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Tytuł:
Determination of soil infiltration rate equation based on soil properties using multiple linear regression
Autorzy:
Harisuseno, Donny
Cahya, Evi N.
Powiązania:
https://bibliotekanauki.pl/articles/1844413.pdf
Data publikacji:
2020
Wydawca:
Instytut Technologiczno-Przyrodniczy
Tematy:
infiltration rate
model performance
multiple linear regression
soil property
Opis:
Infiltration process plays important role in water balance concept particularly in runoff analysis, groundwater recharged, and water conservation. Hence, increasing knowledge concerning infiltration process becomes essential for water manager to gain an effective solution to water resources problems. This study employed multiple linear regression for estimating infiltration rate where the soil properties used as the predictor variable and measured infiltration rate as the response variable. Field measurement was conducted at sixteen points to obtain infiltration rate using double ring infiltrometer and soil properties namely soil porosity, silt, clay, sand content, degree of saturation, and water content. The result showed that measured infiltration rate had an average initial infiltration rate (f0) of 6.92 mm∙min–1 and final infiltration rate (fc) of 1.49 mm∙min–1. Soil porosity and sand content showed a positive correlation with infiltration rate by 0.842, 0.639, respectively, while silt, clay, water content, and degree of saturation exhibited a negative correlation by –0.631, –0.743, –0.66 and –0.49, respectively. Three types of regression equations were established based on type of soil properties used as predictor variables. The model performance analysis was conducted for each equation and the result shows that the equation with five predictor variables fMLR_3 = – 62.014 + 1.142 soil porosity – 0.205 clay, – 0.063 sand – 0.301, silt + 0.07 soil water content with R2 (0.87) and Nash–Sutcliffe (0.998) gave the best result for estimating infiltration rate. The study found that soil porosity contributes mostly to the regression equation that indicates great influence in controlling soil infiltration behavior.
Źródło:
Journal of Water and Land Development; 2020, 47; 77-88
1429-7426
2083-4535
Pojawia się w:
Journal of Water and Land Development
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Development of pedotransfer functions for predicting soil bulk density : a case study in Indonesian small island
Autorzy:
Yanti, Evi Dwi
Mulyono, Asep
Djuwansah, Muhammad Rahman
Narulita, Ida
Putra, Risandi Dwirama
Surinati, Dewi
Powiązania:
https://bibliotekanauki.pl/articles/2048512.pdf
Data publikacji:
2021
Wydawca:
Instytut Technologiczno-Przyrodniczy
Tematy:
bulk density
multiple linear regression
pedotransfer function
soil property
Opis:
Unlike many other countries, tropical regions such as Indonesia still lack publications on pedotransfer functions (PTFs), particularly ones dedicated to the predicting of soil bulk density. Soil bulk density affects soil density, porosity, water holding capacity, drainage, and the stock and flux of nutrients in the soil. However, obtaining access to a laboratory is difficult, time-consuming, and costly. Therefore, it is necessary to utilise PTFs to estimate soil bulk density. This study aims to define soil properties related to soil bulk density, develop new PTFs using multiple linear regression (MLR), and evaluate the performance and accuracy of PTFs (new and existing). Seven existing PTFs were applied in this study. For the purposes of evaluation, Pearson’s correlation (r), mean error (ME), root mean square error (RMSE), and modelling efficiency (EF) were used. The study was conducted in five soil types on Bintan Island, Indonesia. Soil depth and organic carbon (SOC) are soil properties potentially relevant for soil bulk density prediction. The ME, RMSE, and EF values were lower for the newly developed PTFs than for existing PTFs. In summary, we concluded that the newly developed PTFs have higher accuracy than existing PTFs derived from literature. The prediction of soil bulk density will be more accurate if PTFs are applied directly in the area that is to be studied.
Źródło:
Journal of Water and Land Development; 2021, 51; 181-187
1429-7426
2083-4535
Pojawia się w:
Journal of Water and Land Development
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A method of calculation of ship resistance on calm water useful at preliminary stages of ship design
Autorzy:
Żelazny, K.
Powiązania:
https://bibliotekanauki.pl/articles/359058.pdf
Data publikacji:
2014
Wydawca:
Akademia Morska w Szczecinie. Wydawnictwo AMSz
Tematy:
approximate method of resistance calculation
bulk carrier
multiple linear regression
Opis:
During preliminary stages of ship design, decisions on ship properties are made only with little knowledge on ship hull geometry – a ship designer has only the basis dimensions at his disposal. Therefore on these initial stages of ship design, methods of calculation of ship properties (eg. resistance) on the basis of basic design criteria are indispensable. The article presents a new method of calculation of bulk carriers resistance which proves exact even with a minimum number of geometrical parameters of a ship’s hull.
Źródło:
Zeszyty Naukowe Akademii Morskiej w Szczecinie; 2014, 38 (110); 125-130
1733-8670
2392-0378
Pojawia się w:
Zeszyty Naukowe Akademii Morskiej w Szczecinie
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Integration of Overall Equipment Effectiveness and Six Sigma Approach to Minimize Product Defect and Machine Downtime
Autorzy:
Nurprihatin, Filscha
Rembulan, Glisina Dwinoor
Andry, Johanes Fernandes
Lubis, Maulidina
Widiwati, Ivana Tita Bella
Vaezi, Ali
Powiązania:
https://bibliotekanauki.pl/articles/27324207.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
overall equipment effectiveness
six sigma
multiple linear regression
DMAIC
packaging quality
Opis:
This study was conducted in a company that produces palm oil-based products such as cooking oil and margarine. The study aimed to encounter defects in packaging pouches. This study integrated the overall equipment effectiveness (OEE) with the six sigma DMAIC method. The OEE was performed to measure the efficiency of the machine. Three factors were measured in OEE: availability, performance, and quality. These factors were calculated and compared to the OEE world-class value. Then, the Multiple Linear Regression was performed using SPSS to determine the correlation between measurement variables toward the OEE value. Lastly, the six sigma method was implemented through the DMAIC approach to find the solution and improve the packaging quality. Supposing the recommendations are implemented, the OEE is expected to increase from 82% to 85%, with availability ratio, performance ratio, and quality ratio at, 99%, 86%, and 99.8%, respectively.
Źródło:
Management and Production Engineering Review; 2023, 14, 4; 71--91
2080-8208
2082-1344
Pojawia się w:
Management and Production Engineering Review
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Applying the Wastewater Quality Index for Assessing the Effluent Quality of Recently Upgraded Meet Abo El-koum Wastewater Treatment Plant
Autorzy:
Ayoub, Mohamed
El-Morsy, Ahmed
Powiązania:
https://bibliotekanauki.pl/articles/1838434.pdf
Data publikacji:
2021
Wydawca:
Polskie Towarzystwo Inżynierii Ekologicznej
Tematy:
assessment
evaluation
multiple linear regression
quality
wastewater
WWTP
wastewater treatment plant
Opis:
The wastewater quality index (WWQI) can be defined as a single value, which reflects the overall wastewater quality related to its input constituent parameters. The major objective of the present study was to investigate the suitability of the effluent quality from Meet Abo El-koum wastewater treatment plant in Egypt for safe disposal based on the wastewater quality index approach. Moreover, statistical analysis was applied to develop a simple model using multiple linear regression (MLR) for accurate prediction of WWQI depending on different wastewater quality parameters. The results indicate good quality of the treated wastewater for safe disposal in general. Moreover, it is apparent that about 17% of the WWQI values reached excellent quality referring to the classification of the WWQI levels. For greater simplicity, a relationship between BOD5 and COD was deduced using linear regression, so that the results of the BOD5 analyses that appear after five days can be skipped. This approximation can be used to calculate WWQI on a specific day given the results of the treated wastewater analyses on that day.
Źródło:
Journal of Ecological Engineering; 2021, 22, 2; 128-133
2299-8993
Pojawia się w:
Journal of Ecological Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The reasons of the regional differences of agricultural land prices - Hungarian case study from Hungary
Powody regionalnego zróżnicowania cen ziemi rolniczej - studium przypadku Węgier
Autorzy:
Fekete-Farkas, M.
Toth-Naar, Z.
Baranyai, Z.
Szucs, I.
Vinogradov, S.A.
Powiązania:
https://bibliotekanauki.pl/articles/866227.pdf
Data publikacji:
2013
Wydawca:
The Polish Association of Agricultural and Agribusiness Economists
Tematy:
reason
regional differentiation
agricultural land
land price
multiple linear regression
Hungary
Opis:
Agricultural land market in Hungary is in process of development, the prices of land is times lower compared to land prices in old member states of the EU. Because of their lower income Hungarian nationals do not have substantial possibilities to acquire ownership over land like nationals of the old member states of the EU. In order to preserve the agricultural sector from shocks that might arise from the differences in land prices and income with the rest of EU, Hungary as the others Central and Eastern European new member states countries (the Czech Republic, Estonia, Latvia, Lithuania, Poland and Slovakia) during the accession negotiations in 2003 was granted the possibility to maintain existing national provisions restricting the acquisition of agricultural land or forests. Based on the data provided by the Hungarian Farm Accountancy Data Network and by the Hungarian Central Statistic Office the authors examined the specific impacts of factors influencing on arable land prices.
Na podstawie danych z węgierskiego FADN i Centralnego Biura Statystycznego zbadano szczegółowe oddziaływanie czynników wpływających na ceny gruntów ornych w różnych regionach Węgier. W badaniach prowadzonych przy pomocy analizy regresji wielokrotnej wykazano, że na ceny gruntów ornych wpływają różne czynniki, w zależności od regionu Węgier.
Źródło:
Roczniki Naukowe Stowarzyszenia Ekonomistów Rolnictwa i Agrobiznesu; 2013, 15, 1
1508-3535
2450-7296
Pojawia się w:
Roczniki Naukowe Stowarzyszenia Ekonomistów Rolnictwa i Agrobiznesu
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Life Factor Approach to the Yield Prediction: a Comparison with a Technological Approach in Reliability and Accuracy
Autorzy:
Lykhovyd, Pavlo
Powiązania:
https://bibliotekanauki.pl/articles/124852.pdf
Data publikacji:
2019
Wydawca:
Polskie Towarzystwo Inżynierii Ekologicznej
Tematy:
artificial neural network
life factor
multiple linear regression
technological factor
yield modelling
Opis:
There are a number of various approaches to the development of yield predictive models in agriculture. One of the most popular ones is based on the yield modeling from the parameters of crop cultivation technology. However, there is another view on the yield prediction models, which is based on the use of life factors as yielding parameters. Our study is devoted to the comparison of a conventional technological approach to the yield prediction with a less prevalent approach of life factor based yield modeling. The testing of two approaches was performed by using the yielding data of sweet corn cultivated in the field trials under the drip-irrigated conditions of the Southern Ukraine, under the different technological treatments, viz. plowing depth, nutrition, and crop density. We developed two multiple linear regression models to compare their efficiency in the yielding predictions. One of the models used cultivation technology parameters as the inputs while the other used life factors as the inputs. Life factors were expressed in numeric values by using the following converter: total water consumption of the crop was used as the factor of water, the total sum of positive temperatures was used as the factor of heat, and the total sum of the main nutrients (NPK) available in the soil was used as the factor of nutrition. The results of the study proved an equal accuracy and reliability of the studied models of sweet corn yields, which is obvious from the values of RSQ. RSQ of the both studied regression models was 0.897. However, additional check of the modeling approaches applied in the feed-forward artificial neural network showed that the life factor based model with the RSQ value of 0.953 provided better yield predictions than the technologically based model with the RSQ value of 0.913. Therefore, we concluded that the life factor approach should be preferred to the technological approach in the development of yield predictive models for agriculture.
Źródło:
Journal of Ecological Engineering; 2019, 20, 6; 177-183
2299-8993
Pojawia się w:
Journal of Ecological Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Relationships between selected traits in maize (Zea mays L.). Part 2. Multiple linear regression
Współzależność pomiędzy wybranymi cechami kukurydzy (Zea mays L.). Cz. 2. Liniowa regresja wielokrotna
Autorzy:
Bocianowski, Jan
Cyplik, Adrian
Szulc, Piotr
Kobus-Cisowska, Joanna
Powiązania:
https://bibliotekanauki.pl/articles/336819.pdf
Data publikacji:
2019
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Maszyn Rolniczych
Tematy:
maize
multiple linear regression
grain yield
kukurydza
liniowa regresja wielokrotna
plon ziarna
Opis:
The study comprised of 13 maize cultivars, evaluated at two years in a randomized complete block design, with four replicates. To assess the quantitative impact of individual traits on the grain yield the multiple regression analysis was used. We observed grain yield and seven quantitative traits: SPAD, length of ears, number of kernels in row, damage of maize caused by P. nubilalis, infection of maize by Fusarium spp., number of ears and content of chlorophyll a.
Badanie obejmowało 13 odmian kukurydzy, analizowanych w dwóch latach w doświadczeniach polowych, w układzie bloków losowanych kompletnych, w czterech powtórzeniach. Do oceny wpływu poszczególnych cech ilościowych na plon ziarna zastosowano analizę funkcji regresji wielokrotnej. Obserwowano plon ziarna i siedem cech ilościowych: SPAD, długość kolby, liczba ziaren w rzędzie, procent roślin uszkodzonych przez P. nubilalis, porażenie przez Fusarium, liczba kolb oraz zawartość chlorofilu a.
Źródło:
Journal of Research and Applications in Agricultural Engineering; 2019, 64, 3; 15-19
1642-686X
2719-423X
Pojawia się w:
Journal of Research and Applications in Agricultural Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Predicting the properties of corrugated base papers using multiple linear regression and artificial neural networks
Autorzy:
Adamopoulos, S
Karageorgos, A.
Rapti, E.
Birbilis, D.
Powiązania:
https://bibliotekanauki.pl/articles/52433.pdf
Data publikacji:
2016
Wydawca:
Sieć Badawcza Łukasiewicz - Instytut Technologii Drewna
Tematy:
prediction
paper property
multiple linear regression
artificial neural network
linerboard
recovered fibre
Źródło:
Drewno. Prace Naukowe. Doniesienia. Komunikaty; 2016, 59, 198
1644-3985
Pojawia się w:
Drewno. Prace Naukowe. Doniesienia. Komunikaty
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Micro-EDM process modeling and machining approaches for minimum tool electrode wear for fabrication of biocompatible micro-components
Autorzy:
Puthumana, G.
Powiązania:
https://bibliotekanauki.pl/articles/99444.pdf
Data publikacji:
2017
Wydawca:
Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
Tematy:
biocompatibility
microdevice
electrical discharge machining
modeling
multiple linear regression
artificial neural networks
Opis:
Micro-electrical discharge machining (micro-EDM) is a potential non-contact method for fabrication of biocompatible micro devices. This paper presents an attempt to model the tool electrode wear in micro-EDM process using multiple linear regression analysis (MLRA) and artificial neural networks (ANN).The governing micro-EDM factors chosen for this investigation were: voltage (V), current (I), pulse on time (Ton) and pulse frequency (f). The proposed predictive models generate a functional correlation between the tool electrode wear rate (TWR) and the governing micro-EDM factors. A multiple linear regression model was developed for prediction of TWR in ten steps at a significance level of 90%. The optimum architecture of the ANN was obtained with 7 hidden layers at an R-sq value of 0.98. The predicted values of TWR using ANN matched well with the practically measured and calculated values of TWR. Based on the proposed soft computing-based approach towards biocompatible micro device fabrication, a condition for the minimum tool electrode wear rate (TWR) was achieved.
Źródło:
Journal of Machine Engineering; 2017, 17, 3; 97-111
1895-7595
2391-8071
Pojawia się w:
Journal of Machine Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Hybrid wavelet transform – MLR and ANN models for river flow prediction: Case study of Brahmaputra river (Pancharatna station)
Autorzy:
Khandekar, Sachin Dadu
Aswar, Dinesh Shrikrishna
Sabale, Pandurang Digamber
Khandekar, Varsha Sachin
Bajad, Mohankumar Namdeorao
Powiązania:
https://bibliotekanauki.pl/articles/36074310.pdf
Data publikacji:
2024
Wydawca:
Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Wydawnictwo Szkoły Głównej Gospodarstwa Wiejskiego w Warszawie
Tematy:
wavelet transform
artificial neural network
multiple linear regression
streamflow
Daubechies wavelet
time series
Opis:
In this research, discrete wavelet transform (DWT) is combined with MLR and ANN to develop WMLR and WANN hybrid models, respectively, for the Brahmaputra river (Pancharatna station) flow forecasting. Daily flow data for the period of 10 year were decomposed (up to fifth level) into detailed and approximation coefficients (using Daubechies wavelets db1, db2, db3, db8 and db10) which were fed as input to MLR and ANN to get the predicted discharge values two days, four days, seven days and 14 days ahead. For all lead times, the WMLR-db10 model was found to be superior as compared to WANN-db1, WANN-db2, WANN-db3, WANN-db8, WMLR-db1, WMLR-db2, WMLR-db3, WMLR-db8 and single MLR and ANN models. During testing period, the values of determination coefficient (R2) and RMSE for WMLR-db10 model for two-, four-, seven- and 14-day lead time were found to be, respectively, 0.996 (751.87 m3·s–1), 0.991 (1,174.80 m3·s–1), 0.984 (1,585.02 m3·s–1), and 0.968 (2,196.46 m3·s–1). Also, it was observed that for lower order wavelets (db1, db2, db3) WANN’s performance was better, and for higher order wavelets (db8, db10) WMLR’s performance was better. Correspondingly, it was observed that all hybrid models’ efficiency increased with increase in the decomposition level.
Źródło:
Scientific Review Engineering and Environmental Sciences; 2024, 33, 1; 69-94
1732-9353
Pojawia się w:
Scientific Review Engineering and Environmental Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Modeling Pollution Index Using Artificial Neural Network and Multiple Linear Regression Coupled with Genetic Algorithm
Autorzy:
Abdulkareem, Iman Ali
Abbas, Abdulhussain A.
Dawood, Ammar Salman
Powiązania:
https://bibliotekanauki.pl/articles/2068477.pdf
Data publikacji:
2022
Wydawca:
Polskie Towarzystwo Inżynierii Ekologicznej
Tematy:
Shatt Al-Arab river
comprehensive pollution index
multiple linear regression
artificial neural network
genetic algorithm
Opis:
Shatt Al-Arab River in Basrah province, Iraq, was assessed by applying comprehensive pollution index (CPI) at fifteen sampling locations from 2011 to 2020, taking into consideration twelve physicochemical parameters which included pH, Tur., TDS, EC, TH, Na+, K+, Ca+2, Mg+2, Alk., SO4-2, and Cl-. The effectiveness of multiple linear regression (MLR) and artificial neural network (ANN) for predicting comprehensive pollution index was examined in this research. In order to determine the ideal values of the predictor parameters that lead to the lowest CPI value, the genetic algorithm coupled with multiple linear regression (GA-MLR) was used. A multi-layer feed-forward neural network with backpropagation algorithm was used in this study. The optimal ANN structure utilized in this research consisted of three layers: the input layer, one hidden layer, and one output layer. The predicted equation of the comprehensive pollution index was created using the regression technique and used as an objective function of the genetic algorithm. The minimum predicted comprehensive pollution index value recommended by the GA-MLR approach was 0.3777.
Źródło:
Journal of Ecological Engineering; 2022, 23, 3; 236--250
2299-8993
Pojawia się w:
Journal of Ecological Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
IS MULTIPLE LINEAR REGRESSION THE PROPER TOOL OF MODELLING A BEHAVIOUR OF REAL SYSTEMS?
Autorzy:
Nowak, Jacek
Powiązania:
https://bibliotekanauki.pl/articles/453283.pdf
Data publikacji:
2009
Wydawca:
Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Katedra Ekonometrii i Statystyki
Tematy:
model of real system
multiple linear regression
real system structure
discrete automaton
„black box” modelling
quality of approximation
Opis:
Methodological assumption that multiple linear regression is an adequate tool of modelling the behaviour of real systems is checked. To do this the experiment is organised on the basis of simple “real” system represented as finite discrete automaton. Main result is that in situation of “black box” modelling the approximation of output variables with multiple linear regressions (from several samples and under different conditions) may not fulfil any of criterions of feasible approximation of systems behaviour, also in situations where real relation between input and output variables is strictly linear and only one of variables is omitted.
Źródło:
Metody Ilościowe w Badaniach Ekonomicznych; 2009, 10, 1; 194-206
2082-792X
Pojawia się w:
Metody Ilościowe w Badaniach Ekonomicznych
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A comparative analysis of artificial neural network predictive and multiple linear regression models for ground settlement during tunnel construction
Autorzy:
Zou, Baoping
Chibawe, Musa
Hu, Bo
Deng, Yansheng
Powiązania:
https://bibliotekanauki.pl/articles/27312113.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
budowa
tunel
osiadanie gruntu
regresja liniowa wielokrotna
sieć neuronowa sztuczna
tunnel
construction
ground settlement
multiple linear regression
artificial neural network
Opis:
Ground settlement during and after tunnelling using TBM results in varying dynamic and static load action on the geo-stratum. It is an undesirable effect of tunnel construction causing damage to the surface and subsurface infrastructure, safety risk, and increased construction cost and quality issues. Ground settlement can be influenced by several factors, like method of tunnelling, tunnel geometry, location of tunnelling machine, machine operational parameters, depth & its changes, and mileage of recording point from starting point. In this study, a description and evaluation of the performance of the artifcial neural network (ANN) was undertaken and a comparison with multiple linear regression (MLR) was carried out on ground settlement prediction. The performance of these models was evaluated using the coefficient of determination R2, root mean square error (RMSE) and mean absolute percentage error (MAPE). For ANN model, the R2, RMSE and MAPE were calculated as 0.9295, 4.2563 and 3.3372, respectively, while for MLR, the R2, RMSE and MAPE, were calculated as 0.5053, 11.2708, 6.3963 respectively. For ground settlement prediction, both ANN and MLR methods were able to predict significantly accurate results. It was further noted that the ANN performance was higher than that of the MLR.
Źródło:
Archives of Civil Engineering; 2023, 69, 2; 503--515
1230-2945
Pojawia się w:
Archives of Civil Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An investigation of the effects of moderator variables on the lower heating value estimation of lignite deposits in Turkey
Badanie wpływu zmiennych moderatora na szacowanie wartości opałowej złóż węgla brunatnego w Turcji
Autorzy:
Aksoy, Mehmet
Powiązania:
https://bibliotekanauki.pl/articles/27311661.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Instytut Gospodarki Surowcami Mineralnymi i Energią PAN
Tematy:
lignite deposit
lower heating value
multiple linear regression
moderator analysis
proximate analysis
węgiel brunatny
wartość opałowa
wielokrotna regresja liniowa
analiza moderatora
analiza techniczna
Opis:
Turkey has 19.3 billion tons of lignite reserves and the vast majority of these Neogene lignite deposits are preferred for use in thermal power plants due to their low calorific value. The calorific value of lignite used in thermal power plants for electricity generation must be kept under constant control. In the control of calorific value, the estimation of the lower and higher heating values (LHV and HHV) of lignite is of great importance. In the literature, there are many studies that establish a relationship between the heating values of coal and proximate and ultimate analysis variables. In the studies dealing with proximate analysis data, it is observed that although the coefficients of the obtained multiple linear regression models (MRM) are statistically insignificant, these models are used to predict heating values because of the meaningful correlation coefficient. In this study, it is investigated whether moderator variables are effective on LHV estimation with proximate analysis data collected from forty-one lignite basins in different regions of Turkey, and a moderator variable analysis (MVA) model is developed to be used for the prediction of LHV. As a result of the study, it is found that the proposed MVA model is in accordance with observation values (coefficient of determination R2 = 0.951), and absolute and standard errors are also small. Therefore, it is concluded that the use of MVA to estimate the LHV of Turkey’s lignite is found to be more statistically meaningful.
Turcja posiada 19,3 mld ton zasobów węgla brunatnego, a zdecydowana większość tych neogeńskich złóż węgla brunatnego jest preferowana do wykorzystania w elektrowniach cieplnych ze względu na ich niską wartość opałową. Wartość opałowa węgla brunatnego wykorzystywanego w elektrowniach ciepłowniczych do produkcji energii elektrycznej musi być stale kontrolowana. W procesie kontroli wartości opałowej bardzo ważne jest oszacowanie wartości opałowej i ciepła spalania węgla brunatnego. W literaturze istnieje wiele badań, które ustalają związek między wartościami opałowymi węgla a zmiennymi analizy przybliżonej (technicznej) i końcowej. W badaniach dotyczących danych analizy technicznej zaobserwowano, że chociaż współczynniki uzyskanych modeli wielokrotnej regresji liniowej (MRM) są statystycznie nieistotne, modele te są wykorzystywane do przewidywania wartości opałowych ze względu na znaczący współczynnik korelacji. W niniejszym artykule zbadano, czy zmienne moderatora są skuteczne w szacowaniu wartości opałowej (LHV) na podstawie danych z analizy technicznej zebranych z czterdziestu jeden zagłębi węgla brunatnego w różnych regionach Turcji, a także opracowano model analizy zmiennych moderatora (MVA), który ma być wykorzystywany do przewidywania LHV. W wyniku badań stwierdzono, że proponowany model MVA jest zgodny z wartościami obserwacji (współczynnik determinacji R2 = 0,951), a błędy bezwzględne i standardowe są również niewielkie. W związku z tym stwierdzono, że wykorzystanie MVA do oszacowania LHV tureckiego węgla brunatnego jest statystycznie uzasadnione.
Źródło:
Gospodarka Surowcami Mineralnymi; 2023, 39, 3; 199--216
0860-0953
Pojawia się w:
Gospodarka Surowcami Mineralnymi
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-15 z 17

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