Soil and vegetation responses to amendment with pulverized classified paper waste

Busby, Ryan R.; Torbert, H. Allen

doi:10.1016/j.still.2019.104328

Cited by 4 publications

(2 citation statements)

References 18 publications

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“…For Layer 4, also known as defuzzification layer, the weighted value of rules is estimated. As shown in Equation (11), individual adaptive nodes in this layer are fully connected to the corresponding normalization node, coming from fuzzy rule layer.…”

Section: Anfismentioning

confidence: 99%

“…7,8 Also, landfilling, which is the conventional approach to eliminate the appearance of plastic wastes in the environment, has proved to be an insufficient approach due to the negative effects that the plastic wastes have on the quality of the soil and water resources where various species and plants are living. [9][10][11][12][13][14][15] Incineration is another approach to remove plastic wastes. However, it is not prominent since toxic gases will release, causing air pollution.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of terephthalic acid yield in aqueous hydrolysis of polyethylene terephthalate

Abedsoltan

Zoghi

Mohammadi

2023

J of Applied Polymer Sci

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Aqueous hydrolysis is used to chemically recycle polyethylene terephthalate (PET) due to production of high‐quality terephthalic acid (TPA), the PET monomer. PET hydrolysis depends on various factors including PET size, catalyst concentration, and reaction temperature. So, modeling PET hydrolysis by considering the effective factors can provide useful information for material researchers to specify how to design and run these reactions. It will save time, energy, and materials by optimizing the hydrolysis conditions. Machine learning algorithms enable to design models to predict the output results. For the first time, 381 experimental data were gathered to model aqueous hydrolysis of PET. Effective factors on PET hydrolysis were connected to the TPA yield. The logistic regression was applied to rank the effective factors. Two algorithms were proposed, artificial neural network multi‐layer perceptron (ANN‐MLP) and adaptive network‐based fuzzy inference system (ANFIS). The dataset was divided into training, validating, and testing sets to train, validate, and test the models, respectively. The models predicted TPA yield sufficiently where the ANFIS model outperformed. R‐squared (R2) and Root Mean Square Error (RMSE) loss functions were employed to measure the efficiency of the models and evaluate their performance.

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Section: Anfismentioning

confidence: 99%