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A PRACTICAL MODEL FOR FULL-SCALE OPTIMISATION OF THE ANAEROBIC DIGESTION PROCESS Stephen R Smith and Jin Liu Department of Civil and Environmental Engineering, Imperial College London Email s.r.smith@imperial.ac.uk Background Overall 80%

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A PRACTICAL MODEL FOR FULL-SCALE OPTIMISATION OF THE ANAEROBIC DIGESTION PROCESS

Stephen R Smith and Jin Liu

Department of Civil and Environmental Engineering, Imperial College London Email s.r.smith@imperial.ac.uk

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Background

  • Overall 80% of sewage sludge in the UK is treated using anaerobic digestion (AD)
  • Anaerobic digestion is a practical and cost-effective method for the stabilisation

and treatment of residual sewage sludge

  • It is also a significant producer of renewable

energy in form of biogas

  • There is still a lack of practical modelling tools

available to guide operators and maximise the energy output

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AD is a dynamic system affected by multiple factors:

Hydraulic retention time (HRT) Temperature Dry solids (DS) Mixing efficiency Sludge composition Primary SAS ratio Sludge age Volatile fatty acid (VFA) Ammonia Asset age Volatile solids (VS) Organic loading rate Iron dosing Pretreatment

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Data Summary

Routinely collected parameters:

  • Temperature
  • DS
  • HRT

Additional parameters collected at specific sites:

  • VS, sludge age, primary SAS ratio, VFA etc.
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Data Summary

100 200 300 400 500 600 700 800 900 1,000

Site1 Site2 Site3 Site4 Site5 Site6 Site7 Site8 Site9 Site10 Site11 Site12 Site13 Site14 Site15 Site16 Site17 Site18 Site19 Site20 Site21 Site22 Site23 Site24 Site25 Site26 Site27 Site28 Site29 Site30 Site31 Site32 Site33 Site34 Site35 Site36 Site37 Site38 Site39 Site40 Site41 Site42 Site43 Site44 Site45 Site46 Site47 Site48 Site49 Site50 Site51 Site52 Site53 Site54 Site55 Site56 Site57 Site58 Site59 Site60 Site61 Site62 Site63 Site64 Site65 Site66 Company 1 Company 2 Company 3 Company 4

Biogas yield (m3/t DS)

2013-2017 2011-2016 2014-2016 2009-2016

Conventional mesophilic anaerobic digestion (MAD) dataset

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Multiple Regression Analysis

Simple linear multiple regression:

𝑧 = 𝑐1𝑦1+𝑐2𝑦2 + …+𝑐𝑜𝑦𝑜 +c

  • Significant predictors are selected

based on P< 0.05 However, the full-scale AD data are clustered

  • Categorical factor: multi-level

regression

  • Centering approach: evaluating the

relative changes

200 300 400 500 600 700 0.0 2.0 4.0 6.0 8.0 10.0 12.0 Biogas yield (m3/t DS) DS feed (%) Site12 Site14 THP Site 3 THP Site 6 Simple regression line

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Multiple Regression Analysis

Conventional MAD model:

Biogas yield = 230.9 * (Ln(Temperature) - 3.6) + 136.2*(Ln(HRT) - 3.0) - 224.8 * (Ln(DS) - 1.5) + 75.5 * ((Ln(HRT) - 3.0) * (Ln(DS) - 1.5)) + site factor

Variation explained: 0.46% 2.55% 5.31% 0.11% 42.42%

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SLIDE 8

Model Validation – Conventional MAD

200 400 600

08/2011 11/2011 02/2012 05/2012 08/2012 11/2012 02/2013 05/2013 08/2013 11/2013 02/2014 05/2014 08/2014 11/2014 02/2015 05/2015 08/2015 11/2015 02/2016 05/2016

Biogas yield (m3/t DS) Data involved in model generation (2011 - 2016) Site 42

R2=0.65, P<0.001

0.00 200.00 400.00 600.00

04/2016 06/2016 08/2016 10/2016 12/2016 02/2017 04/2017 06/2017 08/2017 10/2017 12/2017 02/2018 04/2018 06/2018 08/2018 10/2018 12/2018 02/2019

Biogas yield (m3/t DS) Independent datasets (2016 - 2019) Site 38

R2=0.59, P<0.001

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SLIDE 9

Expansion of Advanced AD

Increases:

  • Loading rate
  • Biogas yield
  • VS reduction
  • Pathogen kill
  • Dewaterability

Thermal hydrolysis process (THP)

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Combined Conventional-THP MAD Model Development

Apply conventional MAD model into THP datasets

200 400 600 800 200 400 600 800 1000

Observed biogas yield (m3/t DS) Predicted biogas yield (m3/t DS)

THP site 1 THP site 2 THP site 3 THP site 4 THP site 5 THP site 6

y = 0.95x + 22.44 R2 = 0.72 P<0.001

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Combined Conventional-THP MAD Model Development

Conventional MAD model : Biogas yield = 230.9 * (Ln(Temperature) - 3.6) + 136.2*(Ln(HRT) - 3.0) - 224.8 * (Ln(DS) - 1.5) + 75.5 * ((Ln(HRT) - 3.0) * (Ln(DS) - 1.5)) + site factor Combined conventional-THP MAD model : Biogas yield = 265.3 * (Ln(Temperature) - 3.6) + 133.7 * (Ln(HRT) - 3.0) - 216.4 * (Ln(DS) - 1.5) + 61.7*((Ln(HRT) - 3.0) * (Ln(DS) - 1.5)) + site factor

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Combined Conventional-THP MAD Model

Impact of HRT, DS and temperature on performance

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Optimisation Strategies

Net energy balance - changing HRT

Net daily biogas gas Τ m3 day = Biogas volume1 − Biogas volume2

= DS 100 × Digester volume HRT

1

× BY − Digester volume HRT2 × BY + x = DS 100 × Digester volume × BY × HRT2 − HRT

1 × BY + x

HRT

1 × HRT2

  • Biogas volume1 (m3/day) = the volume of biogas produced when HRT is equal to HRT1
  • Biogas volume2 (m3/day) = the volume of biogas produced when HRT is equal to HRT2
  • BY (m3/t DS) = the biogas yield when HRT is equal to HRT1
  • x (m3/t DS) = the relative change of biogas yield when HRT is changed from HRT1 to HRT2
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Optimisation Strategies

Net energy balance - changing HRT

  • Digester volume: 2000 m3
  • Feed Volume: 100 m3/day
  • HRT: 20 days
  • DS: 5%
  • Total sludge feed: 5.0 t DS/ day
  • BY: 400 m3/t DS
  • Biogas produced: 2000 m3/ day

Increasing daily feed volume to 133.3 m3/day

  • Digester volume: 2000 m3
  • Feed Volume: 133.3 m3/day
  • HRT: 15 days
  • DS: 5%
  • Total sludge feed: 6.7 t DS/ day
  • BY: 358 m3/t DS
  • Biogas produced: 2387 m3/ day

19.4% increase in biogas volume

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Optimisation Strategies

Net energy balance - changing temperature and DS

  • 2

2 4 6 31 to 33 33 to 35 35 to 37 37 to 39 39 to 41 Net Energy out from 1 t wet sludge (kWh) Temperature increase (oC) 2.7% DS 3.0% DS 4.5% DS 7.9% DS

Energy required to heat 1 t wet sludge is 2.3 kWh. 2.7% DS is the lower 5% percentile range value of monthly average operational data for conventional MAD sites; 3.0% is the break point sludge feed DS for a positive net energy balance for MAD; 4.5% and 7.9% DS are the mean values of monthly average operational data for conventional and THP MAD sites, respectively.

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Conclusions

  • The first time that simple operational models of the AD process have been

developed based on full-scale operational data

  • Important to balance the three key operational parameters (temperature, HRT,

and DS) to optimise the energy balance of the process

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Acknowledgements

  • This research is sponsored by Anglian Water Services Ltd, Severn Trent Plc,

Thames Water Utilities Limited, United Utilities Group Plc and Yorkshire Water Services Ltd

  • The views expressed in the presentation are those of the authors and do not

necessarily represent the companies supporting the research