IFAC-PapersOnLine最新文献

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.

Faced with the increasing obsolescence of products and complex systems, this study proposes a mapping of possible strategies to extend their lifespan for the customer. With a focus on post-obsolescence resilience, we explore approaches such as reducing degradation depth, reducing recovery time, and increasing degradation time. These solutions aim to mitigate environmental impact, promote sustainability, and address the growing challenges related to resource management. By emphasizing these perspectives, this research contributes to the evolution of customer practices towards reactive management of obsolescence to delay the End-Of-Life (EOL) of the system/product.

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.

The implementation of an in-line control strategy for primary drying relies on the availability of the product temperature and sublimation front position. Such measurements are often inaccessible or difficult to obtain directly without interfering with the drying trajectory, thus motivating the design of in-line estimators. This article specifically addresses this issue, taking advantage of a global mass loss measurement and spatial characterization of vials along the chamber. The estimator proposed uses a least square algorithm and simplified models to recalibrate the heat transfer coefficient related to different vial locations. Results allow comparing the quality of the estimations with regard to the product temperature and cycle time predictions under parametric disturbances and plant/model mismatch.

Causality is a fundamental relationship in the physical world, around which almost all activities of human life revolve. Causal inference refers to the process of determining whether an event or action caused a specific outcome, which involves the evaluation of cause-and-effect relationships in data. This paper presents a new approach to discover latent causal representations of crucial variables in easy-to-obtain data. The proposed method takes a form of trade-off between compression of input data and the causality between the learnt latent variables and critical variables, thereby removing the irrelevant information contained in input data and obtaining the decoupled, strongest causal factors. By introducing variational bounds and specific configurations, the optimisation objective is relaxed to a tractable problem. The approach compacts causal discovery and inference into one model, which is flexible to downstream tasks and parsimonious in the parameters. A case study on an exhaust-emission dataset shows that the proposed method improves the predictive performance over the baseline model, which is a variational information bottleneck model with the same hyperparameters.

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.

The predictive performance of soft sensors deteriorates over time which is called the performance change of a soft sensor. These changes occur due to differences between the current characteristics of the process or plant and the soft sensor model. The deviation is a type of plant-model mismatch (PMM). Initially, this mismatch may be acceptable. However, over time, the PMM can become so large that it affects the prediction quality of the soft sensor and may become unacceptable. This paper develops a new method to evaluate the impact of PMM on closed-loops with soft sensors. Using coprime factorisation and small-gain theory, a performance-change index is developed to characterise the PMM-induced performance degradation. Then, a performance-based online PMM detection method is proposed using this performance-change index. To validate the effectiveness of the proposed algorithm, we use a numerical example and a continuous stirred tank reactor (CSTR). It is shown that that the proposed index can detect the change of the PMM.

The dynamic flux balance model (DFBA) is a constrained-based optimization modeling approach that has gained popularity for describing microbial cultures but has not been thoroughly investigated for mammalian cell cultures due to their relative complexity. This research aims to identify a DFBA model with minimal constraints and associated parameters to predict data for the fed-batch operation of a mammalian CHO cell culture. The Bayesian Information Criterion (BIC) is used to find a minimal set of kinetic constraints. The resulting DFBA model is used to predict 24 metabolites, biomass, and titer with 85 parameters that has a lower BIC and higher R² as compared to previously reported kinetic models.

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.