Effect of dataset size on modeling and monitoring of chemical processes

Zheng, Li; Ying, Yu; Pan, Xinghua; Karim, M. Nazmul

doi:10.1016/j.ces.2020.115928

Cited by 6 publications

(4 citation statements)

References 24 publications

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“…These indices are defined in Equation ( 1) and represent the residuals of the developed models. These indices can be utilized to assess the accuracy of predictions and identify instances where the input variables deviate from the norm [49]. They aid to find outliers or unexpected behavior by measuring the distance between the observed data of GOB's explanatory physical characteristics and the RVP predictions.…”

Section: Resultsmentioning

confidence: 99%

Parametric and Nonparametric Approaches of Reid Vapor Pressure Prediction for Gasoline Containing Oxygenates: A Comparative Analysis Using Partial Least Squares, Nonlinear, and LOWESS Regression Modelling Strategies with Physical Properties

Issa

2024

Modelling and Simulation in Engineering

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This study provides insights into the challenges involved in predicting the Reid vapor pressure (RVP) of gasoline-oxygenate blends (GOB), which is an important indicator of fuel quality and compliance with environmental and performance standards. Given the enormous variety of gasoline compositions and ratios available, there is a significant demand for a fast, straightforward, and cost-effective technique to predict RVP without relying on costly instruments or complicated spectral measurements that involve numerous input variables. A comparative performance analysis has been performed for different regression modelling strategies for predicting RVP in GOB, which is valuable for researchers and practitioners in the petroleum industry for saving time and money. Parametric and nonparametric approaches were compared using partial least squares regression (PLSR), nonlinear regression (NLR), and nonparametric regression (NPR) models. Locally weighted scatterplot smoothing (LOWESS) approach was applied to the NPR model. The gasoline’s physical characteristics (distillation curves and density) formed the basis for the analysis of these models’ performances. Acceptable error metrics have been reached for root mean square error of calibration and prediction (RMSEC and RMSEP) values, for the PLSR, NLR, and NPR models, which are 4.790, 6.235, 4.739, 6.149, 3.968, and 6.029, respectively, which are close for those reported in literature. The NPR model eliminates parametric constraints and allows for a different kind of data structure to emerge. The established models here demonstrate a sound ability to overcome barriers by omitting the use of inconvenient spectral measurements to save expense and simplify data calibration, making them a promising approach for RVP detection of GOB. This finding aids in the development of more accurate RVP prediction models and contributes to the optimization of fuel formulations.

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Section: Resultsmentioning

confidence: 99%

Parametric and Nonparametric Approaches of Reid Vapor Pressure Prediction for Gasoline Containing Oxygenates: A Comparative Analysis Using Partial Least Squares, Nonlinear, and LOWESS Regression Modelling Strategies with Physical Properties

Issa

2024

Modelling and Simulation in Engineering

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“…Regrettably, the achieved accuracy was considerably low, resulting in a MAP@0.5 of only 18% and 22% for the respective stages. After consulting various references [43,52], it was evident that the scale and diversity of the dataset are crucial for deep learning. However, the training samples currently employed are far from sufficient.…”

Section: Discussionmentioning

confidence: 99%

“…To obtain accurate detection results with deep learning technology, it is usually necessary to use a training dataset with rich scenes, a large scale, and accurate labeling [43]. In this study, data augmentation strategies are used to process the collected high-resolution wheat images to address the issue of insufficient samples and enable the model to have stronger generalization ability.…”

Section: Data Augmentationmentioning

confidence: 99%

YOLOv7-MA: Improved YOLOv7-Based Wheat Head Detection and Counting

Meng,

Li,

et al. 2023

Remote Sensing

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Detection and counting of wheat heads are crucial for wheat yield estimation. To address the issues of overlapping and small volumes of wheat heads on complex backgrounds, this paper proposes the YOLOv7-MA model. By introducing micro-scale detection layers and the convolutional block attention module, the model enhances the target information of wheat heads and weakens the background information, thereby strengthening its ability to detect small wheat heads and improving the detection performance. Experimental results indicate that after being trained and tested on the Global Wheat Head Dataset 2021, the YOLOv7-MA model achieves a mean average precision (MAP) of 93.86% with a detection speed of 35.93 frames per second (FPS), outperforming Faster-RCNN, YOLOv5, YOLOX, and YOLOv7 models. Meanwhile, when tested under the three conditions of low illumination, blur, and occlusion, the coefficient of determination (R2) of YOLOv7-MA is respectively 0.9895, 0.9872, and 0.9882, and the correlation between the predicted wheat head number and the manual counting result is stronger than others. In addition, when the YOLOv7-MA model is transferred to field-collected wheat head datasets, it maintains high performance with MAP in maturity and filling stages of 93.33% and 93.03%, respectively, and R2 values of 0.9632 and 0.9155, respectively, demonstrating better performance in the maturity stage. Overall, YOLOv7-MA has achieved accurate identification and counting of wheat heads in complex field backgrounds. In the future, its application with unmanned aerial vehicles (UAVs) can provide technical support for large-scale wheat yield estimation in the field.

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“…Bongiorno et al [ 33 ] constructed sample sets ranging from 10 to 50,000 to study the effect of dataset size on model training performance and found that approximately 200 examples were generally sufficient to train a machine learning algorithm, and increasing the number of training samples did not significantly improve the accuracy of the results. Li et al [ 34 ] proposed an indicator

, which was used to assess the model structure to analyze the minimum size of data to construct a valid model. The verification found that with the increase in the number of samples of the modeling dataset, the model became stable, as the

index converged to zero.…”

Section: Introductionmentioning

confidence: 99%

Sample Size Requirements of a Pharmaceutical Material Library: A Case in Predicting Direct Compression Tablet Tensile Strength by Latent Variable Modeling

Cao,

Shen,

Zhao

et al. 2024

Pharmaceutics

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The material library is an emerging, new data-driven approach for developing pharmaceutical process models. How many materials or samples should be involved in a particular application scenario is unclear, and the impact of sample size on process modeling is worth discussing. In this work, the direct compression process was taken as the research object, and the effects of different sample sizes of material libraries on partial least squares (PLS) modeling in the prediction of tablet tensile strength were investigated. A primary material library comprising 45 materials was built. Then, material subsets containing 5 × i (i = 1, 2, 3, …, 8) materials were sampled from the primary material library. Each subset underwent sampling 1000 times to analyze variations in model fitting performance. Both hierarchical sampling and random sampling were employed and compared, with hierarchical sampling implemented with the help of the tabletability classification index d. For each subset, modeling data were organized, incorporating 18 physical properties and tableting pressure as the independent variables and tablet tensile strength as the dependent variable. A series of chemometric indicators was used to assess model performance and find important materials for model training. It was found that the minimum R2 and RMSE values reached their maximum, and the corresponding values were kept almost unchanged when the sample sizes varied from 20 to 45. When the sample size was smaller than 15, the hierarchical sampling method was more reliable in avoiding low-quality few-shot PLS models than the random sampling method. Two important materials were identified as useful for building an initial material library. Overall, this work demonstrated that as the number of materials increased, the model’s reliability improved. It also highlighted the potential for effective few-shot modeling on a small material library by controlling its information richness.

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Effect of dataset size on modeling and monitoring of chemical processes

Cited by 6 publications

References 24 publications

Parametric and Nonparametric Approaches of Reid Vapor Pressure Prediction for Gasoline Containing Oxygenates: A Comparative Analysis Using Partial Least Squares, Nonlinear, and LOWESS Regression Modelling Strategies with Physical Properties

Parametric and Nonparametric Approaches of Reid Vapor Pressure Prediction for Gasoline Containing Oxygenates: A Comparative Analysis Using Partial Least Squares, Nonlinear, and LOWESS Regression Modelling Strategies with Physical Properties

YOLOv7-MA: Improved YOLOv7-Based Wheat Head Detection and Counting

Sample Size Requirements of a Pharmaceutical Material Library: A Case in Predicting Direct Compression Tablet Tensile Strength by Latent Variable Modeling

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