Recovery of Heavy Metals from Mining and Electroplating Industries Effluents

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Soil and Water".

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 8333

Special Issue Editors


E-Mail Website
Guest Editor
1. Micro Pollutant Research Centre, Civil Engineering, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia
2. Camborne School of Mines, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Penryn TR10 9FE, UK
Interests: microbiology; pharmaceutical; wastewater; phytoremediation
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Applied Microbiology, Faculty of Applied Sciences, Taiz University, Taiz 6803, Yemen
Interests: mycology, bacteriology, environmental technologies, clinical wastes, wastewater, antimicrobial resistance, sterilization, supercritical carbon dioxide; bio-nanotechnology; oxidative enzymes

Special Issue Information

Dear Colleagues,

The metal finishing chemicals industry is expected to increase by 4.0% in the forecast period 2018 to 2025. As a result, large quantities of heavy metals ions in wastewater and the natural water systems are expected to increase accordingly. Currently, the bacterial biomass is becoming more promising for metal ions adsorption. However, many of the limitations are restricting the application of free bacterial cell biomass in environmental technology. The current trends aim to develop novel adsorbents with high affinity and selectivity to adsorb metal ions from mining, electroplating industries effluents, since these effluents have unique characteristics compared to the other wastewater. The removal efficiency is investigated based on the kinetic and isotherms models, while the sufficient information on the heavy metals behaviors to the adsorption process is still lacking in the large scale. This special issue aims to gather the recent findings on recovering of heavy metals from mining and electroplating industries effluents by advanced adsorption process based on the selective adsorbent and machine learning prediction models, which are the key features to provide a full acknowledge on this topic and presenting a developed adsorbent more efficacy to remove heavy metal from such effluents to minimize the heal risk for human and environment.

  • Assessment of heavy metals concentrations in mining and electroplating industries effluents
  • Development novel and selective adsorbents from agriculture wastes and nanocomposite with high affinity for heavy metals removal
  • Heavy metals behavior for the adsorbents based on the machine learning
  • Techno-economic analysis for the applicability of the adsorbents 
  • Effectiveness of treated effluents for safe disposal or reuse for irrigation

Dr. Adel Al-Gheethi
Dr. Efaq Ali Noman
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • heavy metals
  • adsorption
  • nanoparticles
  • water
  • wastewater
  • techno-economic analysis

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

18 pages, 4096 KiB  
Article
Steel Slag and Limestone as a Rock Filter for Eliminating Phosphorus from Domestic Wastewater: A Pilot Study in a Warm Climate
by Syahrul Nizam Maarup, Rafidah Hamdan, Norzila Othman, Adel Al-Gheethi, Sadeq Alkhadher, M. M. Abd El-Hady and S. El-Sayed Saeed
Water 2023, 15(4), 657; https://doi.org/10.3390/w15040657 - 8 Feb 2023
Cited by 2 | Viewed by 2459
Abstract
Phosphorus input with excessive use of fertilizers and manure as one of the main sources of nutrient pollution has increased recently in the wastewater as result of intensive farming and industrialized and densely populated areas. The novelty of the current work lies in [...] Read more.
Phosphorus input with excessive use of fertilizers and manure as one of the main sources of nutrient pollution has increased recently in the wastewater as result of intensive farming and industrialized and densely populated areas. The novelty of the current work lies in improving a Vertical Aerated Rock Filter (VARF) using steel slag and limestone media to enhance the efficiency of a rock filter (RF) to eliminate total phosphorus (TP) from domestic wastewater. RF was designed with steel slag and limestone (calcium hydroxide) as a pilot scale called vertical aerated steel slag filter (VASSF) and optimized based on hydraulic loading rates (HLR) (0.16 to 5.44 m3/m3 day) and airflow rates ranging from 3 to 10 L/min. The highest removal for the design of the laboratory scale steel slag filter (LSSSF) was achieved by approximately 58%, while for the laboratory-scale limestone filter (LSLSF), it was 64%. The VASSF achieved a removal percentage at 30% of TP, biological oxygen demand (BOD; 89%), chemical oxygen demand (COD; 75%), total suspended solids (TSS; 73%), and total coliforms (TC; 96%), recorded with 7 L/min of an airflow rate and 1.04 m3/m3.day of hydraulic loading rate (HLR) at potential of hydrogen (pH) 7.3 and 5.09 mg/L of dissolved oxygen (DO). These findings indicated that the steel slag is higher than limestone in TP removal, because of ion exchange between phosphorus hydrolysis and the adsorption process. Moreover, in the pilot study, the removal efficiency needs more investigation to determine the best conditions for TP considering the temperature, which is unstable, and presence of other pollutants, which might negatively affect the removal efficiency under unstable conditions. Full article
Show Figures

Figure 1

16 pages, 1686 KiB  
Article
Prediction of Wastewater Treatment Plant Performance Using Multivariate Statistical Analysis: A Case Study of a Regional Sewage Treatment Plant in Melaka, Malaysia
by Sofiah Rahmat, Wahid Ali Hamood Altowayti, Norzila Othman, Syazwani Mohd Asharuddin, Faisal Saeed, Shadi Basurra, Taiseer Abdalla Elfadil Eisa and Shafinaz Shahir
Water 2022, 14(20), 3297; https://doi.org/10.3390/w14203297 - 19 Oct 2022
Cited by 5 | Viewed by 5324
Abstract
The wastewater quality index (WWQI) is one of the most significant methods of presenting meaningful values that reflect a fundamental characteristic of wastewater. Therefore, this study was performed to develop a prediction approach using WWQI for a regional wastewater treatment plant (WWTP) in [...] Read more.
The wastewater quality index (WWQI) is one of the most significant methods of presenting meaningful values that reflect a fundamental characteristic of wastewater. Therefore, this study was performed to develop a prediction approach using WWQI for a regional wastewater treatment plant (WWTP) in Melaka, Malaysia. The regional system of WWTP provides a huge amount of registered data due to the many parameters recorded daily. A multivariate statistical analysis approach was applied to analyze the database. In this approach, principal component analysis (PCA) was used to reduce the dimensionality of datasets obtained from the field municipal WWTP, and multiple linear regression (MLR) was used to predict the performance of WWQI. Seven principal component analyses were derived where the eigenvalue was above 1.0, explaining 71.01% of the variance. A linear relationship was observed (R2 = 0.85), p-value < 0.05, and residual values were uniformly distributed above and below the zero baselines. Therefore, the coefficients of the WWQI model are directly dependent on influent biological oxygen demand (BOD), effluent BOD, influent chemical oxygen demand (COD), and effluent COD values. The experimental results showed that the model performed well and can be used to predict WWQI for each WWTP individually and provide better achievements. Full article
Show Figures

Figure 1

Back to TopTop