# A Review of Energy and Sustainability KPI-Based Monitoring and Control Methodologies on WWTPs

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## Abstract

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## 1. Introduction

## 2. Materials and Methods

#### 2.1. Problem Formulation

#### 2.2. Literature Review Planning Protocol

- Search questions:
- Which KPIs are used?
- Which KPI-based methods for monitoring, control, or optimization are used?
- How were these KPI-based methods tested and which simulators were used?
- What is the performance of these KPI-based methods?

- Exclusion criteria:
- Monitoring, control, and optimization works whose methodologies are not based on a KPI or any efficiency index;
- Works that have not been tested in real or simulated WWTPs;
- Works dated before the year 2012;

- Quality criterion:
- Works based on KPI or efficiency index tested in real or simulated WWTPs.
- Works that compare its techniques with others.

#### 2.3. Search Process

#### 2.4. Publications over the Years

## 3. Results

#### 3.1. Energy and Sustainability KPI-Based Monitoring

#### 3.1.1. Monitoring of Efficiency

- Daily value of the efficiency of the pump system ($\eta $);
- Efficiency trend (${\eta}_{t}$) calculated using a rolling window median for the previous 90 days;
- Fluctuation in the trend ${\eta}_{f}={\eta}_{t}-\eta $;
- Ageing of the pump ($\tau $);
- Potential of new failures (Z) that is equal to 0 if the system registers 15 consecutive days with ${\eta}_{f}<0$, otherwise $Z=1$.

#### 3.1.2. Functional Performance Monitoring

#### 3.1.3. Eco-Efficiency Monitoring

- Energy per population equivalent [kWh/PE];
- Waste sludge production per population equivalent [kg/PE];
- Environmental impacts of chemicals (from LCA method) [mPt/PE];

- COD removed [kg/PE];
- Methane production per population equivalent. [l/PE].

#### 3.2. KPI-Based Control and Optimization Methodologies

#### 3.2.1. Efficiency Control and Optimization

#### 3.2.2. Eco-Efficiency Optimization

## 4. Discussion

#### 4.1. KPI-Based Monitoring Methodologies

#### 4.2. KPI-Based Control and Optimization Methodologies

#### 4.3. BSM Simulators

## 5. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## Abbreviations

AE | Aeration Energy |

ANN | Artificial Neural Network |

BOD | Biological Oxygen Demand |

BSM1 | Benchmark Simulation Model No. 1 |

BSM2 | Benchmark Simulation Model No. 2 |

COD | Chemical Oxygen Demand |

DEA | Data Envelopment Analysis |

DMOPSO | Dynamic Multiobjective Particle Swarm Optimization |

DO | Dissolved Oxygen |

EE | Electrical Energy |

EPI | Energy Performance Indicator |

EQ | Effluent Quality |

EQI | Effluent Quality Index |

FLC | Fuzzy Logic Controller |

GI | Green Index |

HRBF | Hierarchical Radial Basis Function |

KPI | Key Performance Indicator |

LCA | Life Cycle Assessment |

LCCA | Life Cycle Cost Analysis |

MPC | Model Predictive Control |

NO | Nitric Oxide |

OCI | Overall Cost Index |

PE | Pump Energy |

PI | Proportional–Integral |

PID | Proportional–Integral–Derivative |

PLC | Programmable Logic Controller |

PLI | Pollution Index |

SCADA | Supervisory Control and Data Acquisition |

SLR | Systematic Literature Review |

STOAT | Sewage Treatment Operation and Analysis over Time |

SVI | Sludge Volume Index |

TN | Total Nitrogen |

WQI | Water Quality Index |

WTEI | Water Treatment Energy Index |

WWTP | Wastewater Treatment Plant |

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**Figure 1.**A generic example of a monitoring, control or optimization method ($M(\xb7)$) that uses data from process variables (${x}_{1},{x}_{2},\dots ,{x}_{n}$) and KPIs to provide some output(s) ($\mathbf{u}$) to optimize the WWTP. $\Theta $ are the hyper-parameters of $M(\xb7)$.

**Figure 2.**Publications’ distribution over the years between 2012 and 2022, related to KPI-based methodologies for WWTPs. No papers were found in the years 2012 and 2022 that fit the objectives of this review.

**Figure 3.**Overview of the organization of Section 3.

**Figure 4.**Diagram illustrating the proposed procedure in [48].

**Figure 5.**Diagram illustrating the proposed method in [59].

**Figure 6.**Diagram illustrating the proposed method in [63].

Reference | KPI Type | KPI(s) | Plant |
---|---|---|---|

[51] | Operational | $COD$ in the effluent. | BSM |

[53] | Operational | $COD$ in the effluent | BSM |

[54] | Operational | Energy consumption obtained from $COD$ and $T{N}_{removal}$ performance indicators. | BSM |

[50] | Energy | Efficiency based on fuzzy rules and the daily value of 5 KPIs. | Real WWTP |

[55] | Operational | $SVI$ obtained | |

[45,49] | Energy | WTEI (12) | Real WWTP |

[44] | Energy | Global Energetic Index (GEI) (2) | Real WWTP |

[48] | Energy | Energy Performance Indicators: $EP{I}_{BOD}$, $EP{I}_{TN}$, $EP{I}_{SL}$ and $EP{I}_{SL\&Tr}$ | Real WWTP |

[57] | Eco | Through DEA and LCA. Outputs: $CO{D}_{removed}$, $ME{T}_{prod}$ | STOAT simulator |

[56] | Eco | Eco-efficiency $\theta $ obtained by DEA from resources consumed (costs), desirable outputs (TSS and COD) and undesirable output (indirect green-house gases) | Real WWTP |

[58] | Eco | $GI$ (14) | SuperPro Designer 8.5 |

Reference | KPI Type | KPI(s) | Plant |
---|---|---|---|

[62] | Energy | Model Predictive Control that takes into account the economic performance index (energies and costs), $JECO$ (15) | BSM |

[61] | Energy | Objective functions based on $AE$, $PE$ and $EQ$ that are models based on regression kernel functions | BSM/Real |

[59] | Energy | OCI | BSM |

[68] | Eco Energy | KPIs, (18) to (25), to select data that respect to: environmental requirements, Global Treatment Yield ($GT$) and Standardizes $GT$ ($SGT$); and to optimized energy consumption, $PLI$, Global Abatement ($GPAB$) and $WQI$ | Real |

[65,66,67] | Eco Energy | $N/E$ Index, ration between the amount of nitrogenated compounds eliminated and the energy consumed | BSM |

[63] | Eco Energy | OCI, EQI and the percentage of times that the pollutant levels exceed the legal limits | SIMBA/Benchmark Simulation Model No. 2 (BSM2) Real |

[69] | Eco Energy | LCCA (based on costs with energy (${C}_{e}$) and chemical (${C}_{c}$) products, costs of transporting (${C}_{t}$) and disposing (${C}_{s}$) the sludge, biogas production (${C}_{bio}$), and miscellaneous (${C}_{mis}$) costs) and LCA (based on energy consumption, eutrophication potential and greenhouse gas emission) | GPS-X |

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## Share and Cite

**MDPI and ACS Style**

de Matos, B.; Salles, R.; Mendes, J.; Gouveia, J.R.; Baptista, A.J.; Moura, P.
A Review of Energy and Sustainability KPI-Based Monitoring and Control Methodologies on WWTPs. *Mathematics* **2023**, *11*, 173.
https://doi.org/10.3390/math11010173

**AMA Style**

de Matos B, Salles R, Mendes J, Gouveia JR, Baptista AJ, Moura P.
A Review of Energy and Sustainability KPI-Based Monitoring and Control Methodologies on WWTPs. *Mathematics*. 2023; 11(1):173.
https://doi.org/10.3390/math11010173

**Chicago/Turabian Style**

de Matos, Bárbara, Rodrigo Salles, Jérôme Mendes, Joana R. Gouveia, António J. Baptista, and Pedro Moura.
2023. "A Review of Energy and Sustainability KPI-Based Monitoring and Control Methodologies on WWTPs" *Mathematics* 11, no. 1: 173.
https://doi.org/10.3390/math11010173