Peat-based sorbents for the removal of oil spills from water surface: Application of artificial neural network modeling

Cojocaru, Corneliu; Macoveanu, Matei; Creţescu, Igor

doi:10.1016/j.colsurfa.2011.05.036

Cited by 109 publications

(69 citation statements)

References 39 publications

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“…The most important phase for building ANN model is the training of the network. During the training process the weights and biases of a feed-forward neural network are adjusted systematically in order to minimize the residual error between network outputs (predictions) and targets (experimental data) [11,23,24]. There is a variety of training algorithms.…”

Section: Ann Theoreticalmentioning

confidence: 99%

“…There is a variety of training algorithms. The most used classes of training methods for feed-forward neural networks are the back-propagation (BP) algorithms [11,17,18,23,24]. Training of ANN using BP algorithm is an iterative optimization process applied for performance function minimization by adjusting the network weights and biases appropriately.…”

Section: Ann Theoreticalmentioning

confidence: 99%

“…MSE), η is the learning rate and k is the integer index indicating the epoch (iteration) in learning phase. Once the ANN was trained the optimal weights and biases are saved and the neural network model can be used for simulation and optimization [11,23]. …”

Section: Ann Theoreticalmentioning

confidence: 99%

See 2 more Smart Citations

Artificial neural network model for desalination by sweeping gas membrane distillation

Khayet

Cojocaru

2013

Desalination

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Sweeping gas membrane distillation process (SGMD) has been used for desalination and its performance index, defined as the product of the distillate flux and the salt rejection factor, has been modeled using artificial neural network (ANN) methodology. A feed-forward ANN has been developed for prediction of the performance index based on a set of 53 different experimental SGMD tests. A feed solution of 30 g/L sodium chloride was used in all experiments and the salt rejection factors were found to be greater than 99.4%. The individual and interaction effects of the input variables, namely the feed inlet temperature, the feed flow rate or the feed circulation velocity, and the air flow rate or the air circulation velocity, on the SGMD performance index have been investigated. The optimum point was determined by means of Monte Carlo simulation. The obtained optimal conditions were a feed inlet temperature of 69°C, an air flow rate of 34.5 L/min (i.e. 2.02 m/s air circulation velocity) and a feed flow rate of 160 L/h (i.e. 0.155 m/s liquid circulation velocity). Under these operating conditions a performance index of 1.493×10−3 kg/m 2 .s has been achieved experimentally being the maximal SGMD performance index obtained inside the region of experimentation.

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Section: Ann Theoreticalmentioning

confidence: 99%

Section: Ann Theoreticalmentioning

confidence: 99%

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Artificial neural network model for desalination by sweeping gas membrane distillation

Khayet

Cojocaru

2013

Desalination

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“…Hussein et al [6], studied about carbonized peat bagasse where the result showed that the fibres extracted from bagasse and carbonized at 300 °C for 2 hours were found to have a high performance for sorption, recovery and recycling of heavy oils, even the viscous ones. Peat-based sorbents has been tested by Cojocaru et al [7], where the removal efficiency of 99.21% was achieved. Sawdust has also been tested, where oleic acid grafted sawdust (OGSD) had the best sorption capacity for crude oil as well as weathered oil [8].…”

Section: Introductionmentioning

confidence: 99%

Response Surface Methodology Optimization of Oil Removal Using Banana Peel as Biosorbent

2017

MJAS

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Improper waste management of oil causes environmental pollution particularly water pollution. Conventional method in treating oil contaminants are expensive and have low removal efficiency, hence the use of natural adsorbent is suggested because of its reliability and affordability. This study aimed to use banana peel as a biosorbent for oil removal and the application of response surface methodology to identify the optimum conditions of oil removal. The adsorptive capacity of household wastes derived from banana peel towards oil were studied by using different parameters such as oil concentration, adsorbent dosage and contact time. Maximum oil adsorption of 98.86% was achieved using banana peel. The optimum conditions for oil removal were found at a dosage of 4.94 g, for 10.29 hours contact time and the initial oil concentration as high as 8.74%. The rate of oil adsorption was mathematically described as a function of experimental parameters and was modelled through response surface methodology via Box-Behnken design. The results showed that the responses of oil adsorption were significantly affected by the quadratic term of oil concentration, adsorbent dosage and contact time. The statistical analysis was performed by ANOVA which indicated good correlation of experimental parameters.Keywords: banana peel, oil removal, response surface methodology Abstrak Pengurusan sisa minyak yang tidak teratur menyebabkan pencemaran alam sekitar terutamanya pencemaran air. Kaedah konvensional untuk merawat pencemaran minyak adalah mahal dan tahap penyingkiran minyaknya adalah rendah, maka penggunaan penjerap semulajadi adalah dicadangkan kerana kebolehannya menyerap minyak dan lebih menjimatkan. Matlamat kajian ini adalah untuk menggunakan kulit pisang sebagai biopenjerap untuk penyingkiran minyak dan aplikasi kaedah gerak balas permukaan untuk mengenal pasti keadaan optimum kapasiti penyingkiran minyak. Kapasiti penjerapan daripada sisa rumah yang diambil daripada kulit pisang terhadap minyak telah dikaji dengan menggunakan parameter yang berbeza seperti kepekatan minyak, kuantiti penjerap dan tempoh masa. Penjerapan minyak secara maksimum, pada kadar 98.86% telah dicapai oleh kulit pisang. Keadaan optimum untuk menyingkirkan minyak adalah pada kadar 4.94 g, 10.29 jam dan kepekatan minyak sebanyak 8.74%. Kadar penjerapan minyak telah diterangkan secara matematik dalam fungsi parameter eksperimen dan telah dimodelkan melalui reka bentuk Box-Behnken (kaedah gerak balas permukaan). Hasil kajian menunjukkan bahawa tindak balas penjerapan minyak sangat dipengaruhi oleh kepekatan minyak, kuantiti penjerap dan tempoh masa. Analisis statistik ANOVA menunjukkan korelasi yang baik bagi setiap parameter eksperimen.Kata kunci: kulit pisang, penyingkiran minyak, kaedah gerak balas permukaan

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“…They include rice husk ash (Vlaev et al, 2011), barely straw (Husseien et al, 2009) and peat-based sorbents (Cojocaru et al, 2011) as well as sugarcane bagasse (SB) from the production of cane sugar. Even though SB is produced in huge abundance every year, only a small proportion of it is reused as biofuel and pulp and paper products (Rowell & Keany, 1991).…”

Section: Introductionmentioning

confidence: 99%

Oil-Water Adsorptive Properties of Chemically Treated Sugarcane Bagasse

Utomo¹,

Yi²,

Zhonghuan³

et al. 2015

ENRR

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Among the contaminants plaguing our waters today, oil remains one of the most pervasive and challenging contaminant to remove. Oil pollution occurs not only through factory discharge, but also by accident spills from the fuel of the vehicle or the transportation of oil. Sugarcane bagasse (SB) is an abundant agricultural by-product containing almost half of cellulose and one quarter of lignin. After chemical treatments SB can be modified their hydrophobicity leading to improve its oil adsorptive properties. In a column experiment containing 1 g of SB, oil was able to be adsorbed from oil and water mixture by, from the highest to the lowest uptake, AASB, ASSB, NSB, SSB and BSB with the average oil adsorption capacity of 13.0 mL/g, 11.25 mL/g, 10.50 mL/g, 9.0 mL/g and 8.75 mL/g respectively. The results were concurrently meeting the result of material characterisation using FTIR, where acetic acid treated SB (AASB) consists of high lignin leading to high hydrophobicity. On the other hand, BSB showed the lowest oil adsorption capacity and more hydrophilic due to the lowest amount of lignin present in SB. The result showed a potential use of natural material of SBs with high lignin content to tackle oil spill in water environment.

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Peat-based sorbents for the removal of oil spills from water surface: Application of artificial neural network modeling

Cited by 109 publications

References 39 publications

Artificial neural network model for desalination by sweeping gas membrane distillation

Artificial neural network model for desalination by sweeping gas membrane distillation

Response Surface Methodology Optimization of Oil Removal Using Banana Peel as Biosorbent

Oil-Water Adsorptive Properties of Chemically Treated Sugarcane Bagasse

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