Artificial neural network model with the parameter tuning assisted by a differential evolution technique: The study of the hold up of the slurry flow in a pipeline

Lahiri, Sandip Kumar; Ghanta, Kartik Chandra

doi:10.2298/ciceq0902103l

Cited by 37 publications

(12 citation statements)

References 12 publications

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“…Other parameter values of ANN model were determined from studies in literature [31][32][33] and by experts' opinions ( Table 7). The replacement times obtained with ANN model for the first 3 and the last 3 out of 120 periods are shown in Table 8.…”

Section: Prediction Of Replacement Time Using Annmentioning

confidence: 99%

Determining optimal machine part replacement time using a hybrid ANN-GA model

Gökler

Boran

2020

Scientia Iranica

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Companies must determine the replacement time of machine parts correctly since it affects their production costs and efficiencies. For this, it is aimed to determine the most appropriate replacement time to minimize cost per unit. In this study, it is proposed to develop a hybrid Artificial Neural Network (ANN)-Genetic Algorithm (GA) model to predict replacement time without using a cost model. At first, a replacement cost model is developed to calculate replacement times to use in training the neural network. Nevertheless, the cost model needs complex mathematical calculations. GA is used instead of the cost model to determine replacement time, and thus, to achieve fast learning for the neural network. The hybrid ANN-GA model was applied to predict replacement time of bladder in tire manufacturing. Furthermore, ANN and GA models, which were developed to increase the prediction accuracy of the hybrid model, were used. The hybrid ANN-GA model showed better solution according to the performance statistics than the other ANN and GA models. The values indicate that the hybrid model is in good agreement with the cost model. Thus, it is recommended that the hybrid model is used instead of the cost model.

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Section: Prediction Of Replacement Time Using Annmentioning

confidence: 99%

Determining optimal machine part replacement time using a hybrid ANN-GA model

Gökler

Boran

2020

Scientia Iranica

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“…There are few reports on the measurement of phase flowrates or phase fractions of slurry flow using soft computing techniques. However, the parameters for considerations of pipe design, such as pressure drop [67], hold-up [68,69] and critical velocity [70,71] have been predicted with soft computing techniques. It is anticipated that more progress in developing the measurement systems incorporating traditional sensors and soft computing techniques would be achieved for the measurement of gas-liquid, gas-solid and liquid-solid flows in the next few years.…”

Section: Soft Computing Techniquesmentioning

confidence: 99%

Application of soft computing techniques to multiphase flow measurement: A review

Yan

Wang

Wang³

et al. 2018

Flow Measurement and Instrumentation

113

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“…In the work of Lahiri and Ghanta (2009b), an ANN model for the hold-up in solid-liquid slurry flow in a pipeline is optimized using a classic variant of DE. Nine inputs (pipe diameter, particle diameter, solid concentration and density, liquid density and viscosity, maximum packing concentration) and one output (hold-up ratio) were considered, and the objective function of the optimization was to obtain minimum average absolute relative error on the test set.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

The use of differential evolution algorithm for solving chemical engineering problems

Drăgoi

Curteanu

2016

Reviews in Chemical Engineering

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AbstractDifferential evolution (DE), belonging to the evolutionary algorithm class, is a simple and powerful optimizer with great potential for solving different types of synthetic and real-life problems. Optimization is an important aspect in the chemical engineering area, especially when striving to obtain the best results with a minimum of consumed resources and a minimum of additional by-products. From the optimization point of view, DE seems to be an attractive approach for many researchers who are trying to improve existing systems or to design new ones. In this context, here, a review of the most important approaches applying different versions of DE (simple, modified, or hybridized) for solving specific chemical engineering problems is realized. Based on the idea that optimization can be performed at different levels, two distinct cases were considered – process and model optimization. In both cases, there are a multitude of problems solved, from different points of view and with various parameters, this large area of successful applications indicating the flexibility and performance of DE.

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Artificial neural network model with the parameter tuning assisted by a differential evolution technique: The study of the hold up of the slurry flow in a pipeline

Cited by 37 publications

References 12 publications

Determining optimal machine part replacement time using a hybrid ANN-GA model

Determining optimal machine part replacement time using a hybrid ANN-GA model

Application of soft computing techniques to multiphase flow measurement: A review

The use of differential evolution algorithm for solving chemical engineering problems

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