Generalized decay and artificial neural network models for fixed-Bed phenolic compounds adsorption onto activated date palm biochar

Dalhat, M.A.; Mu’azu, Nuhu Dalhat; Essa, Mohammed H.

doi:10.1016/j.jece.2020.104711

Cited by 31 publications

(12 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The commonly studied organic compounds, nutrients, pharmaceuticals, drugs, pesticides, and PCPs are cephalexin, chlorothalonil pesticide, heptachlor, triamterene, chlorophenol (CP), paracetamol, phenol, and phosphate [64,65,67,[69][70][71][72][73]. Besides batch experiments, the performance of various column studies was also evaluated using AI tools [74].…”

Section: Pcps From the Aqueous Phasementioning

confidence: 99%

Applications of artificial intelligence in water treatment for optimization and automation of adsorption processes: Recent advances and prospects

Alam

Ihsanullah

Naushad

et al. 2022

Chemical Engineering Journal

209

View full text Add to dashboard Cite

Section: Pcps From the Aqueous Phasementioning

confidence: 99%

Applications of artificial intelligence in water treatment for optimization and automation of adsorption processes: Recent advances and prospects

Alam

Ihsanullah

Naushad

et al. 2022

Chemical Engineering Journal

209

View full text Add to dashboard Cite

“…ANN is widely applied to optimize the process parameters of pollutant adsorption on biochar (Salawu et al 2022;Mondal et al 2016Mondal et al , 2017Mukherjee et al 2022). For example, Dalhat et al employed a FANN coupled with nonlinear regression generalized decay function (GEDF) to optimize the adsorption process of o-cresol and phenol on a fixed bed of biochar (Dalhat et al 2021). The ANN model combined with contour method, Garson algorithm and performance decomposition was applied to determine the relative importance of operating variables.…”

Section: Optimization Of Experimental Conditionsmentioning

confidence: 99%

“…The physicochemical properties of biochar can be improved by modifying the surface of biochar with functional groups (Wang et al 2022b;Yang et al 2022b;Zuo et al 2021). Further, the experimental settings of adsorption process, such as adsorbent dosage , initial concentration of adsorbate (Dalhat et al 2021;Li et al 2020c), adsorption time, stirring speed (Jabar and Odusote 2021; Komnitsas et al 2017), pH, coexisting ions (Tang et al 2022;Wang et al 2020a) and temperature (Zeng et al 2018), have an important impact on adsorption efficiency. The adsorption performance of biochar is closely related to the synthesis and adsorption process of biochar, but there are many influencing factors.…”

Section: Introductionmentioning

confidence: 99%

Synthesis optimization and adsorption modeling of biochar for pollutant removal via machine learning

Zhang

Chen

et al. 2023

Biochar

View full text Add to dashboard Cite

Due to large specific surface area, abundant functional groups and low cost, biochar is widely used for pollutant removal. The adsorption performance of biochar is related to biochar synthesis and adsorption parameters. But the influence factor is numerous, the traditional experimental enumeration is powerless. In recent years, machine learning has been gradually employed for biochar, but there is no comprehensive review on the whole process regulation of biochar adsorbents, covering synthesis optimization and adsorption modeling. This review article systematically summarized the application of machine learning in biochar adsorbents from the perspective of all-round regulation for the first time, including the synthesis optimization and adsorption modeling of biochar adsorbents. Firstly, the overview of machine learning was introduced. Then, the latest advances of machine learning in biochar synthesis for pollutant removal were summarized, including prediction of biochar yield and physicochemical properties, optimal synthetic conditions and economic cost. And the application of machine learning in pollutant adsorption by biochar was reviewed, covering prediction of adsorption efficiency, optimization of experimental conditions and revelation of adsorption mechanism. General guidelines for the application of machine learning in whole-process optimization of biochar from synthesis to adsorption were presented. Finally, the existing problems and future perspectives of machine learning for biochar adsorbents were put forward. We hope that this review can promote the integration of machine learning and biochar, and thus light up the industrialization of biochar. Graphical Abstract

show abstract

“…In the context of fixed bed adsorption, "small data" sets are used to construct empirical models. Commonly used methods to build empirical models from breakthrough data include the design of experiments and neural network modeling approaches (Dalhat et al 2021;Schio et al 2021). These data-driven models may be used for process design or optimization, but they lack mechanistic merit and should not be used to predict breakthrough behavior outside the range of experimental conditions used for model calibration.…”

Section: Introductionmentioning

confidence: 99%

Fixed bed adsorption of water and air contaminants: analysis of breakthrough curves using probability distribution functions

Chu

Hashim

2022

Chemical Engineering Communications

View full text Add to dashboard Cite

Simple fixed bed models such as the Bohart-Adams and Thomas equations are often used to describe the breakthrough characteristics of water and air contaminant adsorption in fixed bed adsorbers. However, these popular models are confined to correlating highly symmetric breakthrough curves. The present study investigates the feasibility of using two probability distributions (normal and log-normal) to track asymmetric breakthrough curves for water and air contaminant adsorption in fixed bed columns (ciprofloxacin, ammonium, hydrogen chloride, and hydrogen sulfide). The normal and log-normal probability distributions provided accurate fits to the slightly asymmetric ciprofloxacin breakthrough curve. They also provided a good representation of the overall shape of the ammonium breakthrough curve, but failed to describe the leakage of ammonium during the initial period of column operation. The log-normal distribution was able to match the asymmetric HCI and H2S breakthrough curves. The normal distribution, by comparison, failed to describe these two asymmetric breakthrough curves.Because the log-normal distribution has a floating inflection point, it was much more successful in describing the asymmetric HCI and H2S breakthrough curves that were sharp initially and 2 then broadened significantly as the column approached saturation. The ability of the normal distribution to track such asymmetric breakthrough data was impeded by its invariant inflection point.

show abstract

Generalized decay and artificial neural network models for fixed-Bed phenolic compounds adsorption onto activated date palm biochar

Cited by 31 publications

References 60 publications

Applications of artificial intelligence in water treatment for optimization and automation of adsorption processes: Recent advances and prospects

Applications of artificial intelligence in water treatment for optimization and automation of adsorption processes: Recent advances and prospects

Synthesis optimization and adsorption modeling of biochar for pollutant removal via machine learning

Fixed bed adsorption of water and air contaminants: analysis of breakthrough curves using probability distribution functions

Contact Info

Product

Resources

About