Applications of artificial neural networks in chemical engineering

Himmelblau, D. M.

doi:10.1007/bf02706848

Cited by 222 publications

(109 citation statements)

References 27 publications

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“…Artificial neural networks (ANNs) are universal approximators [5] and have received numerous applications [6]. The literature, as indicated by [7], points out their ability to recognize highly nonlinear relations and to organize disperse data in a nonlinear mode in the context of empirical or hybrid modeling.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Based Modeling and Operational Optimization of Biomass Gasification Processes

Souza¹,

Nemer²,

Barreto³

et al. 2012

Gasification for Practical Applications

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Section: Introductionmentioning

confidence: 99%

Neural Network Based Modeling and Operational Optimization of Biomass Gasification Processes

Souza¹,

Nemer²,

Barreto³

et al. 2012

Gasification for Practical Applications

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“…Linear and nonlinear ARMAX-type models are readily used to represent dynamic systems 10,11 . Neural network methods have also been proposed to model the dynamics of batch processes 12 . More recently, researchers have investigated the use of multivariate statistical partial least-squares (PLS) models as a means to predict product quality in batch reactors 13 .…”

Section: Model Typesmentioning

confidence: 99%

Control and Optimization of Batch Chemical Processes

Bonvin

François

2017

Coulson and Richardson's Chemical Engineering

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A batch process is characterized by the repetition of time-varying operations of finite duration. Due to the repetition, there are two independent "time" variables, namely, the run time during a batch and the batch index. Accordingly, the control and optimization objectives can be defined for a given batch or over several batches. This chapter describes the various control and optimization strategies available for the operation of batch processes. These include online and run-to-run control on the one hand, and repeated numerical optimization and optimizing control on the other. Several case studies are presented to illustrate the various approaches Keywords Batch control, predictive control, iterative learning control, run-to-run control, batch process optimization, dynamic optimization, optimizing control, run-to-run optimization.

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“…For example, regression by splines usually requires moderate data sets (Friedman, 1991) while neural networks usually require large data sets (Himmelblau, 2000).…”

Section: Algorithm Implementationmentioning

confidence: 99%

Large scale optimization of a sour water stripping plant using surrogate models

Quirante

2016

Computers & Chemical Engineering

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Please cite this article as: Quirante, Natalia., & Caballeroa, José A., Large scale optimization of a sour water stripping plant using surrogate models.Computers and Chemical Engineering http://dx.doi.org/10. 1016/j.compchemeng.2016.04.039 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.Large scale optimization of a sour water stripping plant using surrogate models We present a case study of the optimization of a sour water stripping plant in which we simultaneously consider economics, heat integration and environmental impact using the ReCiPe indicator, which incorporates the recent advances made in Life Cycle Assessment (LCA).The optimization strategy guarantees the convergence to a local optimum inside the tolerance of the numerical noise.

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Applications of artificial neural networks in chemical engineering

Cited by 222 publications

References 27 publications

Neural Network Based Modeling and Operational Optimization of Biomass Gasification Processes

Neural Network Based Modeling and Operational Optimization of Biomass Gasification Processes

Control and Optimization of Batch Chemical Processes

Large scale optimization of a sour water stripping plant using surrogate models

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