Analysis of Operational Heat Exchanger Network Robustness via Interval Arithmetic

Floquet, Pascal; Hétreux, Gilles; Hétreux, Raphaële Théry; Payet, Lucille

doi:10.1016/b978-0-444-63428-3.50238-1

Cited by 6 publications

(8 citation statements)

References 7 publications

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“…Exchangers 2 and 4 are detached from the cluster from a 3D perspective. This observation is in line with the conclusions of the initial study (Floquet et al, 2016).…”

Section: Principal Component Analysis For Interval-valued Datasupporting

confidence: 92%

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Interval-Valued Data Learning for Robustness of Energy Recovery Systems

Ouaret

Floquet

Belaud

et al. 2021

31st European Symposium on Computer Aided Process Engineering

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Modeling of energy recovery systems, Heat Exchanger Networks (HEN) as an example, are complex processes due to many parameters involved and the lack of knowledge on the impact of operational variables on the response. These variables are often affected by different types of uncertainty as to measurement errors, computation errors, or imprecision related to the underlying method. The uncertainty in the data may be treated by considering, rather than a single value for each variable, the interval of values in which it may fall, histograms, or other multivalued data: symbolic data. This work aims to use, when possible, the symbolic data analysis to adapt the classical mathematical HEN models. It deals with the study of continuous interval data through suitable Principal Component Analyses and Regression for two purposes: clustering exchanges (i) classification of exchangers to detect those impacted by uncertainty factors and (ii) evaluation of the relationship between the different process parameters (inlet temperature, heat transfer coefficient, etc.) on interval data. The new method has been tested on a real data set and the numerical results are reported. The symbolic approach provides a simple way to study a great number of scenarios.

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“…Exchangers 2 and 4 are detached from the cluster from a 3D perspective. This observation is in line with the conclusions of the initial study (Floquet et al, 2016).…”

Section: Principal Component Analysis For Interval-valued Datasupporting

confidence: 92%

“…Such data need to be visualized, synthesized, and compared on factor spaces. This paper is in a complementary perspective to Floquet et al's work (Floquet et al, 2016). They have initiated the robustness analysis of a simple exchange networks using interval arithmetic.…”

Section: Introductionmentioning

confidence: 94%

Interval-Valued Data Learning for Robustness of Energy Recovery Systems

Ouaret

Floquet

Belaud

et al. 2021

31st European Symposium on Computer Aided Process Engineering

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“…The temperature of an inlet stream of a HEN is subject to variations following the normal distribution that was characterised as part of the previous steady-state regime. The simulation of the model response of a HEN is detailed in Floquet et al (2016). Eq.…”

Section: Hen Behaviour Simulationmentioning

confidence: 99%

Flexibility Assessment of Heat Exchanger Networks: From a Thorough Data Extraction to Robustness Evaluation

Payet

Hétreux

et al. 2018

Chemical Engineering Research and Design

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Due to process variabilities and operational modifications, operating parameters of Heat Exchanger Network (HEN) may alter its output temperatures. Nevertheless, the impact of these disturbances depends largely on the topology of the HEN. As a consequence, it can be relevant to evaluate the flexibility of a HEN after its synthesis. Flexibility of a HEN refers to the ability of a system to operate at a finite number of set points. In this framework, the implementation of this property is broken down into several aspects. In this contribution, the first level of flexibility concerning the robustness (ability of the system to absorb disturbances without changing utility flowrates) is addressed and compared to other contribution, this criterion is not formulated as a generic one but as a criterion that strongly depends on the studied process. As a consequence, to evaluate its value, the first step is to perform an enhanced data collection by identifying the most frequent disturbances and by pointing out the critical streams i.e. the streams whose output temperature absolutely needs to be kept into a strict interval; then, given this information, a robustness criterion can be formulated for a given HEN. In this paper, a methodology relying on several models is developed to address this issue: a Mass Equilibrium Summation enthalpy non-linear model (MESH) dedicated to the enhanced data collection, a Mixed Integer Linear Programming (MILP) model used for the HEN synthesis and finally a linear model developed for the modeling of the HEN response to disturbances. This methodology is first illustrated through a basic academic example and finally applied to an industrial case study.

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“…By post-evaluating several HEN alternatives and estimating their robustness towards on-site disturbances, a linear formulation was established. Using linear programming, Floquet et al (2016) introduced a methodology based on interval arithmetic (IA) concepts for modelling impacts of process parameters fluctuations on the behaviour of the HEN. Assuming that some parameters p can vary on an interval [pmin, pmax], a linear system can be solved where the left term involves interval matrices (fluctuations of the HEN characteristics, except topology) and right hand side involves fluctuations of input temperature of the HEN.…”

Section: Robustness In Henmentioning

confidence: 99%

“…A perturbation is then characterized by its mean value and a standard deviation. The HEN model used is the same as presented in Floquet et al (2016). In the latter article, it is established that the basic model to evaluate output temperatures from input temperatures for an isolated counter-current heat exchanger is a linear expression displayed in Eq.…”

Section: Step 2a: Hen Simulation In Presence Of Gaussian Disturbancesmentioning

confidence: 99%

Definition of a Robustness Indicator for Assessment of Heat Exchanger Network Performances

Payet¹,

Hétreux²,

Floquet³

2017

Computer Aided Chemical Engineering

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Open Archive Toulouse Archive Ouverte-Robustness: ability to absorb disturbances ("noises" with low amplitude) without changing the flow rates of utilities.-Resilience: ability to absorb large disturbances by acting upon utility flow rates or network topology.-Adaptability: ability to operate over several operating points (production campaigns, seasons influence on process temperatures…)-Management of intermittency in processes: ability to manage temporal mismatch between hot and cold sources mainly in batch processes through storage means. The first step towards HEN flexibility, concerning a robustness criterion, is developed in this paper.

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Analysis of Operational Heat Exchanger Network Robustness via Interval Arithmetic

Cited by 6 publications

References 7 publications

Interval-Valued Data Learning for Robustness of Energy Recovery Systems

Interval-Valued Data Learning for Robustness of Energy Recovery Systems

Flexibility Assessment of Heat Exchanger Networks: From a Thorough Data Extraction to Robustness Evaluation

Definition of a Robustness Indicator for Assessment of Heat Exchanger Network Performances

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