Dynamic Modelling, Process Control, and Monitoring of Selected Biological and Advanced Oxidation Processes for Wastewater Treatment: A Review of Recent Developments
Abstract
:1. Introduction
2. System Identification/Modelling
2.1. Modelling Biological Treatment Processes—ASPs
2.2. Modelling Biological Treatment Processes—SBR
2.3. Modelling other Biological Wastewater Treatment Processes
2.4. Modelling AOPBased Treatment Processes
3. Controlling Treatment Processes
3.1. Controlling Biological Treatment Processes—ASP
3.2. Controlling Biological Treatment Processes—SBR
3.3. Controlling other Biological Treatment Processes
3.4. Controlling AOPBased Treatment Processes
4. Monitoring Treatment Processes
4.1. Hardware Sensors
4.2. Soft Sensors
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Acronyms
ABAC  AmmoniaBased Aeration Control 
ABR  Ada Boost Regression 
ACO  Ant Colony Optimization 
ACR  Anaerobic Contact Reactor 
AD  Anaerobic Digestion 
ADM  Anaerobic Digestion Model 
AE  Aeration Energy 
AECI  Aeration Energy Cost Index 
AFBR  Anaerobic FluidizedBed Reactor 
AFNN  Adaptive Fuzzy Neural Network 
AFR  Anaerobic Filter Reactor 
AI  Artificial Intelligence 
ALD  Approximate Linear Dependence 
ALK  Alkalinity 
AnMBR  Anaerobic Membrane Bioreactor 
ANN  Artificial Neural Network 
AOP  Advanced Oxidation Process 
ARMA  Autoregressive Moving Average Stochastic Model 
ARMAX  AutoRegressive Moving Average with eXogenous input 
ARX  AutoRegressive with eXogenous input model 
AS  Activated Sludge 
ASI  Absorbance Slope Index 
ASM  Activated Sludge Model 
ASP  Activated Sludge Process 
BAF  Biological Aerated Filter 
BN  Bayesian Network 
BOD_{5}  5Days Biochemical Oxygen Demand 
BOD_{st}  ShortTerm Biochemical Oxygen Demand 
BoostingIPWPLS  Boosting–iterative predictor weighting–partial least square 
BPFANN  Backpropagation Function Artificial Neural Network 
BSM  Benchmark Simulation Model 
CAS  Conventional Activated Sludge 
CC  Correlation Coefficient 
CDI  Chronic Daily Intake 
CNN  Convolutional Neural Network 
COD  Chemical Oxygen Demand 
CuNP  Copper Nanoparticle 
CV  Controlled Variable 
DAS  Differential Absorbance Spectra 
DDM  DataDriven Model 
DenseNet  Densely Connected Convolutional Network 
DL  Deep Learning 
DO  Dissolved Oxygen 
DOM  Dissolved Organic Matter 
EB  Electron Beam 
EC  Electrical Conductivity 
EEM  Excitation–Emission Matrix 
EGSB  Expanded Granular Sludge Bed 
EPS  Extracellular Polymeric Substance 
EQI  Effluent Quality Index 
FB  Feedback 
FF  Feedforward 
FFNN  Feedforward Neural Network 
FI  Fluorescence Index 
FLC  Fuzzy Logic Control 
FOPTD  FirstOrder Plus Time Delay 
FPE  Final Prediction Error 
FPI  Fractional order Proportional Integral 
FPLS  Fuzzy Partial Least Square 
FPLSDBN  Fuzzy Partial Least SquareBased Dynamic Bayesian Network 
GA  Genetic Algorithm 
GBR  Gradient Boost Regression 
GD  Gradient Descent 
GHG  Greenhouse Gas 
HGO  High Gain Observer 
HQ  Hazard Quotient 
HRT  Hydraulic Retention Time 
HW  Hammerstein–Wiener Model 
IAC  Iterative Adaptive Critic 
IAE  Integral of Absolute Error 
IFAS  Integrated Fixed Film Activated Sludge 
IMC  Internal Model Control 
ISE  Integral Square Error 
ISE  IonSelective Electrode 
IWA  International Water Association 
IWW  Industrial Wastewater 
IWWTP  Industrial Wastewater Treatment Plant 
KLD  Kilo Liter per Day 
LM  Levenberg–Marquardt algorithm 
LNMIIT  LNM Institute of Information Technology 
LQ  Linear Quadratic 
MATLAB  Matrix Laboratory 
MBBR  Moving Bed Biofilm Reactor 
MBR  Membrane Bioreactor 
MGPR  Multivariate Gaussian Processes Regression 
MIMO  MultiInput MultiOutput 
MLFCN  Machine Learning Fully Connected Network 
MLPANN  MultiLayer Perceptron Artificial Neural Network 
MLR  Multivariate Linear Regression 
MLSS  Mixed Liquor Suspended Solids 
MLVSS  Mixed Liquor Volatile Suspended Solids 
MOPC  MultiObjective Predictive Control 
MPC  Model Predictive Control 
MRVM  Multivariate Relevant Vector Machine 
MSE  Mean Squared Error 
MV  Manipulated Variable 
MWW  Municipal Wastewater 
MWWTP  Municipal Wastewater Treatment Plant 
NARX  Nonlinear AutoRegressive with eXogenous input model 
NARXNN  Nonlinear AutoRegressive eXogenous Neural Network 
NMC  Nonlinear Internal Model Control 
NMPC  Nonlinear Model Predictive Control 
NP  Nanoparticle 
NTP  NonThermal Plasma 
NOM  Natural Organic Matter 
O&G  Oil and Grease 
OCI  Operational Cost Index 
OFPC  Output Feedback Predictive Control 
OR  Oxygen Requirement 
ORP  OxidationReduction Potential 
OUR  Oxygen Uptake Rate 
PARAFAC  Parallel Factor Analysis 
PDS  Peroxydisulfate 
PI  Proportional Integral 
PID  Proportional Integral Derivative 
PMBC  Process ModelBased Control 
PRBS  PseudoRandom Binary Sequence 
PRWWTP  Petroleum Refinery Wastewater Treatment Plant 
PSO  Particle Swarm Optimization 
PVA  Polyvinyl Alcohol 
RAS  Return Activated Sludge 
RB49  Reactive Blue 49 
RBB  Reactive Black B 
RBC  Rotating Biological Contactor 
QFT  Quantitative Feedback Theory 
RBFNN  Radial Basis Function Neural Network 
RFR  Random Forest Regression 
RLS  Recursive Least Square 
RMSE  Root Mean Square Error 
RNN  Recurrent Neural Network 
ROS  Reactive Oxygen Species 
SAFF  Submerged Aerobic Fixed Film 
SBR  Sequencing Batch Reactor 
SCADA  Supervisory Control and Data Acquisition 
SIMO  SingleInput MultiOutput 
SISO  SingleInput SingleOutput 
SME  Small And Medium Enterprise 
SMP  Soluble Microbial Product 
SOFNN  SelfOrganizing Fuzzy Neural Network 
SOPTD  SecondOrder Plus Time Delay 
SOUR  Specific Oxygen Uptake Rate 
SQP  Sequential Quadratic Programming 
SRF  Sludge Recirculating Flow 
SRT  Sludge Retention Time 
SS  Suspended Solids 
SSC  Supervisory Sequential Controller 
SVI  Sludge Volume Index 
SVM  Support Vector Machine 
T  Temperature 
TDS  Total Dissolved Solids 
TF  Transfer Function 
TMOOA  Transfer MultiObjective Optimization Algorithm 
TMP  Transmembrane Pressure 
TN  Total Nitrogen 
TOC  Total Organic Carbon 
TP  Total Phosphorous 
TPANN  TwoPart ANN 
TrOC  Trace Organic Contaminant 
TSS  Total Suspended Solids 
UASB  Upflow Anaerobic Sludge Blanket 
UI  Unknown Input 
UIS  Unknown Input State 
UV  Ultraviolet 
UVA  Ultraviolet Absorbance 
UVVIS  Ultraviolet–Visible 
VFA  Volatile Fatty Acid 
VFD  Variable Frequency Drive 
WRBFNN  Wavelet Radial Basis FunctionBased Neural Network 
WWTP  Wastewater Treatment Plant 
XWTP  Xiangcheng Drinking Water Treatment Plant 
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Model  Description  Application 

ASM1  Biological conversion of organic matter into biomass and carbon dioxide in ASPs  Most widely used model to design/simulate conventional ASPs 
ASM2  Extension of ASM1, includes the conversion of nitrogen and phosphorus compounds  Predicting behaviour of nitrogen removal processes 
ASM2d  Extension of ASM2, includes additional details and factors affecting the performance of the treatment process  Predicting process performance and behaviour of nitrogen removal 
ASM3  Extension of ASM2, includes phosphorus removal through biological processes  Predicting behaviour of nitrogen and phosphorus removal processes 
ASM4  Extension of ASM2, includes phosphorus removal through chemical precipitation  Predicting behaviour of nitrogen and phosphorus removal processes 
ASM7  Comprehensive model, combination of ASM1, ASM2, ASM4  Predicting behaviour of nitrogen and phosphorus removal processes 
Wastewater Treatment Process  Challenges Ahead of Dynamic Modelling  Actions Taken 

Biological 


AOPbased 


Type of Control Method  Control Strategy  Note 

Linear control  P/PI/PID [40,75,87,88,89,90,91,92,93,94]  Proportional control (P):

IMC [88,95,96] 
 
Pole replacement [97,98,99] 
 
Cascade [12,87,88,100,101,102,103,104,105,106] 
 
FF [107,108] 
 
Adaptive [39,79,91,92,99,109] 
 
Optimal [81,82] 
 
MPC [73,79,80,99] 
 
Nonlinear control  Geometric nonlinear [110,111] 

Gain scheduling [99,110] 
 
NMPC [12,112] 
 
NMC [113,114,115] 
 
AIbased control  Expert system (Knowledgebased) [102,106] 

Fuzzy logicbased [40,91,106,116] 
 
ANNbased [75,76,79,95,96,112,117,118,119,120] 
 
Natureinspired algorithmbased [121] 
 
Hybrid AIbased [93,122,123,124,125] 

Wastewater Treatment Process  Influent Wastewater Data Origin  Reactor Size  Dynamic Process Model  Control Strategy  Control Parameters  Study Focus and Other Information  Reference 

ASP  Visakhapatnam MWWTP, India  Full scale: two anoxic tanks, 663 ${\mathrm{m}}^{3}$ each, three aerobic tanks, 883 ${\mathrm{m}}^{3}$ each  ASM1 in BSM1  PI, FPI, MPC, FLC  CVs: ${\mathrm{D}\mathrm{O}}_{\mathrm{r}}$, ${\left[{\mathrm{N}\mathrm{O}}_{3,}^{}\right]}_{\mathrm{r}}$; MVs: aeration rate, internal recycling flow rate  Simulation; controller tuning methods: iSIMC for PI, a proposed method for FPI  [40] 
ASP  Default data for typical MWW data embedded in BSM1  Full scale: two anoxic tanks, three aerobic tanks  BSM1  Datadriven iterative adaptive critic (IAC) control  CVs: DO, ${\left[{\mathrm{N}\mathrm{O}}_{3,}^{}\right]}_{\mathrm{r}}$; MVs: oxygen transfer coefficient, internal recycling flow rate  Simulation; outperformance of IAC over PID  [109] 
ASP  Default data for typical MWW data embedded in BSM2  Full scale  System identification: adaptive fuzzy neural network (AFNN) in BSM2  Datadriven MOPC alongside TMOOA  CVs: DO, ${\mathrm{N}\mathrm{O}}_{2,\mathrm{r}}^{}$; MVs: oxygen transfer coefficient, internal recycle flow rate  Simulation  [126] 
ASP  Făcăi WWTP, Craiova, Romania  Full scale: anoxic tank, 3375 ${\mathrm{m}}^{3}$ and aerobic tank, 15,000 ${\mathrm{m}}^{3}$  A modified ASM by Nejjari et al. [127]  Adaptive multivariable control  CVs: DO, inlet wastewater concentration; MVs: aeration rate, RAS rate  Simulation  [39] 
ASP  Toulouse City sewer system, France  Pilot scale: a bioreactor, 0.03 ${\mathrm{m}}^{3}$  ASM1      Simulation; sampling time: 20 min  [41] 
ASP  IWW, unknown food industry  Pilot scale: a bioreactor, 0.1 ${\mathrm{m}}^{3}$  System identification: FOPTDTF by graphical method  Adaptive Gain scheduling  CV: DO; MV: aeration rate  Simulation and implementation; controller tuning method: polezero allocation  [99] 
ASP  Unknown WWTP  Full scale: one anoxic tank and two aerobic tanks  ASM1 in SIMBA toolbox, MATLAB  Dual QFT loop  CVs: DO, ${\left[{\mathrm{N}\mathrm{O}}_{3,}^{}\right]}_{\mathrm{e}\mathrm{f}\mathrm{f}}$; MV: aeration rate  Simulation  [128] 
ASP  Kartuzy WWTP, Northern Poland  Full scale: four aerobic tanks  ASM2 in SIMBA toolbox, MATLAB  Hierarchical twolevel NMPC  CV: DO; MV: aeration rate  Simulation and implementation; sampling time: 5 min  [101] 
ASP  LNMIIT WWTP, Jaipur, India  Pilot scale: Capacity of 125 KLD    PID implemented in a PLC controller  CV: DO; MV: aeration rate  Simulation and implementation; aeration rate regulation by installing VFD  [129] 
ABAC  Nine Springs WWTP, Madison, WI, USA  Pilot scale: five anoxic and aerobic tanks, 2180 L total volume  ASM1 in BSM1  Cascade of PIP controllers  CV: ${\left[{\mathrm{N}\mathrm{H}}_{4}^{+}\right]}_{\mathrm{e}\mathrm{f}\mathrm{f}}$; MV: aeration rate  Simulation and implementation  [87] 
ABAC  Default data for typical MWW data embedded in BSM1  ASM1 in BSM1  Cascade of FLCPI controllers  CV: DO; MV: aeration rate  Simulation; controller tuning method: IMCbased for PI, sampling time: 15 min  [88]  
SBR  Swarzewo WWTP, Poland  Full scale: three anoxic tanks, 5000${\mathrm{m}}^{3}$, three aerobic tanks, 6500 ${\mathrm{m}}^{3}$  ASM2 in SIMBA toolbox, MATLAB  Cascade supervisory sequential controller (SSC)NMPC  CV: DO; MV: aeration rate  Simulation; sampling time: 2 min  [12] 
SBR  Unknown (data is available in the study)  Full scale  ASM2d in MATLAB  Fuzzy control  CV: DO; MV: oxygen transfer coefficient  Simulation  [130] 
SBR  Cerlà WWTP, Spain  Pilot scale    On/off, PID, fuzzy control implemented in Labwindows^{®}  CV: DO; MV: aeration rate  Simulation and implementation; outperformance of fuzzy control  [131] 
Oxidation ditch  Yumoto WWTP, Japan  Full scale  ASM3 and ASM2d in WEST  Combination of FB and FF  CV: OR; MV: aeration rate  Simulation and implementation  [108] 
MBR  Unknown WW  Pilot scale  FFNN, RBFNN, NARXNN  IMC  CVs: flux, TMP; MV: permeate pump voltage  Simulation  [62] 
Series of AD+MBR  Mixture of WW from Palermo WWTP, Italy, and synthetic WW  Pilot scale: anaerobic tank, 62 L, anoxic tank, 102 L, aerobic tank, 211 L, MBR tank, 36$\mathrm{L}$  Integration of modified ASM2d and physical submodel  Cascade of PI controllers  CVs: DO, ${\left[{\mathrm{N}\mathrm{H}}_{4}^{+}\right]}_{\mathrm{e}\mathrm{f}\mathrm{f}}$, ${\left[{\mathrm{N}\mathrm{O}}_{2,}^{}\right]}_{\mathrm{e}\mathrm{f}\mathrm{f}}$; MV: aeration flow rate  Simulation  [103,132] 
Upflow anaerobic fixed bed reactor  Synthetic WW, COD = 8300 mg/L  Lab scale: cylindrical reactor, 1.8 L    Rulebased supervisory control  CVs: pH, gas flow rate, methane content; MV: influent flow rate  Simulation and implementation; sampling time: 2.5 min for pH, 30 min for gas flow rate  [102] 
Upflow sludge bed filter  Synthetic ethanolcontained WW representing winery WW  Pilot scale: 1150 L  Modified ADM1  Cascade of PID controllers  CVs: [VFAs]_{eff}, Q_{methane, eff}; MV: influent flow rate  Simulation; controller tuning method: ISE  [104] 
AnMBR  Carraixet WWTP, Valencia, Spain  Full scale: anaerobic tank, 1300 L, two membrane tanks, 800 L each  Resistanceinseries filtration  Hierarchical control, lower layer: PID and on/off, upper layer: fuzzy and rulebased controllers  CVs: fouling rate, TMP, membrane permeability, SRF; MVs: influent flow rate, backflushing initiation and duration, etc.  Simulation; controller tuning: trial and error for PID, IAE for fuzzy  [106] 
UV/H_{2}O_{2}  Synthetic PVAcontained WW  Lab scale: series of two photoreactors, 0.92 L total volume  System identification: ARX, NARX, HW  FBPID  CV: effluent pH; MV: ${\left[{\mathrm{H}}_{2}{\mathrm{O}}_{2}\right]}_{\mathrm{i}\mathrm{n}}$  Simulation; controller tuning method: IAE, sampling time: 8 min  [74] 
UV/H_{2}O_{2}  Synthetic PVAcontained WW  Lab scale: series of two photoreactors 0.92 L total volume  System identification: ARX, ARMAX, standard TF, statespace  MPC  CVs: ${\left[\mathrm{T}\mathrm{O}\mathrm{C}\right]}_{\mathrm{e}\mathrm{f}\mathrm{f}}$, ${\left[{\mathrm{H}}_{2}{\mathrm{O}}_{2}\right]}_{\mathrm{r}\mathrm{e}\mathrm{s}\mathrm{i}\mathrm{d}\mathrm{u}\mathrm{a}\mathrm{l}}$; MVs: ${\left[{\mathrm{H}}_{2}{\mathrm{O}}_{2}\right]}_{\mathrm{i}\mathrm{n}}$, feed flow rate; Disturbance: ${\left[\mathrm{P}\mathrm{V}\mathrm{A}\right]}_{\mathrm{f}\mathrm{e}\mathrm{e}\mathrm{d}}$  Simulation; sampling time: 30 s  [73] 
Catalytic ozonation  Synthetic WW: paranitrophenol solution, COD = 500 mg/L  Lab scale: reactor, 19 L  Greybox identification: combining experimental data with mass balance    CVs: ${\left[{\mathrm{O}}_{3,\mathrm{g}\mathrm{a}\mathrm{s}}\right]}_{\mathrm{o}\mathrm{u}\mathrm{t}\mathrm{l}\mathrm{e}\mathrm{t}}$, UVA_{340,eff}; MV: ozonator power  Simulation; sampling time: 8 s  [83] 
Ozonation disinfection  Xiangcheng WWTP (XWTP), Suzhou, China  Full scale  System identification: RBFNN trained by PSO  Adaptive MPC  CVs: ozone exposure, ${\left[{\mathrm{O}}_{3,\mathrm{g}\mathrm{a}\mathrm{s}}\right]}_{\mathrm{r}\mathrm{e}\mathrm{s}\mathrm{i}\mathrm{d}\mathrm{u}\mathrm{a}\mathrm{l}}$; MVs: ${\mathrm{Q}}_{{\mathrm{O}}_{3},\mathrm{i}\mathrm{n}\mathrm{l}\mathrm{e}\mathrm{t}}$, ${\left[{\mathrm{O}}_{3,\mathrm{g}\mathrm{a}\mathrm{s}}\right]}_{\mathrm{i}\mathrm{n}\mathrm{l}\mathrm{e}\mathrm{t}}$  Simulation and implementation  [79] 
Catalytic ozonation  Synthetic WW: paranitrophenol solution, COD = 500 mg/L  Pilot scale: Reactor, 19 L  System identification TF method, parameter estimation by LM algorithm  Optimal linear quadratic (LQ) control  CVs: ${\left[{\mathrm{O}}_{3,\mathrm{g}\mathrm{a}\mathrm{s}}\right]}_{\mathrm{o}\mathrm{u}\mathrm{t}\mathrm{l}\mathrm{e}\mathrm{t}}$; UVA_{340,outlet}; MV: ozonator power  Simulation  [81,82] 
UV and UV/TiO_{2} disinfection  MiaoLi City sewer system, Taiwan  Lab scale  System identification: BPFNN  Manual control  CV: Total coliform counts in the effluent, MV: ${\mathrm{Q}}_{\mathrm{W}\mathrm{W},\mathrm{i}\mathrm{n}}$  Simulation and implementation  [133] 
Fenton  Synthetic textile WW (PVA+ Reactive Blue 49 (RB49) and Reactive Black B (RBB) dyes)  Lab scale: initial pH adjusting tank, 0.9 L, main oxidation tank, 1.2 L, second pH adjusting tank, 1.2 L, settling tank, 0.9 L  System identification: BPFNN  ANNbased control  CVs: ${\mathrm{O}\mathrm{R}\mathrm{P}}_{\mathrm{o}\mathrm{x}\mathrm{i}\mathrm{d}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{a}\mathrm{n}\mathrm{k}}$, ${\mathrm{p}\mathrm{H}}_{\mathrm{o}\mathrm{x}\mathrm{i}\mathrm{d}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{a}\mathrm{n}\mathrm{k}}$; MVs: ${\mathrm{F}}^{2+}$ dosage, ${\mathrm{H}}_{2}{\mathrm{O}}_{2}$ dosage  Simulation and implementation; sampling time: 30 min  [76] 
Wastewater Treatment Process  Motivations for Implementing Process Control  Limitations 

Biological 


AOPbased 


Estimated Parameter  Indicators  Source of Dataset  Modelling Method  Highlights  Reference 

${\mathrm{T}\mathrm{S}\mathrm{S}}_{\mathrm{e}\mathrm{f}\mathrm{f}}\phantom{\rule{0ex}{0ex}}{\mathrm{C}\mathrm{O}\mathrm{D}}_{\mathrm{e}\mathrm{f}\mathrm{f}}\phantom{\rule{0ex}{0ex}}{\mathrm{T}\mathrm{N}}_{\mathrm{e}\mathrm{f}\mathrm{f}}$  Combination of influent parameters (Q_{in}), bioreactor parameters (${\mathrm{D}\mathrm{O}}_{\mathrm{r}}$, ${\left[{\mathrm{N}\mathrm{H}}_{4}^{+}\right]}_{\mathrm{r}},$ ${\left[{\mathrm{N}\mathrm{O}}_{3}^{}\right]}_{\mathrm{r}}$, ${\mathrm{A}\mathrm{L}\mathrm{K}}_{\mathrm{r}}$), effluent parameters (${\left[{\mathrm{N}\mathrm{H}}_{4}^{+}\right]}_{\mathrm{e}\mathrm{f}\mathrm{f}}$, ${\mathrm{A}\mathrm{L}\mathrm{K}}_{\mathrm{e}\mathrm{f}\mathrm{f}}$)  Biological treatment unit, unknown WWTP  ANN  Good model fitness for all parameters: ${\mathrm{T}\mathrm{S}\mathrm{S}}_{\mathrm{e}\mathrm{f}\mathrm{f}}$: ${\mathrm{R}}^{2}$ = 0.9; ${\mathrm{C}\mathrm{O}\mathrm{D}}_{\mathrm{e}\mathrm{f}\mathrm{f}}$: ${\mathrm{R}}^{2}$ = 0.88; ${\mathrm{T}\mathrm{N}}_{\mathrm{e}\mathrm{f}\mathrm{f}}$: ${\mathrm{R}}^{2}$ = 0.91  [152] 
${\mathrm{T}\mathrm{D}\mathrm{S}}_{\mathrm{e}\mathrm{f}\mathrm{f}}\phantom{\rule{0ex}{0ex}}{\mathrm{B}\mathrm{O}\mathrm{D}}_{5,\mathrm{e}\mathrm{f}\mathrm{f}}\phantom{\rule{0ex}{0ex}}{\mathrm{C}\mathrm{O}\mathrm{D}}_{\mathrm{e}\mathrm{f}\mathrm{f}}$  ${\mathrm{T}\mathrm{D}\mathrm{S}}_{\mathrm{i}\mathrm{n}}$, ${\mathrm{T}\mathrm{S}\mathrm{S}}_{\mathrm{i}\mathrm{n}}$, ${\mathrm{B}\mathrm{O}\mathrm{D}}_{5,\mathrm{i}\mathrm{n}}$, ${\mathrm{C}\mathrm{O}\mathrm{D}}_{\mathrm{i}\mathrm{n}}$, ${\mathrm{p}\mathrm{H}}_{\mathrm{i}\mathrm{n}}$, ${\mathrm{T}\mathrm{P}}_{\mathrm{i}\mathrm{n}}$ and ${\mathrm{T}\mathrm{N}}_{\mathrm{i}\mathrm{n}}$  Biological treatment unit, Shokouhieh WWTP, Qom, Iran  Ada Boost Regression (ABR), Gradient Boost Regression (GBR), and Random Forest Regression (RFR)  Outperformance of GBR in predicting target parameters: ${\mathrm{T}\mathrm{D}\mathrm{S}}_{\mathrm{e}\mathrm{f}\mathrm{f}}$: CC = 0.962, RMSE = 30.3 mg/L; ${\mathrm{B}\mathrm{O}\mathrm{D}}_{5,\mathrm{e}\mathrm{f}\mathrm{f}}$: CC = 0.9, RMSE = 4.6 mg/L; ${\mathrm{C}\mathrm{O}\mathrm{D}}_{\mathrm{e}\mathrm{f}\mathrm{f}}$: CC = 0.75, RMSE = 9.6 mg/L  [153] 
${\mathrm{B}\mathrm{O}\mathrm{D}}_{5,\mathrm{e}\mathrm{f}\mathrm{f}}\phantom{\rule{0ex}{0ex}}{\mathrm{C}\mathrm{O}\mathrm{D}}_{\mathrm{e}\mathrm{f}\mathrm{f}}\phantom{\rule{0ex}{0ex}}{\mathrm{T}\mathrm{N}}_{\mathrm{e}\mathrm{f}\mathrm{f}}$ Sludge Volume Index (SVI)  Combination of influent parameters (V_{in}, ${\mathrm{C}\mathrm{O}\mathrm{D}}_{\mathrm{i}\mathrm{n}}$, ${\mathrm{T}\mathrm{S}\mathrm{S}}_{\mathrm{i}\mathrm{n}}$, ${\mathrm{p}\mathrm{H}}_{\mathrm{i}\mathrm{n}}$, ${\left[{\mathrm{C}\mathrm{l}}^{}\right]}_{\mathrm{i}\mathrm{n}}$), bioreactor parameters (T, SRT, ${\left[{\mathrm{N}\mathrm{H}}_{4}^{+}\right]}_{\mathrm{r}}$), effluent parameters (${\mathrm{T}\mathrm{S}\mathrm{S}}_{\mathrm{e}\mathrm{f}\mathrm{f}}$, ${\left[{\mathrm{N}\mathrm{H}}_{4}^{+}\right]}_{\mathrm{e}\mathrm{f}\mathrm{f}}$, ${\mathrm{p}\mathrm{H}}_{\mathrm{e}\mathrm{f}\mathrm{f}}$, ${\left[{\mathrm{C}\mathrm{l}}^{}\right]}_{\mathrm{e}\mathrm{f}\mathrm{f}}$)  Biological treatment unit, Beijing WWTP, China  Multivariate Linear Regression (MLR), Multivariate Relevant Vector Machine (MRVM) and Multivariate Gaussian Processes Regression (MGPR) models  Multioutput soft sensor with good performance  [154] 
${\mathrm{C}\mathrm{O}\mathrm{D}}_{\mathrm{e}\mathrm{f}\mathrm{f}}$  Combination of influent parameters (Q_{in}, ${\mathrm{C}\mathrm{O}\mathrm{D}}_{\mathrm{i}\mathrm{n}}$, ${\mathrm{B}\mathrm{O}\mathrm{D}}_{5,\mathrm{i}\mathrm{n}}$, ${\mathrm{T}\mathrm{S}\mathrm{S}}_{\mathrm{i}\mathrm{n}}$, ${\mathrm{p}\mathrm{H}}_{\mathrm{i}\mathrm{n}}$, ${\left[{\mathrm{N}\mathrm{H}}_{4}^{+}\right]}_{\mathrm{i}\mathrm{n}}$), bioreactor parameters (${\mathrm{D}\mathrm{O}}_{\mathrm{r}}$, ${\mathrm{O}\mathrm{R}\mathrm{P}}_{\mathrm{r}}$, RAS flow rate, recycling mixture flow rate)  Biological treatment unit, unknown WWTP  Adaptive estimation: Combination of Hammerstein with wavelet neural networks, adaptive weighted fusion, and approximate linear dependence (ALD) analysis  Outperformance of adaptive model (error% = 6.41)  [155] 
${\mathrm{C}\mathrm{O}\mathrm{D}}_{\mathrm{e}\mathrm{f}\mathrm{f}}\phantom{\rule{0ex}{0ex}}{\mathrm{T}\mathrm{P}}_{\mathrm{e}\mathrm{f}\mathrm{f}}\phantom{\rule{0ex}{0ex}}{\left[{\mathrm{N}\mathrm{H}}_{4}^{+}\right]}_{\mathrm{e}\mathrm{f}\mathrm{f}}$  Combination of ${\mathrm{C}\mathrm{O}\mathrm{D}}_{\mathrm{i}\mathrm{n}}$, ${\mathrm{T}\mathrm{P}}_{\mathrm{i}\mathrm{n}}$, ${\left[{\mathrm{N}\mathrm{H}}_{4}^{+}\right]}_{\mathrm{i}\mathrm{n}}$, reaction time, aeration rate, SRT, MLVSS, filling time, bioreactor parameters (T, SRT, ${\left[{\mathrm{N}\mathrm{H}}_{4}^{+}\right]}_{\mathrm{r}}$)  SBR, Ekbatan WWTP, Tehran, Iran  RBFNN and multilayer perceptron artificial neural networks (MLPANN)  Good performance of both models; Superior accuracy of MLPANN for all target parameters; Higher R^{2} and lower RMSE in MLPANN for both training and test data  [156] 
${\mathrm{T}\mathrm{S}\mathrm{S}}_{\mathrm{e}\mathrm{f}\mathrm{f}}\phantom{\rule{0ex}{0ex}}{\mathrm{B}\mathrm{O}\mathrm{D}}_{5,\mathrm{e}\mathrm{f}\mathrm{f}}\phantom{\rule{0ex}{0ex}}{\mathrm{C}\mathrm{O}\mathrm{D}}_{\mathrm{e}\mathrm{f}\mathrm{f}}$  Four different combinations: Only ${\mathrm{T}\mathrm{S}\mathrm{S}}_{\mathrm{i}\mathrm{n}}$, Only ${\mathrm{B}\mathrm{O}\mathrm{D}}_{5,\mathrm{i}\mathrm{n}}$, Only ${\mathrm{C}\mathrm{O}\mathrm{D}}_{\mathrm{i}\mathrm{n}}$, All ${\mathrm{T}\mathrm{S}\mathrm{S}}_{\mathrm{i}\mathrm{n}}$, ${\mathrm{B}\mathrm{O}\mathrm{D}}_{5,\mathrm{i}\mathrm{n}}$, and ${\mathrm{C}\mathrm{O}\mathrm{D}}_{\mathrm{i}\mathrm{n}}$.  Biological treatment unit; Doha West WWTP  ANN  The best performed inputoutput models: ${\mathrm{C}\mathrm{O}\mathrm{D}}_{\mathrm{i}\mathrm{n}}$${\mathrm{C}\mathrm{O}\mathrm{D}}_{\mathrm{e}\mathrm{f}\mathrm{f}}$: R = 0.923, MSE = 0.014; ${\mathrm{C}\mathrm{O}\mathrm{D}}_{\mathrm{i}\mathrm{n}}$${\mathrm{B}\mathrm{O}\mathrm{D}}_{5,\mathrm{e}\mathrm{f}\mathrm{f}}$: R = 0.951, MSE = 0.061; ${\mathrm{C}\mathrm{O}\mathrm{D}}_{\mathrm{i}\mathrm{n}}$${\mathrm{T}\mathrm{S}\mathrm{S}}_{\mathrm{e}\mathrm{f}\mathrm{f}}$: R = 0.987, MSE = 0.021  [151] 
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Parsa, Z.; Dhib, R.; Mehrvar, M. Dynamic Modelling, Process Control, and Monitoring of Selected Biological and Advanced Oxidation Processes for Wastewater Treatment: A Review of Recent Developments. Bioengineering 2024, 11, 189. https://doi.org/10.3390/bioengineering11020189
Parsa Z, Dhib R, Mehrvar M. Dynamic Modelling, Process Control, and Monitoring of Selected Biological and Advanced Oxidation Processes for Wastewater Treatment: A Review of Recent Developments. Bioengineering. 2024; 11(2):189. https://doi.org/10.3390/bioengineering11020189
Chicago/Turabian StyleParsa, Zahra, Ramdhane Dhib, and Mehrab Mehrvar. 2024. "Dynamic Modelling, Process Control, and Monitoring of Selected Biological and Advanced Oxidation Processes for Wastewater Treatment: A Review of Recent Developments" Bioengineering 11, no. 2: 189. https://doi.org/10.3390/bioengineering11020189