WO2021038222A1

WO2021038222A1 - Autonomous wastewater treatment system

Info

Publication number: WO2021038222A1
Application number: PCT/GB2020/052047
Authority: WO
Inventors: Sonja OSTOJIN; Peter Skipworth
Original assignee: Environmental Monitoring Solutions Ltd
Current assignee: Environmental Monitoring Solutions Ltd
Priority date: 2019-08-30
Filing date: 2020-08-27
Publication date: 2021-03-04
Anticipated expiration: 2022-02-28
Also published as: GB201912451D0; GB202202555D0; GB2601267A

Abstract

There is described an autonomous wastewater treatment system comprising: at least one sensor to measure the turbidity of wastewater, said sensor being linked to a Monitoring Station (MST); a flow meter at an inlet to a wastewater treatment work (WwTW) to measure flow of wastewater into the WwTW, said flow meter being linked to the Monitoring Station (MST); a HUB for receiving sensor data from one or more MST, wherein the HUB runs a Fuzzy Logic algorithm to estimate the phosphate concentration of the wastewater from the turbidity measurement and calculates the appropriate dosing of metal salt; and a Control Station (CST) which issues a control signal to a metal salt dosing pump to optimise phosphate removal. There is also described a method and a kit related thereto.

Description

Autonomous Wastewater Treatment System

Field of the invention

The present invention relates to an autonomous data driven wastewater treatment system.

More particularly, this invention relates to an innovative wastewater treatment control technology, based on Artificial Intelligence, which is used to control a wastewater treatment process for the removal of phosphate by addition of iron or aluminium salts; and to a method of autonomous control of phosphate removal in wastewater treatment.

The wastewater treatment system of the invention measures turbidity at a wastewater treatment works (WwTW) inlet with a turbidity sensor. One or more Monitoring Stations (MSTs) collects data on turbidity, pH, temperature and flow at the WwTW inlet.

Fuzzy logic is then implemented on a HUB, using the inlet turbidity data to estimate phosphate concentration. The Fuzzy Logic output is then used to calculate dosing in 1/hr using flow and “a recipe” (all hosted on HUB). The HUB issues a pump dosing signal to a Control Station (CST) and the CST sends a command signal to the pump (one or more pumps) that dose iron salts for the removal of the phosphate.

Background of the invention

European water operators are faced with increasingly stringent permit levels for phosphate concentration in final effluent discharged from Wastewater Treatment Works (WwTW). Problems of eutrophication and other ecological disruption are increasing in receiving waters with point source nutrient contributions from WwTW a large contributor. Eutrophication is the enrichment of a water body with nutrients. This process induces growth of plants and algae resulting in oxygen depletion and serious ecological impact. Eutrophication is almost always induced by wastewater phosphorous levels resulting from fertilisers, human waste and detergents.

Under the EU Water Framework Directive, all water bodies were due to be returned to a “good” status both chemically and ecologically by 2015. By 2016, 35% of water bodies had achieved good or high status, a decline from the 37% achieved in 2011 (Joint Nature Conservation Committee, 2017).

Part of the UK Environment Agency’s response to this decline has been to tighten phosphate consents for wastewater treatment works (WwTW) outfalls.

At larger WwTW, sophisticated processes, sensing and treatment control are in place. However, at small WwTW (<50,000 Pop Equivalent), the treatment train can be simple and less flexible, and site footprints offer little scope for expansion. Moreover, large investments are unaffordable and cannot be justified on wastewater throughput.

On many small WwTW the common treatment method for the removal of phosphate or the reduction of phosphate levels, is the use of salts of two- and three-valent metals. The metal ions interact with soluble phosphate salts to create a highly dispersed colloid phosphate sediment. Aluminium and iron additives (e.g. aluminium sulfate, ferric sulfate or ferric chloride) are widely used to precipitate out phosphate. This is considered an “insurance approach”, whereby enough chemical additives are added to cope with assumed/adverse conditions, rather than actual prevailing conditions. However, this approach has disadvantages in that it results in the overuse of chemicals. Furthermore, it is becoming ineffective due to the competing priorities of phosphate and iron concentration permit levels in wastewater. Additionally, this approach is very wasteful in an age where resource and energy waste is becoming a focus and less acceptable.

It is estimated that in the UK alone there are -12,000 such WwTW that may have reducing permit levels and/or a background need to reduce additive levels.

A surrogate approach is used by several water companies for estimating difficult-to-measure parameters on WwTW. Turbidity is a good surrogate for determination of phosphate coming into WwTW. Many wastewater operations are geared around diurnal or flow-proportional control algorithms to dose metal salts.

Our co-pending International patent application No. PCT/GB2019/000065 describes an autonomous in-sewer flow control system comprising a Flow Control Device (FCD), adapted to control flow of water in a sewer; and a Monitoring and Control System (MCS) which monitors water levels and issues commands to the FCD. The autonomous in-sewer flow control system is a wireless, self-powered, autonomous hardware platform, which is visible and configurable over the internet.

We have now developed an innovative wastewater treatment control technology based on Artificial Intelligence which is used to control the addition of iron or aluminium additives in an in situ predetermined amount to remove or reduce phosphate as part of the wastewater treatment process.

Fuzzy controllers can be applied to almost any kind of process. They are particularly suitable for controlling time- varying, ill-defined and nonlinear systems (Fiter et al. 2005).

However, we have found that linear relationships with phosphate have shown gross inefficiencies. Fuzzy sets have been applied to many fields and have shown performance similar to skilled experts (Sugeno 1985). The present invention relies upon the use of Fuzzy controllers in determining the variable concentrations of phosphate in wastewater entering WwTW.

Summary of the Invention

An object of the present invention is to provide an autonomous, intelligent system for wastewater treatment control.

The system of the present invention comprises a wireless, modular technology which is easily integratable with existing on-site equipment (sensors and pumps) at WwTW.

The system of the present invention uses a Fuzzy Logic algorithm, similar to that utilised in co-pending International patent application No. PCT/GB2019/000065, to autonomously regulate addition of iron salts to reduce the phosphate content of wastewater. The autonomous regulation of the addition of iron salts or other metal salts is operated by a Fuzzy Logic (FL) algorithm informed by local real time turbidity data, which non-linearly correlates to phosphate levels in the wastewater. Thus, according a first aspect of the invention there is provided an autonomous wastewater treatment system comprising: at least one sensor to measure the turbidity of wastewater, said sensor being linked to a Monitoring Station (MST); a flow meter at an inlet to wastewater treatment works (WwTW) to measure flow of wastewater into the WwTW, said flow meter being linked to the Monitoring Station (MST); a HUB for receiving sensor data from the MST, wherein the HUB runs a Fuzzy Logic algorithm to estimate the phosphate concentration of the wastewater from the turbidity measurement and calculates the appropriate dosing of metal salt; and a Control Station (CST) which issues a control signal to a metal salt dosing pump to optimise phosphate removal.

The autonomous wastewater treatment system of the invention may optionally include a sensor adapted to measure pH and/ or temperature of wastewater.

Sensor to Measure Turbidity of Wastewater

In England water quality turbidity is governed by ISO 7027 “Water Quality: Determination of Turbidity”.

ISO 7027 specifies two quantitative methods using for determination of turbidity of water using optical nephelometers or turbidimeters. Nephelometry is a procedure for measurement of diffuse radiation, applicable to water of low turbidity, for example, drinking water. The units of turbidity from a calibrated nephelometer are called Nephelometric Turbidity Units (NTU) and signifies that the instrument is measuring scattered light from the sample at a 90- degree angle from the incident light. FNU stands for Formazin Nephelometric Units and also signifies that the instrument is measuring scattered light from the sample at a 90-degree angle from the incident light. FNU is often used when referencing the ISO 7027 (European) turbidity method.

Turbidimetry is a procedure for measurement of the attenuation of a radiant flux, more applicable to highly turbid waters, for example, wastewaters or other cloudy waters. Turbidimetry measures the loss of intensity of transmitted light due to the scattering effect of particles suspended in it. Light is passed through a filter creating a light of known wavelength which is then passed through a solution to be measured. A photoelectric cell collects the light which passes through the cuvette. A measurement is then given for the amount of absorbed light. Although it is within the scope of the present invention for the sensor to comprise a nephelometer or a turbidimeter or a combination of two or more of a nephelometer or a turbidimeter. However, preferably the sensor comprises one turbidimeter.

The determination of phosphate levels in wastewater, e.g. turbidity determination, may be carried out as the wastewater enters the WwTW.

Hub for Determining the Phosphate Content by Turbidity Measurement

In the system of the present invention a Monitoring and Control System (MCS) measures the turbidity levels in wastewater entering the WwTW from the data generated by the sensor. The measured data is input to a Fuzzy Logic (FL) control algorithm hosted on a Hub (HUB). The FL control algorithm estimates the phosphate concentration and influences the quantity of metal salts added to the wastewater to create a colloidal phosphate sediment. The HUB communicates with the Control Station (CST) and issues a command for the dispensing of metal salts.

The MCS will generally comprise a three-module solution:

HUB module will link to the Monitoring Station (MST or MST+), relaying data and configuration files, running the analysis and AI-based control algorithm, issuing commands to the Control Station (CST) which issues commands in turn to the dosing equipment, and linking to an internet dashboard.

MST+: The MST+ incorporates more power options than the current MSTs, for its new role and can receive up to four inputs. It uses existing inputs for the flow meter and turbidity meter at the WwTW inlet.

It should be understood by the person skilled in the art that when reference is made herein to MST, MST+ may also be suitably utilised.

In use, the FL control algorithm is hosted on the HUB which receives data from the MSTs and sends control signals to the CST.

The HUB can optionally communicate with an online Dashboard using wireless mobile telecommunications technology, such as 3G. The Dashboard provides visibility of the data and system status, and also allows remote configuration of the FL and related level and communication parameters. There are three aspects of wireless communication:

1. The modules communicate with each other using a proprietary radio communication protocol.

2. The HUB communicates over the GSM network with the web hosted Dashboard.

3. The modules can be programmed via Bluetooth and an App.

Control System monitors phosphate levels in wastewater and issues commands

The autonomous wastewater treatment system of the invention generally operates to remove dissolved phosphates. Such dissolved phosphates are usually converted into solids by chemical precipitation so that they can be removed by sedimentation or filtration processes. This is normally done by addition of metal salts, e.g. aluminium salts, such as aluminium sulfate; or iron salts, such as ferric sulfate, ferric chloride, and the like, to react with the soluble phosphates to form solid precipitates.

However, it will be understood by the person skilled in the art that dosing of the metal salts can be performed at various stages in the wastewater treatment process. Consequently, the autonomous wastewater treatment system of the present invention may be positioned at different stages of the wastewater treatment process.

Fuzzy Logic (FL)

Fuzzy Logic is a universal approximator and can learn underlying input/output dependencies using a small set (a few days, e.g. seven or more days) of recorded data. FL is particularly suited to the wastewater application of the present invention, in that phenomena can be understood but their behaviour are characterised by variability. FL algorithms can capture, for example, expert knowledge, the conclusions of laboratory and field experiments, and modelling outputs around a particular phenomenon, and cope with their variability.

Fuzzy Logic systems are based on linguistic descriptions of complex systems. Such systems do not demand knowledge of mathematical modelling. Fuzzy Logic systems allow the application of “human language” to describe the problems and their “fuzzy”’ solutions. This is achieved by using Membership Functions and a Rule Base, both developed based on an existing knowledge about system that can be presented as a set of IF-THEN sentences. Each Membership Function imitates a linguistic approach which is used to describe some condition in every day descriptive usage (high, low, etc.). The rule set is based on fuzzy reasoning which employs linguistic rules in the form of IF {condition} - THEN {action} statements. There is a relationship between membership functions and rule sets. The membership values control the degree to which each of the IF - THEN rules will contribute to the control decision.

In wastewater, FL has been used in: detection (e.g. blockage detection; state detection in anaerobic wastewater treatment; CSO performance optimisation and management in near- real-time and control applications (e.g. pump station control and optimisation of energy use); control of additives in treatment; control of an activated sludge plant; energy saving in the aeration process; in-line control of non-linear pH neutralisation; optimisation of nitrogen removal and aeration energy consumption in wastewater treatment plants). Phosphate Removal

To maximise phosphate removal one or more processes can be applied:

1) Phosphate in solid form can be removed using filtration/solids settlement processes. This generally occurs in the primary treatment stage.

However, much of the phosphate will be dissolved in the wastewater stream. These dissolved phosphates can be removed by the secondary and tertiary processes:

2) A proportion of the dissolved phosphates will be removed by the biological process at the treatment works. This is done by growing microorganisms that can absorb and store phosphorus as polyphosphate. The phosphorus is incorporated into the biomass which is then separated from the treated water at the end of the process. This stage is associated with the secondary treatment processes. 3) The remaining dissolved phosphates can be chemically precipitated to convert into solids that can be removed with filtration processes. This is normally done using addition of metal salts to react with the soluble phosphate to form solid precipitates. This is explicitly applied as a tertiary treatment process. Dosing can be performed at various stages in the wastewater treatment process.

In a preferred embodiment of the invention the removal of phosphates comprises chemical precipitation by the addition of metal salts to react with the soluble phosphate to form solid precipitates that can be removed with filtration processes. The wastewater treatment system will generally comprise 3 modular elements (MCS modules):

MST with Sensors (one or more)

Hub (HUB) Control Station (CST).

Technical Details and Features

In the system of the present invention the control algorithm uses flow data and turbidity data provided by a sensing network and uses Fuzzy Logic to estimate phosphate levels as input data and makes decisions based on this data to adjust the amount of metal salts added to the wastewater.

The present invention provides an innovative wastewater treatment control technology based on Artificial Intelligence. It is used to control the addition of metal salt additives to remove phosphate as part of the wastewater treatment process.

• Reduce risk of breaching permitted consents.

• Prevent overuse and waste of chemical additives.

• Easily integrate with existing instruments at wastewater treatment works.

• Reduce carbon footprint of wastewater treatment process.

• Eliminate requirements for expensive human intervention.

• Reduce requirements for laboratory analysis. The system is self-managing and easily deployed. Through the optimisation of metal additives, this technology has the potential to save thousands of pounds; benefitting water companies, their customers and the environment.

The benefits of the system of the invention:

1. Cost and ease of deployment - The system of the present invention is much cheaper and easier to deploy than existing technologies. This is particularly applicable on smaller WwTW’s where expensive Real-Time Control systems are not viable.

2. Reduce additive use - Current treatment control is often wasteful. The system of the present invention can save on the use of additives while maintaining permitted consent levels.

3. Reduce energy use - The over-addition of chemicals is also wasteful in terms of energy used to produce the chemicals and the overall carbon-footprint of the treatment process.

4. Reduce environmental impact - Controlling the amount of phosphate discharged from wastewater treatment plants is paramount to maintain the ecological balance within receiving watercourses

5. Easy integration - The system of the present invention can be easily integrated with existing standard flow and quality instruments already installed on small wastewater treatment works. 6. Observability - The web-based dashboard of the system of the present invention enables observation of performance trends, faults and alarms, and integration with existing control room systems. 7. Adaptability - The system of the present invention is able to sense, think, act and adapt to changing circumstances. This powerful technology is able to mimic human reasoning, executing narrow tasks efficiently and safely.

8. Asset stretching - The AI based technology aligns with the current requirement for intelligent systems that best leverage existing assets.

The system of the present invention is a technology with embedded fuzzy-logic AI. It is modular, robust and easily deployed, with an inbuilt communications network and a web- hosted dashboard. A module monitors inlet conditions, use measurement as input for an embedded AI (Fuzzy logic) algorithm to calculate phosphate concentration. Information is relayed to the Hub (HUB) which decides dosing rates. A further module (a Control Station, or CST) issues commands to the dosing pumps and monitors pump health.

This low-cost autonomous system senses, thinks and acts, adapting to changing circumstances. It can mimic human reasoning, executing narrow tasks efficiently and safely. This powerful technology can take away the need for expensive human intervention and provide pronounced improvements on current processes and systems for significant cost benefit. According to a further aspect of the invention there is also provided a method of autonomous wastewater treatment comprising: arranging at least one sensor adapted to measure the turbidity of wastewater; measuring the turbidity of wastewater in a WwTW inlet; measuring the flow of wastewater at an inlet to a wastewater treatment works (WwTW), said flow meter being linked to the Monitoring Station (MST); relaying the turbidity and flow measurements to a HUB and applying Fuzzy Logic to the measurements to estimate the phosphate content of the wastewater and determine the appropriate dosing pump rate; and relaying commands to a Control Station (CST) wherein said Control Station issues commands to metal salt dosing pump(s) in order to optimise phosphate removal.

According to a yet further aspect of the invention there is provided a kit suitable for use as an autonomous wastewater treatment system, said kit comprising: at least one sensor adapted to measure the turbidity of wastewater, said sensor being linked to a Monitoring Station (MST); a flow meter at an inlet to wastewater treatment work (WwTW) to measure flow of wastewater into the WwTW, said flow meter being linked to the Monitoring Station (MST); a HUB for receiving sensor data from the MST, wherein the HUB runs a Fuzzy Logic algorithm to estimate the phosphate concentration of the wastewater from the turbidity measurement and calculates the appropriate dosing of metal salt; and a Control Station (CST) which issues a control signal to a metal salt dosing pump to optimise phosphate removal. Optionally the kit may include a sensor adapted to measure pH and/ or temperature of wastewater.

The invention will now be described, by way of example only and with reference to the accompanying figures, in which:

Figure 1: illustrates “Dummy” Iron (Fe) Dosing Decisions using Fuzzy Logic.

Claims

1. An autonomous wastewater treatment system comprising: at least one sensor to measure the turbidity of wastewater, said sensor being linked to a Monitoring Station (MST); a flow meter at an inlet to a wastewater treatment work (WwTW) to measure flow of wastewater into the WwTW, said flow meter being linked to the Monitoring Station (MST); a HUB for receiving sensor data from one or more MST, wherein the HUB runs a Fuzzy Logic algorithm to estimate the phosphate concentration of the wastewater from the turbidity measurement and calculates the appropriate dosing of metal salt; and a Control Station (CST) which issues a control signal to a metal salt dosing pump to optimise phosphate removal.

2. An autonomous wastewater treatment system according to claim 1 wherein the sensor comprises one turbidimeter.

3. An autonomous wastewater treatment system according to any one of the preceding claims wherein the turbidity estimation is carried out as the wastewater enters the WwTW.

4. An autonomous wastewater treatment system according to any one of the preceding claims wherein the measured turbidity data in the wastewater entering the WwTW is input to a Fuzzy Logic (FL) control algorithm hosted on a HUB.

5. An autonomous wastewater treatment system according to claim 4 wherein the FL control algorithm estimates the phosphate concentration and influences the quantity of metal salts added to the wastewater.

6. An autonomous wastewater treatment system according to any one of the preceding claims wherein the HUB communicates with a Control Station (CST) and issues a command for the dispensing of metal salts.

7. An autonomous wastewater treatment system according to any one of the preceding claims wherein the HUB receives data from the sensor and sends control signals to the CST.

8. An autonomous wastewater treatment system according to any one of the preceding claims wherein the HUB communicates with an online Dashboard.

9. An autonomous wastewater treatment system according to claim 8 wherein the HUB communicates with an online Dashboard using wireless mobile telecommunications technology.

10. An autonomous wastewater treatment system according to any one of the preceding claims wherein the phosphate removal comprises one or more of: removal of phosphate in solid form by filtration/solids settlement processes; removal of dissolved phosphate with a biological process at the treatment works; and removal of dissolved phosphate by chemically converting the phosphate into a solid form and removal by filtration/solids settlement processes.

11. An autonomous wastewater treatment system according to claim 10 wherein dissolved phosphates are converted into solids by chemical precipitation.

12. An autonomous wastewater treatment system according to claim 11 wherein dissolved phosphates are converted into solids by addition of metal salts.

13. An autonomous wastewater treatment system according to claim 12 wherein the metal salts comprise aluminium salts or iron salts.

14. An autonomous wastewater treatment system according to claim 13 wherein the aluminium salts include aluminium sulfate.

15. An autonomous wastewater treatment system according to claim 13 wherein the iron salts include ferric sulphate or ferric chloride.

16. An autonomous wastewater treatment system according to claim 12 wherein the converted phosphate solids are removed by sedimentation or filtration.

17. An autonomous wastewater treatment system according to any one of claims 5 to 16 wherein the system applies Fuzzy Logic to turbidity level data provided by one or more sensors and calculates the amount of metal salts to be added to the wastewater.

18. An autonomous wastewater treatment system according to any one of the preceding claims which may optionally include a sensor adapted to measure pH and/ or temperature of wastewater.

19. A method of autonomous wastewater treatment comprising: arranging at least one sensor adapted to measure the turbidity of wastewater; measuring the turbidity of wastewater in a WwTW inlet; measuring the flow of wastewater at an inlet to wastewater treatment works (WwTW), said flow meter being linked to the Monitoring Station (MST); relaying the turbidity and flow measurements to a HUB and applying Fuzzy Logic to the measurements to estimate the phosphate content of the wastewater; and relaying the determined additive dosing rate to a Control Station wherein said Control Station issues commands to a metal salt dosing pump in order to optimise phosphate removal.

20. A method of autonomous wastewater treatment according to claim 19 wherein the sensor comprises one or more turbidimeters.

21. A method of autonomous wastewater treatment according to any one of claims 19 to

20 wherein the turbidity measurement is carried out as the wastewater enters the WwTW.

22. A method of autonomous wastewater treatment according to any one of claims 19 to

21 wherein the measured turbidity data in the wastewater entering the WwTW is input to a

Fuzzy Logic (FL) control algorithm hosted on a HUB.

23. A method of autonomous wastewater treatment according to claim 22 wherein the FL control algorithm estimates the phosphate concentration and influences the quantity of metal salts added to the wastewater.

24. A method of autonomous wastewater treatment according to any one of claims 19 to

23 wherein the HUB communicates with a Control Station (CST) and issues a command for the dispensing of metal salts.

25. A method of autonomous wastewater treatment according to any one of claims 19 to

24 wherein the HUB receives data from the sensor and sends control signals to the CST.

26. A method of autonomous wastewater treatment according to any one of claims 19 to

25 wherein the HUB communicates with an online Dashboard.

27. A method of autonomous wastewater treatment according to claim 26 wherein the HUB communicates with an online Dashboard using wireless mobile telecommunications technology.

28. A method of autonomous wastewater treatment according to any one of claims 19 to 27 wherein the phosphate removal comprises one or more of: removal of phosphate in solid form by filtration/solids settlement processes; removal of dissolved phosphate with a biological process at the treatment works; and removal of dissolved phosphate by chemically converting the phosphate into a solid form and removal by filtration/solids settlement processes.

29. A method of autonomous wastewater treatment according to claim 28 wherein dissolved phosphates are converted into solids by chemical precipitation.

30. A method of autonomous wastewater treatment according to claim 29 wherein dissolved phosphates are converted into solids by addition of metal salts.

31. A method of autonomous wastewater treatment according to claim 30 wherein the metal salts comprise aluminium salts or iron salts.

32. A method of autonomous wastewater treatment according to claim 31 wherein the aluminium salts include aluminium sulfate.

33. A method of autonomous wastewater treatment according to claim 31 wherein the iron salts include ferric sulphate or ferric chloride.

34. A method of autonomous wastewater treatment according to claim 30 wherein the converted phosphate solids are removed by sedimentation or filtration.

35. A method of autonomous wastewater treatment according to any one of claims 23 to 34 wherein the system applies Fuzzy Logic to turbidity level data provided by one or more sensors and calculates the amount of metal salts to be added to the wastewater.

36. A method according to any one of claims 23 to 35 wherein the system may optionally include a sensor adapted to measure pH and/ or temperature of wastewater.

37. A kit suitable for use as an autonomous wastewater treatment system, said kit comprising: at least one sensor adapted to measure the turbidity of wastewater, said sensor being linked to a Monitoring Station (MST); a flow meter at an inlet to wastewater treatment works (WwTW) to measure flow of wastewater into the WwTW, said flow meter being linked to the Monitoring Station (MST); a HUB for receiving sensor data from the MST, wherein the HUB runs a Fuzzy Logic algorithm to estimate the phosphate concentration of the wastewater from the turbidity measurement and calculates the appropriate dosing of metal salt; and a Control System which issues a control signal to a metal salt dosing pump to optimise phosphate removal.

38. A kit according to claim 37 which may optionally include a sensor adapted to measure pH and/ or temperature of wastewater.

39. A kit according to claims 37 or 38 wherein the sensor comprises one turbidimeter.

40. A kit according to any one of claims 37 to 39 wherein the turbidity measurement is carried out as the wastewater enters the WwTW.

41. A kit according to any one of claims 37 to 40 wherein the measured phosphate/ turbidity data in the wastewater entering the WwTW is input to a Fuzzy Logic (FL) control algorithm hosted on a HUB.

42. A kit according to claim 41 wherein the FL control algorithm estimates the phosphate concentration and influences the quantity of metal salts added to the wastewater.

43. A kit according to any one of claims 37 to 42 wherein the HUB communicates with a Control Station (CST) and issues a command for the dosing of metal salts.

44. A kit according to any one of claims 37 to 43 wherein the HUB receives data from the sensor and sends control signals to the CST.

45. A kit according to any one of claims 37 to 44 wherein the HUB communicates with an online Dashboard.

46. A kit according to claim 45 wherein the HUB communicates with an online Dashboard using wireless mobile telecommunications technology.

47. A kit according to any one of claims 37 to 46 wherein the phosphate removal comprises one or more of: removal of phosphate in solid form by filtration/solids settlement processes; removal of dissolved phosphate with a biological process at the treatment works; and removal of dissolved phosphate by chemically converting the phosphate into a solid form and removal by filtration/solids settlement processes.

48. A kit according to claim 47 wherein dissolved phosphates are converted into solids by chemical precipitation.

49. A kit according to claim 48 wherein dissolved phosphates are converted into solids by addition of metal salts.

50. A kit according to claim 49 wherein the metal salts comprise aluminium salts or iron salts.

51. A kit according to claim 50 wherein the aluminium salts include aluminium sulfate.

52. A kit according to claim 50 wherein the iron salts include ferric sulphate or ferric chloride.

53. A kit according to claim 49 wherein the converted phosphate solids are removed by sedimentation or filtration.

54. A kit according to any one of claims 42 to 53 wherein the system applies Fuzzy Logic to turbidity level data provided by one or more sensors and calculates the amount of metal salts to be added to the wastewater.

55. An autonomous wastewater treatment system, a method or a kit substantially as herein described and with reference to the accompanying figure(s).