Application of Organic and Inorganic Wastes in Clay Brick Production: A Chemometric Approach

Vasić, Milica; Radojević, Zagorka; Pezo, Lato

doi:10.1002/9781119242598.ch8

Cited by 1 publication

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“…The gathered database of the considered 302 cases is first examined using Principal Components Analysis to test the variability of the parameters, and then, the input and output parameters (as previously shown in Table I) were selected for ANN modeling. Artificial Neural Networks were built following the procedure which was the most efficient as per the previous studies on a similar subject [13,14,[32][33][34][35]. A multilayer perceptron model consisting of three layers was used for building the network, while constantly minimizing the differences between the network predicted and experimental results.…”

Section: Statistical Analysis and Artificial Neural Network Modelingmentioning

confidence: 99%

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An artificial neural network-based prediction model for utilization of coal ash in production of fired clay bricks: A review

et al. 2021

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This study analyzed the last 20 years` data available on power plant coal ashes used in clay brick production. The statistical analysis has been carried out for a total of 302 cases based on the relevant parameters reported in the literature. The chemical composition of the clays and coal ashes, percentage incorporation and maximum particle size of ash, size of fired samples, peak firing temperature, and the corresponding soaking time were selected as inputs for modeling. The product characteristics i.e. open porosity, water absorption, and compressive strength was taken as output parameters. An artificial neural network model has been developed and showed a satisfactory fit to experimental data and predicted the observed output variables with the overall coefficient of determination (r2) of 0.972 during the training period. Besides, the reduced chi-square, mean bias error, root mean square error, and mean percentage error were utilized to check the correctness of the obtained model, which proved the network generalization capability. The sensitivity analysis of the model suggested that the quantity of Na2O coming from brick clays, the percentages of SiO2 and K2O coming from ashes, and MgO coming from clays were the most influential parameters in descending order for the ash-clay composite bricks` quality, mostly owing to the influence of fluxes during firing.

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Section: Statistical Analysis and Artificial Neural Network Modelingmentioning

confidence: 99%

“…Thus, Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm is utilized within the program. The coefficient of determination (r 2 ), reduced chi-square (χ 2 ), mean bias error, and also root mean square and mean percentage errors were used to check the correctness of the obtained model [34,36].…”

Section: Statistical Analysis and Artificial Neural Network Modelingmentioning

confidence: 99%

An artificial neural network-based prediction model for utilization of coal ash in production of fired clay bricks: A review

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

Application of Organic and Inorganic Wastes in Clay Brick Production: A Chemometric Approach

Cited by 1 publication

References 37 publications

An artificial neural network-based prediction model for utilization of coal ash in production of fired clay bricks: A review

An artificial neural network-based prediction model for utilization of coal ash in production of fired clay bricks: A review

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