This article describes a dynamic, control relevant, mechanistic model of the TEALARC liquified natural gas process. The model is to be used for both steady-state and dynamic controllability analysis. The model therefore needs to be computationally light, but still include enough complexity such as to study the impact of capacity constraints on the control structure. Structured assumptions have been used to obtain simplified representations of gas/liquid flows and thermodynamic properties. The steady-state operating points of the dynamic model have been adapted to a given steady-state process design model. The paper demonstrates that the model is well suited for operability analysis. Steady-state and dynamic characteristics are illustrated.
There is increasing interest in utilizing fishers’ knowledge to better understand the marine environment, given the spatial extent and temporal resolution of fishing vessel operations. Furthermore, fishers’ knowledge is part of the best available information needed for sustainable harvesting of stocks, marine spatial planning and large-scale monitoring of fishing activity. However, there are difficulties with integrating such information into advisory processes. Data is often not systematically collected in a structured manner and there are issues around sharing of information within the industry, and between industry and research partners. Decision support systems for fishing planning and routing can integrate relevant information in a systematic way, which both incentivizes vessels to share information beneficial to their operations and capture time sensitive big datasets for marine research. The project Fishguider has been developing such a web-based decision support tool since 2019, together with partners in the Norwegian fishing fleet. The objectives of the project are twofold: 1) To provide a tool which provides relevant model and observation data to skippers, thus supporting sustainable fishing activity. 2) To foster bidirectional information flow between research and fishing activity by transfer of salient knowledge (both experiential and data-driven), thereby supporting knowledge creation for research and advisory processes. Here we provide a conceptual framework of the tool, along with current status and developments, while outlining specific challenges faced. We also present experiential input from fishers’ regarding what they consider important sources of information when actively fishing, and how this has guided the development of the tool. We also explore potential benefits of utilizing such experiential knowledge generally. Moreover, we detail how such collaborations between industry and research may rapidly produce extensive, structured datasets for research and input into management of stocks. Ultimately, we suggest that such decision support services will motivate fishing vessels to collect and share data, while the available data will foster increased research, improving the decision support tool itself and consequently knowledge of the oceans, its fish stocks and fishing activities.
This article shows how controlled variables (CVs) of the regulatory control layer in a liquefied natural gas (LNG) plant can be chosen as linear combinations of measurements using self-optimizing control principles. By self-optimizing control, the CVs are chosen such that the set points of the CVs remain close to steadystate optimal despite disturbances, thus reducing the need for online reoptimization. Several methods for calculation of linear combinations within this framework are compared. Self-optimizing control design can also be used in the process design phase to place measurements by reducing a maximum candidate set of measurements to a best possible subset of measurements giving an acceptable loss. This article proposes a relatively simple method for successive selection (SS) of measurements and compares this approach to a more comprehensive branch-and-bound (BB) method for selection of measurements. The results indicate that, although the BB method gives lower average losses for very small subsets, the methods are comparable with respect to average losses for medium and large subsets, and the SS method outperforms the BB method in terms of computational load.
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