Approximation of the discharge coefficient of differential pressure flowmeters using different soft computing strategies

Dayev, Zh.A.; Kairakbaev, A. K.; Yetilmezsoy, Kaan; Bahramian, Majid; Sihag, Parveen; Kıyan, Emel

doi:10.1016/j.flowmeasinst.2021.101913

Cited by 22 publications

(8 citation statements)

References 33 publications

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“…The experimental and predicted viscosity values in the scatter plot (Figure 1c) were found in close correlation, lay around 45 oblique line, and were within 10% error line. The NSE value of the model was 0.9628 showing acceptable prediction accuracy (Dayev et al, 2021). Thus, the Carreau-Yasuda model can be effectively used to develop a DVM for MFRD.…”

Section: Rheological Modeling Of Marjoram Fortified Rice Doughmentioning

confidence: 86%

“…The accuracy of the trained models was evaluated using scatter plots, where a prediction error of less than 10% was considered acceptable . Additionally, quantitative measures such as Nash-Sutcliffe efficiency (NSE), SI%, MSE, and correlation coefficient (R) were used to assess the prediction accuracy of the models (Dayev et al, 2021). The mathematical forms of NSE and R are shown in Equations ( 16) and ( 17), respectively.…”

Section: Loss Functions and Performance Comparisonmentioning

confidence: 99%

“…where _ P is the predicted viscosity from the trained model. The distinction between NSE and R 2 is that NSE is typically utilized in the validation process (Dayev et al, 2021), while R 2 is employed in model development (Lee et al, 2012). The best model is determined based on the highest R, R 2 , Adjusted R 2 , R 2 (testing), NSE, and the lowest SI %, MSE and MSE(validation) Hosseini et al, 2021;Panigrahi et al, 2021;.…”

Section: Loss Functions and Performance Comparisonmentioning

confidence: 99%

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Rheological modeling of marjoram fortified rice dough: Empirical and machine learning approach

Vishwakarma

Mandliya

Dalbhagat

et al. 2023

J Food Process Engineering

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The rheological properties (viscosity) of marjoram fortified rice dough (MFRD) inside extruder are helpful for product development, process control, and product quality. This study aims to predict viscosity of MFRD through regression analysis of existing empirical models and machine learning (ML) techniques. At first, MFRD was subjected to steady shear analysis (SSA) at various temperatures (60–100°C) and shear rates (1–50 s−1). The SSA data was split into training and validation sets in 70:30 ratio. Rheological modeling was conducted using various empirical models. Hyperparameter tuning was performed using existing MATLAB functions to develop ML models. The SSA revealed that MFRD exhibited pseudoplastic behavior, with viscosity decreasing with increasing temperature up to 70°C and then increasing again, most probably due to starch gelatinization. The regression analysis indicated satisfactory results for Power Law, Carreau, Cross, Sisko, Carreau‐Yasuda model (R2: 0.9849–0.9992) was deemed the best based on Akaike information criterion. A dual variable model (DVM) was then developed using this model, and its coefficient was calculated more efficiently through prepareSurfaceData (R2: 0.8865) MATLAB function than lsqcurvefit (R2: 0.8280) function. In ML model development, Levenberg–Marquardt was the most effective for artificial neural networks (ANNs), grid search for support vector, and Bayesian optimization for decision tree, ensemble, and gaussian process regression (GPR). Only ANN and GPR models had R2(testing) of 1 during training, but GPR model (Nash‐Sutcliffe efficiency: 0.9995) outperformed others during validation. Therefore, GPR model can be used to accurately predict MFRD viscosity and DVM for simulation study. Practical applications Shear profile and temperature within the extruder change frequently, affecting the viscosity of MFRD and the extrudate quality. The present study develops a dual variable viscosity prediction model using regression, which can be utilized in simulating extrusion process of MFRD. Using ML approach, the GPR model was found to be better than other models in accurately predicting MFRD viscosity at a range of shear and temperature conditions between 60–100°C and 3–50 s−1, respectively.

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Section: Rheological Modeling Of Marjoram Fortified Rice Doughmentioning

confidence: 86%

Section: Loss Functions and Performance Comparisonmentioning

confidence: 99%

Section: Loss Functions and Performance Comparisonmentioning

confidence: 99%

See 1 more Smart Citation

Rheological modeling of marjoram fortified rice dough: Empirical and machine learning approach

Vishwakarma

Mandliya

Dalbhagat

et al. 2023

J Food Process Engineering

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“…Using observed data, multiple linear regression is utilized to discover the link between input and output variables. For nonlinear and difficult issues, MLR is frequently used [33,34]. MLRs are a multivariate statistical approach that fits a linear equation to observed data to represent the linear correlations between the dependent variable y and two or more independent variables.…”

Section: Multiple Linear Regressionmentioning

confidence: 99%

Assessment of Soft Computing Techniques for the Prediction of Compressive Strength of Bacterial Concrete

et al. 2022

Self Cite

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In this investigation, the potential of M5P, Random Tree (RT), Reduced Error Pruning Tree (REP Tree), Random Forest (RF), and Support Vector Regression (SVR) techniques have been evaluated and compared with the multiple linear regression-based model (MLR) to be used for prediction of the compressive strength of bacterial concrete. For this purpose, 128 experimental observations have been collected. The total data set has been divided into two segments such as training (87 observations) and testing (41 observations). The process of data set separation was arbitrary. Cement, Aggregate, Sand, Water to Cement Ratio, Curing time, Percentage of Bacteria, and type of sand were the input variables, whereas the compressive strength of bacterial concrete has been considered as the final target. Seven performance evaluation indices such as Correlation Coefficient (CC), Coefficient of determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Bias, Nash-Sutcliffe Efficiency (NSE), and Scatter Index (SI) have been used to evaluate the performance of the developed models. Outcomes of performance evaluation indices recommend that the Polynomial kernel function based SVR model works better than other developed models with CC values as 0.9919, 0.9901, R2 values as 0.9839, 0.9803, NSE values as 0.9832, 0.9800, and lower values of RMSE are 1.5680, 1.9384, MAE is 0.7854, 1.5155, Bias are 0.2353, 0.1350 and SI are 0.0347, 0.0414 for training and testing stages, respectively. The sensitivity investigation shows that the curing time (T) is the vital input variable affecting the prediction of the compressive strength of bacterial concrete, using this data set.

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“…The hit rates of the eight models in error ranges [-0.001%,0.001%], [-0.002%,0.002%], and [-0.003%, 0.003%] are shown in Table 5 and Figure 8. To further verify the performance of the ESSA-DELM model, this study also adopted mean absolute error (MAE), the root mean square error (RMSE) and the mean absolute percentage error (MAPE), determination coefficient (R 2 ), and Nash-Sutcliffe Efficiency (NSE) [35] as the evaluation criteria. The corresponding results among the eight prediction models are shown in Table 6.…”

Section: Prediction Model Of End-point Phosphorus Content Based On Essa-delmmentioning

confidence: 99%

Using Enhanced Sparrow Search Algorithm-Deep Extreme Learning Machine Model to Forecast End-Point Phosphorus Content of BOF

Quan¹,

Li²,

Cui³

et al. 2021

Preprint

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：An effective technology for predicting the end-point phosphorous content of basic oxygen furnace (BOF) can provide theoretical instruction to improve the quality of steel via controlling the hardness and toughness. Given the slightly inadequate prediction accuracy in the existing prediction model, a novel hybrid method was suggested to more accurately predict the end-point phosphorus content by integrating an enhanced sparrow search algorithm (ESSA) and a multi-strategy with a deep extreme learning machine (DELM) as ESSA-DELM in this study. To begin with, the input weights and hidden biases of DELM were randomly selected, resulting in that DELM inevitably had a set of non-optimal or unnecessary weights and biases. Therefore, the ESSA was used to optimize the DELM in this work. For the ESSA, the Trigonometric substitution mechanism and Cauchy mutation were introduced to avoid trapping in local optima and improve the global exploration capacity in SSA. Finally, to evaluate the prediction efficiency of ESSSA-DELM, the proposed model was tested on process data of the converter from the Baogang steel plant. The efficacy of ESSA-DELM was more superior to that of other DELM-based hybrid prediction models and conventional models. The result demonstrated that the hit rate of end-point phosphorus content within ±0.003%, ±0.002%, and ±0.001% was 91.67%, 83.33%, and 63.55%, respectively. The proposed ESSA-DELM model could possess better prediction accuracy compared with other models, which could guide field operations.

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Approximation of the discharge coefficient of differential pressure flowmeters using different soft computing strategies

Cited by 22 publications

References 33 publications

Rheological modeling of marjoram fortified rice dough: Empirical and machine learning approach

Rheological modeling of marjoram fortified rice dough: Empirical and machine learning approach

Assessment of Soft Computing Techniques for the Prediction of Compressive Strength of Bacterial Concrete

Using Enhanced Sparrow Search Algorithm-Deep Extreme Learning Machine Model to Forecast End-Point Phosphorus Content of BOF

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