2022
DOI: 10.1016/j.arabjc.2022.104025
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Modelling and optimization of crude oil removal from surface water via organic acid functionalized biomass using machine learning approach

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Cited by 17 publications
(4 citation statements)
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“…Thermokinetic profile of dried C. sinensis peels revealed three phases of degradation. The initial weight loss took place at 0 ˚C to 150˚C (~10%) corresponded to the dehydration of weakly bonded water molecules on the peel surface [6,12]. The second weight loss (~40%) was observed between 150-350˚C and mainly related to the dehydration, decomposition of glucose linkages, cellulosic materials and depolymerization reactions [2,9,50].…”
Section: Characterization Of C Sinensis Peels With Ft-ir Fib-sem and Tgamentioning
confidence: 99%
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“…Thermokinetic profile of dried C. sinensis peels revealed three phases of degradation. The initial weight loss took place at 0 ˚C to 150˚C (~10%) corresponded to the dehydration of weakly bonded water molecules on the peel surface [6,12]. The second weight loss (~40%) was observed between 150-350˚C and mainly related to the dehydration, decomposition of glucose linkages, cellulosic materials and depolymerization reactions [2,9,50].…”
Section: Characterization Of C Sinensis Peels With Ft-ir Fib-sem and Tgamentioning
confidence: 99%
“…The residual mass was found as 17.5% at 1000˚C (Figure 9). Considering that the adsorption of crude oil is carried out at room temperature, as stated in the li-terature, the data obtained from the TGA results show that C. sinensis peel can be used effectively in crude oil sorption [12].…”
Section: Characterization Of C Sinensis Peels With Ft-ir Fib-sem and Tgamentioning
confidence: 99%
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“…On the other hand, an adaptive neuro-fuzzy inference system (ANFIS) model represents a hybrid artificial intelligence model that combines the valuable attributes of neural networks and fuzzy logic. It excels in decoding complex industrial processes with minimal steady-state error [78]. Response surface methodology (RSM) offers several advantages, including the generation of precise empirical models that accurately predict the optimal response of industrial processes.…”
Section: Modelling and Optimization Of Anaerobic Digestionmentioning
confidence: 99%