Process integration for biological sulfate reduction in a carbon monoxide fed packed bed reactor

Prediction of heavy metal removal performance of sulfate-reducing bacteria using machine learning

A robust modeling approach for predicting heavy metal removal by sulfate-reducing bacteria (SRB) is currently missing. In this study, four machine learning models were constructed and compared to predict the removal of Cd, Cu, Pb, and Zn as individual ions by SRB. The CatBoost model exhibited the best predictive performance across the four subsets, achieving R2 values of 0.83, 0.91, 0.92, and 0.83 for the Cd, Cu, Pb, and Zn models, respectively. Feature analysis revealed that temperature, pH, sulfate concentration, and C/S (the mass ratio of chemical oxygen demand to sulfate) had significant impacts on the outcomes. These features exhibited the most effective metal removal at 35 °C and sulfate concentrations of 1000-1200 mg/L, with variations observed in pH and C/S ratios. This study introduced a new modeling approach for predicting the treatment of metal-containing wastewater by SRB, offering guidance for optimizing operational parameters in the biological sulfidogenic process.

Predictive modelling and optimization of an airlift bioreactor for selenite removal from wastewater using artificial neural networks and particle swarm optimization

Selenite (Se⁴⁺) is the most toxic of all the oxyanion forms of selenium. In this study, a feed forward back propagation (BP) based artificial neural network (ANN) model was developed for a fungal pelleted airlift bioreactor (ALR) system treating selenite-laden wastewater. The performance of the bioreactor, i.e., selenite removal efficiency (RE_selenite) (%) was predicted through two input parameters, namely, the influent selenite concentration (IC_selenite) (10 mg/L - 60 mg/L) and hydraulic retention time (HRT) (24 h - 72 h). After training and testing with 96 sets of data points using the Levenberg-Marquardt algorithm, a multi-layer perceptron model (2-10-1) was established. High values of the correlation coefficient (0.96 ≤ R ≤ 0.98), along with low root mean square error (1.72 ≤ RMSE ≤ 2.81) and mean absolute percentage error (1.67 ≤ MAPE ≤ 2.67), clearly demonstrate the accuracy of the ANN model (> 96%) when compared to the experimental data. To ensure an efficient and economically feasible operation of the ALR, the process parameters were optimized using the particle swarm optimization (PSO) algorithm coupled with the neural model. The RE_selenite was maximized while minimizing the HRT for a preferably higher range of IC_selenite. Thus, the most favourable optimum conditions were suggested as: IC_selenite - 50.45 mg/L and HRT - 24 h, resulting in RE_selenite of 69.4%. Overall, it can be inferred that ANN models can successfully substitute knowledge-based models to predict the RE_selenite in an ALR, and the process parameters can be effectively optimized using PSO.

Novel microbial consortia facilitate metalliferous immobilization in non-ferrous metal(loid)s contaminated smelter soil: Efficiency and mechanisms

Exposure to toxic metals from nonferrous metal(loid) smelter soils can pose serious threats to the surrounding ecosystems, crop production, and human health. Bioremediation using microorganisms is a promising strategy for treating metal(loid)-contaminated soils. Here, a native microbial consortium with sulfate-reducing function (SRB1) enriched from smelter soils can tolerate exposures to mixtures of heavy metal(loid)s (e.g., As and Pb) or various organic flotation reagents (e.g., ethylthionocarbamate). The addition of Fe²⁺ greatly increased As³⁺ immobilization compared to treatment without Fe²⁺, with the immobilization efficiencies of 81.0% and 58.9%, respectively. Scanning electronic microscopy-energy dispersive spectroscopy, X-ray diffraction, and X-ray photoelectron spectroscopy confirmed that the As³⁺ immobilizing activity was related to the formation of arsenic sulfides (AsS, As₄S₄, and As₂S₃) and sorption/co-precipitation of pyrite (FeS₂). High-throughput 16S rRNA gene sequencing of SRB1 suggests that members of Clostridium, Desulfosporosinus, and Desulfovibrio genera play an important role in maintaining and stabilizing As³⁺ immobilization activity. Metal(loid)s immobilizing activity of SRB1 was not observed at high and toxic total exposure concentrations (220-1181 mg As/kg or 63-222 mg Pb/kg). However, at lower concentrations, SRB1 treatment decreased bioavailable fractions of As (9.0%) and Pb (28.6%) compared to without treatment. Results indicate that enriched native SRB1 consortia exhibited metal(loid) transformation capacities under non-toxic concentrations of metal(loid)s for future bioremediation strategies to decrease mixed metal(loid)s exposure from smelter polluted soils.

Recent advances in biological removal of nitroaromatics from wastewater

Various nitroaromatic compounds (NACs) released into the environment cause potential threats to humans and animals. Biological treatment is valued for cost-effectiveness, environmental friendliness, and availability when treating wastewater containing NACs. Considering the significance and wide use of NACs, this review focuses on recent advances in biological treatment systems for NACs removal from wastewater. Meanwhile, factors affecting biodegradation and methods to enhance removal efficiency of NACs are discussed. The selection of biological treatment system needs to consider NACs loading and cost, and its performance is affected by configuration and operation strategy. Generally, sequential anaerobic-aerobic biological treatment systems perform better in mineralizing NACs and removing co-pollutants. Future research on mechanism exploration of NACs biotransformation and performance optimization will facilitate the large-scale application of biological treatment systems.

Effect of carbon source and metal toxicity for potential acid mine drainage (AMD) treatment with an anaerobic sludge using sulfate-reduction

This study compares sulfate-reduction performance in an anaerobic sludge with different carbon sources (ethanol, acetate, and glucose). Also, the toxic effect of copper was evaluated to assess its feasibility for possible acid mine drainage (AMD) treatment. Serological bottles with 1.5 g VSS/L and 150 mL of basal medium (0.67 g COD/g SO₄^2- at a 7-8 pH) were used to determine the percentage of electron equivalents, maximum specific methanogenic (SMA), and sulfide generation activities (SGA). The copper effect was evaluated in a previously activated sludge in batch bioassays containing different concentrations of copper (0-50 mg/L), 3 gVSS/L, and 150 mL of basal medium (0.67 g COD/g SO₄^2-). Carbon source bioassays with glucose obtained the best results in terms of the SGA (1.73 ± 0.34 mg S^2-/g VSS•d) and SMA (10.41 mg COD-CH₄/g VSS•d). The electron flow in the presence of glucose also indicated that 21.29 ± 5.2% of the metabolic activity of the sludge was directed towards sulfidogenesis. Copper toxicity bioassays indicated that a considerable decline in metabolic activity occurs above 10 mg/L. The 20%IC, 50%IC, and 80%IC were 4.5, 14.94, and 35.31 mg Cu/L. Compared to the other carbon sources tested, glucose proved to be a suitable electron donor since it favors sulfidogenesis. Finally, copper concentrations above 15 mg/L inhibited metabolic activity in the toxicity bioassays.

Recent advances in heavy metal recovery from wastewater by biogenic sulfide precipitation

Biological sulfide precipitation by sulfate reducing bacteria (SRB) is an emerging technique for the recovery of heavy metals from metal contaminated wastewater. Advantages of this technique include low capital cost, ability to form highly insoluble salts, and capability to remove and recover heavy metals even at very low concentrations. Therefore, sulfate reduction under anaerobic conditions has become a suitable alternative for the treatment of wastewaters that contain metals. However, bioreactor configurations for recovery of metals from sulfate rich metallic wastewater have not been explored widely. Moreover, the recovered metal sulfide nanoparticles could be applied in various fields such as solar cells, dye degradation, electroplating, etc. Hence, metal recovery in the form of nanoparticles from wastewater could serve as an incentive for industries. The simultaneous metal removal and recovery can be achieved in either a single-stage or multistage systems. This paper aims to present an overview of the different bioreactor configurations for the treatment of wastewater containing sulfate and metal along with their advantages and drawbacks for metal recovery. Currently followed biological strategies to mitigate sulfate and metal rich wastewater are evaluated in detail in this review.

Biological remediation of acid mine drainage: Review of past trends and current outlook

Formation of acid mine drainage (AMD) is a widespread environmental issue that has not subsided throughout decades of continuing research. Highly acidic and highly concentrated metallic streams are characteristics of such streams. Humans, plants and surrounding ecosystems that are in proximity to AMD producing sites face immediate threats. Remediation options include active and passive biological treatments which are markedly different in many aspects. Sulfate reducing bacteria (SRB) remove sulfate and heavy metals to generate non-toxic streams. Passive systems are inexpensive to operate but entail fundamental drawbacks such as large land requirements and prolonged treatment period. Active bioreactors offer greater operational predictability and quicker treatment time but require higher investment costs and wide scale usage is limited by lack of expertise. Recent advancements include the use of renewable raw materials for AMD clean up purposes, which will likely achieve much greener mitigation solutions.

A novel carbon monoxide fed moving bed biofilm reactor for sulfate rich wastewater treatment

In this study, a moving bed biofilm reactor was used for biodesulfuruization using CO as the sole carbon substrate. The effect of hydraulic retention time (HRT), sulfate loading rate and CO loading rate on sulfate and CO removal was examined. At 72, 48 and 24 h HRT, the sulfate removal was 93.5%, 91.9% and 80.1%, respectively. An increase in the sulfate loading reduced the sulfate reduction efficiency, which, however, was improved by increasing the CO flow rate into the MBBR. Best results in terms of sulfate reduction (>80%) were obtained for low inlet sulfate and high CO loading conditions. The CO utilization was very high at 85% throughout the study, except during the last phase of the continuous bioreactor operation it was around 70%. An artificial neural network based model was successfully developed and optimized to accurately predict the bioreactor performance in terms of both sulfate reduction and CO utilization. Overall, this study showed an excellent potential of the moving bed biofilm bioreactor for efficient sulfate reduction even under high loading conditions.