Global sensitivity analysis of a feedback-controlled stochastic process model

Savolainen, Jouni

doi:10.1016/j.simpat.2013.04.003

Cited by 3 publications

(4 citation statements)

References 14 publications

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“…Usually, the nature of process models is either deterministic—for example, first‐principles models—or stochastic—for example, black box models or metamodels—depending on the KPIs. However, to account for operational uncertainty, this work introduces the use of a so‐called “hybrid stochastic–deterministic model” that can be formulated as

K P I = {false\sum}_{k = 1}^{K} true(1 + q_{k} true) {\cdot K P I}_{k}

where

K

represents the total number of tasks,

q_{k}

is the counter representing the number of times task

k

is repeated because of failures, and

{K P I}_{k}

is the KPI specific to task

k

. Depending on the level of automation of the task,

{K P I}_{k}

can be either distributed or constant.…”

Section: Methodsmentioning

confidence: 99%

Uncertainty‐conscious methodology for process performance assessment in biopharmaceutical drug product manufacturing

et al. 2017

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This work presents an uncertainty‐conscious methodology for the assessment of process performance—for example, run time—in the manufacturing of biopharmaceutical drug products. The methodology is presented as an activity model using the type 0 integrated definition (IDEF0) functional modeling method, which systematically interconnects information, tools, and activities. In executing the methodology, a hybrid stochastic–deterministic model that can reflect operational uncertainty in the assessment result is developed using Monte Carlo simulation. This model is used in a stochastic global sensitivity analysis to identify tasks that had large impacts on process performance under the existing operational uncertainty. Other factors are considered, such as the feasibility of process modification based on Good Manufacturing Practice, and tasks to be improved is identified as the overall output. In a case study on cleaning and sterilization processes, suggestions were produced that could reduce the mean total run time of the processes by up to 40%. © 2017 American Institute of Chemical Engineers AIChE J, 64: 1272–1284, 2018

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K P I = {false\sum}_{k = 1}^{K} true(1 + q_{k} true) {\cdot K P I}_{k}

where

K

represents the total number of tasks,

q_{k}

is the counter representing the number of times task

k

is repeated because of failures, and

{K P I}_{k}

is the KPI specific to task

k

. Depending on the level of automation of the task,

{K P I}_{k}

can be either distributed or constant.…”

Section: Methodsmentioning

confidence: 99%

Uncertainty‐conscious methodology for process performance assessment in biopharmaceutical drug product manufacturing

et al. 2017

View full text Add to dashboard Cite

show abstract

“…Sensitivity analysis is the prescriptive or quantitative analysis of the effect of model inputs (including model parameters) on model outputs [ 2 ]. In general, one might be interested in which parameters have the greatest impact on the output, and which parameters have negligible impact [ 3 ]. Model parameter sensitivity analysis can diagnose the model structure and identify the key parameters of the model, which is a key step in model establishment and application [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

“…Song et al [ 10 ] used variance-based sensitivity method and GRSA method to conduct global sensitivity analysis on headless rivet model and Ten-bar structure model. Savolainen [ 3 ] used the Sobol method based on variance to conduct a global sensitivity analysis of feedback control stochastic process models, and discussed how to use global sensitivity analysis in dynamic and stochastic process modeling cases. Scholars such as Zhou [ 11 ] introduced the sparse grid integration method into the calculation of the sensitivity index based on variance and applied it to the automobile front axle model.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Sensitivity analysis of disease-information coupling propagation dynamics model parameters

Yang

Liu

2022

PLoS ONE

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The disease-information coupling propagation dynamics model is a widely used model for studying the spread of infectious diseases in society, but the parameter settings and sensitivity are often overlooked, which leads to enlarged errors in the results. Exploring the influencing factors of the disease-information coupling propagation dynamics model and identifying the key parameters of the model will help us better understand its coupling mechanism and make accurate recommendations for controlling the spread of disease. In this paper, Sobol global sensitivity analysis algorithm is adopted to conduct global sensitivity analysis on 6 input parameters (different cross regional jump probabilities, information dissemination rate, information recovery rate, epidemic transmission rate, epidemic recovery rate, and the probability of taking preventive actions) of the disease-information coupling model with the same interaction radius and heterogeneous interaction radius. The results show that: (1) In the coupling model with the same interaction radius, the parameters that have the most obvious influence on the peak density of nodes in state AI and the information dissemination scale of the information are the information dissemination rate βI and the information recovery rate μI. In the coupling model of heterogeneous interaction radius, the parameters that have the most obvious impact on the peak density of nodes in the AI state of the information layer are: information spread rate βI, disease recovery rate μE, and the parameter that has a significant impact on the scale of information spread is the information spread rate βI and information recovery rate μI. (2) Under the same interaction radius and heterogeneous interaction radius, the parameters that have the most obvious influence on peak density of nodes in state SE and the disease transmission scale of the disease layer are the disease transmission rate βE, the disease recovery rate μE, and the probability of an individual moving across regions pjump.

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Greenhouse gas emissions during timber and concrete building construction —A scenario based comparative case study

Sandanayake

Lokuge

Zhang

et al. 2018

Sustainable Cities and Society

102

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Global sensitivity analysis of a feedback-controlled stochastic process model

Cited by 3 publications

References 14 publications

Uncertainty‐conscious methodology for process performance assessment in biopharmaceutical drug product manufacturing

Uncertainty‐conscious methodology for process performance assessment in biopharmaceutical drug product manufacturing

Sensitivity analysis of disease-information coupling propagation dynamics model parameters

Greenhouse gas emissions during timber and concrete building construction —A scenario based comparative case study

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