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Prediction of the Discharge Coefficient of a Labyrinth Weir Type D by an Artificial Neural Network Method

Belaabed Faris, Arabet Leila

Archives of Hydro-Engineering and Environmental Mechanics

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2024

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Vol. 71, nr 1

59--72

EN

This study presents the use, and its advantages, of artificial intelligence methods to predict the discharge coefficient (Cw), considering the approach conditions of the labyrinth weir type D. The study suggests modifying the training and validation rates in AI tools, which are often fixed without proper justification in previous studies. Unlike most studies that use geometric dimensions as inputs, this work focuses on the approach conditions (the emplacement of the labyrinth weir and filling the alveoli upstream and downstream) of the labyrinth weir type D. The results, based on laboratory experiments, show that these modified inputs significantly impact the efficiency and cost of constructing the weir. Moreover, the Cw predictions based on these inputs are highly satisfactory compared to laboratory test results. In terms of training and validation ratios, the study confirms that the optimal ratio is 70/30 for accurate and highly satisfactory predictions.

2

Estimation of grade and recovery in the concentration of barite tailings by the flotation using the MLR and ANN analyses

Deniz Vedat, Umucu Yakup, Deniz Orcun T.

Physicochemical Problems of Mineral Processing

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2022

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Vol. 58, iss. 5

art. no. 150646

EN

This study aimed to find optimal models in a comparative framework to estimate the recovery and grade of barite concentrate obtained from the rougher flotation of the barite tailings. Therefore, firstly, the effect of four operating parameters (flotation time, pH, collector dosage, and depressant dosage) on the rougher flotation of the barite tailings containing 37.23% BaSO4 was experimentally investigated. Secondly, two models called the multivariable linear regression (MLR) and the artificial neural network (ANN) were used for the estimation of the recovery and grade of the barite concentrate for the rougher flotation optimization. The R2 values found from the MLR and ANN models were 0.828 and 0.995 for the concentrate recovery, and 0.977 and 0.960 for the barite concentrate grade, respectively. In the comparison of the models determined, it was found that the ANN model expressed quite well than the MLR models, especially for the recovery of the rougher concentrate.

3

Using artificial neural network (ANN) for prediction of sediment loads, application to the Mellah catchment, northeast Algeria

Bouzeria H., Ghenim A. N., Khanchoul K.

Journal of Water and Land Development

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2017

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no. 33

47--55

EN

In this study, we present the performances of the best training algorithm in Multilayer Perceptron (MLP) neural networks for prediction of suspended sediment discharges in Mellah catchment. Time series data of daily suspended sediment discharge and water discharge from the gauging station of Bouchegouf were used for training and testing the networks. A number of statistical parameters, i.e. root mean square error (RMSE), mean absolute error (MAE), coefficient of efficiency (CE) and coefficient of determination (R2) were used for performance evaluation of the model. The model produced satisfactory results and showed a very good agreement between the predicted and observed data. The results also showed that the performance of the MLP model was capable to capture the exact pattern of the sediment discharge data in the Mellah catchment.

PL

W niniejszej pracy przedstawiono działanie najlepszego algorytmu sieci neuronowych z użyciem wielowarstwowego perceptronu do przewidywania odpływu zawiesiny ze zlewni rzeki Mellah. Do treningu i testowania sieci użyto serii czasowych dobowego odpływu zawiesiny i odpływu wody z profilu wodowskazowego Bouchegouf. Do oceny działania modelu wykorzystano szereg parametrów statystycznych, takich jak pierwiastek ze średniego błędu kwadratowego, średni błąd bezwzględny, współczynnik wydajności i współczynnik determinacji. Model dawał zadowalające wyniki i wykazywał bardzo dobrą zgodność między obserwowanymi i przewidywanymi danymi. Wyniki świadczą także, że model jest w stanie wychwycić szczegółowy wzorzec odpływu zawiesiny ze zlewni rzeki Mellah.

4

Prediction of freeze-thaw resistance of GGBFS concrete based on ANN models

Wawrzeńczyk J., Kłak A.

Architecture Civil Engineering Environment

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2015

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Vol. 8, no. 4

61--66

EN

In the paper the neural network modelling approach was used to construct an ANNs model to investigate the influence of mix proportion on freeze–thaw GGBFS concrete internal cracking resistance. The simplest way to prevent the internal cracking due to freeze–thaw cycles is the good air-entrainment with an adequate air void spacing. The first step in developing the network was collecting the data set containing the information about mix proportion parameters, characteristics of physical structure of hardened concrete (absorption, permeability, compressive strength) and freeze–thaw durability test results obtained in laboratory using the method of polish standard PN-B-06250:1988. The collected data dealt with normal and high strength concretes made with cements: CEM I, CEM II/A,B-S and CEM III/A, air-entrained or not. The four classes of freeze–thaw durability were used in assessing the concrete resistance to internal cracking.

PL

W referacie przedstawiono rezultaty wybranych analiz wykonanych przy użyciu sztucznych sieci neuronowych aby zbadać wpływ składu betonu na jego mrozoodporność. Najprostszym sposobem zapewnienia mrozoodporności jest właściwe napowietrzenie z odpowiednim rozstawem porów powietrznych. Pierwszym etapem tworzenia sieci było zebranie informacji zawierających informację o składzie mieszanki betonowej, właściwościach fizycznych i strukturze wewnętrznej badanych betonów (absorpcja kapilarna, przepuszczalność, wytrzymałość na ściskanie) oraz przeprowadzenie testów mrozoodporności zgodnie z PN-B-06250:1988. Zebrane dane dotyczyły betonów wykonanych na cementach CEM I, CEM II/A,B-S i CEM III/A, napowietrzonych lub nienapowietrzonych. W zależności od straty wytrzymałości próbek zamrażanych metodą zwykłą PN po 150 cyklach zamrażania-rozmrażania wprowadzono pojęcie 4 klas mrozoodporności.