A critical review of state-of-the-art technologies for electroplating wastewater treatment

Kamar, Mohamed T.; Elattar, Hoda; Mahmoud, Ahmed S.; Peters, Robert W.; Mostafa, Mohamed K.

doi:10.1080/03067319.2022.2098486

Cited by 17 publications

(7 citation statements)

References 138 publications

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“…Evaporation, adsorption, precipitation, membrane separation, electrocoagulation, and ion exchange are the common methods employed to eliminate heavy metals, including Cu ions, from industrial wastewater sources [9,10]. The adsorptionbased wastewater treatment method has encouraged researchers' motivation because of its simplicity of implementation and design, easy recovery of heavy metals, relatively low operation cost, and the feasibility of reusing the spent sorbent [11][12][13]. Adsorption is also characterized by ease of operation, and generation of low amounts of residue (e.g., sludge) [13,14].…”

Section: Introductionmentioning

confidence: 99%

Predicting Cu(II) Adsorption from Aqueous Solutions onto Nano Zero-Valent Aluminum (nZVAl) by Machine Learning and Artificial Intelligence Techniques

et al. 2023

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Predicting the heavy metals adsorption performance from contaminated water is a major environment-associated topic, demanding information on different machine learning and artificial intelligence techniques. In this research, nano zero-valent aluminum (nZVAl) was tested to eliminate Cu(II) ions from aqueous solutions, modeling and predicting the Cu(II) removal efficiency (R%) using the adsorption factors. The prepared nZVAl was characterized for elemental composition and surface morphology and texture. It was depicted that, at an initial Cu(II) level (Co) 50 mg/L, nZVAl dose 1.0 g/L, pH 5, mixing speed 150 rpm, and 30 °C, the R% was 53.2 ± 2.4% within 10 min. The adsorption data were well defined by the Langmuir isotherm model (R2: 0.925) and pseudo-second-order (PSO) kinetic model (R2: 0.9957). The best modeling technique used to predict R% was artificial neural network (ANN), followed by support vector regression (SVR) and linear regression (LR). The high accuracy of ANN, with MSE < 10−5, suggested its applicability to maximize the nZVAl performance for removing Cu(II) from contaminated water at large scale and under different operational conditions.

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Section: Introductionmentioning

confidence: 99%

Predicting Cu(II) Adsorption from Aqueous Solutions onto Nano Zero-Valent Aluminum (nZVAl) by Machine Learning and Artificial Intelligence Techniques

et al. 2023

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“…Traditionally, electroplating has been utilized to obtain protective coatings in the jewelry industry. However, this approach carries significant environmental consequences, including generating toxic heavy metals, gases, and harmful waste [25,26]. Recently, there has been a growing focus on investigating electroplating technology [27][28][29][30][31] due to its high environmental impact.…”

Section: Introductionmentioning

confidence: 99%

PVD for Decorative Applications: A Review

et al. 2023

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Physical Vapor Deposition (PVD) is a widely utilized process in various industrial applications, serving as a protective and hard coating. However, its presence in fields like fashion has only recently emerged, as electroplating processes had previously dominated this reality. The future looks toward the replacement of the most hazardous and toxic electrochemical processes, especially those involving Cr(VI) and cyanide galvanic baths, which have been restricted by the European Union. Unfortunately, a complete substitution with PVD coatings is not feasible. Currently, the combination of both techniques is employed to achieve new aesthetic features, including a broader color range and diverse textures, rendering de facto PVD of primary interest for the decorative field and the fashion industry. This review aims to outline the guidelines for decorative industries regarding PVD processes and emphasize the recent advancements, quality control procedures, and limitations.

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“…Water treatment techniques are imperative in the electroplating process as they ensure the removal of impurities and the attainment of high‐quality plating 18‐22 . One popular method employed in water treatment is ion exchange, which entails the replacement of ions present in the water with ions on a solid resin 23 . However, this method is limited by the potential exhaustion of the resin and the requirement for a frequent resin replacement.…”

Section: Introductionmentioning

confidence: 99%

“…Despite its high efficiency, this method is hindered by its high energy consumption and the potential for membrane fouling. Precipitation is also utilized for the elimination of dissolved metals and other contaminants from the water 23,24 . However, this technique may result in the generation of sludge and the requirement for high chemical dosage 25 .…”

Section: Introductionmentioning

confidence: 99%

Chromoionophoric probe‐anchored mesoporous silica nanospheres for rapid and reliable naked‐eye detection of Ni(II) ions in petroleum products and removal from electroplating wastewater

Al‐bonayan

Alamrani

Ibarhiam

et al. 2023

J of Molecular Recognition

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This paper presents the expansion of an optical, chemical sensor that can rapidly and reliably detect, quantify, and remove Ni(II) ions in oil products and electroplating wastewater sources. The sensor is based on mesoporous silica nanospheres (MSNs) that have an extraordinary surface area, uniform surface morphology, and capacious porosity, making them an excellent substrate for the anchoring of the chromoionophoic probe,3′‐{(1E,1′ E)‐[(4‐chloro‐1,2 phenylene)bis (azaneylylidene)]‐bis(methaneylylidene)}bis(2‐hydroxybenzoic acid) (CPAMHP). The CPAMHP probe is highly selective and sensitive to Ni(II), enabling it to be used in naked‐eye colorimetric recognition of Ni(II) ions. The MSNs provide several accessible exhibited sites for uniform anchoring of CPAMHP probe molecules, making it a viable chemical sensor even with the use of naked‐eye sensing. The surface characters and structural analysis of the MSNs and CPAMHP sensor samples were examined using various techniques. The CPAMHP probe‐anchored MSNs exhibit a clear and vivid color shift from pale yellow to green upon exposure to various concentrations of Ni(II) ions, with a reaction time down to approximately 1 minute. Furthermore, the MSNs can serve as a base to retrieve extremely trace amounts of Ni(II) ions, making the CPAMHP sensor a dual‐functional device. The calculated limit of recognition for Ni(II) ions using the fabricated CPAMHP sensor samples is 0.318 ppb (5.43 × 10−9 M). The results suggest that the proposed sensor is a promising tool for the sensitive and reliable detection of Ni(II) ions in petroleum products and for removing Ni(II) ions in electroplating wastewater; the data indicate an excellent removal of Ni (II) up to 96.8%, highlighting the high accuracy and precision of our CPAMHP sensor.

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A critical review of state-of-the-art technologies for electroplating wastewater treatment

Cited by 17 publications

References 138 publications

Predicting Cu(II) Adsorption from Aqueous Solutions onto Nano Zero-Valent Aluminum (nZVAl) by Machine Learning and Artificial Intelligence Techniques

Predicting Cu(II) Adsorption from Aqueous Solutions onto Nano Zero-Valent Aluminum (nZVAl) by Machine Learning and Artificial Intelligence Techniques

PVD for Decorative Applications: A Review

Chromoionophoric probe‐anchored mesoporous silica nanospheres for rapid and reliable naked‐eye detection of Ni(II) ions in petroleum products and removal from electroplating wastewater

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