Relative Prediction Error Research Articles

Research on load prediction of low-calorific fuel fired gas turbine based on data and knowledge hybrid model

The high-precision load prediction technology plays a vital role in load control and health management for gas turbines. Low-calorific fuel fired gas turbines pose an especially significant challenge for load prediction due to the frequent and wide fluctuations of gas parameters. First, an improved hybrid-dimension physical-based model is proposed, which is corrected based on segmented operation data and integrates zero-dimensional thermodynamic knowledge of gas turbines with three-dimensional fluid dynamics simulation knowledge. Secondly, an optimal data-driven model is proposed through conducting a systematic comparative study on four popular machine learning models with two types of input variables feature extraction. Then, a novel hybrid model is proposed based on the improved physical-based model, optimal data-driven model, and the principle of minimizing hybrid model errors. The average relative prediction error of the hybrid model over the complete operation range is 0.68%. Compared with the physical-based model and the data-driven model, the proposed hybrid model can improve the prediction accuracy by 63% and 29%, respectively. As the high-calorific fuel fired gas turbine system is relatively simple and has a small range of fuel fluctuations, the method established in this paper can be extended to high-calorific fuel fired gas turbines, which is of great significance for promoting the dynamic balance of loads in a new type of electric power system based on new energy.

Simulation of landslide dam failure due to overtopping considering vertical soil particles distribution

Accurate prediction of potential landslide dam breaches is crucial for timely and effective emergency responses to the resulting flood. Although many models for landslide dam overtopping have been proposed in past studies, most of these models did not consider the heterogeneity of dam materials, which is important in reflecting the erodibility of soil. In this study, a new mathematical model for landslide dam breaches due to overtopping was proposed based on a constructed vertical distribution model of dam particle size. Two real-world dams, the Tangjiashan landslide dam and Baige landslide dam, were used to evaluate the performance of the proposed model. In addition, the proposed model was compared with four existing dam failure models, and sensitivity analysis was conducted on the median particle size and particle size distribution. The results demonstrate that the computational outcomes of the proposed model align more closely with the actual results, indicating that the proposed model outperforms the four existing models in accurately simulating the landslide dam failure process. It also suggests that ignoring the variation of dam material particle size along the vertical direction inside the dam body would increase the prediction relative errors of key breach outcomes.

Residual Life Prediction of Rotating Machinery Guided by Quantum Deep Neural Network

In order to avoid the low prediction rate and high evaluation rate in estimating the service balance life of rotating machinery, this paper presents a quantum gene chain encoded bidirectional neural network (QGCCBNN) for estimating the service balance life of rotating machinery. In QGCCBNN, quantum bidirectional transmission mechanism has been developed. In order to improve the global optimization ability and convergence speed, we have developed a quantum gene chain encoding method to transform the gradient descent into the data transmission and updating. Because of the advantages of QGCCBNN in nonlinear estimation ability and convergence speed, the proposed QGCCBNN for predicting the remaining service life of rotating machinery can achieve higher prediction precision and optimization.The predicted value of the proposed method for the remaining service life of double row roller bearings is 6.33h (actual value is 7.17h), with a prediction error of only 0.84h and a relative prediction error of only 11.72%. The experimental results demonstrate the effectiveness of the proposed method.

Enantiomeric purity determination of lactofen formulations through Chemometric Deconvolution of partially overlapped chromatographic profiles

Lactofen is a chiral restricted-use herbicide, classified as toxicity class I by the Environmental Protection Agency (EPA), commonly used to control broadleaved weeds. While the observed herbicidal activity mostly derives from the (S)-(+)-enantiomer, formulations have been manufactured as a mixture of both enantiomers. In this work, we present a quantitative method for the enantiomeric determination of lactofen in commercial formulations based on the development of a second-order calibration to assist the selectivity of chiral separation under reversed-phase liquid chromatographic mode coupled to diode array detection. Chromatographic conditions were aimed to minimize analysis time and solvent consumption. This approach is feasible because second-order calibration allows for the quantification of overlapping bands and analytes in the presence of interferences. Chromatographic-spectral matrices were processed by unfolded partial least-squares coupled to residual bilinearization. The proposed methodology was evaluated through validation samples, achieving relative errors of predictions of 2–4 %. Finally, commercial formulations were subjected to minimal sample treatment and analyzed, and figures of merit for real samples were determined. The described methodology results suitable for the determination of enantiomeric purity and quality control in commercial lactofen formulations.

Real-time determination of combustion degree by laser-induced breakdown spectroscopy

The combustion of fuels in industries is one of the sources of greenhouse gases, posing a considerable threat to human health and the natural environment. The monitoring of the combustion process and the measurement of combustion degrees, therefore, are crucial and significant. In this work, an experimental system was developed based on Laser-induced breakdown spectroscopy (LIBS). Charcoals with different combustion degrees were taken as samples, and in the LIBS spectra of charcoals, the characteristic line of C and the molecular bands of CN and CaO were observed. Moreover, a univariate calibration model based on the exponential fitting curve was constructed to predict the combustion degree according to the variation of the C I line intensity under different combustion degrees. Furthermore, different algorithms including support vector machine (SVM), partial least squares (PLS), random forest (RF), and convolutional neural network (CNN) were applied to establish the multivariate calibration model, and the prediction performance was compared according to the 10-fold cross-validation. Particle swarm optimization (PSO) was used to optimize hyperparameters of the selected SVM-based multivariate calibration model. Principal component analysis (PCA) and linear discriminant analysis (LDA) were employed to extract spectral features and reduce dimensions for the improvement of the model's performance, with the final root mean square error of prediction (RMSEP) and mean relative error (MRE) of the LDA-PSO-SVM calibration model of 0.035 and 3.877%, respectively. Finally, c. The results prove feasible to realize the real-time determination of combustion degree to control carbon emissions and monitor carbon concentration through the LIBS-based method with machine learning, which also provides new ideas for the application of LIBS in the atmospheric field.

Cloud maps highlighting dynamic characteristics of surface signal to improve time-varying wear evaluation accuracy

This paper proposes a high accuracy time-varying wear evolution method with cloud maps highlighting dynamic characteristics of surface signal. Firstly, the cloud map method is established by arrangement of measured data, thus time-varying wear evolution process is visualized on a series of plane figures for preliminary qualitative analysis. Then, high accuracy recognition of time-varying wear states is realized by cloud map shape parameters, including kurtosis and 1-D kernel density function highlighting distribution information of surface signal. The relative recognition degree by cloud map shape parameters are higher than those of friction coefficient, Root-Mean-Square (RMS) deviation and fractal dimension. Finally, high accuracy time-varying wear life prediction is realized by cloud map size growing speed highlighting waviness information of surface signal. The relative prediction error is reduced from more than 20% to less than 10% compared with friction coefficient and roughness.

Spectrophotometric determination of celecoxib and tramadol in the new approved formulated dosage form using principle component regression assistive model

Celecoxib and tramadol have been combined in a novel FDA-approved medication to address acute pain disorders requiring opioid treatment when other analgesics proved either intolerable or ineffective. The absorbance spectra of celecoxib and tramadol exhibit significant overlap, posing challenges for their individual quantification. This study introduces a spectrophotometric quantification approach for celecoxib and tramadol using a principle component regression assistive model to assist resolving the overlapped spectra and quantifying both drugs in their binary mixture. The model was constructed by establishing calibration and validation sets for the celecoxib and tramadol mixture, employing a five-level, two-factor experimental design, resulting in 25 samples. Spectral data from these mixtures were measured and preprocessed to eliminate noise in the 200–210 nm range and zero absorbance values in the 290–400 nm range. Consequently, the dataset was streamlined to 81 variables. The predicted concentrations were compared with the known concentrations of celecoxib and tramadol, and the errors in the predictions were evidenced calculating root mean square error of cross-validation and root mean square error of prediction. Validation results demonstrate the efficacy of the models in predicting outcomes; recovery rates approaching 100 % are demonstrated with relative root mean square error of prediction (RRMSEP) values of 0.052 and 0.164 for tramadol and celecoxib, respectively. The selectivity was further evaluated by quantifying celecoxib and tramadol in the presence of potentially interfering drugs. The model demonstrated success in quantifying celecoxib and tramadol in laboratory-prepared tablets, producing metrics consistent with those reported in previously established spectrophotometric methods.

A deep learning-based surrogate model for probabilistic analysis of high-speed railway tunnel crown settlement in spatially variable soil considering construction process

In reality, many high-speed railway tunnels are built by sequential excavation method in spatially variable soil. This study introduces an efficient deep learning-based solution for the probabilistic analysis of tunnel crown settlements considering the spatial variability of surrounding soil. The two-dimensional convolutional neural network (2D-CNN) is employed to uncover the implicit correlation between the soil elastic modulus random field and tunnel crown settlements. The correlation coefficient (R) of the surrogate model is greater than 0.9664. Meanwhile, the mean relative error of the predictions remains within 2.38%, under various scales of fluctuation (SOFs). An additional 10,000 samples were produced to assess the practical performance of the trained model in probabilistic analysis. The relative errors of predictions generally fall within 5%, with a minimum confidence interval of 98.15%. These results demonstrate that the developed 2D-CNN model can effectively substitute the traditional method in predicting tunnel crown settlements considering soil spatial variability.

Determination of valsartan and pitavastatin using synchronous spectrofluorimetry and augmented least squares chemometric models: A comparative study with greenness and blueness assessment.

Hypertension and hyperlipidemia are two common conditions that require effective management to reduce the risk of cardiovascular diseases. Among the medications commonly used for the treatment of these conditions, valsartan and pitavastatin have shown significant efficacy in lowering blood pressure and cholesterol levels, respectively. In this study, synchronous spectrofluorimetry coupled to chemometric analysis tools, specifically concentration residual augmented classical least squares (CRACLS) and spectral residual augmented classical least squares (SRACLS), was employed for the determination of valsartan and pitavastatin simultaneously. The developed models exhibited excellent predictive performance with relative root mean square error of prediction (RRMSEP) of 2.253 and 2.1381 for valsartan and pitavastatin, respectively. Hence, these models were successfully applied to the analysis of synthetic samples and commercial formulations as well as plasma samples with high accuracy and precision. Besides, the greenness and blueness profiles of the determined samples were also evaluated to assess their environmental impact and analytical practicability. The results demonstrated excellent greenness and blueness scores with AGREE score of 0.7 and BAGI score of 75 posing the proposed method as reliable and sensitive approach for the determination of valsartan and pitavastatin with potential applications in pharmaceutical quality control, bioanalytical studies, and therapeutic drug monitoring.

A developed convolutional neural network model for accurately and stably predicting effective thermal conductivity of gradient porous ceramic materials

Accurate and stable prediction of the effective thermal conductivity (ETC) of porous ceramic materials is of great significance for their application in areas such as optimizing the design of thermal barrier coatings and improving energy conversion efficiency. Porous ceramic materials exhibit complex porous structures with gradient porosity distributions that pose a great challenge for effective medium theory (EMT) and conventional convolutional neural networks (CNN) in attempting to accurately and stably predict the ETC of porous media. In this study, a CNN model that integrates self-attention and a multiscale feature-fusion mechanism is proposed to predict the ETC of porous media with greater accuracy and stability. The integration of the self-attention and multiscale feature-fusion mechanisms enhances the CNN's ability to learn long-range dependencies and preserve detailed information. The optimization of the CNN's accuracy and stability is visually illustrated using gradient-weighted class activation mapping (Grad-CAM) for ETC. Additionally, by employing the proposed gradient quartet structure generation set (QSGS), a gradient porous ceramic media dataset comprising 10 000 images was built to train the CNN model. Finally, the prediction results and relative error distribution of the ETC were compared across different models. Our model demonstrated improvements in statistical metrics, including a 33.7 % decrease in mean error, 25.2 % decrease in median error, and 59.6 % decrease in maximum error. The decreases in these metrics and Grad-CAM for the ETC strongly demonstrates that the model proposed in this work greatly improved the accuracy and stability of predicting the ETC of ceramic materials with gradient porosity distributions.

Aqueous size exclusion chromatography applied to polymer analysis: experimental conditions and molecular weight calibration curves

Aqueous mode size exclusion chromatography (SEC) was employed for the analysis and construction of molecular weight (MW) calibration curves of three water-soluble polymers, namely, polyethylene glycol, polyethylene oxide, and polyacrylic acid sodium salt. Several calibration curves were obtained, varying chromatographic conditions such as columns arrangement, ionic strength, temperature and pH, in addition trends in polymeric chromatographic behavior were examined. The variation in SEC distribution coefficients at different temperatures was found to be below 10 %, indicating that the studied polymers follow an ideal SEC mechanism under the tested conditions. Thus, differences in chromatographic behavior were ascribed to changes in polymer configuration induced by media and/or temperature. These variations in morphology were consistent with the observed SEC behavior.Regarding MW calibration, polynomial regression models ranging from first to fifth order were applied, and the most adequate ones were selected based on their fit and prediction capabilities. Third order polynomials were the preferred models for polyethylene glycol and polyacrylic acid sodium salt, independently of chromatographic conditions. Meanwhile for polyethylene oxide, either third or fifth-order polynomial models were optimal depending on the chromatographic conditions. All the selected regression models presented coefficients of multiple determination (R2) above 0.990, while achieving relative errors of prediction (REP%) in MW ranging from 0.3 to 4 % for cross-validation.

Quantitative analysis of four PAHs in oily sludge by surface-enhanced Raman spectroscopy (SERS) combined with partial least squares regression (PLS) based on a novel nano-silver-silicon coupling substrate

Polycyclic aromatic hydrocarbons (PAHs) present in oily sludge generated by the petroleum and petrochemical industries have emerged as a prominent concern within the realm of environmental conservation. The precise determination of PAHs holds immense significance in both petroleum geochemistry and environmental protection. In this study, a combination of surface-enhanced Raman spectroscopy (SERS) and solid–liquid extraction was employed for the screening of PAHs in oily sludge. Methanol was utilized as the extraction solvent for PAHs, while nanosilver-silicon coupling substrates were employed for their detection. The SERS spectrum was acquired using a portable Raman spectrometer. The nano silver-silicon coupling substrate exhibits excellent uniformity, with relative standard deviations (RSDs) of Phenanthrene, Fluoranthrene, Fluorene and Naphthalene (Phe, Flt, Flu and Nap) being 2.8%, 1.08%, 1.41%, and 5.44% respectively. Moreover, the limits of detection (LODs) achieved remarkable values of 0.542 μg/g, 0.342 μg/g, 0.541 μg/g, and 5.132 μg/g. The quantitative analysis of PAHs in oily sludge was investigated using SERS technology combined with partial least squares (PLS). The optimal PLS calibration model was optimized by combining spectral preprocessing methods and using the SiPLS (Synergy interval partial least squares)-VIP (Variable Importance in Projection) hybrid variable selection strategy. The prediction performance of the D1st (First derivative)-WT (Wavelet transform)-SiPLS-VIP-PLS model was deemed satisfactory, as evidenced by high R2P values of 0.9851, 0.9917, and 0.9925 for Phe, Flt, and Flu respectively; additionally, the corresponding MREP values were found to be 0.0580, 0.0668, and 0.0669 respectively. However, for Nap analysis, the D1st-WT-PLS model proved to be a better calibration model with an R2P value of 0.9864 and an MREP (Mean relative error of prediction) value of 0.0713. In summary, SERS technology combined with PLS based on different spectral pretreatment methods and mixed variable selection strategies is a promising method for quantitative analysis of PAHs in oily sludge, which will provide new ideas and methods for the quantitative analysis of PAHs in oily sludge.

Providing a Photovoltaic Performance Enhancement Relationship from Binary to Ternary Polymer Solar Cells via Machine Learning.

Ternary polymer solar cells (PSCs) are currently the simplest and most efficient way to further improve the device performance in PSCs. To find high-performance organic photovoltaic materials, the established connection between the material structure and device performance before fabrication is of great significance. Herein, firstly, a database of the photovoltaic performance in 874 experimental PSCs reported in the literature is established, and three different fingerprint expressions of a molecular structure are explored as input features; the results show that long fingerprints of 2D atom pairs can contain more effective information and improve the accuracy of the models. Through supervised learning, five machine learning (ML) models were trained to build a mapping of the photovoltaic performance improvement relationship from binary to ternary PSCs. The GBDT model had the best predictive ability and generalization. Eighteen key structural features from a non-fullerene acceptor and the third components that affect the device's PCE were screened based on this model, including a nitrile group with lone-pair electron, a halogen atom, an oxygen atom, etc. Interestingly, the structural features for the enhanced device's PCE were essentially increased by the Jsc or FF. More importantly, the reliability of the ML model was further verified by preparing the highly efficient PSCs. Taking the PM6:BTP-eC9:PY-IT ternary PSC as an example, the PCE prediction (18.03%) by the model was in good agreement with the experimental results (17.78%), the relative prediction error was 1.41%, and the relative error between all experimental results and predicted results was less than 5%. These results indicate that ML is a useful tool for exploring the photovoltaic performance improvement of PSCs and accelerating the design and application with highly efficient non-fullerene materials.

OLE! Dairy model

The process of intensification of the dairy sector has been characterized in recent decades by the increase in milk production per hectare, the increase in livestock density, the inclusion of more concentrates in the diet, and the improvement of the genetic merit of dairy cows. The use of models has productive, environmental, and economic advantages. The objectives of the study were to describe a new model, “OLE! Dairy model”, to (a) simulate the biophysical performance of a pasture-based dairy production system; (b) evaluate the predictive capacity of the model with a set of statistical parameters, comparing its results with the biophysical performance of experimental studies of dairy farm systems, and (c) calibrate by adjusting the technical coefficient. The experimental design combines two feeding strategies with a different proportion of pasture in the diet and two animal genotypes. We make a description of the biophysical component and the calculations proposed in the “OLE! Dairy model”. Then a variety of parameters was calculated for model testing, including the Mean Squared Error, the Relative Prediction Error, the square root of the MSE, the Concordance Correlation Coefficient, and the Model Efficiency. The model presented a good predictive capacity for stocking rate and concentrate, pasture, and reserve intake. The predictive capacity of the model for individual production and area production improves after performing a rapid calibration, which allows for avoiding overestimations or underestimations that generate erroneous measurements in the planning and management of milk production systems, and can be adjusted to different conditions of production of the region.

Surface temperature and power generation efficiency of PV arrays with various row spacings: A full-scale outdoor experimental study

Quantifying the relationship between surface temperature and power generation efficiency of solar photovoltaics (PV) is critical to their practical implementation. Although empirical models have been developed on this, they were mainly based on indoor laboratory tests, ignoring a practically significant arrangement factor. Therefore, a combined outdoor experimental and empirical study on PV array systems considering various row spacings was undertaken through this study. Experimental results indicated that solar irradiance shows a relatively more prominent effect than row spacing and wind speed in outdoor environments. During the practical arrangement, it is only necessary to ensure that there is no shadow between the adjacent rows of PV arrays in practical applications, and the possible cooling effect brought by excessive row spacing may not be pursued. However, this does not mean that row spacing can be ignored when predicting surface temperature and power generation efficiency. Based on the data from our long-term experimental tests, empirical models to predict solar PV's surface temperature and power generation efficiency were developed, considering various row spacings. Comparison showed that the models developed in this study can reduce the mean relative error of the previous model's predictions from 20% to 5%, which proves the necessity of including row spacing in the models. This study's developed models and research outcomes pave the way for future large-scale practical analysis of solar PV arrays.

Prediction of hydrogel swelling states using machine learning methods

AbstractIn the field of material informatics, artificial neural networks (ANNs) contribute to the investigation of the processing‐structure‐properties‐performance relationship of materials. This inspires us to leverage the capabilities of ANNs to decode properties of hydrogels, thereby customizing these active materials for sensors or actuators. In the current work, we introduce an approach to predict discrete swelling states of temperature‐responsive hydrogels, especially PNIPAAm, based on their synthesis parameters, utilizing ANN models. To build the database, we analyze literature on temperature‐responsive hydrogels and compile essential synthesis parameters. The corresponding data points related to these synthesis parameters are then extracted. We propose different variants of ANN models and compare their accuracy on the acquired dataset. The selected model can predict the swelling states of hydrogel samples within the test dataset with relative prediction error of 0.11. This approach is applied to predict the expected properties. Subsequently, the hydrogels can be synthesized, and their properties can be experimentally verified. Our approach can be extended to other types of hydrogels and in the prediction of additional properties. The identified synthesis parameters serve as a valuable foundation for the expansion of the database with further literature resources. An enriched database will enhance the performance of the data‐driven model, thereby improving its predictive capabilities.

UV imaging for the rapid at-line content determination of different colourless APIs in their tablets with artificial neural networks

This paper presents a novel high-resolution and rapid (50 ms) UV imaging system, which was used for at-line, non-destructive API content determination of tablets. For the experiments, amlodipine and valsartan were selected as two colourless APIs with different UV induced fluorescent properties according to the measured solid fluorescent spectra. Images were captured with a LED-based UV illumination (385–395 nm) of tablets containing amlodipine or valsartan and common tableting excipients. Blue or green colour components from the RGB colour space were extracted from the images and used as an input dataset to execute API content prediction with artificial neural networks. The traditional destructive, solution-based transmission UV measurement was applied as reference method. After the optimization of the number of hidden layer neurons it was found that the relative error of the content prediction was 4.41 % and 3.98 % in the case of amlodipine and valsartan containing tablets respectively. The results open the possibility to use the proposed UV imaging-based system as a rapid, in-line tool for 100 % API content screening in order to greatly improve pharmaceutical quality control and process understanding.

Vis-NIR spectroscopic discriminant analysis of aflatoxin B1 excessive standard in peanut meal as feedstuff materials

A fast, simple and reagent-free detection method for aflatoxin B1 (AFB1) is of great significance to food safety and human health. Visible and near-infrared (Vis-NIR) spectroscopy was applied to the discriminant analysis of AFB1 excessive standard of peanut meal as feedstuff materials. Two types of excessive standard discriminant models based on spectral quantitative analysis with partial least squares (PLS) and direct pattern recognition with partial least squares-discrimination analysis (PLS-DA) were established, respectively. Multi-parameter optimization of Norris derivative filtering (NDF) was used for spectral preprocessing; the two-stage wavelength screening method based on equidistant combination-wavelength step-by-step phase-out (EC-WSP) was used for wavelength optimization. A rigorous sample experimental design of calibration-prediction-validation was utilized. The calibration and prediction samples were used for modeling and parameter optimization, and the selected model was validated using the independent validation samples. For quantitative analysis-based, the positive, negative and total recognition-accuracy rates in validation (RARV+, RARV-, and RARV) were 84.8 %, 74.6 % and 79.8 %, respectively; but, the relative root mean square error of prediction was as high as 51.0 %. For pattern recognition-based, the RARV+, RARV-, and RARV were 93.3 %, 90.5 % and 91.9 %, respectively. Moreover, the number of wavelengths N was drastically reduced to 17, and the discrete wavelength combination was in NIR overtone frequency region. The results indicated that, the EC-WSP-PLS-DA model achieved significantly better discrimination effect. Thus demonstrated that Vis-NIR spectroscopy has feasibility for the excessive standard discrimination of aflatoxin B1 in feedstuff materials.

Elemental quantitation and evaluation of hydrocarbon in shale using fiber-optic laser induced breakdown spectroscopy

To meet the demands of in situ analysis, a fiber-optic laser-induced breakdown spectroscopy (FO-LIBS) system was established to predict the elemental concentrations and pyrolytic parameters. The key parameters, including pulse energy, gate delay, type of surrounding gas, and flow rate of gas, were optimized to improve the signal-to-noise ratio of the spectral lines, and the evolution characteristics of the plasma morphology in different gas atmospheres were investigated. Under the optimized experimental conditions, C, H, Si, Ca, Fe, and Mg were calibrated using pellet samples based on partial least squares regression, and the average relative prediction error of the natural samples was lower than 10%. In addition, the support vector regression method was utilized to predict pyrolytic parameters such as TOC, Pg, and Tmax with the R2 of all calibration curves being higher than 0.97. The abundance and generation potential of hydrocarbons were evaluated using the predicted TOC and Pg, and Tmax could contribute to the thermal maturity of the kerogen.

Diagnostic accuracy of estimated glomerular filtration rate in pediatric oncology patients.

Objective: To compare eGFR using different equations with 24-hour creatinine clearance Methodology: We collected data of standardized 24-hour creatinine clearance of pediatric oncology patients for a period of one year and compared the efficiency of various equations for estimation of creatinine clearance with it. For error estimation we used mean relative prediction error (MRPE) and root mean square error (RMSE) and for accuracy estimation we used Lin’s correlation. Results: Out of 100 study participants, 57 were males and 43 were females. Mean age of the population studied was 11.8 while mean BMI was 13.95. Most common tumor reported was osteosarcoma. Out of 18 equations evaluated only 5 showed an accuracy of 0.80 or more, while SEM of none of the equation is near to the desired value (zero). Minimum SEM was found to be for Schwartz (2012) equation. Conclusions: According to our study none of the equations in current clinical practices showed a good correlation with creatinine clearance adjusted according to body surface area. Keywords: eGFR, creatinine clarence, renal function, pediatric oncology.