A Comparative Study Between Least Square Support Vector Machine(Lssvm) and Multivariate Adaptive Regression Spline(Mars) Methods for the Measurement of Load Storing Capacity of Driven Piles in Cohesion Less Soil

Chugh, Mohit; Thumsi, Shraddha Satish; Keshri, Vivek

doi:10.18178/ijscer.4.2.189-194

Cited by 4 publications

(6 citation statements)

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“…where parameters V j,min y , V j,max y , V j,min u , V j,max u , V j,min ∆u , and Vj,max ∆u are dimensionless controller constants analogous to the cost weights used for constraint softening; ε k ≥ 0 is the scalar QP slack variable used for constraints softening; s j y is the scale factor for the j-th plant output; y j,min and y j,max (i) are lower and upper limits for the j-th plant output and the i-th prediction horizon step; u j,min and u j,max (i) are lower and upper limits for the j-th MV and the i-th prediction horizon step; ∆u j,min and ∆u j,max (i) are (14) where u target (k+i|k) are n u MV target values corresponding to u(k+i|k). Parameter ε k is scalar QP slack variable at control interval k (dimensionless) used for constraint softening and ρ ε is constraint violation penalty weight (dimensionless).…”

Section: Resultsmentioning

confidence: 99%

“…If the MARS technique of regression analysis on time series is used, the autoregressive model can be obtained. Many works have been published that discussed the MARS method (e.g., [14,19,51]).…”

Section: Figurementioning

confidence: 99%

“…Training data are split into several splines on an equivalent interval basis. The data are in every spline split into many subgroups, and several knots are created that can be placed between different input variables or different intervals in the same input variable to separating subgroups [14]. To verify the performance of the model being created, the model is verified on the test data.…”

Section: Figurementioning

confidence: 99%

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Model Predictive Control of UCG: An Experiment and Simulation Study

Kačur

Flegner

Durdán

et al. 2019

ITC

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Underground coal gasification (UCG) is a potential technology that enables to mine coal without traditional mining equipment. The coal is gasified deep in underground and produced syngas is processed on the surface. The most important technical problem in UCG is unstable quality of syngas and control. This paper proposes advanced control based on an adaptive predictive controller. The maintaining of desired calorific value depends on flow rates of gasification agents injected to the underground geo-reactor and controlled exhaust. The paper proposes a physical model of UCG technology and applies a method of multivariate adaptive regression splines (MARS) to model the gasification process. This method satisfactorily approximates nonlinearity in the process variables. The paper proposes adaptive model predictive control (MPC) using online model estimation and applied it on the MARS model of UCG that imitates the real process. The results have shown that optimization of manipulation variables can replace manual control in UCG. Getting better quality of syngas depends on setpoints, optimized manipulation variables, and constraints used in MPC. In simulations, the adaptive MPC has shown better performance in comparison with manual and PI control.

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Section: Resultsmentioning

confidence: 99%

Section: Figurementioning

confidence: 99%

Section: Figurementioning

confidence: 99%

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Model Predictive Control of UCG: An Experiment and Simulation Study

Kačur

Flegner

Durdán

et al. 2019

ITC

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“…Multivariate Adaptive Regression Splines (MARS) is the method of regression that was developed by [29]. Many works have been published that discussed the MARS method [26,[30][31][32][33][34]. It is a non-parametric regression technique that looks like an extension of linear models.…”

Section: Multivariate Adaptive Regression Splinesmentioning

confidence: 99%

“…The data are, in each spline, split into many subgroups, and several knots are created that can be placed between different input variables or different intervals in the same input variable to separating subgroups [31].…”

Section: Volmentioning

confidence: 99%

A Comparative Study of Data-Driven Modeling Methods for Soft-Sensing in Underground Coal Gasification

et al. 2019

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Underground coal gasification (UCG) is a technological process, which converts solid coal into a gas in the underground, using injected gasification agents. In the UCG process, a lot of process variables can be measurable with common measuring devices, but there are variables that cannot be measured so easily, e.g., the temperature deep underground. It is also necessary to know the future impact of different control variables on the syngas calorific value in order to support a predictive control. This paper examines the possibility of utilizing Neural Networks, Multivariate Adaptive Regression Splines and Support Vector Regression in order to estimate the UCG process data, i.e., syngas calorific value and underground temperature. It was found that, during the training with the UCG data, the SVR and Gaussian kernel achieved the best results, but, during the prediction, the best result was obtained by the piecewise-cubic type of the MARS model. The analysis was performed on data obtained during an experimental UCG with an ex-situ reactor.

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Prediction of Temperature and Carbon Concentration in Oxygen Steelmaking by Machine Learning: A Comparative Study

et al. 2022

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The basic oxygen steelmaking process (BOS) faces the issue of the absence of information about the melt temperature and the carbon concentration in the melt. Although deterministic models for predicting steelmaking process variables are being developed in metallurgical research, machine-learning models can model the nonlinearities of process variables and provide a good estimate of the target process variables. In this paper, five machine learning methods were applied to predict the temperature and carbon concentration in the melt at the endpoint of BOS. Multivariate adaptive regression splines (MARS), support-vector regression (SVR), neural network (NN), k-nearest neighbors (k-NN), and random forest (RF) methods were compared. Machine modeling was based on static and dynamic observations from many melts. In predicting from dynamic melting data, a method of pairing static and dynamic data to create a training set was proposed. In addition, this approach has been found to predict the dynamic behavior of temperature and carbon during melting. The results showed that the piecewise-cubic MARS model achieved the best prediction performance for temperature in testing on static and dynamic data. On the other hand, carbon predictions by machine models trained on joined static and dynamic data were more powerful. In the case of predictions from dynamic data, the best results were obtained by the k-NN-based model, i.e., carbon, and the piecewise-linear MARS model in the case of temperature. In contrast, the neural network recorded the lowest prediction performance in more tests.

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A Comparative Study Between Least Square Support Vector Machine(Lssvm) and Multivariate Adaptive Regression Spline(Mars) Methods for the Measurement of Load Storing Capacity of Driven Piles in Cohesion Less Soil

Cited by 4 publications

References 0 publications

Model Predictive Control of UCG: An Experiment and Simulation Study

Model Predictive Control of UCG: An Experiment and Simulation Study

A Comparative Study of Data-Driven Modeling Methods for Soft-Sensing in Underground Coal Gasification

Prediction of Temperature and Carbon Concentration in Oxygen Steelmaking by Machine Learning: A Comparative Study

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