**Appendices and nomenclature**


**79**

**Author details**

University, Chongqing, China

Sciences, Chongqing, China

Jixiang Yang1,2

provided the original work is properly cited.

\*Address all correspondence to: jixiang504@hotmail.com

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

1 School of Urban Construction and Environmental Engineering, Chongqing

2 Chongqing Institute of Green and Intelligent Technology, Chinese Academy of

*Approaches for Modeling Anaerobic Granule-Based Reactors*

*DOI: http://dx.doi.org/10.5772/intechopen.90201*

*Approaches for Modeling Anaerobic Granule-Based Reactors DOI: http://dx.doi.org/10.5772/intechopen.90201*

*Bacterial Biofilms*

the ANN.

**Acknowledgements**

(cstc2018jszx-zdyfxmX0013).

**Appendices and nomenclature**

ANN Artificial neural network BOD Biological oxygen demand

HRT Hydraulic retention time IBM Individual-based model

OLR Organic loading rate PF Plug-flow reactors Q Reactor flow rate

T Temperature

TKN Total Kjeldahl nitrogen TSS Total suspended solids VFA Volatile fatty acid

VSS Volatile suspended solids

CFD Computational fluid dynamics COD Chemical oxygen demand CSTRs Continuous stirred reactors

OLR Volumetric organic loading rate

r The distance from the granule center RC Reactor compartmentalization

TCOD Volumetric total chemical oxygen demand

ri The volumetric substrate conversion rate in the granule Si The substrate concentration of component i in the granule

Si,sur The substrate concentration of component i in the granule surface

Di The diffusion coefficient of substrate I EPS Extracellular polymeric substances

BP Back propagation

The RC and CFD strategies can both be applied to obtain a reactor hydraulic model that can be further integrated with a kinetic model for modeling effluent quality. The RC strategy manually divides a sludge bed reactor into several virtual reactors. The division does not have to fit the real flow conditions in the reactor. Alternatively, the CFD strategy can provide more details for reactor understanding

Parameter calibration for ADM1 is required before being integrated with a hydraulic model—this is a difficult task. Alternatively, most applied BP neural networks can accurately model concentrations of components in effluent, although the involved reactor is still a black box because the BP neural network completely ignores all bioprocesses in the reactor. An algorithm could be programed for ADM1 calibration by applying the high calibrating capacity of

and manipulation while being integrated with a kinetic model.

This chapter was supported by the Youth Innovation Promotion Association (NO.2019375) and Key Research Project from Chongqing City

ADM1 Reactor modeling with anaerobic digestion model 1

AMIMO Multiple inputs and multiple outputs ANAMMOX An anaerobic ammonium oxidation ANFIS Adaptive neuro-fuzzy inference system

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