application in fault classification is studied through an industrial application.

Hydrogen technologies are playing an increasing role in transportation and industry. Key for taking maximum advantage out of the great gravimetric energy density of hydrogen are feasible and safe storage and transfer concepts. For the successful implementation of a hydrogen-based society with several green industrial initiatives, it is indispensable to develop an infrastructure of liquid hydrogen (LH2) terminals and tanker ships with the capability to bunker LH2. Liquid hydrogen might be used to store and transport large quantities of hydrogen. LH2 is a cryogenic fluid, so double-walled vacuum insulated tanks are required to keep it cold for long periods of time until further distribution or use. Whether transportation sector or industry, to date there is little knowledge on safety related issues available. Main concern when handling hydrogen are accidental releases that can lead to integrity damage on materials and structures, fires, and explosions. Assuming that LH₂ infrastructures will be widely deployed and in use all over the globe, accidents are possible to occur during loading and unloading of LH2 bunkering facilities. Therefore, it is necessary to conduct a detailed risk assessment that focuses on system resilience to improve the capabilities of the facility to keep its functionality up when errors occur. This study refers to the LH2 storage tank installed in the LH2 terminal in Kobe, Japan. This stationary storage tank is an essential element of the terminal that was constructed by the Hydrogen Energy Supply Chain Technology Research Association (HySTRA). HySTRA aims at the distribution of hydrogen in liquified form by ship from Australia to Japan. To date, there is only little data available regarding the facility in Kobe. Nevertheless, due to the novelty of the technology the risk for accidents to occur might be higher than in conventional fuel distribution terminals. Accidents might happen due to technical failures, human errors, or external causes such as natural events. The consequences could be catastrophic. Some of these may expose structures and personnel to extreme low temperatures, fires, and explosions which may hinder bunkering operations of the facility in Kobe. This study gives an overview on possible scenarios that lead to loss of containment and provides an insight in the process of evaluating system resilience during such a scenario. This work details with a framework for assessing system resilience applied to LH2 storage facilities. The system resilience calculation involves the examination of a critical hydrogen accident database and provides suitable preventive and mitigative safety barriers.

The classic concepts of reliability, maintainability and availability (RAM) are well established, but their link with obsolescence and shortages in today’s industrial context remains poorly explored. This paper proposes an exploratory study to model the standard definitions of RAM and relate them to obsolescence and shortages, extending their scope beyond the usual physical and software components. Our initial results suggest that failure to take these phenomena into account systematically leads to an overestimate of availability. Research avenues are presented to quantify these overestimates.

Smart industry and Industry 4.0 are terms which are often used interchangeably. They characterise industry that capitalises on optimising processes through the successful integration of advanced digitalisation and manufacturing technologies, while applying sound organisation and human factors management principles. Equipping the current and future generation professionals with the necessary skills and personal qualities needed for the transition to Industry 4.0, and its extension to Industry 5.0 has been targeted by academic and professional education. Lessons learned from existing studies point to problem-based learning as an effective mechanism for the internalisation of interdisciplinary concepts, methods, and technologies. This paper outlines the formulation and experience gained from educational activities within the context of a smart industry postgraduate MSc course. The aim was to bring together methods for process and data integration, technologies such as machine learning, and management aspects, targeting domains relevant to smart industry. An educational activity was designed relevant to risk prediction within the asset management of wind farms. With scenarios of diverse criticality assumptions, marking the importance of Industry 5.0, results obtained from the educational activity show that students excelling in individual dimensions of smart industry are valuable contributors in a team setting, but a sound holistic understanding and competences across all three pillars of smart industry are needed for best learning objectives.