AU2020101377A4

AU2020101377A4 - A process and device for on-line detection of chemical oxygen demand (cod) and biological oxygen demand (bod) in water

Info

Publication number: AU2020101377A4
Application number: AU2020101377A
Authority: AU
Inventors: Roshan Karri; Vishy Karri
Original assignee: Expert 365 Pty Ltd
Current assignee: Expert 365 Pty Ltd
Priority date: 2020-07-15
Filing date: 2020-07-15
Publication date: 2020-08-20
Anticipated expiration: 2028-07-15

Abstract

A method for use in quantifying chemical oxygen demand (COD) and biological oxygen demand (BOD), the method being performed using one or more electronic processing devices, the method including: receiving, from at least one Ph sensor, one turbidity senros, Dissolved Oxygen (DO) sensor, one of the nitrate sensor, nitrite sensor and ammonium sensors with at least one maximum solar irradiance value; determining COD based on one set of the Ph, turbidity, DO, nitrate, nitrite and ammonium based at least in part on the maximum solar irradiance value obtaining, from a meteorological data source, meteorological data for a geographical region corresponding to the area of waterbody for a prior time period; determining at least one evapotranspiration value for the prior time period based on at least some of the meteorological data; and calculating the COD and then taking the COD as input alogn with Ph, turbidity, DO, nitrate, nitrite and ammonium based at least in part on the maximum solar irradiance value to calculate the BOD value. 6/6 INPUTS Processing unit Ph, turbidity, Dissolved Oxygen, nitrate, nitrite and ammonium I solar irradiance OUTPUT COD PUTS BOD Fig. 5C: Neural network flow chart of on-line COD and BOD

Description

6/6 INPUTS Processing unit Ph, turbidity,

I Dissolved Oxygen, nitrate, nitrite and ammonium OUTPUT solar irradiance

COD PUTS BOD

Fig. 5C: Neural network flow chart of on-line COD and BOD

Editorial Note 2020101377 There is only thirty pages of the description

A PROCESS AND DEVICE FOR ON-LINE DETECTION OF CHEMICAL OXYGEN DEMAND AND BIOLOGICAL OXYGEN DEMAND IN WATER

Background of the Invention

[0001] This invention relates to a process and device for use in on-line detection of chemical oxygen demand and biological oxygen demand in water.

Description of the Prior Art

The chemical oxygen demand (COD) and biological oxygen demand (BOD) have been used extensively as an indicator of water quality and forms a mandatory requirement for compliance in establishing water quality. Whether the water is treated, stored, potable or a waste, there is a need to understand the oxygen demands to assess health impacts when released into mainstream. The prior art highlighted here shows methods currently in use for monitoring of both COD and BOD in drinking water. A simple way to explain the difference between COD and BOD is the amount of oxygen requirement in both these parameters. COD is the amount of oxygen required for the chemical oxidation of total organic and in-organic matter in water whereas BOD is the amount of oxygen which is consumed by bacteria while decomposing organic matter under aerobic conditions. Current methods used in detection of BOD typically rely on bioassay procedure that measures the oxygen consumed by bacteria from the decomposition of organic matter (Sawyer and McCarty, 1978). The change in DO concentration is measured over a given period of time in water samples at a specified temperature. While calculating the oxygen demand, the carbonaceous stage is taken into account. This stage is almost completed in 5 days, which means that most of the organic content of the sewage is oxidized under aerobic conditions in 5 days. Hence, BOD for 5 days is calculated and often represented as BOD5. Typically, the measurements are performed by taking a water sample and incubating it over a 5 day or 20 day period while monitoring the dissolved oxygen concentration every 12 hours. In a sample containing high concentrations of BOD, the sample should be diluted to ensure that the original oxygen level present in the water sample is not fully consumed. There are several requirements for the water used in the dilution of the sample. Distilled water should not be used, as microorganisms require certain salts to carry out metabolises. Thus, potassium, sodium, calcium, magnesium, iron, and ammonium salts are added to the dilution water. Also, the water should be buffered between a pH range of 6.5 to 8.5 with phosphate buffer. In some cases, certain water samples may require a "seed" of viable microorganisms to complete the degradation process. As a general rule in determining BOD values, at least 2 mg/L of oxygen must be used over the course of the experiment, but at least 0.5 mg/L must remain in the final sample. The oxygen concentration can be measured by the Winkler Titration Method (for dissolved oxygen concentration), or by utilizing a dissolved oxygen probe. A complete description of the procedure for the handling of samples, making dilution water, incubating samples, and determining oxygen concentration may be found in Standard Methods for the Examination of Water and Wastewater (1998).

In general, the utilization of oxygen by micro-organisms is considered to be a pseudo-first order process. In a closed system (no re-aeration does not take place), the rate of oxygen consumption is commonly described by

L = Lee-kt eqn.1

Wherein t is time, L is the concentration of oxygen at time t. Lo is the original concentration of oxygen in a sample, and k is the rate constant, which is generally around 0.17/day for sewage waste. Eqn 1 is used to draw the line representing the removal of oxygen.

A similar expression can be used to describe the oxidation of BOD in the sample as it is the inverse of the oxygen consumption,

L = Lo - Lee-kt eqn.2

where L is the concentration of biodegradable organic matter at time t, Lo is the original concentration of biodegradable organic matter, and k and t are the same variables as in eqn.1.

Traditionally, we are concerned with the amount of oxygen required to oxidize a BOD sample over a 5-day period. This time period of 5 days was established years ago in England and resulted from the fact that it required 5 days for water in most streams to reach the ocean. The microorganisms continue to exert an oxygen demand on the stream after this time and the ultimate BOD can be determined by conducting the experiment over a 20- day period. The ultimate BOD allows for an accurate calculation of the Lo in eqn.2 above.

An alternate method in determining Lo is to measure the BOD over a 5-day period, fit the

data to Equation 2 using a k value of 0.17, and solving for Lo. However, experience has

shown that this method does not work due to the non-first-order nature of the microbial degradation process. The ultimate BOD, Lo, can be determined using the Thomas slope

method (Snoeyink and Jenkins, 1980) illustrated, which linearizes the data in the form

1/3 () = (Lok)"1/ + /_k' t__ 0 eqn.3

where t is time, y is the BOD in mg/L at time t (L in Equation 2), Lo is the original

concentration of biodegradable organic matter, and k is the rate constant. Note that equation 3 is the equation of a line, where

(4k)-'/= they - intercept, b km 641= the slope of the line, a

By substitution, k = 6b/a and Lo = 1/(ka3 ). By plotting an experimental data set of lab

measurements (BOD as function of time) according to eqn.3, the rate constant and ultimate BOD can be estimated.

In another method using microbial fuel cells, an anode and cathode compartments (working volume of 25 ml each) were separated by a cation exchange membrane (Nafion, Dupont Co., USA). Graphite felt (50 x 50 x 3 mm, GF series, ElectroSynthesis Co., USA) were used as electrodes with platinum wire connecting them through resistance of 10 Ohms and a multi- meter (Keithley Co., USA). This method was popularised by Byung Hong Kim, In Seop Chang, Geun Cheol Gil et.al., (Biotechnology Letters 25: 541-545, 2003). As shown by Byung Hong Kim etl., the anode compartment was kept anoxic by purging with nitrogen gas

(100 ml min- 1 ). Air (100 ml min- 1 ) was purged into the cath- ode compartment in order to supply 02 needed for the electrochemical reaction. The cathode compart- ment contained 50 mM phosphate buffer (pH 7) with 100 mM NaCl as the electrolyte, and the anode com partment wastewater diluted with the electrolyte. The microbial fuel cell was operated in a batch mode. The anode content (25 ml) was replaced by diluted waste- water as fuel. The

microbial fuel cells were placed in a temperature-controlled chamber controlled at 30 C. The potential difference (PD) between anode and cathode was measured using a multimeter and recorded every 5 min through a data acquisition system. The measured PD was converted to current according to the relation- ship of PD = current x resistance. Coulomb, which is expressed as current x time, was calculated by inte- grating the current over the time from the start point of experiment to the time where current was decreased to 5% of maximum current. Experiments were conducted using separate microbial fuel cells, and results were presented as average values of BOD5. In separate investigations, Hikuma M, Suzuki H, Yasuda T, Karube I, Suzuki S et.al have estimated BOD using living immobilised yeast (Eur. J Appl. Microbiol. Biotechnol. 8: 289-297) while Hyun CK, Tamiya E, Takeuchi T, Karube I, Inoue have developed novel BOD sensor based on bacterial luminescence. (Biotechnol. Bioeng. 41: 1107-1111). Other significant attempts to detect BOD have been using optical fibre sensors (Preininger C, Klimant I, Wolfbeis OS (1994) and fluorescence technique by Reynolds DM, Ahmad SR (Water Res. 31: 2012-2018: 1997) who claimed a rapid and direct determination of BOD.

In all these methods to measure BOD, the major issue has been in calibration of the equipment and correlating various concentrations of BOD to measurable parameters such as light flux, potential difference, bacterial luminescence, immobilised yeast and so on. The accuracy of the measurements was valid to the matrix of experimentation. One of the uncertainties has been problem of accurately identifying the colony numbers when natural colony increase associated with changes in temperature and time.

Detection of COD by synchronous fluorescence spectra is also evident in the literature (Jin Hur, Bo-Mi Lee, Tae-Hwan Lee and Dae-Hee Park), as this method allows for rapid detection. A novel rapid methodology for the determination of COD based on photoelectrochemical oxidative degration principle (PECOD) was proposed by Huijun Zhao, Dianlu Jiang, Shanqing Zhang, Kylie Catterall and Richard John (Anal. Chem. 2004, 76, 1, 155-160). With this new method, the extend of degradation of dissolved organic matter in water sample was measured simply by direct quantifying the extent of electron transfer. At a TiO, nanoporous film electrode during an exhaustive photoelectron-catalytic degradation of organic matter in a thin layer photoelectrochemical cell. The PECOD method demonstrated in this work is a direct method. The method in their principle measured theoretical COD values due to the extraordinary high oxidation, efficiency and accuracy of charge measurement. The other methods for COD are comparable to the BOD as highlighted above. In all those cases a reliable on-line detection of both COD and BOD became a challenge as the models proposed were specific to the type of the water and revised models needed to be retrofitted when the water samples were changed.

[0002] A Any reference in this specification to prior publication (or information derived from it), or to any matter which is known, is not, and should not be taken as an acknowledgment or admission or any form of suggestion that the prior publication (or information derived from it) or known matter forms part of the common general knowledge in the field of endeavour to which this specification relates.

Summary of the Present Invention

[00031 In one broad form an aspect of the present invention seeks to provide a process and device for use in performing on-line detection of COD and BOD in water, the method being performed using one or more electronic processing devices, the method including: receiving, from at least one potential for hydrogen (Ph) sensor located in water , at least two sensors for separate dissolved oxygen (DO) of water and nitrogen content, at least one sensor measuring ammonium content in water, at least two sensors measuring turbidity and total suspended solids (TSS); estimating most COD and BOD of water as two separate calculations, based on the sensors output along with temperature and solar irradiance from meteorological data source, meteorological data for a geographical region corresponding to the water location and time period; determining indicative of total nitrogen content at least one solar irradiance value for the prior time period based on at least some of the meteorological data; and calculating COD and BOD in mg/L of water.

[0004] In one embodiment, the method includes: determining total nitrogen from nitrite, nitrate and ammonium values using different techniques; determining a maximum nitrogen value from the plurality of calculated nitrogen values; sensing Ph, turbidity and DO of water and calculating the COD and BOD, as two separate estimations, based on at least in part on the maximum total nitrogen, Ph, turbidity and DO value together with mean solar irradiance and temperature of the location of the water body.

[0005] In one embodiment, at least one ammonium measurement is determined using in-situ sensors and subsequently calculating nitrogen from ammonium.

[0006] In one embodiment the method includes obtaining the solar irradiance and temperature from meteorological data based on location of the water body.

[00071 In one embodiment the method includes obtaining the meteorological data using at least one of: file transfer protocol (FTP); and; an application programming interface (API).

[0008] In one embodiment the method includes: comparing the solar irradiance with monthly average for the location of the water body; and determining higher values for subsequent calculations.

[0009] In one embodiment the method includes: receiving a plurality of Ph, turbidity, DO from a plurality of sensors; determining all highest measurements from those plurality of measurements; and identifying those parameters as input parameters for the calculation of COD and BOD.

[0010] In one broad form an aspect of the present invention seeks to provide apparatus for use in performing on-line detection of COD and BOD for a water body, the apparatus including: at least one sensor for each of the Ph, turbidity, DO, nitrates, nitrites and ammonium detection and a meteorological data source via one or more communications networks, the one or more electronic processing devices being configured to: receive, from the at least one sensor for each of the Ph, turbidity, DO, nitrates, nitrites and ammonium detection; in response to estimating COD and BOD in water at any given instance: obtaining, from a meteorological data source, meteorological data for a geographical region corresponding to the location of water body for a prior time period.

[0011] In one embodiment the one or more electronic processing devices are configured to provide an indication of the determined COD and BOD area of water over the subsequent time period.

[0012] In one embodiment the apparatus includes a plurality of Ph, turbidity, DO, nitrates, nitrites and ammonium detection sensors, the plurality of Ph, turbidity, DO, nitrates, nitrites and ammonium detection sensors being coupled together via a sensor communication network.

[0013] In one embodiment the apparatus includes: a gateway unit configured to act as a parent node in the sensor communication network; and a plurality of sensor units each including a respective Ph, turbidity, DO, nitrates, nitrites and ammonium detection sensors and configured to receive the sensor communication network and transfer the Ph, turbidity, DO, nitrates, nitrites and ammonium detection measurements to the one or more electronic processing devices.

[0014] In one embodiment the one or more electronic processing devices are coupled to the gateway unit and the meteorological data source via the Internet.

[0015] It will be appreciated that the broad forms of the invention and their respective features can be used in conjunction, interchangeably and/or independently, and reference to separate broad forms is not intended to be limiting.

Brief Description of the Drawings

[0016] Various examples and embodiments of the present invention will now be described with reference to the accompanying drawings, in which:

[00171 Figure 1 is a flow chart of an example of a method for use in both measuring input parameters and determining COD and BOD;

[0018] Figure 2 is a schematic diagram of an example of a distributed architecture;

[0019] Figure 3 is a schematic diagram of an example of a server processing system;

[0020] Figure 4 is a schematic diagram of an example of a client processing system; and

[0021] Figures 5A to 5C are a flow chart of an example of a method and apparatus for use in determining COD and BOD on-line.

Detailed Description of the Preferred Embodiments

[0022] An example of a method for use in measuring on-line COD and BOD will now be described with reference to Figure 1.

[0023] The method will typically be performed using one or more electronic processing devices, which may be provided in the form of discrete devices such as servers or personal computers, or shared computer processing resources which may be obtained using Internet based cloud computing services. The one or more electronic processing devices will typically be coupled to one or more communication networks to allow data to be received or transferred as required to perform the steps of the method.

[0024] In step 100, the method involves receiving, from at least one sensor measurement for Ph, turbidity, DO, nitrates, nitrites and ammonium detection sensors, the plurality of Ph, turbidity, DO, nitrates, nitrites and ammonium detection sensors, embodiments of the method may utilise a plurality of Ph, turbidity, DO, nitrates, nitrites and ammonium detection sensors which may immersed in flowing water to allow multiple measurements to be obtained.

[0025] Step 110 involves then involves determining total nitrogen content based on the nitrates and nitrites.

[0026] Step 120 Based on the ammonium measurement, calculate the nitrogen content from the ammonium.

[0027] Having obtained two separate nitrogen calculations from 110 and 120 above, the subsequent step 130 involves obtaining, from solar irradiance from meteorological data source, meteorological data for a geographical region corresponding to the water body location. The meteorological data source may be provided by an organisation that provides weather services, such as the Bureau of Meteorology in Australia. Typically, the meteorological data source will facilitate access to meteorological data such as weather observations and forecasts for different geographical regions. This access may be facilitated via the Internet, for instance by using File Transfer Protocol (FTP) or an Application Programming Interface (API). Accordingly, the meteorological data may be readily accessed via a suitable Internet-enabled interface of the meteorological data source.

[0028] In step 140, the method involves determining one value for the time period tested, as an average, along with meteorological data. The average values for the parameters from

[100] may be determined in a variety of different ways, such as by calculating the mean values of the measured parameters. The solar irradiance value may be directly obtained from the meteorological data. In some cases, the solar irradiance values may be obtained using different sources/techniques should the data for the location is not available for a given time and day at the water source.

[0029] Next, step 150 involves calculating the COD and BOD quantities based on part on water parameter data collected from [110] - [140].

[0030] Accordingly, COD and BOD quantities can be determined without the need for incubation, filtering techniques or any other time-consuming methods cited.

[0031] It should be noted in particular that the use of meteorological data for the time period to determine the solar irradiance value will mean that the water body has had an exposure of such irradiance over a period of time during the day.

[0032] Further optional implementation features of the method will now be described.

[0033] As mentioned above, the solar irradiance value may be determined in different ways, and thus in some implementations, the method may include determining a plurality of solar irradiance values using different techniques, determining a maximum solar irradiance value from the plurality of solar irradiance values; and then calculating the COD and BOD based at least in part on the maximum solar irradiance value. This may provide a more conservative approach to ensure the COD and BOD is calculated based on a highest solar irradiance scenario, and thus avoid the situation where a solar irradiance calculation based on a particular set of parameters might not accurately model the full extent of solar irradiance in certain circumstances. The use of a maximum solar irradiance ensures possibility of under estimating COD and BOD which has a higher risk of inhibiting water quality.

[0034] Implementations of the method may involve calculating solar irradiance values using the meteorological data, for instance by using known calculation methods such as the linear regression (LR), Angstrom-Prescott-Page (APP) and the artificial neural network (ANN) models

[00351 For each of the above three models the basic dataset for a given location encompass solar radiation (H), minimum and maximum temperatures (Tmin and Tmax), Total rainfall and evaporation (Lr and Le), and sunshine duration (S) over a period of time. A comprehensive dataset is available for many meteorological sites to get the monthly averages. This dataset is also important for ANN training and testing.

[0036] The linear regression methods (LR) involves the following methodology:

The monthly average daily extra-terrestrial solar radiation, Ho, on a horizontal surface can be calculated from the following equation (Elminir et al., 2007):

Ho =H2 6 Iok2430180eq. 00- (cospcos6sino + singsin6 eqn.4

where Io is the solar constant, k is the eccentric correction factor of the earth's orbit, p is the latitude of the location (degrees), 6 is the solar declination angle (degrees) and to is the sunset hour angle (degrees). The currently accepted and commonly used Io value is set at 1367 W/m 2, which is recommended by the World Radiation Centre (Montero et al., 2009; Page,

1986; Stine, 1985). The eccentric correction factor k for each day can be calculated (Yorukoglu and Celik, 2006):

k = 1 + 0.033cos (3 ") eqn.5

where dn is the day number counted from beginning of the year (where dn = 1 for 1st of January).

The declination angle 6 and sunset hour angle co for each day of the year can be calculated below (Cooper, 1969; Luque and Hegedus, 2003):

= 23.45sin(36X 24+dn)) eqn.6

w cos 1 (-tanptan8) eqn.7

Lastly, the maximum possible sunshine duration, So, for each day is calculated from (Soler

and Gopinathan, 1994):

So = 2 eqn.8

To gauge the accuracy of the models, the (RMSE) was used.

Linear Regression (LR) Analysis

Having established the values from mathematical equations above for each day, the averages were obtained which forms the linear regression model shown below:

H = aTmin + bTmax + cL + dL + eH + f S +g SO eqn.9

where a to g are regression coefficients, to be determined empirically.

On the other hand, the Angstrom-Prescott-Page (APP) method for calculating solar irradiance can be expressed using the formula:

= a+ b eqn.10 Ho SO

where a and b are the empirical (or Angstrom) coefficients. The coefficient a can be interpreted as the fraction of the monthly average solar radiation (H/Ho) entering the atmosphere when there is a complete cloud cover (Ahmad and Tiwari, 2011). The second coefficient b defines the rate of change of H/Ho with respect to the sunshine duration ratio (S/So). It is an index of the latitudinal variation (Ahmad and Tiwari, 2011). To determine the values for coefficients a and b, the plot clearness index (H/Ho)vs. sunshine duration ratio (S/So) is plotted to get a linear trend-line.

[0037] The OLL network was proposed and developed by Ergezinger and Thomsen. This method is based on the linearization of the activation function, thus leading to a linear optimization problem in each layer. The error made when linearizing the activation function is accounted for by introducing a penalty term. This penalty term, whose influence is varied, is to maintain optimum convergence for the network. The OLL neural network is then optimized in an iterative procedure, where for each iteration, the corresponding weights are optimized by solving a set of linear equations. It uses the

Figure below shows a basic OLL neural network structure. Inputs xi are connected to the hidden layer with connect weights rai and the hidden neuron are connected to the output neurons with connection weights sa. The b neurons in the output layer have a pure linear activation function and hidden neurons have a sigmoid activation function. The training algorithm for the OLL model described below:

xo=1 zo=1 ~~* r10

xi hi XA1l z -------

X2 '' h2 XA2 Y2

Optimization Layer-by-Layer ANN Architecture

Step 1: Initialization Initial values for the weights R and S, where R and S are the weight vectors between the hidden-input and output-hidden layers respectively, penalty constant p and the number of iterations are defined. Weight vectors R and S are to be optimized in order to minimize the error function.

Step 2: Optimization of Output Layer Weights The optimum weight for S, S°"""', is obtained using:

S''"'""'= A-' * b eqn.11

with

P

A= $zazjI" a, j= 0,..., A eqn.12 p=1

P

b= $ azt" a = 0,..., A eqn.13 p=1

where P is the total number of training data, za, and z are the scalar outputs of the hidden neurons of training data p and tP is the target output value.

Update the weights S and calculate the RMS error using below:

P K

) 1> tk" -Yk) ErrorRMS p=1 k=1 PK eqn.14

where K is the total number of output neurons, tj is the target output value for neuron k and training data p and yk is the network output value for training data p.

Step 3: Optimization of Hidden Layer Weights The new updated weight R is then defined asRtest, as shown:

AROP"'mum = A•b eqn.15

where

for a# h P B XmSh linearized S [(iSha linearized p=1 b=1

wh ere i, m = 0,...,I1 h = 0,..., A

fora = h P B

A = [xi sba linearized Xmsbhlinearized - ) sbalinearized XXAa Xixm| eqn.17 p=1 b=1

wh ere i, m = 0,...,I1 h = 0,..., A

and

P B b = l(tp - yb )Shh linearizedx eqn. 18 p=1 b=1

wh ere m = 0,..., I h = 0,..., A

where sbalinearized and sbhiinearized are the linearized weights from neuron b of the output layer to neurons a and h in the hidden layer (of training data P), XAa is the second derivative of the activation function XAa, Sba and Sbh are the connection weights between the output-hidden layer.

Once AR°""is obtained, the new update weight can be defined as:

R Rold +AR optmum eqn.19

Step 4: Testfor Completion RMS error, ERMst", was then calculated comparing theRest matrix with S or SPu"' matrices calculated in Step 3. a. ERMStest < E The hidden layer weight matrix R is updated R = Rtest. Decrease the influence of the penalty term by decreasing g. Proceed to Step 5. b.~test>E b. ERMSts ;> E

Increase the influence of p and repeat Step 4a.

Step 5: Process Termination If RMS error solar radiation is not within the desired range, repeat Step 2, else the training process is ceased.

[00381 It will thus be appreciated that calculation of the solar irradiance value using the any of the three methods will require access to meteorological data including observations of a range of different measurements including the maximum temperature, the minimum temperature, the rain fall and evaporation.

[0039] Additionally, the solar irradiance value may be determined based on a value calculated by the meteorological data source and included in the meteorological data. As mentioned above multiple techniques can be used and a highest value may be used for calculating the COD and BOD.

[00401 In order to more specifically account for the particular water body in the COD and BOD on line calculation, the method may include determining a solar irradiance value for the particular location of the water body along with nitrogen from nitrates and nitrates and separate calculation of nitrogen from ammonium alone.

[0041] In particular, this may involve determining nitrogen from ammonium as a separate calculation to nitrogen from nitrates and nitrite.

[0042] It should be appreciated that the particular set of water data that is stored may depend on the specific techniques used for determining the solar irradiance on water body value and/or the COD and BOD. In any event, the water COD and BOD data may be stored in a database or the like and in some examples may be organised into tables for each crop.

[0043] It will be appreciated that the nitrogen from nitrates and ammonium is calculated using the formulas:

a) N-NO3= Measured NO3 /4.43 eqn.20

b) N-Nh4= Measured NH4*14/17 eqn.21

[0044] With regard to the meteorological data, which is used for at least determining the solar irradiance value, this may be obtained based on location data for the water body. For example, a geographical location for a particular site to which this method is to be applied may be used to obtain meteorological data corresponding to that location. In some cases, meteorological data may only be available for neighbouring regions, and the method may either use meteorological data for the closest region or may use averaged values of the meteorological data for more than one neighbouring region if desired.

[00451 Particular implementations of the method may include obtaining the meteorological data using file transfer protocol (FTP) or an application programming interface (API). The particular technique used for accessing the meteorological data will largely depend on the techniques supported by the meteorological data source.

[0046] The specific types of meteorological data that may be obtained and used in the method include a relative humidity observation, a maximum temperature observation, a minimum temperature observation, an air temperature observation, a soil surface temperature observation, an atmospheric pressure observation, and a solar radiation exposure observation. The aforementioned observations can support the calculation of evapotranspiration values using the Priestly Taylor method or the Turc method as discussed above, but it should be appreciated that not all of these need to be obtained in all implementations of the method. On the other hand, additional types of meteorological data may also be obtained to support the use of other techniques or for providing extended functionalities.

[00471 As mentioned above, solar irradiance value may be obtained directly as part of the meteorological data. This can be useful to allow comparisons to the values derived from other techniques, but it should be understood that this is not essential.

[0048] In another aspect, an apparatus may be provided for use in performing on-line detection of COD and BOD for a particular water body. The apparatus may include at least one sensor for each of the Ph, turbidity, DO, nitrate, nitrite and ammonium in water, and one or more electronic processing devices coupled to the at least one or more of the above sensors and a meteorological data source via one or more communications networks. In this regard, the one or more electronic processing devices will be configured to perform the method as described above.

[00491 In some implementations, the one or more electronic processing devices may be configured to provide an indication of the calculated COD and BOD via app.

[0050] As mentioned above, a plurality of sensors may be used, and in this respect the apparatus may include the plurality of sensors, which may be coupled together via a sensor communication network. In one example, the apparatus may include a gateway unit configured to act as a parent node in the sensor communication network and a plurality of sensor units. Each sensor unit may include a respective Ph, turbidity, DO, nitrate, nitrite and ammonium sensors and be configured to act as a child node in the sensor communication network. The gateway unit may in turn be configured to receive measurements from the sensor units and transfer the moisture measurements to the one or more electronic processing devices.

[0051] The one or more electronic processing devices may be coupled to the gateway unit and the meteorological data source via the Internet. In one specific embodiment, the one or more electronic processing devices may be provided using a cloud computing system and the meteorological data source may be accessed via an Internet API.

[0052] In one example, the process is performed by one or more processing systems operating as part of a distributed architecture, an example of which will now be described with reference to Figure 2.

[0053] In this example, the arrangement includes a number of processing systems 201, 203 along with gateway and sensor units 205, 207, each interconnected via one or more communications networks, such as the Internet 202, and/or a number of local area networks (LANs) 204.

[00541 It will be appreciated that the configuration of the networks 202, 204 are for the purpose of example only, and in practice the processing systems 201, 203 and gateway and sensor units 205, 207 can communicate via any appropriate mechanism, such as via wired or wireless connections, including, but not limited to mobile networks, private networks, such as an 802.11 networks, the Internet, LANs, WANs, or the like, as well as via direct or point-to point connections, such as Bluetooth, Zigbee or the like.

[0055] The nature of the processing systems 201, 203 and their functionality will vary depending on their particular requirements. In one particular example, the processing systems 201, 203 represent servers (such as for determining the COD and BOD) and clients (for allowing mining companies, water infrastructure companies, water recycling plants and aquaculture plants to monitor COD and BOD processes or updating data), although this is not essential and is used primarily for the purpose of illustration.

[0056] An example of a suitable processing system 201 is shown in Figure 3. In this example, the processing system 201 includes an electronic processing device, such as at least one microprocessor 300, a memory 301, an optional input/output device 302, such as a keyboard and/or display, and an external interface 303, interconnected via a bus 304 as shown. In this example the external interface 303 can be utilised for connecting the processing system 201 to peripheral devices, such as the communications networks 202, 204, databases 211, other storage devices, or the like. Although a single external interface 303 is shown, this is for the purpose of example only, and in practice multiple interfaces using various methods (e.g. Ethernet, serial, USB, wireless or the like) may be provided.

[00571 In use, the microprocessor 300 executes instructions in the form of applications software stored in the memory 301 to perform required processes, such as communicating with other processing systems 201, 203 or the gateway and/or sensor units 205, 207 depending on the sensor network topology. Thus, actions performed by a processing system 201 are performed by the processor 300 in accordance with instructions stored as applications software in the memory 301 and/or input commands received via the I/O device 302, or commands received from other processing systems 201, 203. The applications software may include one or more software modules, and may be executed in a suitable execution environment, such as an operating system environment, or the like.

[0058] Accordingly, it will be appreciated that the processing systems 201 may be formed from any suitable processing system, such as a suitably programmed computer system, PC, web server, network server, or the like. In one particular example, the processing system 201 is a standard processing system such as a 32-bit or 64-bit Intel Architecture based processing system, which executes software applications stored on non-volatile (e.g., hard disk) storage, although this is not essential. However, it will also be understood that the processing systems 201 could be or could include any electronic processing device such as a microprocessor, microchip processor, logic gate configuration, firmware optionally associated with implementing logic such as an FPGA (Field Programmable Gate Array), or any other electronic device, system or arrangement.

[0059] As shown in Figure 4, in one example, the processing systems 203 include an electronic processing device, such as at least one microprocessor 400, a memory 401, an input/output device 402, such as a keyboard and/or display, and an external interface 403, interconnected via a bus 404 as shown. In this example the external interface 403 can be utilised for connecting the processing system 203 to peripheral devices, such as the communications networks 202, 204, databases, other storage devices, or the like. Although a single external interface 403 is shown, this is for the purpose of example only, and in practice multiple interfaces using various methods (e.g. Ethernet, serial, USB, wireless or the like) may be provided.

[0060] 1 In use, the microprocessor 400 executes instructions in the form of applications software stored in the memory 401 to perform required processes, for example to allow communication with other processing systems 201, 203. Thus, actions performed by a processing system 203 are performed by the processor 401 in accordance with instructions stored as applications software in the memory 402 and/or input commands received from a user via the I/O device 403. The applications software may include one or more software modules, and may be executed in a suitable execution environment, such as an operating system environment, or the like.

[0061] Accordingly, it will be appreciated that the processing systems 203 may be formed from any suitable processing system, such as a suitably programmed PC, Internet terminal, lap-top, hand-held PC, smart phone, PDA, tablet, or the like. Thus, in one example, the processing system 203 is a standard processing system such as a 32-bit or 64-bit Intel Architecture based processing system, which executes software applications stored on non-volatile (e.g., hard disk) storage, although this is not essential. However, it will also be understood that the processing systems 203 can be any electronic processing device such as a microprocessor, microchip processor, logic gate configuration, firmware optionally associated with implementing logic such as an FPGA (Field Programmable Gate Array), or any other electronic device, system or arrangement.

[0062] It will also be noted that whilst the processing systems 201, 203 are shown as single entities, it will be appreciated that this is not essential, and instead one or more of the processing systems 201, 203 can be distributed over geographically separate locations, for example by using processing systems provided as part of a cloud based environment.

[0063] In a preferred implementation, the processing systems 201 may be provided as part of a cloud computing service and will communicate with other elements of the arrangement via the Internet 202. The use of other processing systems 203 in the form of client devices is not essential to the method, but in practice will be advantageous to allow users such as Aquaculture farmers, water companies and mining companies, hospitals or the like to monitor the COD and BOD status for a site. Furthermore, users can interact with the processing systems 201 or data stored on the database 211 to update data for use in the method, such as the input data or Coloforms data, if necessary.

[0064] The gateway and sensor units 205, 207 may be provided as specialised versions of the processing systems 203 as shown in Figure 4, whereby the external interfaces 403 include dedicated sensor interfaces for interfacing with respective soil moisture sensors along with network interfaces as required for the particular sensor network topology. For instance, the gateway unit 205 may differ from the sensor units 207 in terms of the particular network connectivity provided. The gateway unit 205 may include external network connectivity for allowing communications with the processing systems 201 via the Internet or any other external network, along with internal network connectivity for enabling communications within a localised sensor network, such as by using Zigbee or any other suitable networking protocol. On the other hand, the sensor units 2017 may only include internal network connectivity.

[0065] Since the gateway and sensor units 205, 207 may need to be deployed in remote locations these may include localised power sources such as a solar panel and rechargeable batteries. To conserve power, the microprocessor 400 and other hardware used in the gateway and sensor units 205, 207 may be selected for energy efficiency.

[0066] However, it will be appreciated that the above described arrangement is shown as an example only, and numerous other configurations may be used.

[0067] A detailed example of a method for use in on-line detection of COD and BOD will now be described with regard to the flow chart of Figures 5A to 5B.

[0068] In this particular example, it is assumed that the main data processing functionalities of this method are provided as part of a cloud computing service which is able to communicate with meteorological data services and gateway and sensor units having respective soil moisture sensors via the Internet. This example will illustrate a typical loop of

COD and BOD calculations which may be carried out periodically and potentially only at predetermined times of the day.

[0069] As an initial process, the method will involve checking the water field status at step 500. This may include accessing a water field table including particular information regarding a field such as coordinates, date, time, type of water. If the field status is inactive the method may proceed no further, however if the field status is active a determination may be made on whether the field status should be changed to inactive.

[0070] Assuming field status is active, type of water source such as fresh water, effluent, creek water, drinking water or brackish water selection should also be made at next step

[501] at the field status.

[0071] Ph, turbidity, DO, nitrate, nitrite, ammonium measurements will be received at step 502, and in the event that these are received for multiple soil moisture sensors in the same field, these may be processed by averaging or taking a maximum measurement.

[0072] At step 503 based on the sensory input and steady state collection, ensure the steady quantitative collection of the process parameters to avoid large variability.

[0073] At [504] meteorological data will be obtained for the day. In this example, the meteorological data may be obtained from three separate meteorological data sources via Internet APIs.

[0074] The maximum value of the solar irradiance/radiation data may be taken after processing the three models namely: Linear regression, APP and Al. The data is sent to the could at [506]

[0075] Moving on to step 507, the method may then include calculation of COD and BOD in the cloud as function of Ph, turbidity, DO, nitrogen from nitrates, nitrite and ammonium along with the solar irradiance obtained from [504].

[0076] Throughout this specification and claims which follow, unless the context requires otherwise, the word "comprise", and variations such as "comprises" or "comprising", will be understood to imply the inclusion of a stated integer or group of integers or steps but not the exclusion of any other integer or group of integers.

[0077] Persons skilled in the art will appreciate that numerous variations and modifications will become apparent. All such variations and modifications which become apparent to persons skilled in the art, should be considered to fall within the spirit and scope that the invention broadly appearing before described.

Description of the Prior Art

The chemical oxygen demand (COD) and biological oxygen demand (BOD) have been used

extensively as an indicator of water quality and forms a mandatory requirement for

compliance in establishing water quality. Whether the water is treated, stored, potable or a waste, there is a need to understand the oxygen demands to assess health impacts when

released into mainstream. The prior art highlighted here shows methods currently in use for monitoring of both COD and BOD in drinking water. A simple way to explain the difference

between COD and BOD is the amount of oxygen requirement in both these parameters. COD is the amount of oxygen required for the chemical oxidation of total organic and in

organic matter in water whereas BOD is the amount of oxygen which is consumed by bacteria while decomposing organic matter under aerobic conditions. Current methods

used in detection of BOD typically rely on bioassay procedure that measures

the oxygen consumed by bacteria from the decomposition of organic matter (Sawyer and McCarty, 1978). The change in DO concentration is measured over a given period of time in

water samples at a specified temperature. While calculating the oxygen demand, the carbonaceous stage is taken into account. This stage is almost completed in 5 days, which

means that most of the organic content of the sewage is oxidized under aerobic conditions in 5 days. Hence, BOD for 5 days is calculated and often represented as BOD5. Typically, the

measurements are performed by taking a water sample and incubating it over a 5 day or 20 day period while monitoring the dissolved oxygen concentration every 12 hours. In a sample

containing high concentrations of BOD, the sample should be diluted to ensure that the

original oxygen level present in the water sample is not fully consumed. There are several requirements for the water used in the dilution of the sample. Distilled water should not be

used, as microorganisms require certain salts to carry out metabolises. Thus, potassium, sodium, calcium, magnesium, iron, and ammonium salts are added to the dilution water.

Also, the water should be buffered between a pH range of 6.5 to 8.5 with phosphate buffer. In some cases, certain water samples may require a "seed" of viable microorganisms to

complete the degradation process. As a general rule in determining BOD values, at least 2 mg/L of oxygen must be used over the course of the experiment, but at least 0.5 mg/L must

remain in the final sample. The oxygen concentration can be measured by the Winkler

Titration Method (for dissolved oxygen concentration), or by utilizing a dissolved oxygen probe. A complete description of the procedure for the handling of samples, making dilution water, incubating samples, and determining oxygen concentration may be found in Standard Methods for the Examination of Water and Wastewater (1998).

In general, the utilization of oxygen by micro-organisms is considered to be a pseudo-first order process. In a closed system (no re-aeration does not take place), the rate of oxygen

consumption is commonly described by

L = Loe-kt eqn.1

Wherein t is time, L is the concentration of oxygen at time t. Lo is the original concentration

of oxygen in a sample, and k is the rate constant, which is generally around 0.17/day for sewage waste. Eqn 1 is used to draw the line representing the removal of oxygen.

L = Lo - Loe-kt eqn.2

where L is the concentration of biodegradable organic matter at time t, Lo is the original

concentration of biodegradable organic matter, and k and t are the same variables as in eqn.1.

Traditionally, we are concerned with the amount of oxygen required to oxidize a BOD sample over a 5-day period. This time period of 5 days was established years ago in England

and resulted from the fact that it required 5 days for water in most streams to reach the ocean. The microorganisms continue to exert an oxygen demand on the stream after this

time and the ultimate BOD can be determined by conducting the experiment over a 20- day period. The ultimate BOD allows for an accurate calculation of the Lo in eqn.2 above.

shown that this method does not work due to the non-first-order nature of the microbial degradation process. The ultimate BOD, Lo, can be determined using the Thomas slope method (Snoeyink and Jenkins, 1980) illustrated, which linearizes the data in the form t Y (==_1/3 13(Lok) + 1l +6L 0113)I eqn.3 where t is time, y is the BOD in mg/L at time t (L in Equation 2), Lo is the original concentration of biodegradable organic matter, and k is the rate constant. Note that equation 3 is the equation of a line, where

13 (Lok) = they - intercept, b 3 k2J - = the slope of the line, a 641/3

By substitution, k = 6b/a and Lo = 1/(ka3). By plotting an experimental data set of lab

In another method using microbial fuel cells, an anode and cathode compartments (working volume of 25 ml each) were separated by a cation exchange membrane (Nafion, Dupont Co., USA). Graphite felt (50 x 50 x 3 mm, GF series, ElectroSynthesis Co., USA) were used as electrodes with platinum wire connecting them through resistance of 10 Ohms and a multi meter (Keithley Co., USA). This method was popularised by Byung Hong Kim, In Seop Chang, Geun Cheol Gil et.al., (Biotechnology Letters 25: 541-545, 2003). As shown by Byung Hong Kim etl., the anode compartment was kept anoxic by purging with nitrogen gas

(100 ml min- 1 ). Air (100 ml min-1 ) was purged into the cath- ode compartment in order to supply 02 needed for the electrochemical reaction. The cathode compart- ment contained 50 mM phosphate buffer (pH 7) with 100 mM NaCl as the electrolyte, and the anode com partment wastewater diluted with the electrolyte. The microbial fuel cell was operated in a batch mode. The anode content (25 ml) was replaced by diluted waste- water as fuel. The

Detection of COD by synchronous fluorescence spectra is also evident in the literature (Jin Hur, Bo-Mi Lee, Tae-Hwan Lee and Dae-Hee Park), as this method allows for rapid detection.

A novel rapid methodology for the determination of COD based on photoelectrochemical oxidative degration principle (PECOD) was proposed by Huijun Zhao, Dianlu Jiang, Shanqing

Zhang, Kylie Catterall and Richard John (Anal. Chem. 2004, 76, 1, 155-160). With this new method, the extend of degradation of dissolved organic matter in water sample was

measured simply by direct quantifying the extent of electron transfer. At a TiO, nanoporous

film electrode during an exhaustive photoelectron-catalytic degradation of organic matter in a thin layer photoelectrochemical cell. The PECOD method demonstrated in this work is a

direct method. The method in their principle measured theoretical COD values due to the extraordinary high oxidation, efficiency and accuracy of charge measurement. The other

methods for COD are comparable to the BOD as highlighted above. In all those cases a reliable on-line detection of both COD and BOD became a challenge as the models proposed were specific to the type of the water and revised models needed to be retrofitted when the water samples were changed.

Claims

THE CLAIMS DEFINING THE INVENTION ARE AS FOLLOWS:

1) A method for use in performing on-line measurement of COD and BOD for a water body, the method being performed using one or more electronic processing devices, the method including:

a) receiving, from one set of combined measurement of Ph, turbidity, DO, nitrite, nitrite and ammonium and subsequent calculations of nitrogen from nitrite, nitrate along with nitrogen from aluminium as separate calculations.

b) in response to sensory output in determining on-line COD and BOD for a water body:

i) obtaining, from a meteorological data source, meteorological data for a geographical region corresponding to the location of water body;

ii) determining at least one solar irradiance value for the prior time period based on at least some of the meteorological data; and

2) A method according to claim 1, wherein the method includes:

a) determining a plurality of solar irradiance values using different techniques;

b) determining a maximum solar irradiance value from the plurality of solar irradiance values; and

c) calculating the COD as functions of Ph, turbidity, DO, nitrate, nitrite and ammonium based at least in part on the maximum solar irradiance value and subsequently calculating BOD as functions of COD, Ph, turbidity, DO, nitrate, nitrite and ammonium based at least in part on the maximum solar irradiance value.

3) A method according to any one of claims 1 and 2, wherein at least one evapotranspiration value is determined using at least one of:

a) Solar irradiance value calculated using the meteorological data using linear regression method.

b) Solar irradiance value calculated using the meteorological data using a Angstrom Prescott-Page (APP) method; and c) Solar irradiance value calculated using the Artificial Neural Network Method d) and solar irradiance value calculated by the meteorological data source and included in the meteorological data.

4) A method according to any one of claims 1 to 3, wherein the method includes obtaining the meteorological data based on location data for the water body.

) A method according to any one of claims 1 to 4, wherein the method includes obtaining the meteorological data using at least one of:

a) file transfer protocol (FTP); and;

b) an application programming interface (API).

6) Apparatus for use in performing on-line COD and BOD estimation for a water body, the apparatus including:

a) at least one sensor measurement for EC, turbidity, nitrite and nitrate and ammonium;

Ph, turbidity, 100 Dissolved Oxygen, nitrate, nitrite and ammonium Jul 2020

from sensors

Receive Solar Irradiance data 110 Determine maximum solar irradiance from varied calculations

120 2020101377

Calculate Chemical Oxygen Demand

With COD as input along with inputs from 130 Step100 above Calculate Biological Oxygen Demand

Fig 1

203 204 2020101377

201 203

202

201

205

204

207 207

Fig. 2