Recommendations for olive mills waste treatment in hot and dry climate

Alkhalidi, Ammar; Halaweh, Ghada; Khawaja, Mohamad K.

doi:10.1016/j.jssas.2023.03.002

Cited by 11 publications

(12 citation statements)

References 45 publications

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“…The following aqueous solutions were used as references (controls) for the broth microdilution assay: [1]: OSE (2% v/v), [2]: OMW (2% v/v), [3]: OSE (2% v/v) and NaOH (0.2 mM) with pH level 8.75, [4]: OMW (2% v/v) and NaOH (7.9 mM) with pH level 11.89, [5]: AgNO 3 (2 mM), and [6]: AgNO 3 (1 mM). All reference solutions were filtered using sterile syringe filters (with 0.22 μm pore diameter) attached to sterilized syringes (2.5 ml).…”

Section: Antibacterial Tests Of Agnpsmentioning

confidence: 99%

“…Οlive oil production is one of the main economic branches of the countries located in the Mediterranean basin (>90% of the world's olive oil production) [1,2]. This happens because of the mild climate that prevails in these areas and favors the cultivation and growth of olive trees.…”

Section: Introductionmentioning

confidence: 99%

“…However, parallel to this abundant and rapidly increased production, substantial byproducts and wastes are also generated. More specifically at the olive oil production phase [2], only 20% is olive oil, and the remaining 80% consists of waste [1]. This waste is divided into 30% of moist solid waste (pomace) and 50% of liquid waste called Olive Mill Wastewater (OMW) [1].…”

Section: Introductionmentioning

confidence: 99%

“…More specifically at the olive oil production phase [2], only 20% is olive oil, and the remaining 80% consists of waste [1]. This waste is divided into 30% of moist solid waste (pomace) and 50% of liquid waste called Olive Mill Wastewater (OMW) [1]. Concerning OMW, it is characterized by low pH, high concentration in phenols (tannins, polyalchohols, pectins) high biological (12000-63000 mg L −1 ), and chemical (80000-20000 mg L −1 ) oxygen demand values [1,[5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

“…This waste is divided into 30% of moist solid waste (pomace) and 50% of liquid waste called Olive Mill Wastewater (OMW) [1]. Concerning OMW, it is characterized by low pH, high concentration in phenols (tannins, polyalchohols, pectins) high biological (12000-63000 mg L −1 ), and chemical (80000-20000 mg L −1 ) oxygen demand values [1,[5][6][7][8]. All these characteristics render olive oil production's byproducts hazardous for the environment and the ecosystems (terrestrial and aqueous).…”

Section: Introductionmentioning

confidence: 99%

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Green synthesis of silver nanoparticles using olive mill wastewater and olive stones extract and testing their antimicrobial activities against Escherichia coli and Staphylococcus epidermidis

Rigopoulos,

Gkaliouri,

Ioannou

et al. 2024

Nano Ex.

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Plant mediated synthesis of silver nanoparticles is eco-friendly and of low cost. The synthesis involves a reduction of silver ions and is controlled by several independent factors. In this work, silver nanoparticles (AgNPs) were successfully synthesized using olive stone extract (OSE) and olive mill wastewater (OMW) extract. The nanoparticle synthesis was monitored using the plasmon resonance observed in the UV–Vis absorption spectrum, in which a Voigt profile was fitted. The peak wavelength (λ0), the peak area (A), and the Full Width at Half Maximum (FWHM) were the fitting parameters and were used as the response. The independent factors were the incubation temperature, the incubation time, the silver nitrate, extract, and sodium hydroxide concentrations. The influence of these factors was investigated “two factor at a time”, using interaction plots. Strong interaction was observed between all factors, with sodium hydroxide to have a crucial role. The optimum conditions for silver nanoparticle formation were a) OSE (1 % v/v), AgNO3 (2 mM), and NaOH (0.2 mM), and b) OMW (2 % v/v), AgNO3 (1 mM), and NaOH (7.9 mM), showing an absorption maximum at 414 nm, and 410 nm, respectively. The mean diameter of AgNPs using OMW, measured with Transmission Electron Microscopy was 12.87±4.84 nm. Both types of AgNPs showed antibacterial action against Staphylococcus epidermidis (S. epidermidis) and Escherichia coli (E. coli), using the broth microdilution assay. Both nanoparticle types inhibited bacterial growth up to one dilution higher than reference samples.

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Section: Antibacterial Tests Of Agnpsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Green synthesis of silver nanoparticles using olive mill wastewater and olive stones extract and testing their antimicrobial activities against Escherichia coli and Staphylococcus epidermidis

Rigopoulos,

Gkaliouri,

Ioannou

et al. 2024

Nano Ex.

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Machine Learning Modeling of Anchovy Waste Treatment Using Solar Drying

Mohammed,

Zakaria,

Younes

et al. 2024

Heat Trans

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This study aims to valorize coproducts from the anchovy processing chain by obtaining compounds of interest through the implementation of environmentally friendly and energy‐efficient techniques. These methods, which also apply to other fresh anchovy waste coproducts, seek to minimize the environmental pollution associated with conventional systems. The investigation focused on the application of solar drying as a treatment of anchovy waste. The resulting data were employed to model the drying behavior of anchovy waste using five machine learning algorithms. A thermokinetic study was conducted under both natural and forced convection solar drying to establish the optimal conditions for drying and storing anchovy heads, which are a significant source of high‐quality proteins for human and animal nutrition. Drying kinetics were examined at three temperatures (60°C, 70°C, and 90°C) and two airflow rates (150 and 300 m3/h). The study identified air drying temperature as the most critical factor affecting the drying kinetics of anchovy wastes. Machine learning modeling of anchovy waste solar drying was conducted, and evaluated models were RNN, LSTM, GRU, LightGBM, and CatBoost. CatBoost demonstrated superior performance in predicting moisture content. It achieved the lowest Mean Squared Error of 1.1491e − 06, the lowest Mean Absolute Error of 0.0006265, and the highest coefficient of determination (R2) of 99.99%. The comparative analysis highlighted distinct differences in the predictive accuracy of the models, with CatBoost emerging as the most effective.

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