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Developing a data-driven soft sensor to predict silicate impurity in iron ore flotation concentrate

Pural Yusuf Enes

Physicochemical Problems of Mineral Processing

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2023

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Vol. 59, iss. 5

art. no. 169823

EN

Soft sensors are mathematical models that estimate the value of a process variable that is difficult or expensive to measure directly. They can be based on first principle models, data-based models, or a combination of both. These models are increasingly used in mineral processing to estimate and optimize important performance parameters such as mill load, mineral grades, and particle size. This study investigates the development of a data-driven soft sensor to predict the silicate content in iron ore reverse flotation concentrate, a crucial indicator of plant performance. The proposed soft sensor model employs a dataset obtained from Kaggle, which includes measurements of iron and silicate content in the feed to the plant, reagent dosages, weight and pH of pulp, as well as the amount of air and froth levels in the flotation units. To reduce the dimensionality of the dataset, Principal Component Analysis, an unsupervised machine learning method, was applied. The soft sensor model was developed using three machine learning algorithms, namely, Ridge Regression, Multi-Layer Perceptron, and Random Forest. The Random Forest model, created with non-reduced data, demonstrated superior performance, with an R-squared value of 96.5% and a mean absolute error of 0.089. The results suggest that the proposed soft sensor model can accurately predict the silicate content in the iron ore flotation concentrate using machine learning algorithms. Moreover, the study highlights the importance of selecting appropriate algorithms for soft sensor developments in mineral processing plants.

2

Application perspective of digitalneural networks in the context of marine technologies

Konon V., Konon N.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

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2022

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Vol. 16 No. 4

743--747

EN

This study is focused on the issue of digital neural networks’ implementation in the context of maritime industry. Various algorithms of such networks in the terms of the marine technologies have been reviewed in the current study in order to evaluate the effectiveness of the methodology and to propose a new concept of an artificial neural network’s application in this way. Fire-detection system simulation based on the thermal imagers’ data input had been developed to assess the efficiency of the concept suggested with a multi-layer perceptron (MLP) algorithm integrated into the designed 3d-model.

3

Development of a neural statistical model for the prediction of relative humidity levels in the region of Rabat-Kenitra, North West Morocco

El Azhari Kaoutar, Abdallaoui Badreddine, Dehbi Ali, Abdalloui Abdelaziz, Zineddine Hamid

Journal of Water and Land Development

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2022

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no. 54

13--20

EN

This article accounts for the development of a powerful artificial neural network (ANN) model, designed for the prediction of relative humidity levels, using other meteorological parameters such as the maximum temperature, minimum temperature, precipitation, wind speed, and intensity of solar radiation in the Rabat-Kenitra region (a coastal area where relative humidity is a real concern). The model was applied to a database containing a daily history of five meteorological parameters collected by nine stations covering this region from 1979 to mid-2014. It has been demonstrated that the best performing three-layer (input, hidden, and output) ANN mathematical model for the prediction of relative humidity in this region is the multi-layer perceptron (MLP) model. This neural model using the Levenberg-Marquard algorithm, with an architecture of [5-11-1] and the transfer functions Tansig in the hidden layer and Purelin in the output layer, was able to estimate relative humidity values that were very close to those observed. This was affirmed by a low mean squared error (MSE) and a high correlation coefficient (R), compared to the statistical indicators relating to the other models developed as part of this study.

4

Application of Water Quality Index and Multivariate Statistical Techniques to Assess and Predict of Groundwater Quality with Aid of Geographic Information System

Dawood Ammar S., Jabbar Mushtak T., Al-Tameemi Hayfaa H., Baer Eric M.

Journal of Ecological Engineering

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2022

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Vol. 23, nr 6

189--204

EN

In this study, the groundwater quality and spatial distribution of the Basra province in the south of Iraq was assessed and mapped for drinking and irrigation purposes. Groundwater samples (n = 41) were collected from deep wells in the study area to demonstrate, estimate and model the Water Quality Index (WQI). The analysis of water samples integrated with GIS-based IDW technique was used to express the spatial variation in the study area with consideration of WQI. The physicochemical parameters, including pH, sodium (Na+), electrical conductivity (EC), chloride (Cl-), total dissolved solids (TDS), calcium (Ca2+), nitrate (NO3-), sulfate (SO42-), magnesium (Mg2+), and bicarbonate (HCO3-) were identified for groundwater quality assessment. The results of calculated WQI classify groundwater into three sorts. The results of WQI showed that 2.5%, 2.5% and 95% of the groundwater samples were classified as poor/very poor/unsuitable for drinking, respectively. The GIS tools integrated with statistical techniques are utilized for spatial distribution and description of water quality. Correlation analysis of groundwater data revealed that some parameters have actually a relationship that is strong with the other parameters and they share a common source of origin. Multivariate statistical techniques, especially cluster analysis (CA) and factor analysis (FA), were applied for the evaluation of spatial variations of forty-one selected groundwater samples. Cluster analysis confirmed that some different locations of wells have comparable sourced elements of water pollution, whereas factor analysis yielded three factors which are accountable for groundwater quality variations, clarifying more than 72% of the total variance of the data and permitted to group the preferred water quality. MultiLayer Perceptron (MLP) models were applied in modeling the water quality index. Comparing different result values of the MLP network suggested that the values of MSE and r for the selected model are 0.1940 and 0.9998, respectively. Finally, it can be revealed that the MLP network precisely predicted the output, i.e. the WQI values.

5

Prognozowanie występowania pęknięć w oparciu o system FEM-MLP

Gajewski J., Nowakowski P., Różyło P.

Logistyka

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2015

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nr 3

1381--1386, CD 1

PL

W pracy przedstawiono analizę wyników badań numerycznych MES poprzez zastosowanie sztucznej sieci MLP. Zaprojektowanie modelu oraz badanie procesu pękania elementu maszynowego wykonano w oprogramowaniu ABAQUS. Model Sztucznej sieci neuronowej został stworzony w aplikacji STATISICA. Analizy umożliwiły określenie wpływu parametrów modelu na występowanie pęknięć.

EN

The paper presents the FEM - ANN analysis. Design and cracking tests of the model was performed in the ABAQUS software . Artificial neural network model was created in STATISTICA application. The analysis made it possible to provide information on the impact of the parameters on the occurrence of cracks.

6

Ferroresonance Suppression in Voltage Transformers

Valverde V., Mazón J., Buigues G., Zamora I.

Przegląd Elektrotechniczny

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2012

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R. 88, nr 1a

137-140

EN

Ferroresonance is a special case of disturbance that involves high levels of overvoltage and overcurrents distortion. This phenomenon can severely affect voltage transformers and its consequences can be catastrophic. This paper analyses different practical solutions against ferroresonance phenomenon in voltage transformers, highlighting the current development of ferroresonance detection techniques. In this regard, this paper introduces a novel ferroresonance detection technique based on Artificial Neural Networks (ANNs).

PL

Ferrorezonans w transformatorze jest zakłóceniem wprowadzającym przepięcia i przeciążenia prądowe. Może on popwodować nawet zniszczenie transformatora. W artykule zaproponowano szereg metod umożliwiających stłumienie ferrorezonansu. Ferrorezonans jest wykrywany dzięki technice bazującej na sztucznych sieciach neuronowych.

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Lokalizacja punktów pomiarowych w systemie do trójwymiarowego pozycjonowania ciała wybranymi metodami sztucznej inteligencji

Czechowicz A., Tokarczyk R.

Archiwum Fotogrametrii, Kartografii i Teledetekcji

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2009

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Vol. 20

67--79

PL

Fotogrametryczny system cyfrowy do pomiaru ciała ludzkiego dla celów badania wad postawy służy do wyznaczania przestrzennego położenia wybranych jego punktów. Wymaga on pomierzenia na zdjęciach cyfrowych trzech grup punktów, zwanych w tytule referatu punktami pomiarowymi: fotopunktów, markerów sygnalizowanych na pacjencie oraz źrenic oczu. Fotopunkty to czarno-białe sygnały pozwalające na orientację w przestrzeni modelu utworzonego ze zdjęć. Markery to styropianowe kulki o średnicy 4÷5 mm sygnalizujące wybrane elementy kośćca umieszczone na powierzchni ciała. Artykuł dotyczy wykorzystania sieci neuronowych do lokalizacji fotopunktów i styropianowych markerów. Zadaniem sieci jest klasyfikacja kolejnych fragmentów obrazu na zawierające obraz fotopunktu, markera lub niezawierające obrazu żadnego z nich. W ramach badań sprawdzono możliwość przeprowadzenia zdefiniowanej powyżej klasyfikacji sieciami o architekturze wielowarstwowego perceptronu (ang. Multi Layer Perceptron –MLP) ze wsteczną propagacją błędu oraz sieciami z radialnymi funkcjami bazowymi RBF (ang. Radial Basis Function Networks). Zweryfikowano przydatność reprezentacji opartej na informacji o rozkładzie wartości gradientu oraz jego kierunku dla celów wykrycia punktów pomiarowych. Wspomniana reprezentacja wywodzi się z badań nad selekcją podobrazów dla potrzeb dopasowania zdjęć lotniczych.

EN

A digital photogrammetric system for making measurements of the human body for the purpose of studying faulty posture is designed to determine the three-dimensional location of selected points in the human body. It requires the measurement of three groups of points on digital images, points referred to in this paper’s title as measurement points, i.e. control points, markers indicated on the patient’s body and pupils of the eyes. Control points are black and white signals permitting the correct orientation in space of a model created from the images. The markers are balls of polystyrene foam of 4-5 mm diameter, placed on the body, which indicate selected elements of the human skeleton. This paper describes the utilisation of neural networks to locate control points and markers. The aim of the networks is to classify consecutive fragments of an image as containing control points, containing markers or not containing any of these features. The research covered evaluation of the possibility of conducting this classification using Multi Layer Perceptron Networks with back propagation of errors as well as with Radial Basis Function Networks. The usefulness of a representation based on information about the distribution of gradient value and direction for the purpose of the detection of measurement points has been verified. This representation comes from earlier research on the selection of subimages for the purpose of matching the aerial pictures.