A survey on artificial neural networks application for identification and control in environmental engineering: Biological and chemical systems with uncertain models

Poznyak, Alexander S.; Chairez, Isaac; Poznyak, Tatyana

doi:10.1016/j.arcontrol.2019.07.003

Cited by 63 publications

(43 citation statements)

References 165 publications

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“…The principle of the developed intelligent technology lies in the functioning throughout the entire brazing process of a pretrained artificial neural network [ 30 , 31 , 32 , 33 ] (ANN ident ), which analyzes information about the temperature of the SCWP assembly elements from the measurement instruments. The technology makes it possible to determine the presence of incorrect information about TP, as well as the possibility to correct it using another pre-trained artificial neural network (ANN).…”

Section: Methodsmentioning

confidence: 99%

The Use of Collections of Artificial Neural Networks to Improve the Control Quality of the Induction Soldering Process

Milov

Tynchenko

Kurashkin

et al. 2021

Sensors

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In industries that implement the technology of induction soldering, various sensors, including non-contact pyrometric ones, are widely used to control the technological process. The use of this type of sensor implies the need to choose a solution that is effective in different operating conditions in terms of the accuracy of the data obtained and the reliability of the measurement equipment and duplication in case of a failure. The present article discusses the development of intelligent technology based on a collection of artificial neural networks, which allows a number of problems associated with technological process control when using pyrometric sensors to be solved: assessing the quality of measurements, correcting measurements when non-standard errors are detected, and controlling the process of induction heating in the absence of reliable readings of the measurement instruments. The collection of artificial neural networks is self-configuring with the use of multicriterion genetic algorithms. The use of the proposed intelligent technology made it possible to improve the control quality of the technological process of the induction brazing of waveguide paths of spacecraft: the overregulation was decreased from 0–20 to 0, and the difference in the heating temperatures of the elements of the brazed waveguide assembly was decreased from 20–100 to 0–10. In addition, the overall process duration decreased and became more stable. When using the classical control technology, the time varied in the range of 20–60 s; when using the proposed technology, it stabilized in the range of 30–35 s.

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Section: Methodsmentioning

confidence: 99%

The Use of Collections of Artificial Neural Networks to Improve the Control Quality of the Induction Soldering Process

Milov

Tynchenko

Kurashkin

et al. 2021

Sensors

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show abstract

“…In spite of these difficulties, a significant number of successful applications of ANNs for dynamic modelling are reported over a wide spectrum of fields [11,26,20,27]. Especially in the process engineering area, ANNs have been extensively used as Nonlinear AutoRegressive eXogenous (NARX) models for dynamic system identification of both univariate (single output) [28,1,29,30,31,21] and multivariate (multi-output) problems [16,32,17,33,14,34]. In the literature, multivariate systems are usually approximated either using a multi-output ANN model or an ensemble of singleoutput ANNs models, where, in the latter case, a set of independent single-output ANN models, each approximating one output as a function of the inputs, is built.…”

Section: Review On Data-driven Dynamic Modelling In Chemical Processesmentioning

confidence: 99%

“…7 1.1. Review on Data-Driven Dynamic Modeling in Chemical Processes. ANNs have become a popular choice for nonlinear dynamic modeling and identification 16,20,21 due to their universal approximation abilities. 14,22 Although they exhibit very powerful capabilities, their usage has two main practical drawbacks: (i) large effort is required to select a good network structure (numbers of layers and the included neurons) and configurations (type of activation function, training algorithm, cost/error function, etc.…”

Section: Introductionmentioning

confidence: 99%

“…ANNs have become a popular choice for nonlinear dynamic modeling and identification ,, due to their universal approximation abilities. , Although they exhibit very powerful capabilities, their usage has two main practical drawbacks: (i) large effort is required to select a good network structure (numbers of layers and the included neurons) and configurations (type of activation function, training algorithm, cost/error function, etc. ), and (ii) the curse of dimensionality, i.e., the increase of the number of inputs causes growth of the number of ANN neurons and consequently the number of parameters (weights and biases) to be set; then, the quantity of data needed for training the ANN grows exponentially with the number of inputs …”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic Surrogate Modeling for Multistep-ahead Prediction of Multivariate Nonlinear Chemical Processes

Shokry

Baraldi

Zio

et al. 2020

Ind. Eng. Chem. Res.

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This work proposes a methodology for multivariate dynamic modeling and multistep-ahead prediction of nonlinear systems using surrogate models for the application to nonlinear chemical processes. The methodology provides a systematic and robust procedure for the development of data-driven dynamic models capable of predicting the process outputs over long time horizons. It is based on using surrogate models to construct several Nonlinear AutoRegressive eXogenous models (NARX), each one approximating the future behavior of one process output as a function of the current and previous process inputs and outputs. The developed dynamic models are employed in a recursive schema to predict the process future outputs over several time steps (multistep-ahead prediction). The methodology is able to manage two different scenarios: 1) one in which a set of input-output signals collected from the process is only available for training, and 2) another in which a mathematical model of the process is available and can be used to generate specific datasets for training. With respect to the latter, the proposed methodology includes a specific procedure for the selection of training data in dynamic modeling based on Design Of Computer Experiment (DOCE) techniques. The proposed methodology is applied to case studies from the process industry presented in the literature. The results show very high prediction accuracies over long time horizons. Also, thanks to the flexibility, robustness and computational efficiency of surrogate modeling, the methodology allows dealing with a wide range of situations, which would be difficult to address using first principle models.

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“…Han, Cheng, Sun, Li, and Di (2019) explored neural networks with feed‐forward control and applied them to water reclamation systems including ammonia estimation. Poznyak, Chairez, and Poznyak (2019) performed a comprehensive examination of artificial neural networks and highlighted knowledge gaps for further investigation. Huang et al (2019) suggest that a fuzzy logic model was better than conventional approaches in estimating effluent quality and gas production in a large anaerobic treatment system.…”

Section: Modelingmentioning

confidence: 99%

Modeling, instrumentation, automation, and optimization of water resource recovery facilities (2019) DIRECT

Sweeney

Kabouris

2020

Water Environment Research

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A survey on artificial neural networks application for identification and control in environmental engineering: Biological and chemical systems with uncertain models

Cited by 63 publications

References 165 publications

The Use of Collections of Artificial Neural Networks to Improve the Control Quality of the Induction Soldering Process

The Use of Collections of Artificial Neural Networks to Improve the Control Quality of the Induction Soldering Process

Dynamic Surrogate Modeling for Multistep-ahead Prediction of Multivariate Nonlinear Chemical Processes

Modeling, instrumentation, automation, and optimization of water resource recovery facilities (2019) DIRECT

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