Conference Agenda

Overview and details of the sessions of this Conference. Please select a date or location to show only sessions at that day or location. Please select a single session for a detailed view (with abstracts and downloads if available). The programme is preliminary and subject to change!

Please note that all times are shown in the time zone of the conference. The current conference time is: 31st Oct 2024, 07:46:06pm EDT

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Session Overview
Session
Active Treatment
Time:
Tuesday, 23/Apr/2024:
3:10pm - 5:00pm

Session Chair: Dorothy J Vesper
Location: Salons A–C


1. First speaker: 3:10-3:35
2. Second speaker: 3:35-4:00
3. Third speaker: 4:00-4:25
4. Fourth speaker: 4:25-4:50

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Presentations

Removal of selenium by biological reduction and surface complexation: removal efficiencies and speciation results

Myriam De Ladurantaye-Noel, Marc Laliberté

Veolia Water Technologies Canada, Canada

Due to its chronic toxicity, selenium is a growing concern in many mining activities. One of the main issues with selenium-contaminated waters is the bioaccumulation of selenium in living organisms in the receiving body. This bioaccumulation is highly dependent on the selenium speciation, because organoselenium species are prone to higher bioaccumulation.

Veolia has developed a new treatment method for selenium removal, which is based on biological reduction of selenium to selenite, its subsequent removal using surface complexation on ferric oxyhydroxide and removal of the solid, followed by further biological oxidation of the treated water. This paper reports on the findings of continuing lab investigations of this new process.

First, it was demonstrated that under standard biological denitrification conditions there is a reduction in selenium valence, with the majority of selenium being reduced to various Se+4 and Se-2 species. Detailed speciation results are presented. It was also found that when the denitrified water is sent to a biological aerobic reactor, most of the remaining reduced selenium species are oxidized back to Se+6 (selenate).

Finally, it was confirmed that after denitrification selenium can be removed by solid separation combined with surface complexation, with varying efficiency depending on the ion considered.

Results from the laboratory tests have shown, in over a year of operation, a significant and constant decrease in concentration of selenium to below 5 µg/L, with Se-2 species concentrations below 0.25 µg/L.



Electrolytic manganese removal from acid rock drainage

Sarah Doyle1, Linda Figueroa2

1Itasca Denver Inc.; 2Colorado School of Mines

Manganese is a common, yet difficult to remove pollutant in acid rock drainage (ARD). High manganese concentrations may occur in ARD from weathering of manganese-containing minerals such as oxides, carbonates, or iron minerals in which manganese substituted for iron. Removal of dissolved manganese is typically accomplished by pH adjustment to 10 to form manganese solids, followed by neutralization before discharge.

Manganese oxidation reaction rates can increase by creating strong oxidizing conditions, typically accomplished by use of a chemical oxidant such as permanganate. However, permanganate dosing requires close monitoring because coloration of discharge occurs if excess reagent is used. Electrolysis creates strong oxidizing conditions at the anode, without the need for chemical oxidants. Electrolysis allows for manganese removal by precipitation of manganese oxides at low pHs. Treatment at low pH can reduce costs associated with pH adjustments required by pH-10 precipitation as manganese hydroxides. Batch and flow-through laboratory experiments were conducted using a synthetic mine water generated from metal sulfates and deionized water. Applied voltages of 3.5V and 5V were used. Copper sheeting was used as the cathode and carbon cloth was used as the anode.

This study demonstrates that manganese can be removed from mining-impacted water using electrolytic methods at room temperature, low pH, and using low-cost materials. Manganese concentrations in pH 3 mine water were reduced from approximately 40 mg/L to less than 2 mg/L in batch tests with applied voltages of 3.5 V and 5 V after 12 hours of treatment. Steady state concentrations for the flow-through tests are similar, with an average manganese concentration of 2.5 mg/L for tests having a hydraulic residence time of 10.7 hours.

Unlike commonly used alkaline addition treatment methods, electrolytic removal does not require adjusting the pH to 10. Electrolytic treatment would therefore require less pH-adjusting chemicals and may also reduce sludge volumes. Electrolytically formed manganese oxide may also have potential for reuse as a sorbent for other water treatment applications at mine sites.



Economical and Environmentally Friendly Adsorption of Arsenic from Mine Drainage: Comparison between CMDS-Bead and GFH

Ki-Rim Lee, Duk-Min Kim, Hye-Lim Kwon, Nam-Kyu Kim, Young-Min Kim, Dae-Gyu Im, Oh-Hun Kwon

Sangji university, Korea, Republic of (South Korea)

The arsenic released from mine drainage can cause major harm to both humans and crops through surface water and groundwater contamination. Coal Mine Drainage Sludge (CMDS) and Granular Ferric Hydroxides (GFH), produced using ferrous hydroxide, exhibit a high point of zero charge (PZC) and strong affinity for arsenic, making them highly effective for arsenic adsorption. Both adsorbents were evaluated using two types of mine drainage and artificial water.

The CMDS-Bead and GFH adsorbents were utilized in batch experiments and column studies. Batch experiments included adsorption isotherm, pH adsorption edge, and kinetics experiments. In the column experiments, CMDS-Bead and GFH adsorbents were employed to treat two different mine drainage samples. Additionally, the electrical conductivity was assessed to evaluate the applicability of the adsorbents in water treatment reactors.

In the pH edge experiments, the adsorption performance of CMDS-Bead and GFH was evaluated over the pH range of 3 to 10. The results showed that GFH exhibited the highest adsorption capacity at pH 3, and as the pH increased, the adsorption of arsenic (As(V)) decreased. In the 30-day adsorption isotherm experiments, Langmuir model was a better fit both CMDS-Bead and GFH adsorbents. In other studies as well, CMDS and GFH were found to be well-fitted to the Langmuir model (Lee at al., 2018; Kumar et al., 2020). The maximum sorption capacities (Qmax) for CMDS-Bead and GFH were evaluated to be 16.2 mg/g and 17.6 mg/g, respectively. In the column experiments, A and B mine drainages with 0.2–0.4 mg/L and 0.3–0.6 mg/L of As, respectively, were treated separately. The Empty Bed Contact Time (EBCT) was 22.4 minutes. To date, CMDS-Bead has not reached the breakthrough point even after passing through 6312 BV and 6214 BV for A and B mine drainages. Likewise, GFH has not reached the breakthrough point even after passing through 6213 BV and 6023 BV for A and B mine drainages. CMDS-Bead satisfied emission standards while being substantially more cost-effective in manufacturing compared to GFH. Moreover, CMDS-Bead and GFH exhibited hydraulic conductivities of 8–9 × 10-3 cm/s and 7–8 × 10-3 cm/s, respectively, demonstrating characteristics similar to well-sorted sands.

CMDS-Bead, being a ferrous hydroxide adsorbent created from the recycling of mine effluent treatment plant sludge, is considered both economical and environmentally friendly. It is anticipated to see widespread use in various applications, particularly in the adsorption of Potentially Toxic Elements like arsenic, such as in mine effluent treatment processes.



Innovative data collection and management strategies for improved water treatment efficiency

Tom Meuzelaar1, Shannon D. Zahuranec1, Alice Alex2, James P. Jonas1

1Life Cycle Geo, United States of America; 2Canadian Nuclear Safety Commission

The performance of active and passive water treatment systems can be negatively influenced by seasonal and diurnal water quality and quantity fluctuations of the feedwater. Treatment performance can be improved by proactively modifying the water management strategy in response to these fluctuations. Employing modern, full, or partially automated configurations as part of the water management strategy can efficiently optimize these types of water treatment systems.

These automated system configurations build upon existing components with emerging technologies resulting in the following innovative data workflow: (a) automated, frequent collection of data at various treatment system monitoring points using sensor-based technologies, (b) automated alarms that can signal remote system upset conditions (compliance exceedances, pump malfunction, clogging, fouling etc.), (c) telemetry-based data upload and ingestion by a cloud-based data management system, (d) automated data cleaning and preparation pipelines, and (e) the use of conventional statistical and computational techniques and, when necessary, more advanced algorithms such as machine learning to analyze incoming data streams.

This workflow promotes intelligent, real-time guidance on water treatment and management decisions, such as treatment methods, dosage frequency, water diversion, and more, can be provided in near real-time through visualization, reporting, dashboards, and PLC controls. Results of the implementation of various components of this workflow demonstrate benefits such as improved treatment efficiency, more reliable operation, compliance with standards at discharge points, and overall reduction in labor, reagent costs, and energy demand. Additionally, transferring all system data, including sensor data, images, operator logs, and legacy PDFs, to a cloud-hosted data warehouse opens significant opportunities for enhancing value extracted from collected data.

An example application is provided for a remote semi-passive treatment system designed to treat waste rock drainage prone to upset conditions due predominantly to large fluctuations in water volume and difficulty staffing an experienced operator. Finally, the authors discuss how this workflow could be used to optimize a full-scale active treatment system with multiple sensor locations and numerous real-time data streams using a digital twin/machine learning approach.



 
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