Gas-phase trichloroethylene removal by Rhodococcus opacus using an airlift bioreactor and its modeling by artificial neural network

A comprehensive review and perspective research in technology integration for the treatment of gaseous volatile organic compounds

Volatile organic compounds (VOCs) are harmful pollutants emitted from industrial processes. They pose a risk to human health and ecosystems, even at low concentrations. Controlling VOCs is crucial for good air quality. This review aims to provide a comprehensive understanding of the various methods used for controlling VOC abatement. The advancement of mono-functional treatment techniques, including recovery such as absorption, adsorption, condensation, and membrane separation, and destruction-based methods such as natural degradation methods, advanced oxidation processes, and reduction methods were discussed. Among these methods, advanced oxidation processes are considered the most effective for removing toxic VOCs, despite some drawbacks such as costly chemicals, rigorous reaction conditions, and the formation of secondary chemicals. Standalone technologies are generally not sufficient and do not perform satisfactorily for the removal of hazardous air pollutants due to the generation of innocuous end products. However, every integration technique complements superiority and overcomes the challenges of standalone technologies. For instance, by using catalytic oxidation, catalytic ozonation, non-thermal plasma, and photocatalysis pretreatments, the amount of bioaerosols released from the bioreactor can be significantly reduced, leading to effective conversion rates for non-polar compounds, and opening new perspectives towards promising techniques with countless benefits. Interestingly, the three-stage processes have shown efficient decomposition performance for polar VOCs, excellent recoverability for nonpolar VOCs, and promising potential applications in atmospheric purification. Furthermore, the review also reports on the evolution of mathematical and artificial neural network modeling for VOC removal performance. The article critically analyzes the synergistic effects and advantages of integration. The authors hope that this article will be helpful in deciding on the appropriate strategy for controlling interested VOCs.

Optimizing the algae-bacteria biofilm reactor for imidacloprid wastewater treatment: An evaluation of hydraulic retention times for enhanced efficiency and energy savings

Recent observations have highlighted the rapidly growing prevalence of emerging contaminants such as Imidacloprid (IMI) within our environment. These insecticidal pollutants, coexisting with more traditional contaminants, have become predominant in aquatic systems, posing risks to both human and ecological well-being. Among the various wastewater treatment approaches tested, biofilm reactors are currently gaining prominence. In this study, we employed an Algae-Bacteria Biofilm Reactor (ABBR) to concurrently address both conventional and emergent contaminants, specifically IMI, over an extended timeframe. Following a 60-day assessment, the ABBR consistently demonstrated removal efficiencies exceeding 85% for total dissolved nitrogen, ammonia nitrogen, and total dissolved phosphorus, and also achieved removal efficacy for the soluble chemical oxygen demand (sCOD). Despite the removal efficiency of IMI (with initial concentration is 1.0 mg/L) in ABBR showed a gradual decline over the extended period, it remained consistently effective over 50% due to the microalgae-mediated free radical reactions, indicating the ABBR's sustained efficiency in long-duration operations. Additionally, applying some non-conventional modifications, like aeration removal and reducing light exposure, demonstrated minimal impact on the reactor's pollutant removal efficiencies, achieving comparable results to the control group (which utilized aeration with a 14:10 light/dark ratio), 0.92 kW h/L/d of electricity can be saved economically, which accentuated the potential for energy conservation. An in-depth analysis of the treated effluents from the ABBRs, using ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) technique, uncovered four potential transformation pathways for IMI. Overall, our findings suggest that these optimized processes did not influence the transformation products of IMI, thereby reaffirming the viability of our proposed optimization.

Neural network-based performance assessment of one- and two-liquid phase biotrickling filters for the removal of a waste-gas mixture containing methanol, α-pinene, and hydrogen sulfide

The performance of one- and two-liquid phase biotrickling filters (OLP/TLP-BTFs) treating a mixture of gas-phase methanol (M), α-pinene (P), and hydrogen sulfide (H) was assessed using artificial neural network (ANN) modeling. The best ANN models with the topologies 3-9-3 and 3-10-3 demonstrated an exceptional capacity for predicting the performance of O/TLP-BTFs, with R2 > 99%. The analysis of causal index (CI) values for the model of OLP-BTF revealed a negative impact of M on P removal (CI = -2.367), a positive influence of P and H on M removal (CI = +7.536 and CI = +3.931) and a negative effect of H on P removal (CI = -1.640). The addition of silicone oil in TLP-BTF reduced the negative impact of M and H on P degradation (CI = -1.261 and CI = -1.310, respectively) compared to the OLP-BTF. These findings suggested that silicone oil had the potential to improve P availability to the biofilm by increasing the concentration gradient of P between the air/gas and aqueous phases. Multi-objective particle swarm optimization (MOPSO) suggested an optimum operational condition, i.e. inlet M, P, and H concentrations of 1.0, 1.1, and 0.3 g m-3, respectively, with elimination capacities (ECs) of 172.1, 26.5, and 0.025 g m-3 h-1 for OLP-BTF. Likewise, one of the optimum operational conditions for TLP-BTF is achievable at inlet concentrations of 4.9, 1.7, and 0.8 g m-3, leading to the optimum ECs of 299.7, 52.9, and 0.072 g m-3 h-1 for M, P, and H, respectively. These results provide important insights into the treatment of complex waste gas mixtures, addressing the interactions between the pollutant removal characteristics in OLP/TLP-BTFs and providing novel approaches in the field of biological waste gas treatment.

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.

Rhodococcus: A promising genus of actinomycetes for the bioremediation of organic and inorganic contaminants

Rhodococcus is a genus of actinomycetes that has been explored by the scientific community for different purposes, especially for bioremediation uses. However, the mechanisms governing Rhodococcus-mediated bioremediation processes are far from being fully elucidated. In this sense, this work aimed to compile the recent advances in the use of Rhodococcus for the bioremediation of organic and inorganic contaminants present in different environmental compartments. We reviewed the bioremediation capacity and mechanisms of Rhodococcus spp. in the treatment of polycyclic aromatic hydrocarbons, phenolic substances, emerging contaminants, heavy metals, and dyes given their human health risks and environmental concern. Different bioremediation techniques were discussed, including experimental conditions, treatment efficiencies, mechanisms, and degradation pathways. The use of Rhodococcus strains in the bioremediation of several compounds is a promising approach due to their features, primarily the presence of appropriate enzyme systems, which result in high decontamination efficiencies; but that vary according to experimental conditions. Besides, the genus Rhodococcus contains a small number of opportunistic species and pathogens, representing an advantage from the point of view of safety. Advances in analytical detection techniques and Molecular Biology have been collaborating to improve the understanding of the mechanisms and pathways involved in bioremediation processes. In the context of using Rhodococcus spp. as bioremediation agents, there is a need for more studies that 1) evaluate the role of these actinomycetes on a pilot and field scale; 2) use genetic engineering tools and consortia with other microorganisms to improve the bioremediation efficiency; and 3) isolate new Rhodococcus strains from environments with extreme and/or contaminated conditions aiming to explore their adaptive capabilities for bioremediation purposes.

Reactive Adsorption of Gaseous Anisole by MCM–41-Supported Sulfuric Acid

To achieve the efficient resource treatment of aromatic volatile organic compounds (VOCs) of high toxicity, this work chose anisole as a representative pollutant and investigated its removal by an MCM–41-supported sulfuric acid (SSA/MCM–41) adsorbent. The results indicate that the SSA/MCM–41 adsorbent exhibited a reactive temperature range of 110–140 °C, in which the anisole removal ratio (Xa) was greater than 95%. The collected breakthrough adsorption data fit the dose–response model. In the comprehensive analysis of the process conditions, reducing the flow rate enhanced the theoretical breakthrough time and adsorption capacity (tB,th and QB,th), while reducing the inlet concentration or raising the bed height resulted in a first increasing and then slightly decreasing trend in the QB,th. As a result, the highest tB,th and QB,th were 73.82 min and 247.56 mg g−1, respectively. The FTIR and 1H/13C NMR results demonstrate that the adsorbed products included both 4-methoxybenzenesulfonic acid and 1-methoxy-4-(4-methoxyphenyl)sulfonylbenzene. Accordingly, the mechanism of reactive adsorption was proposed. Meanwhile, the spent SSA/MCM–41 could be desorbed and regenerated for cyclic reuse. It is believed that the results obtained will assist in promoting the application of the novel gas–solid adsorption approach in VOC treatment.

Reactive Adsorption Performance and Behavior of Gaseous Cumene on MCM-41 Supported Sulfuric Acid

Efficient removal of cumene from gaseous streams and recovery of its derivatives was accomplished using a MCM-41-supported sulfuric acid (SSA/MCM-41) adsorbent. The results indicated that the removal performance of the SSA/MCM-41 for cumene was significantly influenced by the process conditions such as bed temperature, inlet concentration, bed height, and flow rate. The dose–response model could perfectly describe the collected breakthrough adsorption data. The SSA/MCM-41 adsorbent exhibited a reactive temperature region of 120–170 °C, in which the cumene removal ratios (X_c) were greater than 97%. Rising the bed height or reducing the flow rate enhanced the theoretical adsorption performance metrics, such as theoretical breakthrough time (t_B,th) and theoretical breakthrough adsorption capacity (Q_B,th), whereas increasing the inlet concentration resulted in t_B,th shortening and Q_B,th rising. As demonstrated in this paper, the highest t_B,th and Q_B,th were 69.60 min and 324.50 mg g⁻¹, respectively. Meanwhile, the spent SSA/MCM-41 could be desorbed and regenerated for cyclic reuse. Moreover, two recoverable adsorbed products, 4-isopropylbenzenesulfonic acid and 4, 4′-sulfonyl bis(isopropyl-benzene), were successfully separated and identified using FTIR and ¹H/¹³C NMR characterization. Accordingly, the relevance of a reactive adsorption mechanism was confirmed. This study suggests that the SSA/MCM-41 has remarkable potential for application as an adsorbent for the resource treatment of cumene pollutants.

Design of nutrient gas-phase bioreactors: a critical comprehensive review

To reach an efficient and economical gas-phase bioreactor is still one of the most critical challenges in biotechnology engineering. The numerous advantages of gas-phase bioreactors (GPBs) as well as disadvantages of these bioreactors should be exactly recognized, and efforts should be made to eliminate these defects. The first step in upgrading these bioreactors is to identify their types and the results of previous research. In the present work, a summary of the studies carried out in the field of cultivation in these bioreactors, their classification, their components, their principles and relations governing elements, modeling them, and some of their inherent engineering aspects are presented. Literature review showed that inoculation of shoots, roots, adventurous roots, callus, nodal explants, anther, nodal segment, somatic embryo, hairy roots, and fungus is reported in 15, 2, 2, 2, 3, 2, 1, 1, 37, and 5 cases, respectively.

Direct aerobic oxidation (DAO) of chlorinated aliphatic hydrocarbons: A review of key DAO bacteria, biometabolic pathways and in-situ bioremediation potential

Contamination of aquifers and vadose zones with chlorinated aliphatic hydrocarbons (CAH) is a world-wide issue. Unlike other reactions, direct aerobic oxidation (DAO) of CAHs does not require growth substrates and avoids the generation of toxic by-products. Here, we critically review the current understanding of chlorinated aliphatic hydrocarbons-DAO and its application in bioreactors and at the field scale. According to reports on chlorinated aliphatic hydrocarbons-DAO bacteria, isolates mainly consisted of Methylobacterium and Proteobacterium. Chlorinated aliphatic hydrocarbons-DAO bacteria are characterized by tolerance to a high concentration of CAHs and highly efficient removal of CAHs. Trans-1,2-dichloroethylene (t-DCE) is easily transformed biomass for bacteria, followed by 1,2-dichloroethane (1,2-DCA), dichloromethane (DCM), vinyl chloride (VC) and cis-1,2-dichloroethylene (c-DCE). Significant differences in the maximum specific growth rates were observed with different CAHs and biometabolic pathways for DCM, 1,2-DCA, VC and c-DCE degradation have been successfully parsed. Detection of the functional genes etnC and etnE is useful for the determination of active VC DAO bacteria. Additionally, DAO bacteria have been successfully applied to CAHs in new types of bioreactors with satisfactory results. To the best of the authors' knowledge, only one study on DAO-CAHs was conducted in-situ and resulted in 99% CAH removal. Lastly, we put forward future development prospect of chlorinated aliphatic hydrocarbons-DAO.

Multivariate data-based optimization of membrane adsorption process for wastewater treatment: Multi-layer perceptron adaptive neural network versus adaptive neural fuzzy inference system

Application of machine-learning methods to assess the batch adsorption of malachite green (MG) dye on chitosan/polyvinyl alcohol/zeolite imidazolate frameworks membrane adsorbents (CPZ) was investigated in this study. Our previous research results proved the suitability of the CPZ membranes for wastewater decoloring. In the current work, the residence time was combined with the other operational variables i.e., pH, initial dye concentration, and adsorbent dose (AD), to obtain the possible interactions involved in nonequilibrium adsorption. Two well-known soft-computing approaches, multi-layer perceptron adaptive neural network (MLP-ANN) and adaptive neural fuzzy inference system (ANFIS), were selected among different machine learning alternatives and then, comprehensively compared with each other considering reliability and accuracy for a 60 number of runs. The ANFIS structure with nine centers of clusters could predict the adsorption performance better than the ANN approach. Root mean square error (RMSE) and R-square were obtained 0.01822 and 0.9958 for the test data, respectively. The interpretability test resulted a linear trend predicted by the model and disclosed that the maximum value of the removal efficiency (99.5%) could be obtained when the amount of the inputs set to the upper limit. Lastly, the sensitivity analysis uncovered that the residence time has a decisive effect (relevancy factor > 80%) on the removal efficiency. According to the results, ANFIS is an effective and reliable tool to optimize and intensify the membrane adsorption process.

C5b-9 membrane attack complex activated NLRP3 inflammasome mediates renal tubular immune injury in trichloroethylene sensitized mice

Trichloroethylene (TCE) induced occupational medicamentosa-like dermatitis (OMLDT) in patients is accompanied, typically, by renal damage. But the role of C5b-9 and IL-1β in TCE-sensitized mouse renal tubular damage is unclear. This study aimed to investigate whether TCE-sensitized mouse renal tubular epithelial cell damage was induced by NLRP3 inflammasome and whether NLRP3 inflammasome was activated by sublytic C5b-9. In total, 52 specific pathogen-free BALB/c female mice, 6- to 8-week-old, were used for establishing the TCE-sensitized mouse model. Renal tubular epithelial cells were isolated and used for determining the sublytic level of C5b-9. Kidney histological examination, serum neutrophil gelatinase associated lipocalin (NGAL) level were used for kidney damage evaluation. Renal protein levels of C5b-9, NLRP3, ASC, Caspase-1, IL-1β, and IL-18 were measured. The renal lesions, serum NGAL level, renal NLRP3, ASC, Caspase-1 and IL-1β protein levels all increased significantly in TCE sensitized positive group. However, pretreatment with recombinant protein sCD59-Cys inhibited the expression of C5b-9, NLRP3 inflammasome, IL-1β, IL-18, and attenuated renal tubular epithelial cell damage. The sublytic C5b-9 activated NLRP3 inflammasome and aggravated renal tubular epithelial cell damage. Pretreatment with recombinant protein sCD59-Cys blocked the expression of the NLRP3 inflammasome by inhibiting the expression of C5b-9, and alleviating renal tubular epithelial cell damage.