Performance Evaluation and Neural Modeling of Gas-Phase Styrene Removal in One- and Two-Liquid Phase Suspended-Growth Bioreactors

Bioelimination of low methane concentrations emitted from wastewater treatment plants: a review

Sewage from residents and industries is collected and transported to wastewater treatment plants (WWTPs) with sewer networks. The operation of WWTPs results in emissions of greenhouse gases, such as methane (CH₄), mostly due to sludge anaerobic digestion. Amounts of emissions depend on the source of influent, i.e. municipal and industrial wastewater as well as sewer systems (gravity and rising). Wastewater is the fifth-largest source of anthropogenic CH₄ emissions in the world and represents 7-9% of total global CH₄ emissions into the atmosphere. Global wastewater CH₄ emission grew by approximately 20% from 2005 to 2020 and is expected to grow by 8% between 2020 and 2030, which makes wastewater an important CH₄ emitter worldwide. This review initially considers the emission of CH₄ from WWTPs and sewer networks. In the second part, biotechniques available for biodegradation of low CH₄ concentrations (<5% v/v) encountered in WWTPs have been studied. The paper reviews major bioreactor configurations for the treatment of polluted air, i.e. biotrickling filters, bioscrubbers, two-liquid phase bioreactors, biofilters, and hybrid reactor configurations, after which it focuses on CH₄ biofiltration systems. Biofiltration represents a simple and efficient approach to bio-oxidize CH₄ in waste gases from WWTPs. Major factors influencing a biofilter's performance along with knowledge gaps in relation to its application for treating gaseous emissions from WWTPs are discussed.

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

This study evaluated the biological removal of trichloroethylene (TCE) by Rhodococcus opacus using airlift bioreactor under continuous operation mode. The effect of inlet TCE concentration in the range 0.12-2.34 g m^-3 on TCE removal has studied for 55 days. During the continuous bioreactor operation, a maximum of 96% TCE removal was obtained for low inlet TCE concentration, whereas the highest elimination capacity was 151.2 g m^-3 h^-1 for the TCE loading rate of 175.0 g m^-3 h^-1. The carbon dioxide (CO₂) concentration profile from the airlift bioreactor revealed that the degraded TCE has primarily converted to CO₂ with a fraction of organic carbon utilized for bacterial growth. The artificial neural network (ANN) based model was able to successfully predict the performance of the bioreactor system using the Levenberg-Marquardt (LM) back propagation algorithm, and optimized biological topology is 3:12:1. The prediction accuracy of the model was high as the experimental data were in good agreement (R² = 0.9923) with the ANN predicted data. Overall, from the bioreactor experiments and its ANN modeling, the potential strength of R. opacus in TCE biodegradation is proved.

Experimental studies and neural network modeling of the removal of trichloroethylene vapor in a biofilter

In this study, bamboo carrier based lab scale compost biofilter was evaluated to treat synthetic waste air containing trichloroethylene (TCE) under continuous operation mode. The effect of inlet TCE concentration and gas flow rate and its removal was investigated. Maximum TCE removal efficiency was found to be 89% under optimum conditions of inlet 0.986 g/m³ TCE concentration corresponding to a loading rate of 43 g/m³ h and 0.042 m³/h gas flow rate at empty bed residence time (EBRT) of 2 min. For the first time, Artificial Neural Network (ANN) was applied to predict the performance of the compost biofilter in terms of TCE removal. The ANN model used a three layer feed forward based Levenberg-Marquardt algorithm, and its topology consisted of 3-25-1 as the optimum number for the three layers (input, hidden and output). An excellent match between the experimental and ANN predicted the value of TCE removal was obtained with a coefficient of determination (R²) value greater than 0.99 during the model training, validation, testing and overall. Furthermore, statistical analysis of the ANN model performance mediated its prediction accuracy of the bioreactor to treat TCE contaminated systems.

Performance assessment of gas-phase toluene removal in one- and two-liquid phase biotrickling filters using artificial neural networks

The main aim of this work is to study gas-phase toluene removal in one- and two-liquid phase biotrickling filters (O/TLP-BTF) and model the BTF performance using artificial neural networks (ANNs). The TLP-BTF was operated for 60 d in the presence of silicone oil at empty bed residence times (EBRTs) of 120, 60, and 45 s, respectively, and toluene concentrations in the range of 0.9-3.1 g m^-3. A t-test analysis indicated that increasing the silicone oil volume ratio from 5 to 10% v/v, did not significantly improve the TLP-BTF performance (p-value = 0.65 > 0.05). The results from ANN modeling showed that toluene removal was more negatively affected by the inlet concentration (casual index, CI = -5.63) due to the kinetic limitation. The CI values for inlet concentration (+4.01) and liquid trickling rate (-2.45) indicated that the diffusion-limited regime controlled the removal process in the OLP-BTF.

Steady- and transient-state performance of a thermophilic suspended-growth bioreactor for α-pinene removal from polluted air

The removal of α-pinene from polluted air was examined under thermophilic conditions (50°C) in one- and two-liquid phase continuous stirred tank bioreactors (CSTBs). Steady-state experiments were performed at different gas residence times, and α-pinene concentrations, corresponding to inlet loading rates between 0.5 and 38gm(-3)h(-1) in the one-liquid phase CSTB, and between 2.9 and 176gm(-3)h(-1) in the two-liquid phase CSTB. The presence of a second liquid-phase (5% v/v silicone oil) increased the maximum elimination capacity (85.7gm(-3)h(-1)) by ∼6-fold compared to the one-liquid phase CSTB. During transient-state experiments, the CSTB with silicone oil could withstand a higher α-pinene shock load (110gm(-3)h(-1)) than the CSTB operated without silicone oil (70gm(-3)h(-1)). Besides, the thermophilic specific substrate utilization rates were estimated from batch assays, reaching 89 and 340μgα-pinenemgbiomass(-1)d(-1), in the absence of or presence of silicone oil, respectively.

Waste gas biofiltration: advances and limitations of current approaches in microbiology

As confidence in gas biofiltration efficacy grows, ever more complex malodorant and toxic molecules are ameliorated. In parallel, for many countries, emission control legislation becomes increasingly stringent to accommodate both public health and climate change imperatives. Effective gas biofiltration in biofilters and biotrickling filters depends on three key bioreactor variables: the support medium; gas molecule solubilization; and the catabolic population. Organic and inorganic support media, singly or in combination, have been employed and their key criteria are considered by critical appraisal of one, char. Catabolic species have included fungal and bacterial monocultures and, to a lesser extent, microbial communities. In the absence of organic support medium (soil, compost, sewage sludge, etc.) inoculum provision, a targeted enrichment and isolation program must be undertaken followed, possibly, by culture efficacy improvement. Microbial community process enhancement can then be gained by comprehensive characterization of the culturable and total populations. For all species, support medium attachment is critical and this is considered prior to filtration optimization by water content, pH, temperature, loadings, and nutrients manipulation. Finally, to negate discharge of fungal spores, and/or archaeal and/or bacterial cells, capture/destruction technologies are required to enable exploitation of the mineralization product CO(2).

Combined biological and physicochemical waste-gas cleaning techniques

This review presents a general overview of physical, chemical and biological waste-gas treatment techniques such as adsorption, absorption, oxidation and biodegradation, focusing more extensively on combined processes. It is widely recognized that biological waste-gas treatment devices such as biofilters and biotrickling filters can show high performance, often reaching removal efficiencies above 90 % for pollutant concentrations below 5 g/m(3). However, for concentrations exceeding this limit and under transient shock-load conditions that are frequently encountered in industrial situations, a physicochemical gas cleaning process can sometimes be advantageously combined with a biological one. Besides improving the overall treatment efficiency, the non-biological, first-stage process could also serve as a load equalization system by reducing the pollutant load during periodic shock-loads, to levels that can easily be handled in the second-stage bioreactor. This article reviews the operational advantages of integrating different non-biological and biological processes, i.e., adsorption pre-treatment+bioreactor, bioreactor+adsorption post-treatment, absorption pre-treatment+bioreactor, UV pre-treatment+bioreactor, and bioreactor/bioreactor combinations, for waste-gas treatment, where different gas-phase pollutants have been tested.

Effect of oil concentration and residence time on the biodegradation of α-pinene vapours in two-liquid phase suspended-growth bioreactors

Recently, research on the use of binary aqueous-organic liquid phase systems for the treatment of polluted air has significantly increased. This paper reports the removal of α-pinene from a waste air stream in a continuous stirred tank bioreactor (CSTB), using either a single-liquid aqueous phase or a mixed aqueous-organic liquid phase. The influence of gas flow rate, load and pollutant concentration was evaluated as well as the effect of the organic to aqueous phase ratio. Continuous experiments were carried out at different inlet α-pinene concentrations, ranging between 0.03 and 25.1 g m⁻³ and at four different flow rates, corresponding to residence times (RTs) of 120 s, 60 s, 36 s and 26 s. The maximum elimination capacities (ECs) reached in the CSTB were 382 g m⁻³ h⁻¹ (without silicone oil) and 608 g m⁻³ h⁻¹ (with 5%v/v silicone oil), corresponding to a 1.6-fold improvement using an aqueous-organic liquid phase. During shock-loads experiments, the performance and stability of the CSTB were enhanced with 5% silicone oil, quickly recovering almost 100% removal efficiency (RE), when pre-shock conditions were restored. The addition of silicone oil acted as a buffer for high α-pinene loads, showing a more stable behaviour in the case of two-liquid-phase systems.

Neural network models for biological waste-gas treatment systems

This paper outlines the procedure for developing artificial neural network (ANN) based models for three bioreactor configurations used for waste-gas treatment. The three bioreactor configurations chosen for this modelling work were: biofilter (BF), continuous stirred tank bioreactor (CSTB) and monolith bioreactor (MB). Using styrene as the model pollutant, this paper also serves as a general database of information pertaining to the bioreactor operation and important factors affecting gas-phase styrene removal in these biological systems. Biological waste-gas treatment systems are considered to be both advantageous and economically effective in treating a stream of polluted air containing low to moderate concentrations of the target contaminant, over a rather wide range of gas-flow rates. The bioreactors were inoculated with the fungus Sporothrix variecibatus, and their performances were evaluated at different empty bed residence times (EBRT), and at different inlet styrene concentrations (C(i)). The experimental data from these bioreactors were modelled to predict the bioreactors performance in terms of their removal efficiency (RE, %), by adequate training and testing of a three-layered back propagation neural network (input layer-hidden layer-output layer). Two models (BIOF1 and BIOF2) were developed for the BF with different combinations of easily measurable BF parameters as the inputs, that is concentration (gm(-3)), unit flow (h(-1)) and pressure drop (cm of H(2)O). The model developed for the CSTB used two inputs (concentration and unit flow), while the model for the MB had three inputs (concentration, G/L (gas/liquid) ratio, and pressure drop). Sensitivity analysis in the form of absolute average sensitivity (AAS) was performed for all the developed ANN models to ascertain the importance of the different input parameters, and to assess their direct effect on the bioreactors performance. The performance of the models was estimated by the regression coefficient values (R(2)) for the test data set. The results obtained from this modelling work can be useful for obtaining important relationships between different bioreactor parameters and for estimating their safe operating regimes.