Prediction of temperature for various pressure levels using ANN and multiple linear regression techniques: A case study

Jain, Sarika; Rathee, Sarita; Kumar, Ajay; Sambasivam, Anivel; Boadh, Rahul; Choudhary, Tushar; Kumar, Parveen; Singh, Pravin Kumar

doi:10.1016/j.matpr.2022.01.067

Cited by 14 publications

(7 citation statements)

References 21 publications

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“…Multiple linear regression − is a statistical tool for fitting linear relationships between independent and dependent variables and can be used to identify linear relationships between single or multiple variables. Simple linear regression consists of a single element input, and multiple linear regression consists of multiple inputs. X b = { true 1 b 1 ( 1 ) ⋯ b n ( 1 ) 1 b 1 ( 2 ) ⋱ b n ( 2 ) ⋮ ⋮ ⋮ ⋮ 1 b 1 ( m ) ⋯ b n ( m ) } θ = false( θ 1 + θ 2 + ··· + θ i false) T = false( X b T × X b − 1 …”

Section: Data Processing and Machine Learning Modelsmentioning

confidence: 99%

“…Multiple linear regression − is a statistical tool for fitting linear relationships between independent and dependent variables and can be used to identify linear relationships between single or multiple variables. Simple linear regression consists of a single element input, and multiple linear regression consists of multiple inputs. where X b is the feature matrix, b n ( m ) is the feature m of the n th data sample in the data sample, θ is the coefficient vector, and y is the actual value.…”

Section: Data Processing and Machine Learning Modelsmentioning

confidence: 99%

“…Multiple linear regression 21 − 23 is a statistical tool for fitting linear relationships between independent and dependent variables and can be used to identify linear relationships between single or multiple variables. Simple linear regression consists of a single element input, and multiple linear regression consists of multiple inputs.…”

Section: Data Processing and Machine Learning Modelsmentioning

confidence: 99%

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Application of Machine Learning in a Mineral Leaching Process─Taking Pyrolusite Leaching as an Example

Zhang

et al. 2022

ACS Omega

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In this study, several machine learning models were used to analyze the process variables of electric-field-enhanced pyrolusite leaching and predict the leaching rate of manganese, and the applicability of those models in the leaching process of hydrometallurgy was compared. It showed that there was no correlation between the six leaching conditions; in addition to the leaching time, the concentrations of sulfuric acid and ferrous sulfate had great influences on the leaching of pyrolusite. The results of the prediction models showed that the support vector regression model has the best prediction performance, with regression index (R 2) = 0.92 and mean square error = 25.04, followed by the gradient boosting regression model (R 2 > 0.85). In this research, machine learning models were applied to the optimization of the manganese leaching process, and the research process and methods were also applicable to other hydrometallurgical processes for majorization and result prediction.

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Section: Data Processing and Machine Learning Modelsmentioning

confidence: 99%

“…Multiple linear regression − is a statistical tool for fitting linear relationships between independent and dependent variables and can be used to identify linear relationships between single or multiple variables. Simple linear regression consists of a single element input, and multiple linear regression consists of multiple inputs. where X b is the feature matrix, b n ( m ) is the feature m of the n th data sample in the data sample, θ is the coefficient vector, and y is the actual value.…”

Section: Data Processing and Machine Learning Modelsmentioning

confidence: 99%

Section: Data Processing and Machine Learning Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Application of Machine Learning in a Mineral Leaching Process─Taking Pyrolusite Leaching as an Example

Zhang

et al. 2022

ACS Omega

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“…Other modeling techniques like multiple linear regression can also be used. However, it has been shown in the literature that modeling techniques like ANN are better equipped to handle the nonlinearities present in the data compared to multiple linear regression methods . SVRs are used in modeling various applications like Li-ion batteries, grinding circuits, and hydrogen evolution reaction catalysts establishing their capability in modeling complex systems.…”

Section: Introductionmentioning

confidence: 99%

“…However, it has been shown in the literature that modeling techniques like ANN are better equipped to handle the nonlinearities present in the data compared to multiple linear regression methods. 19 SVRs are used in modeling various applications like Li-ion batteries, 16 grinding circuits, 20 and hydrogen evolution reaction catalysts 21 establishing their capability in modeling complex systems. This observation motivates us to explore the applicability of SVRs in modeling and optimization of MSMPR.…”

Section: Introductionmentioning

confidence: 99%

Toward Faster Operational Optimization of Cascaded MSMPR Crystallizers Using Multiobjective Support Vector Regression

Inapakurthi

Naik

Mitra

2022

Ind. Eng. Chem. Res.

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Mixed-suspension mixed-product removal (MSMPR) crystallization process is critical for optimal separation and purification operations in pharmaceutical and fine chemical industries. To achieve this, detailed mathematical model-based optimization is the current practice, which is reported as an extremely time-consuming exercise, prohibiting their online implementation. To facilitate faster optimization, a novel datadriven modeling and optimization algorithm has been proposed in this work. Limited number of high-fidelity data from the computationally expensive model were utilized to build surrogates using support vector regression (SVR). Inputs of MSMPR are assigned different, instead of same, kernel parameters, and multiple kernels were explored to capture the complex dynamics of the crystallization process. Loaded with a sample size estimation algorithm to reduce the computational load on model execution time, the complete formulation is solved using an evolutionary algorithm enabling evolution of optimal SVR-based surrogate models. From the Pareto optimal set of several such models, two instances with differing prediction error were selected and the optimization of MSMPR was performed. The study indicates 3 orders of magnitude faster optimization with the surrogate models and 87.54% savings in the number of expensive function evaluations compared to the physics-based MSMPR model facilitating online optimization of such a process.

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Dual-Band Planar Antenna Design Using Characteristics Mode Analysis for 5G Sub-6 Band and WLAN Application

Kolte,

Bansal,

Kumar

2024

Emerging Trends in Mechatronics

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Prediction of temperature for various pressure levels using ANN and multiple linear regression techniques: A case study

Cited by 14 publications

References 21 publications

Application of Machine Learning in a Mineral Leaching Process─Taking Pyrolusite Leaching as an Example

Application of Machine Learning in a Mineral Leaching Process─Taking Pyrolusite Leaching as an Example

Toward Faster Operational Optimization of Cascaded MSMPR Crystallizers Using Multiobjective Support Vector Regression

Dual-Band Planar Antenna Design Using Characteristics Mode Analysis for 5G Sub-6 Band and WLAN Application

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