Application of linear regression algorithm and stochastic gradient descent in a machine‐learning environment for predicting biomass higher heating value

Ighalo, Joshua O.; Adeniyi, Adewale George; Marques, Gonçalo

doi:10.1002/bbb.2140

“…neuro-fuzzy) within different control loops are applied for network predictive control regarding energy savings [45]. Most of the studies focused on technologies related to DUC, and ML to predict higher heating values of a biomass [46] and energy use and GHG emissions reduction [47]. BD (mainly DM and DC and SI) are applied for data interoperability supporting computational toxicology and chemical safety evaluation [48] and CSI technologies in development activities of the chemical industry [49], [50].…”

Section: Resultsmentioning

confidence: 99%

AI and BD in Process Industry: A Literature Review with an Operational Perspective

Fornasiero¹,

Nettleton²,

Kiebler³

et al. 2021

Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems

3

0

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Among digital technologies, Artificial Intelligence (AI) and Big Data (BD) have proven capability to support different processes, mainly in discrete manufacturing. Despite the fact that a number of AI and BD literature reviews exist, no comprehensive review is available for the Process Industry (i.e. cements, chemical, steel, and mining). This paper aims to provide a comprehensive review of AI and BD literature to gain insights into their evolution supporting operational phases of the Process Industry. Results allow to define the areas where AI/BD are proven to have greater impact and areas with gaps like for example the process control (predictive models) area, machine learning and cyber-physical systems technologies. The sectors lagging behind are Ceramics, Cement and nonferrous metals. Areas to be studied in the future include the interaction between intelligent systems. humans and the external environment, the implementation of AI for the monitoring and optimization of parameters of different operations, ethical and social impact.

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“…A linear regression algorithm simply performs a regression task by predicting the target value (Y) based on the regression which is made by considering the independent variables (X) (Eq. 1) (Kumar and Manjula 2012;Yozgatligil et al, 2013;Sim et al, 2015;Ighalo et al, 2020).…”

Section: Missing Value Imputationmentioning

confidence: 99%

Performance Evaluation of Machine Learning Models with Ensemble Learning Approach in Classification of Water Quality Indices Based on Different Subset of Features

Garabaghi

¹

,

Benzer

²

,

Benzer

³

2022

Preprint

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Since fresh water resources in form of groundwaters which are the most available water resources for human consumption are extremely limited and due to uncontrolled human activities are prone to contamination, it’s of a great importance to constantly monitor the quality of the ground fresh water resources to provide a sustainable drinking water for people as well as protecting the ecosystem. One tool for modeling the water quality of a basin is Water Quality Index (WQI). However, calculating WQI is complicated and time- consuming, therefore, today, scientists are being inclined to propose simpler ways for modeling the quality of the water resources such as machine learning algorithms. In this study the performance of four machine learning algorithms with ensemble learning approach were evaluated to propose a classification model (classifier) with highest performance. Moreover, to identify the most important water quality parameters in the classification process, three feature selection methods with machine learning approach were applied. As a result, among four classifiers, XGBoost showed outstanding performance, with the accuracy of 96.9696% when all the parameters of interest were involved in the classification process. However, in order to make the model cost-effective it is suggested to conduct the classification with optimum parameters which in this case, for the dataset which was used in this study XGBoost classifier is suggested as the best classifier with the maximum accuracy of 95.606% with 10-Fold Cross Validation when seven parameters which were identified by Backward Feature Elimination Feature selector were involved in the classification process.

show abstract

“…Linear regression algorithm simply performs regression task by predicting the target value (Y) based on the regression which is made by considering the independent variables (X) (Eq. 1) (Kumar and Manjula 2012;Yozgatligil et al 2013;Sim et al 2015;Ighalo et al 2020).…”

Section: Dataset Formationmentioning

confidence: 99%

Performance Evaluation of Machine Learning Models with Ensemble Learning approach in Classification of Water Quality Indices Based on Different Subset of Features

Garabaghi

¹

,

Benzer

²

,

Benzer

³

2021

Preprint

View full text Add to dashboard Cite

Since fresh water resources in form of groundwaters which are the most available water resources for human consumption are extremely limited and due to uncontrolled human activities are prone to contamination, it’s of a great importance to constantly monitor the quality of the ground fresh water resources to provide a sustainable drinking water for people as well as protecting the ecosystem. One tool for modeling the water quality of a basin is Water Quality Index (WQI). However, calculating WQI is complicated and time- consuming, therefore, today, scientists are being inclined to propose simpler ways for modeling the quality of the water resources such as machine learning algorithms. In this study the performance of four machine learning algorithms with ensemble learning approach were evaluated to propose a classification model (classifier) with highest performance. Moreover, to identify the most important water quality parameters in the classification process, three feature selection methods with machine learning approach were applied. As a result, among four classifiers, XGBoost showed outstanding performance, with the accuracy of 96.9696% when all the parameters of interest were involved in the classification process. However, in order to make the model cost-effective it is suggested to conduct the classification with optimum parameters which in this case, for the dataset which was used in this study XGBoost classifier is suggested as the best classifier with the maximum accuracy of 95.606% with 10-Fold Cross Validation when seven parameters which were identified by Backward Feature Elimination Feature selector were involved in the classification process.

show abstract

Application of linear regression algorithm and stochastic gradient descent in a machine‐learning environment for predicting biomass higher heating value

Cited by 47 publications

References 44 publications

AI and BD in Process Industry: A Literature Review with an Operational Perspective

AI and BD in Process Industry: A Literature Review with an Operational Perspective

Performance Evaluation of Machine Learning Models with Ensemble Learning Approach in Classification of Water Quality Indices Based on Different Subset of Features

Performance Evaluation of Machine Learning Models with Ensemble Learning approach in Classification of Water Quality Indices Based on Different Subset of Features

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