A model-based framework assisting the design of vapor-liquid equilibrium experimental plans

Duarte, Belmiro P.M.; Atkinson, Anthony C.; Granjo, José F.O.; Oliveira, Nuno M.C.

doi:10.1016/j.compchemeng.2020.107168

Cited by 6 publications

(3 citation statements)

References 62 publications

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“…In this case, it was necessary to use a full, symplectic–centroid plan, which imposes limits on the values of the input parameters [ 56 , 57 , 58 ]. Plans of this type are used in numerous scientific papers, like [ 59 , 60 , 61 ], to determine the effect of each factor on the value of the response function.…”

Section: Experimental Planmentioning

confidence: 99%

Multi-Faceted Analysis of Phase-Change Composite Intended for Autonomous Buildings

Musiał,

Lichołai

2024

Materials

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This paper presents the long-term, holistic results of research into an innovative heat accumulator based on an organic phase-change material in the form of a mixture of aliphatic alkanes, molecular silica sieves, carbon recyclate and epoxy and cement matrices. The research included chemical testing of vacuum soaking of molecular silica sieves with a liquid phase-change material. The results proved an improvement in the heat storage efficiency of the heat accumulators due to the addition of carbon recyclate by 28%, while increasing the heat storage time by 134 min, and a reduction in PCM leakage due to the use of molecular silica sieves. In addition to its cognitive scientific value, another research objective of the work achieved was to obtain response functions in the form of approximating polynomials. They provide a useful, validated and verified tool to predict the physical and chemical characteristics of heat accumulators with different contents of individual components. As part of the ongoing research, technical problems related to leak-proofing assurance and matrix selection for organic phase-change materials were also solved. The solution presented is in line with the issues of efficient use of renewable energy, low-carbon and energy-efficient circular economy.

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Section: Experimental Planmentioning

confidence: 99%

Multi-Faceted Analysis of Phase-Change Composite Intended for Autonomous Buildings

Musiał,

Lichołai

2024

Materials

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“…The model fitting problems were linear with respect to the parameters and were solved using the least squares algorithm [46]. The accumulation of experiments allowed us to converge to the goal since the procedure had convergence properties (see [47]). However, to rationalize the experimental work, we set a given tolerance for the distance between the quality performance and the optimum (O = 0.0), and the cycle ended when it was attained.…”

Section: Design Proceduresmentioning

confidence: 99%

On the Design of Aqueous Emulsions of Colophony Resin

Ingrez

Ferreira

Gameiro³

et al. 2023

Polymers

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Companies regularly face market pressure to develop products faster but they also need to simultaneously incorporate technological constraints, sustainability trends, and customer requirements into their designs, which requires the use of systematic procedures. Firms that exploit natural resources and convert them into high-value products are among them. However, the literature on the application of such systematic approaches to products of this type remains scarce, as they often requrire extensive experimental plans involving the testing and optimization of multiple formulations. Here, we propose a systematic approach to the design of pine-resin-in-water emulsions, which can be used to fabricate pressure-sensitive adhesives. The strategy is customer-centric in the sense that the customers’ specifications are integrated into the decision-making tool used to assess the quality of the formulations obtained through experiments. This tool uses loss functions to assess satisfaction with individual quality characteristics and multi-attribute decision-making methods to integrate them into an overall quality metric. Our framework is aligned with industrial practices and consists of three sequential stages: (i) screening of primary factors; (ii) optimization of secondary factors; and (iii) assessment of the experimental repeatability of the formulations. In each of these stages, the decision-making tool is used to “drive” the process of finding the optimal formulation.

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“…The process of adjusting the model parameters, determining experiments and performing these experiments is iterated until one is satisfied with the parameter estimate θ est . A detailed overview on this iterative model identification workflow and each individual step can be found in [1] or [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Model-Based Design of Experiments for High-Dimensional Inputs Supported by Machine-Learning Methods

2021

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Algorithms that compute locally optimal continuous designs often rely on a finite design space or on the repeated solution of difficult non-linear programs. Both approaches require extensive evaluations of the Jacobian Df of the underlying model. These evaluations are a heavy computational burden. Based on the Kiefer-Wolfowitz Equivalence Theorem, we present a novel design of experiments algorithm that computes optimal designs in a continuous design space. For this iterative algorithm, we combine an adaptive Bayes-like sampling scheme with Gaussian process regression to approximate the directional derivative of the design criterion. The approximation allows us to adaptively select new design points on which to evaluate the model. The adaptive selection of the algorithm requires significantly less evaluations of Df and reduces the runtime of the computations. We show the viability of the new algorithm on two examples from chemical engineering.

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A model-based framework assisting the design of vapor-liquid equilibrium experimental plans

Cited by 6 publications

References 62 publications

Multi-Faceted Analysis of Phase-Change Composite Intended for Autonomous Buildings

Multi-Faceted Analysis of Phase-Change Composite Intended for Autonomous Buildings

On the Design of Aqueous Emulsions of Colophony Resin

Model-Based Design of Experiments for High-Dimensional Inputs Supported by Machine-Learning Methods

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