Browsing by Author "Martin, Ben"

Now showing 1 - 3 of 3
  • Thumbnail Image
    ItemOpen Access
    Insights into the effect of mixed engineered nanoparticles on activated sludge performance
    (Oxford University Press, 2015-07-16) Eduok, Samuel; Hendry, Callum; Ferguson, Robert M. W.; Martin, Ben; Villa, Raffaella; Jefferson, Bruce; Coulon, Frederic
    In this study, the effects, fate and transport of ENPs in wastewater treatment plants (WWTP) were investigated using three parallel pilot WWTPs operated under identical conditions. The WWTPs were spiked with (i) an ENP mixture consisting of silver oxide, titanium dioxide and zinc oxide, and (ii) bulk metal salts. The third plant served as control (unspiked). ENP effects were evaluated for (i) bulk contaminant removal, (ii) activated sludge (AS) process performance, (iii) microbial community structure and dynamics and (iv) microbial inhibition. ENPs showed a strong affinity for biosolids and induced a specific oxygen uptake rate two times higher than the control. The heterotrophic biomass retained its ability to nitrify and degrade organic matter. However, non-recovery of ammonia- and nitrite-oxidizing bacteria such as Nitrosomonas, Nitrobacter or Nitrospira in the ENP spiked reactors suggests selective inhibitory effects. The results further suggest that ENPs and metal salts have antimicrobial properties which can reduce synthesis of extracellular polymeric substances and therefore floc formation. Scanning electron microscopy evidenced selective damage to some microbes, whereas lipid fingerprinting and 454 pyrosequencing indicated a temporal shift in the microbial community structure and diversity. Acidovorax, Rhodoferax, Comamonas and Methanosarcina were identified as nano-tolerant species. Competitive growth advantage of the nano-tolerant species influenced the removal processes and unlike other xenobiotic compounds, ENPs can hasten the natural selection of microbial species in AS.
  • Thumbnail Image
    ItemOpen Access
    Short-term memory artificial neural network modelling to predict concrete corrosion in wastewater treatment plant inlet chambers using sulphide sensors
    (Elsevier, 2025-01-01) Mendizabal, J.; Vernon, D.; Martin, Ben; Bajón-Fernández, Yadira; Soares, Ana
    Sulphide accumulation in lengthy rising mains can lead to significant concrete corrosion and odour issues at manholes and wastewater treatment plants (WWTPs). Monitoring dissolved sulphide, typically relies on auto-sampling or grab samples followed by laboratory analysis, remains underdeveloped. This study aimed to identify sources of concrete corrosion sources at a WWTP inlet chamber and develop a sulphide prediction model using artificial intelligence (AI). A dissolved sulphide sensor was installed at three rising mains (RM1 to RM3) and the combined inlet at a full-scale WWTP, providing a 5-minute resolution data that revealed a daily hydrogen sulphide (H2S) pattern that inversely correlated with the flow rate. RM1 exhibited the highest sulphide load, peaking at 3.6 kg/d during cold months and 4.2 kg/d during warm months. RM3 and RM2 recorded loads of 2.96 kg/d and 0.98 kg/d, respectively, during cold months. A long short-term memory (LSTM) artificial neural network (ANN) model was developed to predict H₂S concentrations at RM1, using flow rate, temperature, and time of day as inputs. The model achieved a root mean square error (RMSE) of 0.34 and a Nash-Sutcliffe efficiency (NSE) of 0.57, accurately predicting the daily H2S pattern. This study's main contributions include insights into sulphide dynamics from high-resolution sensor data, which could support corrosion management as part of a septicity warning system or feedforward control for sulphide treatment. Additionally, the AI-based prediction model offers potential for sensor repurposing, saving both capital and operational costs.
  • Thumbnail Image
    ItemOpen Access
    Techno-economic analysis of sidestream ammonia removal technologies: biological options versus thermal stripping
    (Elsevier, 2022-11-22) Ochs, Pascal; Martin, Ben; Germain-Cripps, Eve; Stephenson, Tom; van Loosdrecht, Mark; Soares, Ana
    Over the past twenty years, various commercial technologies have been deployed to remove ammonia (NH4–N) from anaerobic digestion (AD) liquors. In recent years many anaerobic digesters have been upgraded to include a pre-treatment, such as the thermal hydrolysis process (THP), to produce more biogas, increasing NH4–N concentrations in the liquors are costly to treat. This study provides a comparative techno-economic assessment of sidestream technologies to remove NH4–N from conventional AD and THP/AD dewatering liquors: a deammonification continuous stirred tank reactor (PNA), a nitrification/denitrification sequencing batch reactor (SBR) and thermal ammonia stripping process with an ammonia scrubber (STRIP). The SBR and PNA were based on full-scale data, whereas the STRIP was designed using a computational approach to achieve NH4–N removals of 90–95%. The PNA presented the lowest whole-life cost (WLC) over 40 years, with £7.7 M, while the STRIP had a WLC of £43.9 M. This study identified that THP dewatering liquors, and thus a higher ammonia load, can lead to a 1.5–3.0 times increase in operational expenditure with the PNA and the SBR. Furthermore, this study highlighted that deammonification is a capable and cost-effective nitrogen removal technology. Processes like the STRIP respond to current pressures faced by the water industry on ammonia recovery together with targets to reduce nitrous oxide emissions. Nevertheless, ammonia striping-based processes must further be demonstrated in WWTPs and WLC reduced to grant their wide implementation and replace existing technologies.