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Extensive historical data of a sewage treatment works are required by numerical models in order to simulate the biological processes accurately. However, the data are recorded mostly for daily operational purpose. They are basically not comprehensive enough to meet the modelling’s requirements. A comprehensive sampling protocol to accurately characterise the influent is required in order to determine all model components, which is very time-consuming and expensive. In a project of evaluating a sewage treatment works in Chongqing by using BioWin 4.1 for mathematical modelling, sensitivity analysis was conducted to determine the most critical parameters for process monitoring. It was found that influent characteristics, wasted sludge flow rate, water temperatures, DO levels of the biological tanks and five bio-kinetic parameters were the most influential parameters governing the plant performance. Therefore, apart from monitoring the effluent quality, regular checking of the afore-mentioned influential parameters can help examine the performance of a sewage treatment works. Moreover, operators of the sewage treatment works can conduct “what-if” analysis to determine how these most influential parameters can be adjusted to improve the treatment performance of the sewage treatment works.

Municipal sewage treatment works are essential infrastructures of modern cities. Apart from building new treatment facilities to cater for the city development, many existing sewage treatment works are required to be upgraded in order to meet new requirements, such as tightening of the effluent discharge standards, increase in treatment capacity and reduction in operating costs. Since the treatment processes of municipal sewage treatment works are getting more complicated, mathematical modelling is becoming an increasingly popular tool to assist process design of wastewater treatment [

One of the limitations on using mathematical modelling is the extensive work involved in model calibration. Extensive historical data of a sewage treatment works are required for model calibration. However, the data are recorded mostly for daily operational purpose. So they are basically not comprehensive enough to meet the modelling’s requirements. Therefore, some research works were conducted in order to simplify the modelling procedures [

There are a number of sewage treatment works in Chongqing province. In 2015, about 1012 million tons of wastewater was treated by municipal sewage treatment works in 2015 [

A sewage treatment works in Chongqing was selected for this study. Taojiazhen Sewage Treatment Works (TJZSTW) is located at Jiulongpo district, southwest of the Chongqing metropolitan area. The outlook of the TJZSTW was illustrated in ^{3}/day. However, the current peak loading of one system has already reached more than 3000 m^{3}/day. Therefore, appropriate plant upgrade measures of the TJZSTW have to be designed.

The treated effluent from the TJZSTW is discharged into the Daxi River, eventually to the Yangtze River where Three Gorges are located. Therefore, the discharge standards of the sewage treatment works are stringent and are expected to be tightened from the current Standard 1-B of “Discharge Standard of Pollutants for Municipal Wastewater Treatment Plant” (GB 18918-2002) to Standard 1-A. Since the TJZSTW can represent as a typical plant upgrade cases in Chongqing, the TJZSTW was selected in this study. Major parameters of the discharge standards are summarised in

The key treatment process of the TJZSTW is an A2O Carrousel oxidation ditch process, as illustrated in

Parameter | Discharge Standard | |
---|---|---|

Standard 1-B | Standard 1-A | |

Chemical Oxygen Demand (COD) | 60 mg/l | 50 mg/l |

Biochemical Oxygen Demand (BOD_{5}) | 20 mg/l | 10 mg/l |

Suspended Solid (SS) | 20 mg/l | 10 mg/l |

Total Nitrogen (TN) | 20 mg/l | 15 mg/l |

Ammonia Nitrogen (NH_{4}-N) | 8 mg/l (water temperature > 12˚C) | 5 mg/l (water temperature > 12˚C) |

15 mg/l (water temperature ≤ 12˚C) | 8 mg/l (water temperature ≤ 12˚C) | |

Total Phosphate (TP) | 1 mg/l | 0.5 mg/l |

Colour | 30 | 30 |

pH | 6 - 9 | 6 - 9 |

E.Coli | 10^{4} (/l) | 10^{3} (/l) |

sewage is then pumped to a fine screen and two hydrocyclones operated in parallel to further remove sand and grits which may affect the subsequent biological processes. Afterwards, the sewage enters two sets of A2O systems and Carrousel oxidation ditches, which operate in parallel. Cl_{2}O is dosed into the treated effluent of two final clarifiers before discharging to the Daxi River. The process block diagram is illustrated in

Historical water quality and operational data were collected from the TJZSTW. However, as the data of TJZSTW were recorded for operational purpose, only daily characteristics of influent and effluent as well as influent flow rates were available. A site visit to TJZSTW was then conducted in accompaniment of operators and management staff of the plant to verify the actual design and practice of daily operation of TJZSTW.

In order to build a mathematical model for simulation of the treatment process, comprehensive sampling protocol, with reference to Melcer et al. [

Parameter | Raw Influent | Primary Effluent | Anaerobic Tank | Anoxic Tank | Aerobic Tank | Primary Sludge | Returned Activated Sludge | Wasted Activated Sludge | Secondary Effluent |
---|---|---|---|---|---|---|---|---|---|

Daily Flow | √ | √ | √ | √ | |||||

Hourly Flow | √ | ||||||||

Flow | √ | √ | |||||||

Temperature | √ | √ | √ | ||||||

DO | √ | √ | √ | ||||||

TSS | √ | √ | √ | √ | |||||

VSS | √ | √ | |||||||

MLSS | √ | √ | √ | ||||||

MLVSS | √ | √ | √ | ||||||

COD (Total) | √ | √ | √ | √ | √ | ||||

COD (Filtered with 1.5 μm glass fiber) | √ | √ | √ | √ | |||||

COD (Filtered with 0.45 μm glass fiber after coagulation with ZnSO_{4}) | √ | ||||||||

BOD_{5} | √ | ||||||||

BOD_{5} (Filtered with 0.45 μm glass fiber) | √ | ||||||||

VFA | √ | ||||||||

TKN(Total) | √ | √ | √ | √ | √ | ||||

TKN (Filtered with 1.5 μm glass fiber) | √ | ||||||||

TKN (Filtered with 0.45 μm glass fiber) | √ | ||||||||

NH_{3}-N | √ | √ | √ | ||||||

NH_{3}-N (Filtered with 1.5 μm glass fiber) | √ | √ | √ | ||||||

NO_{3}-N | √ | ||||||||

NO_{3}-N (Filtered with 1.5 μm glass fiber) | √ | √ | √ | ||||||

NO_{2}-N | √ | ||||||||

TP(Total) | √ | √ | √ | √ | √ | ||||

TP (Filtered with 1.5 μm glass fiber) | √ | ||||||||

TP (Filtered with 0.45 μm glass fiber) | √ | ||||||||

PO_{4}-P (Filtered with 1.5 μm glass fiber) | √ | √ | √ | ||||||

PO_{4}-P (Filtered with 0.45 μm glass fiber) | √ | √ | √ | ||||||

pH | √ | √ | √ | ||||||

Alkalinity | √ | √ | √ | ||||||

Calcium | √ | ||||||||

Magnesium | √ |

and Carrousel oxidation ditches which were operated in parallel. The sampling protocol was only conducted in System A, as illustrated in

Model calibration is an iterative process to fine-tune model parameters in order to fit a certain set of data measured from the sampling protocol with the calculated values by the model.

In order to simulate an oxidation ditch, alternative anoxic and aerobic bioreactors were used according to the locations of the surface aerators.

Physical configuration parameters, such as dimensions of water tanks and equipment parameters, were site-specific parameters. These parameters could be found from the design documents of TJZSTW. Sample analyses for all wastewater characteristics mentioned in

Apart from influent characteristics, ASMs also include kinetic and stoichiometric parameters. Examples are growth and decay rates of Ammonia Oxidising Biomass (AOB) and Nitrite Oxidising Biomass (NOB). Default values of these ASM parameters were firstly used as they were developed based on results from numerous sewage treatment plants.

Parameter | Minimum | Maximum | Average | |
---|---|---|---|---|

Influent | Flowrate (m^{3}/day) | 2849 | 3394 | 3152 |

TCOD (mg/l) | 84 | 189 | 136 | |

TKN (mg/l) | 8.9 | 14.1 | 11.2 | |

NH_{4}-N (mg/l) | 7.7 | 12.7 | 10.0 | |

TP (mg/l) | 1.47 | 2.08 | 1.70 | |

PO_{4}-P (mg/l) | 0.72 | 1.27 | 0.93 | |

TSS (mg/l) | 32.7 | 68.7 | 52.6 | |

Effluent | COD (mg/l) | 24.5 | 31.1 | 27.6 |

TKN (mg/l) | 0.20 | 2.13 | 0.84 | |

NH_{4}-N (mg/l) | 0.42 | 0.64 | 0.51 | |

TP (mg/l) | 0.45 | 0.84 | 0.63 | |

PO_{4}-P (mg/l) | 0.26 | 0.65 | 0.38 | |

TSS (mg/l) | 10.8 | 18.2 | 15.6 |

When the model calibration was conducted, a parameter governing the simultaneous nitrification and denitrification (SND) reactions was found necessary to deviate from the default value. This parameter was used to switch off aerobic Ordinary Heterotrophic Organisms (OHO) activity under low DO conditions (that is in anaerobic and anoxic reactors), and triggers denitrification and Phosphorus Accumulating Organisms (PAOs). If the default value (0.05 mg/L) of this switch parameter was used, a higher level of effluent NO_{3} would be resulted. For an oxidation ditch process, simultaneous nitrification and denitrification occur. Therefore, higher threshold to trigger anaerobic activity was proposed. Therefore, the switch parameter was revised to 0.45 mgO_{2}/L in order to fit the model with actual performance of the system, which was still below low DO level of 0.5 mg/L previously tested by Gogina and Gulshin [

The developed model was then validated. The purpose of model validation is to determine the accuracy of the model developed. In order to conduct the model validation, a set of data which was not used for the model development was inputted to the model to determine a set of predicted values (i.e. output of model). This set of predicted values was compared with the range of actually measured values. If the predicted values fall within the range of actually measured values, then the developed model is determined to be valid for the current input data set.

The validated results are illustrated in

the ranges of the measured values of four targeted parameters, represented by interval bars. This proves that the calibrated model can be used for the subsequent analysis.

It is an expensive and time-consuming process to determine all model components. An enormous number of wastewater characteristics imply conducting a comprehensive sampling protocol. Additional tests are even necessary to be conducted in order to determine kinetics parameters [_{i,j}) is defined as the ratio of the percentage change in output variables relative to the percentage change of input variables, as illustrated in Equation (1). In this paper, a 10% change in the input variables is used.

S i , j = Δ y i / y i Δ x i / x i (1)

where S_{i,j} = normalised sensitivity coefficient; x_{i} = ith input variable; Δx_{i} = variation of ith input variable; y_{i} = ith output variable; Δy_{i} = variation of ith output parameter.

Since the variation of input variables is fixed at 10%, a high value of normalised sensitivity coefficient, S_{i,j}, for a particular input variable indicates that the input variable has an important influence on the simulation results (i.e. resulting in greater change of output variable), whereas a zero value of S_{i,j} for an input variable means that the output variable does not depend on that input variable.

Apart from determining the degree of influence of an individual input variable to a particular output variable using Equation (1), a collective influence of a group of input variables to a particular output variable can be determined using a sum of absolute normalised sensitivity coefficients, as defined in Equation (2):

δ j = ∑ i = 1 n | S i , j | (2)

where δ_{j} = collective influence of all input variables to the jth output variable; S_{i,j} = normalised sensitivity coefficient.

A higher value of d_{j} indicates that this particular output variable is highly influenced by this group of input variables, i.e. more degree of sensitivity to this group of input variables.

Similarly, a collective influence of a particular input variable to a group of output variables can be determined using a sum of absolute normalised sensitivity coefficients, as defined in Equation (3):

δ i = ∑ i = 1 n | S i , j | (3)

where δ_{i} = collective influence of all output variables from the ith input variable; S_{i,j} = normalised sensitivity coefficient.

A higher value of d_{i} indicates that this particular input variable highly influences this group of output variables, i.e. more degree of influence to this group of output variables.

In order to conduct a comprehensive sensitivity analysis, eighty-six input variables in BioWin 4.1, covering influent wastewater physical and chemical characteristics, kinetic and stoichiometric parameters associated with Ammonia Oxidising Biomass (AOB) and Nitrite Oxidising Biomass (NOB) were involved in the sensitivity analysis. The water characteristics of effluent were the output variables in the sensitivity analysis.

For the sake of simplicity, only values of the normalised sensitivity coefficient, S_{i,j}, higher than 0.05 are showed in

Output Variables | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|

Filtered TKN | Nitrate N | Soluble PO_{4}-P | NH_{4}-N | Nitrite + Nitrate | TKN | Total P | TSS | Filtered COD | ||

Input Variables | Influent total COD | 0.068 | −0.289 | −0.333 | −0.216 | 0.079 | −0.088 | 0.069 | 0.089 | |

Influent TKN | 0.082 | 0.246 | 0.111 | 0.213 | 0.056 | |||||

Wasted sludge flow rate | 0.082 | −0.054 | −0.067 | 0.052 | −0.057 | −0.060 | ||||

Influent flow | 0.082 | −0.103 | 0.052 | 0.061 | ||||||

Temperature (−10%) | 0.075 | −0.065 | 0.141 | 0.051 | ||||||

Kinetic-endogenous products decay rate | 0.102 | −0.085 | ||||||||

Temperature (+10%) | −0.070 | 0.058 | −0.170 | |||||||

Influent total P | 0.061 | 0.135 | 0.098 | |||||||

Kinetic-AOB-substrate (NH_{4}) half sat. | 0.109 | 0.098 | 0.000 | |||||||

Kinetic-AOB-Aerobic decay rate | 0.109 | 0.098 | 0.075 | |||||||

Kinetic-AOB-Max. spec. growth rate | −0.222 | |||||||||

DO level of AER 4 | 0.061 | |||||||||

Kinetic term for switching off aerobic activity under low DO conditions (+10%) | −0.089 | −0.073 | ||||||||

Readily biodegradable COD | 0.054 | −0.103 | ||||||||

Kinetic term for switching off aerobic activity under low DO conditions (−10%) | 0.084 | 0.071 |

variables. Similarly, the input variable with the highest value of collective normalised sensitivity as defined in Equation (3) is positioned at the top row of

The result shows that the total COD of influent causes the most influence to the effluent quality, not only the effluent COD but also other parameters related to nitrogen and phosphorus. Apart from this input variable, four additional variables, namely TKN, flow rate, total P and readily biodegradable COD, also have the greatest influence to the effluent quality. Therefore, analyses of these five influent parameters are essential to the monitoring and evaluation of the performance of sewage treatment works.

Apart from the aforesaid influent parameters, the wasted sludge flow rate, water temperature and DO level in the aeration zone of oxidation ditch also have significant influence on the effluent quality. On the other hand, the performance of sewage treatment works can be verified by checking the wasted sludge flow rate and water temperature. For example, according to _{4}-P in the effluent (column). However, it will simultaneously cause about 5.2% rise in NH_{4}-N. Alternatively, if the wasted sludge flow rate is dropped, it is anticipated that the level of soluble PO_{4}-P would be increased while the level of NH_{4}-N would be reduced. This piece of information may provide a rough estimation on the adjustment of the wasted sludge flow rate during the operation of TJZSTW.

The remaining top influential parameters are five kinetic parameters, namely the endogenous products decay rate, substrate (NH_{4}) half saturation of AOB, the aerobic decay rate of AOB, Maximum specific growth rate of AOB and Switch off aerobic Ordinary Heterotrophic Organisms (OHO) activity under low DO conditions. Except for the last switch parameter which has been discussed above, default values provided in BioWin 4.1 are used for all these kinetic parameters in this study. It demonstrates that an ordinary set of biological process parameters are sufficient to be used to model the performance of TJZSTW.

In this project, mathematical modelling for the Taojiazhen Sewage Treatment Works (TJZSTW) in Chongqing was developed using BioWin 4.1. It would be very time-consuming and expensive to use a comprehensive sampling protocol to accurately characterise the influent in order to determine all model components. Instead, sensitivity analysis was conducted to determine the most critical parameters for process monitoring. It was found that influent characteristics, wasted sludge flow rate, water temperatures, DO levels of the biological tanks and five bio-kinetic parameters were the most influential parameters governing the plant performance. Therefore, apart from monitoring the effluent quality, regular checking of the afore-mentioned influential parameters can help examine the performance of a sewage treatment works. Moreover, operators of the sewage treatment works can conduct “what-if” analysis to determine how these most influential parameters can be adjusted to improve the treatment performance of the sewage treatment works.

AcknowledgementsThis study was funded by International S&T Cooperation Program of China (ISTCP) (Cooperative Research and Development of Mathematical Modelling Simulation on Municipal Sewage Treatment Process) [2014DFH90030].

Zhou, X., Chen, J., Tang, Y.Q., Ren, J.J., Lee, V.K.C. and Ma, A.Y.W. (2018) Improved Monitoring Protocol for Evaluating the Performance of a Sewage Treatment Works Based on Sensitivity Analysis of Mathematical Modelling. Engineering, 10, 464-476. https://doi.org/10.4236/eng.2018.107032