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Minimisation of Sequencing Batch Reactor Volume by Optimisation of the Hydraulic and Solids Retention Time Presenter: Adamu Abubakar Rasheed PhD student, School of Engineering, University of Aberdeen Supervisor: Dr Davide Dionisi CEng MIChemE

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Minimisation of Sequencing Batch Reactor Volume by Optimisation of the Hydraulic and Solids Retention Time

Presenter: Adamu Abubakar Rasheed

PhD student, School of Engineering, University of Aberdeen

Supervisor: Dr Davide Dionisi CEng MIChemE

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Outline

  • Introduction
  • Approach
  • Methodology
  • Results and Discussions
  • Conclusion
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Introduction

  • Sequencing batch reactor (SBR) is a variant of the activated sludge

process that combines all treatment steps (reaction, clarification) in a single vessel.

  • SBR is uniquely suited for wastewater treatment applications

characterized by low or intermittent flow conditions.

  • The operation is based on a fill-and-draw principle consisting of

five steps as shown below:

Feed (as substrate) Fill (aerated) React Settle Sludge withdrawal Effluent withdrawal Sludge withdrawal Effluent withdrawal

Phases (treatment steps) of the SBR operations cycle

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Introduction

  • The main operating parameters associated with the SBR are:

hydraulic retention time (HRT); solids retention time (SRT); length

  • f treatment phases; and the number of cycles per day
  • SBR volume can be minimised by reducing the HRT, which

consequently increases the organic loading rate (OLR)

  • For a fixed SRT, reducing the HRT will be limited by high biomass

concentration, with a decrease in the settling rate and increase in the aeration requirements per unit of reactor volume.

  • Thus,

decreasing the HRT while keeping the SRT fixed can potentially cause the process to fail.

Q V HRT 

OLR= Q× S

FEED

V = S

FEED

HRT

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Introduction

This potential problems can be overcome by appropriate manipulation of the SRT

  • Decreasing the SRT, at a fixed HRT, decreases the biomass

concentration in the reactor and the oxygen consumption.

  • However, reducing the SRT can potentially compromise the effluent

quality.

  • The question now is: to which point can the HRT and SRT be

decreased for a given wastewater?

eff eff w

X Q X Q X V SRT      In order to minimise the reactor volume, the HRT and SRT need to be reduced (optimised) simultaneously

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Approach

This study addresses the question with both experimental and modelling approaches. 1. Experimental investigations of the behaviour of lab-scale SBRs

  • perated in a range of HRT, SRT and OLR values with synthetic

wastewaters at a fixed composition. 2. To verify if the process performance at the various values of HRT and SRT can be predicted from batch kinetic tests and mathematical modelling.

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Experimental

  • An experiment of ten different SBR runs was carried out on the

wastewater in a lab scale SBR .….(i.e. more runs to be carried out )

  • A synthetic wastewater composed of 1 g/l of glucose was used.
  • The inoculum was a soil from Craibstone farm in Aberdeen.
  • Performance was recorded in terms of substrate removal and

biomass concentration.

Lab-scale glass reactors operating as SBR (picture taken during the settle phase). Feed Effluent 1L reactor volume

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Experimental

Run HRT (days) OLR

(g COD/l/day)

Sludge Withdrawal Rate (ml/day) No of Cycles (per day) Length of the Phases (min) Volume Fed Per Day (ml/day) Fill (aerated) React Settle Effluent Withdrawal

1 4 0.27 250 4 2 300 58 2 250 2 4 0.27 90 4 2 300 58 2 250 3 4 0.27 35 4 2 300 58 2 250 4 4 0.27 18 4 2 300 58 2 250 5 4 0.27 4 2 300 58 2 250 6 1 1.07 1000 4 5 300 55 5 1000 7 1 1.07 350 4 5 300 55 5 1000 8 1 1.07 4 5 300 55 5 1000 9 0.5 2.14 100 4 10 295 55 10 2000 10 0.25 4.28 70 6 10 190 40 10 2000 Table 1. Operational characteristics of the SBR for each run. Sludge withdrawal was done manually.

Experimental design

Glucose concentration in the effluent was measured both as total carbohydrates and as COD

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  • Glucose removal for the first set of runs (HRT = 4 days)
  • > 98 % substrate removal for all the runs (1-5)

Results

SBR performance

100 200 300 400 500 600 700 800 900 1000 1100 1200 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 Total carbohydrates in effluent (mg/l) Time (days)

Run 1: HRT = 4 days; SRT = 4 days Run 2: HRT = 4 days; SRT = 8.7 days Run 3: HRT = 4 days; SRT = 16.3 days Run 4: HRT = 4 days; SRT = 27.3 days Run 5: HRT = 4 days; SRT = 65.3 days

Stable performance

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  • Glucose removal for the second set of runs (HRT ≤ 1 day)
  • Partial substrate removal for runs 6 and 7 (< 27 % glucose removal)

Results

SBR performance

100 200 300 400 500 600 700 800 900 1000 1100 1200 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 Total carbohydrates in effluent (mg/l) Time (days)

Run 6: HRT = 1 day; SRT = 1 day Run 7: HRT = 1 day; SRT = 1.7 days Run 8: HRT = 1 day; SRT = 37 days Run 9: HRT = 0.5 days; SRT = 2.5 days Run 10: HRT = 0.25 days; SRT = 3.1 days Process failed at SRT ≤ 1.7 days

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  • Solids in the reactor for the first set of runs (HRT = 4 days)

Results

SBR performance

250 500 750 1000 1250 1500 1750 2000 2250 2500 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 VSS- mixed mixed reactor (mg/l) Time (days)

Run 1: HRT = 4 days; SRT = 4 days Run 2: HRT = 4 days; SRT = 8.7 days Run 3: HRT = 4 days; SRT = 16.3 days Run 4: HRT = 4 days; SRT = 27.3 days Run 5: HRT = days; SRT = 65.3 days

  • Biomass concentration in the reactor increases with SRT
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  • Solids in the reactor for the second set of runs (HRT ≤ 1 day)

Results

SBR performance

500 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 6000 6500 7000 7500 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 VSS- well mixed reactor (mg/l) Time (days)

Run 6: HRT = 1 day; SRT = 1 day Run 7: HRT = 1 day; SRT = 1.7 days Run 8: HRT = 1 day; SRT = 37 days Run 9: HRT = 0.5 days; SRT = 2.5 days Run 10: HRT = 0.25 days; SRT = 3.1 days

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Results

Caption of the reactors for SRT = 27.3 days (right) vs SRT = 1 day (left)

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Results

eff eff w

X Q X Q X V SRT     

SRT was calculated from the concentrations of solids in the reactor and in the effluent as:

Summary of the results

Run HRT (days) OLR (g COD/l/day) Calculated SRT (days) Total carbohydrates (mg/l) COD (mg/l) Biomass concentration (mg/l) % glucose removal (total carbohydrates)

1 4 0.27 4 17 (2) 91 (7) 470 (55) 98 2 4 0.27 8.7 15 (0.5) 51 (5) 836 (8) 99 3 4 0.27 16.3 14 (3) 62 (16) 1088 (146) 99 4 4 0.27 27.3 11 (2) 43 (6) 1357 (47) 99 5 4 0.27 65.3 3 (2) 18 (14) 1695 (113) 100 6 1 1.07 1 949 (21) 972 (7) 76 (26) 14 7 1 1.07 1.7 801 (9) 815 (32) 190 (42) 27 8 1 1.07 37 3 (3) 13 (3) 6613 (85) 100 9 0.5 2.14 2.5 17 (8) 26 (13) 1680 (42) 98 10 0.25 4.28 3.1 8 (3) 24 (10) 4338 (145) 99

Table 2. Summary of the steady state performance for each SBR run. Standard deviations in brackets. Total carbohydrates and COD are measured in the reactor effluent.

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Results

SBR performance

Table 3. Comparison of the results obtained in this study with other studies reported in the literature

  • From

the results, for glucose wastewater at OLR

  • f

4.28 g COD/l/day: HRT of 0.25 days and SRT of 3.1 days are the minimum values for calculating the reactor volume, while still maintaining acceptable values of the biomass concentration and satisfying effluent quality requirement.

Reference Length of cycle (hour) SRT (days) OLR (g COD/l/day) Beun et al. (2002)

4 4 1.15

Serafirm et al. (2004)

8 10 0.9

Dionisi et al. (2008)

6 4 1

Li et al. (2008)

8 14.5 - 25 1.2

Hajiabadi et al. (2009)

24 5 1.4

Ge et al. (2013)

3 2 – 3.8 1.4 – 2.8

Rodríguez et al. (2013)

8 30 3.24 This study 6 3.1 4.28

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Model approach

  • A kinetic model developed by Dionisi et al. (2016) that calculates

the steady state conditions of SBR was adopted for this approach

  • Batch kinetic tests were carried out on the glucose wastewater at

various initial substrate to biomass ratio, and range of values of the kinetic parameters were estimated for the model application.

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Results

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.2 0.4 0.6 0.8 1 1.2 OUR (kg/m3.day) Time (day) Experimental Fitted

Parameter Values

Initial substrate/biomass 2.4 µmax (day-1) 1.608 KS (kg COD/m3) 0.128 b (day-1) 0.098 YX/S (kg Biomass/ kg COD) 0.579

  • Batch

experiments were carried

  • ut

as respirometric tests which measures the oxygen uptake rate (OUR) as a function of time.

  • Values of the kinetic parameters were used to simulate the steady state

conditions in terms of biomass and substrate concentration at the various values of HRT and SRT.

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Results

200 400 600 800 1000 1200 1400 1600 1800 10 20 30 40 50 60 70 X (mg biomass/l) SRT ( days)

Experimental Model

Steady state predictions of the SBR using the kinetic parameters from the batch test. First set of runs (HRT = 4 days)

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Results

1000 2000 3000 4000 5000 6000 7000 5 10 15 20 25 30 35 40 X (mg biomass/l) SRT ( days)

Experimental Model

  • Of course, the other batch tests will predict a performance slightly

different due to the range of values estimated in the tests.

Second set of runs (HRT ≤ 1 day)

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Results

SRT HRT X              day l day biomass g produced Biomass

 

42 . 1 day l day O g n consumptio Oxygen

2

                SRT HRT X S S

200 400 600 800 1000 1200 5 10 15 20 25 30 35 40 45 50 55 60 65 70 Sludge produced; O2consumed SRT (days)

Sludge produced-exp. Sludge produced-model Oxygen consumed-exp. Oxygen consumed-model

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Conclusion

  • In order to minimize the SBR volume, the HRT and SRT have to be

reduced simultaneously and set to their minimum.

  • Achieving a successful operation at OLR of 4.28 g COD/l/day and

SRT of 3.1 days is better than most of the reported values in the literature for aerobic activated sludge processes.

  • Batch kinetic tests are indeed able to predict the performance of

the process with the appropriate kinetic model and simulation procedure such as the one developed by Dionisi et al. (2016).

  • This strategy of reducing the HRT by decreasing the SRT can

potentially lead to even lower reactor volumes and higher OLR values for successful treatments.

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Acknowledgements

Dr Davide Dionisi CEng MIChemE Materials and Chemical Engineering group, School of Engineering, University of Aberdeen, Aberdeen, AB24 3UE, UK davidedionisi@abdn.ac.uk, phone: +44 (0)1224 272814 Dr Aniruddha Majumder Materials and Chemical Engineering group, School of Engineering, University of Aberdeen, Aberdeen, AB24 3UE, UK a.majumder@abdn.ac.uk

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