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Reducing sludge formation by enhancing biological decay of biomass: a mathematical model

  • Salman S. Alsaeed ORCID logo , Mark I. Nelson ORCID logo EMAIL logo , Ahmed H. Msmali und Maureen Edwards
Veröffentlicht/Copyright: 28. August 2024
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Chemical Product and Process Modeling
Aus der Zeitschrift Chemical Product and Process Modeling Band 19 Heft 4

Abstract

We investigate a model for an activated sludge process that contains a chemical reactor unit attached to a bioreactor. Inside the chemical reactor unit, a process takes place which increases the decay coefficient of heterotrophic biomass. This mimics a number of experimental techniques which are used to decrease the mass of sludge. Such techniques are of growing interest as the activated sludge process produces large volumes of sludge; the costs associated with its disposal are significant. Our primary interest is to investigate how the operation of the chemical reactor unit changes the steady-state concentrations of both the total suspended solids within the biological reactor and the chemical oxygen demand in the effluent stream. The operation of a chemical reactor unit always increases the value of chemical oxygen demand in the effluent stream. The behaviour of the total suspended solids is more complicated; in some cases, the operation of the CRU increases the total suspended solids. For a fixed value of the chemical oxygen demand in the influent stream, we show that both the percentage reduction in the total suspended solids and the chemical oxygen demand in the effluent stream are increasing functions of the soluble substrate in the feed. The same trends occur when the influent composition is fixed, and the disintegration rate inside the chemical reactor unit is increased. This leads to dichotomic behaviour, decreasing the total suspended solids increases the chemical oxygen demand in the effluent stream. There are two consequences of this behaviour. Firstly, there are some waste streams that can not be cleaned using the process configuration considered in this paper. Secondly, the imposition of a target value for the chemical oxygen demand in the effluent stream imposes a maximum achievable reduction in the total suspended solids in the biological reactor. Higher reductions can not be achieved without causing the chemical oxygen demand in the effluent stream to exceed the target value.

Keywords: activated sludge model no. 1 (ASM1); cell lysing; excess activated sludge; sludge disintegration; sludge reduction
Corresponding author: Mark I. Nelson, School of Mathematics and Applied Statistics, University of Wollongong, Wollongong, NSW 2522, Australia, E-mail:

Funding source: University of Wollongong

Award Identifier / Grant number: Unassigned

Acknowledgments

Salman Alsaeed is a PhD student at the University of Wollongong. He gratefully acknowledges the award of a PhD scholarship by Jouf University (Saudi Arabia).

  1. Research ethics: Not applicable.

  2. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interests: None.

  4. Research funding: None declared.

  5. Data availability: The data used in this paper was obtained by integrating the model equations using Matlab. This data is not publically available.

Appendix A: Nomenclature

C

concentration factor. (–)

COD

chemical oxygen demand in the reactor. (mg COD L−1)

CODe

chemical oxygen demand in the effluent stream (mg COD L−1)

CODin

chemical oxygen demand in the influent stream (mg COD L−1)

D

disintegration factor. ((–))

F

flow rate through bioreactor. (L day−1)

K L,A

oxygen transfer coefficient. (day−1)

K O,H

oxygen half-saturation coefficient. (mg O2L−1)

K S

monod constant for biomass. (g COD L−1)

K X

contois coefficient for hydrolysis of particulate biodegradable substrate.

M 2

monod kinetics for readily biodegradable soluble substrate. (–)

M 8h

monod kinetics for the component S 0 with respect to biomass. (–)

R

recycle ratio. (–)

R*

effective recycle parameter. (–)

S O

concentration of soluble oxygen. (mg O2L−1)

S O,in

soluble oxygen concentration in the feed (mg O2L−1)

S O,max

maximum concentration of soluble oxygen. (mg O2L−1)

S S

soluble substrate concentration. (mg COD L−1)

S S,in

concentration of soluble substrate in the feed. (mg COD L−1)

TSS

total suspended solids. (g SS L−1)

V

bioreactor volume. (L)

V*

the ratio of the volume of the bioreactor to that of the chemical reactor unit: V* = V/V S (–)

V S

volume of the chemical reactor unit. (L)

X B,H

concentration of heterotrophic biomass. (mg COD L−1)

X B,H,CRU

concentration of heterotrophic biomass in the chemical reactor unit. (mg COD L−1)

X P

concentration of particulate products arising from biomass decay. (mg COD L−1)

X P,CRU

concentration of particulate products in the chemical reactor unit. (mg COD L−1)

X S

concentration of slowly biodegradable particulates. (mg COD L−1)

X S,CRU

concentration of slowly biodegradable particulates in the chemical reactor unit. (mg COD L−1)

X S,in

concentration of slowly biodegradable particulates in the feed. (mg COD L−1)

Y H

heterotrophic yield coefficient. (–)

b H

heterotrophic decay coefficient. (day−1)

b H,CRU

heterotrophic decay coefficient inside the chemical reactor unit. (day−1)

c 1

conversion factor from COD to SS for X S and X S ( g SS g COD − 1 ).

c 2

conversion factor from COD to TSS for solution X B,H ( g SS g COD − 1 ).

f p

the fraction of dead biomass converted to particulate products.

k h

maximum hydrolysis rate. (day−1)

k sat

saturation kinetics for hydrolysis. (–)

t

time. (day)

μ max,H

maximum specific growth rate for biomass. (day−1)

τ

residence time. (day)

Appendix B: Parameter values

The following are typical values for domestic wastewater at neutral pH and 20 °C [7, Table 5].

K O,H 0.2 mg O2L−1 [16, Table 6]
K S 20.0 mg COD L−1 [16, Table 6]
K X 0.03 g COD g COD − 1 [16, Table 6]
Y H 0.67 – [16, Table 6]
b H 0.22 day−1 [16, Table 6]
f p 0.08 – [16, Table 6]
k h 3.0 day−1 [16, Table 6]
μ max,H 6.0 day−1 [16, Table 6]

The parameter values associated with dissolved oxygen are

K L,A 96 day−1 [17, page 855]
S O,in 2.0 mg O2L−1
S O,max 10.0 mg O2L−1 [17, page 856]

Conversion factors from units of COD to units of TSS are:

c 1 0.75 g SS g COD − 1 [18]
c 2 0.90 g SS g COD − 1 [18]

Typical values for the operation of the settling unit are R = 0.4 and w = 0.1.

The influent composition is given by

S S,in 200 Mg COD L−1 [16, Table 9]
X S,in 100 Mg COD L−1 [16, Table 9]

  1. where V* = V/V S .

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Received: 2022-12-09
Accepted: 2023-11-08
Published Online: 2024-08-28

© 2023 Walter de Gruyter GmbH, Berlin/Boston

Artikel in diesem Heft

  1. Frontmatter
  2. Review
  3. Computational chemistry unveiled: a critical analysis of theoretical coordination chemistry and nanostructured materials
  4. Research Articles
  5. Reducing sludge formation by enhancing biological decay of biomass: a mathematical model
  6. Numerical investigation of discharge pressure effect on steam ejector performance in renewable refrigeration cycle by considering wet steam model and dry gas model
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  8. Energy efficiency in cooling systems: integrating machine learning and meta-heuristic algorithms for precise cooling load prediction
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  10. Temperature optimization model to inhibit zero-order kinetic reactions
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https://doi.org/10.1515/cppm-2022-0077
Schlagwörter für diesen Artikel
activated sludge model no. 1 (ASM1); cell lysing; excess activated sludge; sludge disintegration; sludge reduction

Artikel in diesem Heft

  1. Frontmatter
  2. Review
  3. Computational chemistry unveiled: a critical analysis of theoretical coordination chemistry and nanostructured materials
  4. Research Articles
  5. Reducing sludge formation by enhancing biological decay of biomass: a mathematical model
  6. Numerical investigation of discharge pressure effect on steam ejector performance in renewable refrigeration cycle by considering wet steam model and dry gas model
  7. Natural pigment indigoidine production: process design, simulation, and techno-economic assessment
  8. Energy efficiency in cooling systems: integrating machine learning and meta-heuristic algorithms for precise cooling load prediction
  9. A parametric study on syngas production by adding CO2 and CH4 on steam gasification of biomass system using ASPEN Plus
  10. Temperature optimization model to inhibit zero-order kinetic reactions
  11. Multi-objective Bonobo optimisers of industrial low-density polyethylene reactor
  12. Assessment the thermal performance of square twisted double tube heat exchanger with Al2O3 nanofluid
  13. Short Communication
  14. Layouts and tips for a typical final-year chemical engineering graduation project
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