Estimation of Rate Constants from Multiresponse Kinetic Data

Mezaki, Reiji; Butt, John

doi:10.1021/i160025a021

Cited by 36 publications

(10 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Jutan et al (1977) have recently reported on using a multiresponse kinetic model as part of a multivariable process control procedure for a laboratory reactor. Some other examples have been given by Downie (1976, 1978), Juusola, Mann and Downie (1970), and Mezaki and Butt (1968).…”

Section: Introductionmentioning

confidence: 99%

Kinetic Modelling with Multiresponse Data

Ziegel¹,

Gorman²

1980

Technometrics

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Kinetic Modelling with Multiresponse Data

Ziegel¹,

Gorman²

1980

Technometrics

View full text Add to dashboard Cite

“…The concentration profiles obtained from ALS-MCR deconvolution were fitted to Model I minimizing Eq. (10). Results are presented in Fig.…”

Section: Influence Of Rotational Ambiguitymentioning

confidence: 99%

“…(2), R = D −D = D − CS (2) where the C matrix is calculated by numerical integration of the differential equation system associated with the reaction mechanism. Hard-modeling methods were developed originally in the field of chemical engineering because of the need for algorithms able to retrieve the relevant information from kinetic models [10]. Hard Modeling Multi Curve Resolution (HM-MCR) methods can be classified as global, when they use the full array of responses (D) or factorial, when using the principal components or other related vectors [11].…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Hard- and Soft-Modeling Algorithms for Kinetic Data Processing

Pérez

Baeza-Baeza²,

Llopis

et al. 2016

Int. J. Chem. Kinet.

View full text Add to dashboard Cite

This article critically compares the efficacy of three algorithms, namely Alternating Least‐squares Multi Curve Resolution (ALS‐MCR), Hard Modeling Alternating Least‐squares (HM‐ALS), and classical Hard Modeling Multi Curve Resolution (HM‐MCR) in finding the true values of rate constants associated with a kinetic model. Simulated experiments on the simple system (normalA1⟶normalA2⟶normalA3) indicate that soft‐modeling ALS‐MRC methodology, which is subject only to linear constraints, does not ensure that experimental responses are correctly deconvolved, thus preventing further calculations to determine the true rate constants. Inclusion of the kinetic model in the ALS scheme, which gives rise to the HM‐ALS methodology, was found to yield a correct assessment of the rate coefficients but had a large computational cost. Numerical experiments employing a more complex model (normalA1⇌normalA2⇌normalA3) were also carried out, mainly to evaluate strategies for performing efficient searches on multidimensional multimodal least‐squares surfaces using HM‐ALS and HM‐MCR. This study again revealed the efficiency and reliability of classical HM‐MCR methods. Results from simulations were corroborated by analysis of data from an experimental study of chromate reduction by hydrogen peroxide; the mechanism of which is similar in complexity to those considered in simulations. The present work suggests that HM‐MCR algorithms implementing a multiminimum search strategy are the method of choice for analyzing two‐dimensional kinetic data.

show abstract

“…The first real-life application of Eq. 6 was made by Mezaki and Butt (1968) to a four-component chemical reaction system. They estimated the six parameters at each temperature by minimizing Iv(fl) I, and also by minimizing the total sum of squares, tr[v(O)].…”

Section: Summary Of Applicationsmentioning

confidence: 99%