Wyniki wyszukiwania - BazTech

1

A comparative study of artificial neural networks and multivariate regression for predicting groundwater depths in the Arak aquifer

Hanifian Soadat, Khaleghi Mohammad Reza, Najarchi Mohsen, Jafarnia Reza, Varvani Javad

Acta Geophysica

|

2024

|

Vol. 72, no. 1

419–-432

EN

In recent years, the groundwater resources of Arak plain have been under severe stress, so in some areas, due to the drying up of wells, the depth of wells has increased to access water. In some areas, the groundwater depth is high, which will lead to the salinization of those lands in the future. Regional modeling was used to organize and measure the response of the groundwater resources of Arak plain against the implementation of different management and implementation scenarios. This study aims to investigate the effective factors in the groundwater depth to provide a regional model with multiple linear regression (MLR) methods for Arak plain aquifer. For this purpose, the average groundwater potential maps (GPMs) in the Arak plain, as a dependent variable, and the transmissivity of the aquifer formations, groundwater exploitation values, altitude, average precipitation of the region, the amount of evaporation, and the distance from water resources are considered independent variables and regression analysis is done in SPSS software media. It was done to present a linear model. In the next stage, the presented model was evaluated by applying it to places where its statistics and information were not used to present the model, and finally, by applying this model in the GIS environment, the GPMs for the region were created. The study was prepared. Also, an artificial neural network (ANN) was used to simulate the depth of underground water. The performance of the ANN was measured through parameters such as root-mean-square error (RMSE) and correlation coefficient between real and desired outputs (R). The results of both methods indicate that factors such as the transmissivity of aquifer formations, GPMs drawdown, topography (the height of the well site on the level of the watershed), the groundwater exploitation values at the maximum operating radius of the well, and the distance from water resources are the main factors of GPMs drawdown. But the effectiveness of ANN in estimating GPMs drawdown is higher than the MLR method. The implemented methodology could be generalized to other watersheds with water scarcity problems for groundwater management.

2

The modelling of layered rocks using a numerical homogenisation technique and an artificial neural network

Urbański Aleksander, Ligęza Szymon, Przecherski Piotr

Technical Transactions

|

2023

|

Vol. 120, iss. 1

art. no. e2023007

EN

A method of creating a constitutive model of layered rocks based on an artificial neural network (ANN) is reported in this work. The ANN gives an implicit constitutive function Ʃⁿ⁺¹= F( Ʃⁿ , ΔE), relating the new state of homogenized stresses Ʃⁿ⁺¹ with the old state Ʃⁿ and with the increment of homogenized strains ΔƩ. The first step is to repeatedly run a strain- controlled homogenisation on an uni-dimensional finite element model of a periodic cell with elastic-plastic models (Drucker-Prager) of the components. Paths are created in (Ʃ, E) space, from which, a set of patterns is formed to train the ANN. A description of how to prepare this data and a discussion on ANN training issues are presented. Finally, the procedure based on trained ANN is put into a finite-element code (ZSoil.PC) as a user-delivered constitutive function. The approach is verified by comparing the results of the developed model basing on ANN with a direct (single-scale) analysis, which showed acceptable accuracy.

3

Performance enhancement of pv system using artificial neural network (ANN) as a maximum power point tracking

Bensiali Nadia, Benalia Nadia, Zerzouri Nora

Przegląd Elektrotechniczny

|

2023

|

R. 99, nr 4

262--265

EN

Since the generalization of the use of electricity, energy consumption, conversion and energy storage has led to research and development of new sources of supply. This interest has increased due to the depletion of fossil fuels, their impact on the environment and the waste they generate. Photovoltaic (PV) technology is an attractive solution as a replacement or complementary to conventional sources of electricity supply due to its many advantages. This work focuses on studying, modeling and designing of an advanced controller for a step up converter that uses an artificial neural network (ANN) as a maximum power point tracking (MPPT) algorithm to generate maximum power. The effectiveness was proved for climatic conditions variation. A comparative study between the proposed method and the conventional ones was carried out using Matlab/Simulink software.

PL

Od czasu uogólnienia wykorzystania energii elektrycznej jej zużycie, konwersja i magazynowanie energii doprowadziły do badań i rozwoju nowych źródeł zaopatrzenia. Zainteresowanie to wzrosło ze względu na wyczerpywanie się paliw kopalnych, ich wpływ na środowisko i generowane przez nie odpady. Technologia fotowoltaiczna (PV) jest atrakcyjnym rozwiązaniem jako zamiennik lub uzupełnienie konwencjonalnych źródeł zaopatrzenia w energię elektryczną ze względu na swoje liczne zalety. Praca ta koncentruje się na badaniu, modelowaniu i projektowaniu zaawansowanego sterownika do przetwornicy podwyższającej, która wykorzystuje sztuczną sieć neuronową (ANN) jako algorytm śledzenia punktu mocy maksymalnej (MPPT) do generowania maksymalnej mocy. Skuteczność została wykazana przy zmienności warunków klimatycznych. Badanie porównawcze proponowanej metody z metodami konwencjonalnymi przeprowadzono z wykorzystaniem oprogramowania Matlab/Simulink.

4

Investigation of the possibility of using artificial neural networks in the process of assessing the threat of lightning

Sobieska Emilia, Sobolewski Konrad

Przegląd Elektrotechniczny

|

2023

|

R. 99, nr 1

211--214

EN

The article presents issues related to the possibility of using an artificial neural network to classify factors describing the lightning hazard. It also analyzed the selection of the most optimal type of neural network for this type of task, as well asthe issue of input data, which both teach the neural network and form the basis for the final classification. Finally, the conclusions and assumptions necessary for the practical implementation of the task were drawn. The practical part of the task is planned as the following consideration stage.

PL

W artykule została przedstawiona problematyka związana z możliwością wykorzystania sztucznej sieci neuronowej do przeprowadzenia klasyfikacji współczynników opisujących zagrożenie piorunowe. Przeanalizowano wybór najbardziej optymalnego rodzaju sieci neuronowej dla tego rodzaju zadania, a także kwestię danych wejściowych, które posłużą zarówno do uczenia sieci neuronowej, jak również stanowią podstawę do końcowej klasyfikacji. Na koniec wyciągnięte zostały wnioski oraz założenia niezbędne do praktycznej realizacji zadania. Praktyczna część zadania będzie stanowić kolejny etap rozważań.

5

Comparative analysis of optimization value between artificial neural network and long short-term memory for prediction particulate matter (PM2.5) in Bangkok Thailand

Atiwanwong Thanpitcha, Jantakun Adirek, Sangsongfa Adisak

Przegląd Elektrotechniczny

|

2023

|

R. 99, nr 7

89--95

EN

The crisis that the general public is worried about is particulate matter as small as 2.5 microns, which is invisible to the naked eye, causing a great lack of awareness of health hazards. One of the key goals and visions of government leaders around the world is to tackle PM2.5 particulate matter, but without measurements, reports and predictions, how will it lead to emission reduction and remedial steps? Therefore, the prediction of PM2.5 is considered as the main factor that will help to reduce the pollution of PM2.5. So, Neural networks have been widely used in predictive research, but the problem is What type of neural network would be most suitable for predicting the value of PM2.5? In this research, the predictions were compared between Artificial Neural Network (ANN) and Long Short -Term Memory (LSTM) using values measured from the performance test results with accuracy. The results showed that when the values of similar hyperparameters were given different results, the average ANN accuracy is 91.1460%. The average accuracy LSTM is 96.8496%. The values obtained from the comparison clearly show that for the prediction of PM2.5, the LSTM neural network was significantly more suitable than the ANN neural network.

PL

Kryzys, który niepokoi opinię publiczną, to pył zawieszony o wielkości zaledwie 2,5 mikrona, który jest niewidoczny gołym okiem, powodując ogromny brak świadomości zagrożeń dla zdrowia. Jednym z kluczowych celów i wizji przywódców rządów na całym świecie jest rozwiązanie problemu pyłu zawieszonego PM2,5, ale bez pomiarów, raportów i prognoz, w jaki sposób doprowadzi to do redukcji emisji i działań zaradczych? Dlatego prognoza PM2,5 jest uważana za główny czynnik, który pomoże zmniejszyć zanieczyszczenie PM2,5. Tak więc sieci neuronowe były szeroko stosowane w badaniach predykcyjnych, ale problem polega na tym, jaki typ sieci neuronowej byłby najbardziej odpowiedni do przewidywania wartości PM2,5? W tym badaniu porównano przewidywania między sztuczną siecią neuronową (ANN) a pamięcią długokrótkoterminową (LSTM) przy użyciu wartości zmierzonych z wynikami testu wydajności z dużą dokładnością. Wyniki pokazały, że przy różnych wartościach podobnych hiperparametrów średnia dokładność ANN wynosi 91,1460%. Średnia dokładność LSTM wynosi 96,8496%. Uzyskane z porównania wartości jednoznacznie wskazują, że do predykcji PM2,5 sieć neuronowa LSTM okazała się znacznie bardziej odpowiednia niż sieć neuronowa ANN.

6

Autonomiczna kamera do wykrywania wolnych miejsc parkingowych

Lamża Aleksander

Przegląd Elektrotechniczny

|

2023

|

R. 99, nr 4

192--195

PL

W artykule została opisana architektura systemu autonomicznej kamery do wykrywania wolnych miejsc parkingowych. System został zrealizowany i przetestowany zarówno w warunkach laboratoryjnych, jak i rzeczywistych. Omówione zostały kluczowe elementy systemu, ich współdziałanie, a także możliwości modyfikacji i rozwoju.

EN

The article describes the architecture of an autonomous camera system to detect free parking spaces. The system was implemented and tested both in laboratory and real conditions. The key elements of the system, their interaction, as well as the possibilities of modification and development were discussed.

7

Predicting Young’s modulus of Indian coal measure rock using multiple regression and artificial neutral network

Chakraborty Sayantan, Bisai Rohan, Roy Rohit, Palaniappan Sathish Kumar, Pal Samir Kumar, Rao Karanam Uma Maheshwar

Journal of Sustainable Mining

|

2023

|

Vol. 22, iss. 1

41--54

EN

Accurate information on Young’s modulus (E) is required for simulating rock deformation in mines; on the other hand, it is very cumbersome to obtain in the laboratory and collecting drilled cores in sufficient amounts, especially in the case of soft rocks, is quite impossible. Empirical equations were deducted for - from easily determinable rock properties, and the final model was selected through different statistical strength parameter tests. The generalization of the equation was verified through the normal distribution tests of residues of the equation. R2 came to be 0.609 and was validated using an artificial neural network with an improved value of 0.73.

8

A novel Parkinson's disease detection algorithm combined EMD, BFCC, and SVM classifier

Boualoulou Nouhaila, Mounia Miyara, Nsiri Benayad, Behoussine Drissi Taoufiq

Diagnostyka

|

2023

|

Vol. 24, No. 4

art. no. 2023404

EN

Identifying and assessing Parkinson's disease in its early stages is critical to effectively monitoring the disease's progression. Methodologies based on machine learning enhanced speech analysis are gaining popularity as the potential of this field is revealed. Acoustic features, in particular, are used in a variety of algorithms for machine learning and could serve as indicators of the general health of subjects' voices. In this research paper, a novel method is introduced for the automated detection of Parkinson's disease through speech signal analysis, a support vector machines classifier (SVM) and an Artificial Neural Network (ANN) are used to evaluate and classify the data based on two acoustic features: Bark Frequency Cepstral Coefficients (BFCC) and Mel Frequency Cepstral Coefficients (MFCC). These features are extracted from the denoised signals using Empirical Mode Decomposition (EMD). The most relevant results obtained for a dataset of 38 participants are by the BFCC coefficients with an accuracy up to 92.10%. These results confirm that EMD-BFCC-SVM method can contribute to the detection of Parkinson's disease.

9

Robust estimation based nonlinear higher order sliding mode control strategies for PMSG-WECS

Nazir Awais, Khan Safdar Abbas, Khan Malak Adnan, Alam Zaheer, Khan Imran, Irfan Muhammad, Rehman Saifur, Nowakowski Grzegorz

Bulletin of the Polish Academy of Sciences. Technical Sciences

|

2023

|

Vol. 71, nr 5

art. no. e147063

EN

The wind energy conversion systems (WECS) suffer from an intermittent nature of source (wind) and the resulting disparity between power generation and electricity demand. Thus, WECS are required to be operated at maximum power point (MPP). This research paper addresses a sophisticated MPP tracking (MPPT) strategy to ensure optimum (maximum) power out of the WECS despite environmental (wind) variations. This study considers a WECS (fixed pitch, 3KW, variable speed) coupled with a permanent magnet synchronous generator (PMSG) and proposes three sliding mode control (SMC) based MPPT schemes, a conventional first order SMC (FOSMC), an integral back-stepping-based SMC (IBSMC) and a super-twisting reachability-based SMC, for maximizing the power output. However, the efficacy of MPPT/control schemes rely on availability of system parameters especially, uncertain/nonlinear dynamics and aerodynamic terms, which are not commonly accessible in practice. As a remedy, an off-line artificial function-fitting neural network (ANN) based on Levenberg-Marquardt algorithm is employed to enhance the performance and robustness of MPPT/control scheme by effectively imitating the uncertain/nonlinear drift terms in the control input pathways. Furthermore, the speed and missing derivative of a generator shaft are determined using a high-gain observer (HGO). Finally, a comparison is made among the stated strategies subjected to stochastic and deterministic wind speed profiles. Extensive MATLAB/Simulink simulations assess the effectiveness of the suggested approaches.

10

Performance prediction and control for wastewater treatment plants using artificial neural network modeling of mechanical and biological treatment

Alnajjar Hussein Y.H., Üçüncü Osman

Archives of Environmental Protection

|

2023

|

Vol. 49, no 2

16--29

EN

Biological treatment in wastewater treatment plants appears to be one of the most crucial factors in water quality management and planning. Though, measuring this important factor is challenging, and obtaining reliable results requires signifi cant eff ort. However, the use of artifi cial neural network (ANN) modeling can help to more reliably and cost-effectively monitor the pollutant characteristics of wastewater treatment plants and regulate the processing of these pollutants. To create an artifi cial neural network model, a study of the Samsun Eastern Advanced Biological WWTP was carried out. It provides a laboratory simulation and prediction option for flexible treatment process simulations. The models were created to forecast influent features that would affect effluent quality metrics. For ANN models, the correlation coefficients R-TRAINING and R-ALL are more than 0.8080. The MSE, RMSE, and MAPE were less than 0.8704. The model’s results showed compliance with the permitted wastewater quality standards set forth in the Turkish water pollution control law for the environment where the treated wastewater is discharged. This is a useful tool for plant management to enhance the quality of the treatment while enhancing the facility’s dependability and efficiency.

11

Modelling the high‑temperature deformation characteristics of S355 steel using artificial neural networks

Olejarczyk-Wożeńska Izabela, Mrzygłód Barbara, Hojny Marcin

Archives of Civil and Mechanical Engineering

|

2023

|

Vol. 23, no. 1

art. no. e1, 2023

EN

In this study, artificial neural networks were used to predict the plastic flow behaviour of S355 steel in the process of high-temperature deformation. The aim of the studies was to develop a model of changes in stress as a function of strain, strain rate and temperature, necessary to build an advanced numerical model of the soft-reduction process. The high-temperature characteristics of the tested steel were determined with a Gleeble 3800 thermo-mechanical simulator. Tests were carried out in the temperature range of 400-1450 °C for two strain rates, i.e. 0.05 and 1 s-1. The test results were next used to develop and verify a rheological model based on artificial neural networks (ANNs). The conducted studies show that the selected models offer high accuracy in predicting the high-temperature flow behaviour of S355 steel and can be successfully used in numerical modelling of the soft-reduction process.

12

Prediction and assessment of exposure to electromagnetic field during a helicopter flight

Michałowska Joanna

Przegląd Elektrotechniczny

|

2022

|

R. 98, nr 1

96--99

EN

The paper present the research concerning the value of the electric component of the electromagnetic (EM) energy determined by NHT3DL meter by Microrad with the 01E measuring probe during flight of Robinson R44 Raven helicopter. The point of reference for the recorded measurement was the normative limits of the electromagnetic field (EMF), which can influence a pilot in the course of a flight. Selected studies of the maximum value recorded by the meter was E = 7.68 V/m when landing at an airfield equipped with the VHF (Very High Frequency) omnidirectional radio range (VOR) approach system. The developed model of neural networks is used to predict the value of electric field exposure in the context of flight safety analysis.

PL

W artykule przedstawiono badania dotyczące wartości składowej elektrycznej energii elektromagnetycznej (EM) wyznaczonej miernikiem NHT3DL firmy Microrad z sondą pomiarową 01E podczas lotu śmigłowca Robinson R44 Raven. Punktem odniesienia dla zarejestrowanego pomiaru były granice normatywne pola elektromagnetycznego (PEM), które może oddziaływać na pilota w trakcie lotu. W wybranych pomiarach maksymalna wartość zarejestrowana przez miernik wynosiła E = 7.68 V/m podczas lądowania na lotnisku wyposażonym w system podejścia dookólnego VHF (Very High Frequency) -VOR. Opracowany model sieci neuronowych służy do przewidywania wartości ekspozycji pola elektrycznego w kontekście analizy bezpieczenstwa lotów.

13

Monitoring, diagnosis and localization of the partial shading fault in a photovoltaic power plant with an approach by artificial neural networks

Saada Zakarya, Zebirate Soraya

Przegląd Elektrotechniczny

|

2022

|

R. 98, nr 12

182--186

EN

This paper aims to propose a useful modeling diagnostic method for solar plants. The study was performed on the basis of the localization of the failing panel obtained by an effective comparison of measured output voltages and estimator voltages. The comparison is done with the ideal solar plant using learning approach based on artificial neuronal network (ANN). The partial shading failure was detected by the given equation d²ΔV/dI²>0. The obtained results using MATLAB/Simulink environment show a satisfactory performance in terms of rapidity and precision under variable shading conditions.

PL

Celem artykułu jest zaproponowanie użytecznej metody diagnostycznej modelowania dla elektrowni słonecznych. Badania przeprowadzono na podstawie lokalizacji uszkodzonego panelu uzyskanej poprzez efektywne porównanie zmierzonych napięć wyjściowych i napięć estymatorów. Porównanie jest dokonywane z idealną elektrownią słoneczną przy użyciu podejścia uczenia opartego na sztucznej sieci neuronowej (ANN). Częściowe zacienienie zostało wykryte za pomocą podanego równania d²ΔV/dI²>0. Uzyskane wyniki w środowisku MATLAB/Simulink wykazują zadowalające działanie pod względem szybkości i precyzji w zmiennych warunkach zacienienia.

14

Metody szacowania wskaźnika nagromadzenia odpadów służącego do określenia lokalnego potencjału energetycznego z odpadów komunalnych na terenie gmin w Polsce

Nęcka Krzysztof, Szul Tomasz

Przegląd Elektrotechniczny

|

2022

|

R. 98, nr 12

258--261

PL

W pracy sprawdzono przydatność wybranych metod prognostycznych do szacowania lokalnego wskaźnika ilości generowanych odpadów komunalnych a tym samym potencjału energetycznego odpadów, które będą mogły być wykorzystane w instalacjach termicznego przetwarzania odpadów. Prognozy stawiano w oparciu o metody: sztucznych sieci neuronowych (ANN), drzewa regresyjne (CART), wielozmienną regresję adaptacyjną z użyciem funkcji sklejanych (MARS), losowy las dla regresji (RFR), teorii zbiorów przybliżonych (RST), wzmacniane drzewa regresyjne (SRT) a także metody kombinowane będące połączeniem kilku metod prognostycznych.

EN

In this paper, the usefulness of selected forecasting methods was tested to estimate the local rate of municipal waste generation, and thus the energy potential of waste, which can be utilised in thermal waste treatment plants. Forecasts were made on the basis of the following methods: artificial neural networks (ANN), regression trees (CART), multivariate adaptive regression with glued functions (MARS), random forest for regression (RFR), rough set theory (RST), boosted regression trees (SRT), and combined methods which are a combination of several forecasting methods.

15

Internal model control using artificial neural networks for linear minimum phase systems

Mahmoud Ines, Saidi Imen, Bejaoui Islem

Przegląd Elektrotechniczny

|

2022

|

R. 98, nr 2

45--48

EN

In this paper, we are interested in the internal model control using neural networks in the case of linear minimum phase systems. We propose, to use the neural internal model control to solve the inversion problem of a model M(z) in order to design the IMC controller. An example application is presented and the implementation of the proposed approach is discussed.

PL

W niniejszym artykule interesuje nas sterowanie modelem wewnętrznym za pomocą sieci neuronowych w przypadku liniowych układów o minimalnej fazie. Proponujemy wykorzystanie neuronowego sterowania modelem wewnętrznym do rozwiązania problemu inwersji modelu M(z) w celu zaprojektowania sterownika IMC. Przedstawiono przykładową aplikację oraz omówiono wdrożenie proponowanego podejścia.

16

Application of artificial neural network for speed control of BLDC motor 90KW in electrical bus

Pamuji Feby Agung, Prihantari Kharisma Julieta, Riawan Dedet Candra, Asfani Dimas Anton, Suryoatmojo Heri, Guntur Harus L., Sudarmanta Bambang, Arumsari Nurvita

Przegląd Elektrotechniczny

|

2022

|

R. 98, nr 2

203--210

EN

There are many research on electric vehicles to reduce environmental pollution due to vehicles that use fossil fuels. The advantages of using a BLDC motor are high efficiency, high torque, reduced noise, long lifetime, and easy maintenance. Using of BLDC motors in electric vehicles is sometimes not optimal due to varying set points and presence of loads. Then a speed motor is needed to be controlled so the motor can work properly. In this research using the Artificial Neural Network (ANN) method. The ANN on this speed controller is practical as a 3-phase inverter input voltage control so the speed of BLDC motor can match the set point. In the simulation in this research, controlled based ANN is applied to electric buses with large torque, from the simulation it can be seen that Controlled based ANN can work well.

PL

Istnieje wiele badań dotyczących pojazdów elektrycznych mających na celu zmniejszenie zanieczyszczenia środowiska przez pojazdy wykorzystujące paliwa kopalne. Zalety stosowania silnika BLDC to wysoka sprawność, wysoki moment obrotowy, obniżony poziom hałasu, długa żywotność i łatwa konserwacja. Stosowanie silników BLDC w pojazdach elektrycznych czasami nie jest optymalne ze względu na różne nastawy i obecność obciążeń. Następnie konieczne jest sterowanie prędkością silnika, aby silnik mógł działać prawidłowo. W badaniach wykorzystano metodę Sztucznej Sieci Neuronowej (ANN). SSN na tym regulatorze prędkości jest praktycznym sterowaniem napięcia wejściowego falownika 3-fazowego, dzięki czemu prędkość silnika BLDC może być zgodna z wartością zadaną. W symulacji w niniejszych badaniach, kontrolowany SSN jest stosowany do autobusów elektrycznych o dużym momencie obrotowym, z symulacji widać, że SSN w oparciu o sterowanie może dobrze działać.

17

A RBF artificial neural network to predict a fuel cell maximum power point

Foughali Yahia, Mankour Mohamed, Sekour Mohamed, Azzeddine Hocine Abdelhak, Larbaoui Ahmed, Chaouch Djamel Eddine, Berka Mohammed

Przegląd Elektrotechniczny

|

2022

|

R. 98, nr 7

100--104

EN

In this article, an artificial neural network (ANN) based maximum power point tracker (MPTT) for proton exchange membrane fuel cell (PEMFC) is proposed. For this purpose, a Radial Basis Function Artificial Neural Network (RBF ANN) is used to predict the voltage and the current of a fuel cell maximum power point at different fuel cell operating conditions. To train the proposed artificial neural network, a set of maximum power points defined by their corresponding current and voltage values is generated using a validated electrochemical fuel cell model. To ensure the validity of the ANN, we compare the results found by the ANN to those obtained using the electrochemical PEMFC model. The results show that the developed ANN can accurately and quickly predict current and voltage fuel cells at maximum power point for any operating conditions.

PL

W tym artykule zaproponowano śledzenie maksymalnego punktu mocy (MPTT) oparte na sztucznej sieci neuronowej (ANN) dla ogniwa paliwowego z membraną do wymiany protonów (PEMFC). W tym celu wykorzystuje się sztuczną sieć neuronową Radial Basis Function (RBF ANN) do przewidywania napięcia i prądu punktu maksymalnej mocy ogniwa paliwowego w różnych warunkach pracy ogniwa paliwowego. Aby wytrenować proponowaną sztuczną sieć neuronową, przy użyciu sprawdzonego modelu elektrochemicznego ogniwa paliwowego generowany jest zestaw maksymalnych punktów mocy określonych przez odpowiadające im wartości prądu i napięcia. Aby zapewnić wiarygodność ANN, porównujemy wyniki uzyskane przez ANN z wynikami uzyskanymi przy użyciu elektrochemicznego modelu PEMFC. Wyniki pokazują, że opracowana SSN może dokładnie i szybko przewidywać prąd i napięcie ogniw paliwowych w punkcie maksymalnej mocy w dowolnych warunkach pracy.

18

Comparative analysis of Solar Generation System with 21- CHB-MLI integrated SAPF based ANN and AGPSO tuned PI controller to enhance power quality

Agrawal Seema, Kumar Mahendra, Palwalia D. K.

Journal of Power Technologies

|

2022

|

Vol. 102, nr 4

121--131

EN

This paper represents comparative analysis of artificial neural network (ANN) and AGPSO tuned PI controller based power quality improvement solar generation system. Now a day's Power quality is a major problem due to non-liner load based on power electronics. SAPF is solution to overcome such power quality issues in dynamic manner. With the use of both soft computing controllers based Shunt active power filter, it is tried to reduce harmonics (distortions), compensate reactive power, enhance power quality and power factor correction of supply voltage. System comprises 21-Level cascaded H-bridge inverter supplied from photovoltaic panel, series coupling inductor and self supported DC (capacitor) bus. Voltage harmonics of supplied voltage from PV is reduced by 21-level cascades H-bridge inverter in which switching signal is generated by carrier based in phase level shifted pulse width modulation technique. Incremental conductance (IC) MPPT technique is incorporated to maximize PV panel output. Phase locked loop based unit template generation and Levenberg Marquardt algorithm trained ANN and AGPSO tuned PI controller based DC bus voltage regulation is utilized for current quality improvement in SAPF. Comparative results show the effectiveness of ANN controller than A GPSO tuned PI controller. Suggested model is simulated in Matlab/Simulink 2016(b) for effectiveness.

19

Modeling and optimization of cutting parameters when turning EN-AW-1350 aluminum alloy

Khrouf F., Tebassi H., Yallese M. A., Chaoui K., Haddad A.

International Journal of Applied Mechanics and Engineering

|

2022

|

Vol. 27, no. 2

124--142

EN

An experimental investigation is carried out to examine the effects of various cutting parameters on the response criteria when turning EN-AW-1350 aluminum alloy under dry cutting conditions. The experiments related to the analysis of the influence of turning parameters on the surface roughness (Ra) and material removal rate (MRR) were carried out according to the Taguchi L27 orthogonal array (313) approach. The analysis of variance (ANOVA) was applied to characterizing the main elements affecting response parameters. Finally, the desirability function (DP) was applied for a bi-objective optimization of the machining parameters with the objective of achieving a better surface finish (Ra) and a higher productivity (MRR). The results showed that the cutting speed is the most dominant factor affecting Ra followed by the feed rate and the depth of cut. Moreover, the Artificial Neural Network (ANN) approach is found to be more reliable and accurate than its Response Surface methodology (RSM) counterpart in terms of predicting and detecting the non-linearity of the surface roughness and material removal rate mathematical models. ANN provided prediction models with a precision benefit of 8.21% more than those determined by RSM. The latter is easier to use, and provides more information than ANN in terms of the impacts and contributions of the model terms.

20

Modeling and prediction of thermal conductivity ratio of metal-oxide based nano-fluids using artificial neural network and power law

Hanief Mohammad, Irfan Quresh, Parvez Malik

Chemical and Process Engineering

|

2022

|

Vol. 43, nr 2

159–-163

EN

In this study, the thermal conductivity ratio model for metallic oxide based nano-fluids is proposed. The model was developed by considering the thermal conductivity as a function of particle concentration (percentage volume), temperature, particle size and thermal conductivity of the base fluid and nano-particles. The experimental results for Al2O3, CuO, ZnO, and TiO2 particles dispersed in ethylene glycol, water and a combination of both were adopted from the literature. Artificial neural network (ANN) and power law models were developed and compared with the experimental data based on statistical methods. ANOVA was used to determine the relative importance of contributing factors, which revealed that the concentration of nano-particles in a fluid is the single most important contributing factor of the conductivity ratio.