Compartmental Modelling in chemical engineering: A critical review

Jourdan, Nicolas; Neveux, Thibaut; Potier, Olivier; Kanniche, Mohamed; Wicks, Jeffrey D.; Nopens, Ingmar; Rehman, Usman; Moullec, Yann Le

doi:10.1016/j.ces.2019.115196

Cited by 57 publications

(34 citation statements)

References 101 publications

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“…A vegyipari műveleteket nagyon leegyszerűsítve úgy lehetne definiálni, hogy vagy több anyagot szeretnénk egymással egyesíteni, vagy egy heterogén rendszer alkotóit szeretnénk egymástól szétválasztani. A vegyipari gépészet alapfeladata pedig ezen vegyipari technológiák végrehajtása, rendszerbe foglalása és a hozzá tartozó berendezések megtervezése [1]. A hétköznapi életből példákat hozva ide sorolhatóak szeszgyártási technológiák (a kiindulást jelentő cefre alkoholtartalma túlságosan alacsony, desztillációs művelettel a végtermékben feldúsítjuk), környezetvédelmi technológiák (a füstgázokban lévő kén kalcium-tartalmú vízzel érintkeztetve leválasztható, így nem kerül a légkörbe és nem okoz környezetkárosító kénesőt) vagy a legkülönfélébb műanyagok gyártása (melyek kiindulási anyagát a kőolajfinomítók különböző termékeiből állítanak elő) [2].…”

Section: Bevezetésunclassified

Komplex elemzési módok a vegyipari gépészet területén

Petrik

Jármai

Szepesi

2021

MDT

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A vegyipari gépészet területén belül öt nagyobb területet lehet felsorolni, melyek a művelettan, szilárdságtan, áramlástan, biztonságtechnika és rendszertechnika. A különböző tervezési és ellenőrzési feladatoknál azonban nem lehet élesen szétválasztani ezeket a részterületeket, gyakorlatilag minden esetben komplex módon kell a problémát megközelíteni. Jelen cikkünkben ilyen komplexen vizsgált rendszerekre szeretnénk néhány jellemző példát bemutatni.

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Section: Bevezetésunclassified

Komplex elemzési módok a vegyipari gépészet területén

Petrik

Jármai

Szepesi

2021

MDT

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“… Jourdan et al, 2019 ; Khakzad et al, 2012 ; Khakzad et al, 2013 ; Khan and Amyotte, 2005 ; Marhavilas et al, 2011 ; Markowski, 2007 ; Markowski et al, 2009 ; Tugnoli et al, 2012 ; Word Health Organization, 2020 .…”

Section: Uncited Referencesunclassified

How can process safety and a risk management approach guide pandemic risk management?

Alauddin

Khan

et al. 2020

Journal of Loss Prevention in the Process Industries

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The coronavirus disease (COVID-19) brought the world to a halt in March 2020. Various prediction and risk management approaches are being explored worldwide for decision making. This work adopts an advanced mechanistic model and utilizes tools for process safety to propose a framework for risk management for the current pandemic. A parameter tweaking and an artificial neural network-based parameter learning model have been developed for effective forecasting of the dynamic risk. Monte Carlo simulation was used to capture the randomness of the model parameters. A comparative analysis of the proposed methodologies has been carried out by using the susceptible, exposed, infected, quarantined, recovered, deceased (SEIQRD) model. A SEIQRD model was developed for four distinct locations: Italy, Germany, Ontario, and British Columbia. The learning-based approach resulted in better outcomes among the models tested in the present study. The layer of protection analysis is a useful framework to analyze the effect of different safety measures. This framework is used in this work to study the effect of non-pharmaceutical interventions on pandemic risk. The risk profiles suggest that a stage-wise releasing scenario is the most suitable approach with negligible resurgence. The case study provides valuable insights to practitioners in both the health sector and the process industries to implement advanced strategies for risk assessment and management. Both sectors can benefit from each other by using the mathematical models and the management tools used in each, and, more importantly, the lessons learned from crises.

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“…One assumes that the population consists of homogeneous pools like in simulations of different biological system 54 or chemical reactions 55 ;…”

Section: Assumptionsmentioning

confidence: 99%

Time-Discrete Parameter Identification Algorithms for Two Deterministic Epidemiological Models applied to the Spread of COVID-19

Wacker

Schlüter

2020

Preprint

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The pandemic outbreak of COVID-19 threatens people worldwide. Politicians around the globe have to balance different interests. Accordingly, they make decisions which heavily impact our daily life or even curtail fundamental rights. They consequently base their judgements on advices from scientists. For these reasons, many epidemiological models have appeared and have been famous since the beginning of the twentieth century. However, scientists depend on rich data to evaluate them and their assumptions. As this pandemic outbreak has lead to large data collections by the John Hopkins University since its beginning, we take advantage of this situation and compare two deterministic epidemiological models heavily used - the Susceptible-Infectious-Recovered model (SIR model) and the Susceptible-Infectious-Recovered-Dead model (SIRD model). In contrast to other works which concentrate on future predictions, we aim to investigate the validity of assumptions with respect to available data. For that purpose, instead of using these models forward in time, we propose modified time-discrete versions of the continuous models with relaxing all transfer rates to be time-varying and develop algorithms to predict these coefficients based on the principle of “divide-and-conquer” for the inverse problems. Since Asia, Europe and North America are especially affected by this pandemic, we choose countries from these continents to illustrate our results. In the main part of this article, we escepially discuss our results for Hubei (China). As supplementary material, we illustrate our findings for the aforementioned choice of countries. We finally draw some conclusions for future applications of these models and data acquisition.

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Compartmental Modelling in chemical engineering: A critical review

Cited by 57 publications

References 101 publications

Komplex elemzési módok a vegyipari gépészet területén

Komplex elemzési módok a vegyipari gépészet területén

How can process safety and a risk management approach guide pandemic risk management?

Time-Discrete Parameter Identification Algorithms for Two Deterministic Epidemiological Models applied to the Spread of COVID-19

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