Model-predictive safety: A new evolution in functional safety

Soroush, Masoud; Masooleh, Leila Samandari; Arbogast, Jeffrey E.; Seider, Warren D.; Oktem, Ulku

doi:10.1016/b978-0-12-820027-8.00011-3

Cited by 3 publications

(2 citation statements)

References 55 publications

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“…Modern chemical plants often consist of highly integrated subsystems with a variety of strong interactions, including energy, material, and automation interactions. In recent years, the international competition for producing higher‐quality, less‐expensive products have motivated industries to pay increased attention to product quality monitoring and control 1,2 …”

Section: Introductionmentioning

confidence: 99%

Distributed state estimation from infrequent and delayed measurements in large‐scale processes

2023

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Effective control and monitoring of a process usually require frequent and delay-free measurements of important process output variables. However, these measurements are often either not available or available infrequently with significant time delays. This article presents a method that allows for improving the performance of distributed state estimators implemented on large-scale manufacturing processes. The method uses a sample state augmentation approach that permits using delayed measurements in distributed state estimation. The method can be used with any state estimator, including unscented Kalman filters, extended Kalman filters, and moving horizon state estimators. The method optimally handles the tradeoff between computational time and estimation accuracy in distributed state estimation implemented using a computer with parallel processors. Its implementation and performance are shown using a few simulated examples.

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Section: Introductionmentioning

confidence: 99%

Distributed state estimation from infrequent and delayed measurements in large‐scale processes

2023

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“…Biological networks, 1 power networks, 2 controlled chemical processes, 3‐6 and many others 7‐9 are examples of complex networks. Moreover, modern manufacturing plants are increasingly integrated, 10,11 leading to structural and computational complexities. In recent years, the problem of decomposing a large complex network into a set of interacting small networks that adequately capture the interactions of the original large network has received great attention in many engineering and science fields 12‐15 …”

Section: Introductionmentioning

confidence: 99%

An efficient algorithm for community detection in complex weighted networks

et al. 2021

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Community detection decomposes large‐scale, complex networks “optimally” into sets of smaller sub‐networks. It finds sub‐networks that have the least inter‐connections and the most intra‐connections. This article presents an efficient community detection algorithm that detects community structures in a weighted network by solving a multi‐objective optimization problem. The whale optimization algorithm is extended to enable it to handle multi‐objective optimization problems with discrete variables and to solve the problems on parallel processors. To this end, the population's positions are discretized using a transfer function that maps real variables to discrete variables, the initialization steps for the algorithm are modified to prevent generating unrealistic connections between variables, and the updating step of the algorithm is redefined to produce integer numbers. To identify the community configurations that are Pareto optimal, the non‐dominated sorting concept is adopted. The proposed algorithm is tested on the Tennessee Eastman process and several benchmark community‐detection problems.

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