SYSTEM AND METHOD FOR PREDICTING A PARAMETER ASSOCIATED WITH A WASTEWATER TREATMENT PROCESS

Abstract

A system for predicting an effluent parameter associated with a wastewater treatment process including a predictor module configured to receive a first input dataset comprising a plurality of wastewater inflow parameters to predict a biodegradable type of effluent wastewater; a mechanistic simulator configured to receive the biodegradable type of effluent wastewater and the plurality of wastewater inflow parameters as a second input dataset to predict the effluent parameter.

Claims

1. A system for predicting an effluent parameter associated with a wastewater treatment process including a predictor module configured to receive a first input dataset comprising a plurality of wastewater inflow parameters and predict a biodegradable group, the predictor module further configured to correlate the biodegradable group to the corresponding inflow parameters; a mechanistic simulator configured to receive the biodegradable group and the plurality of wastewater inflow parameters as a second input dataset to produce the effluent parameter as a simulated output.

2. The system of claim 1, further including a characterization module configured to correlate the plurality of wastewater inflow parameters with at least one industry type, the characterization module arranged in data communication with the predictor module.

3. The system of claim 2, wherein the characterization module includes a plurality of characterization databases, the plurality of characterization databases include at least one correlation table between the at least one biodegradable group and the corresponding inflow wastewater parameters, obtained from at least one specific industry.

4. The system of claim 1, wherein the predictor module is arranged to receive the plurality of wastewater inflow parameters from at least one physical sensor and at least one soft sensor.

5. The system of claim 1, wherein the predictor module includes a machine learning module configured to learn the correlation between the plurality of wastewater inflow parameters with at least one biodegradable type.

6. The system of claim 1, wherein the biodegradable group is one of the following groups: —(i.) biodegradable soluble, (ii.) non-biodegradable soluble, (iii.) slowly biodegradable colloidal, (iv.) slowly biodegradable particulates and (v.) non-biodegradable particulates.

7. The system of claim 1, wherein the plurality of wastewater inflow parameters include at least two of the following: —input chemical oxygen demand (COD), total organic carbon (TOC), solids content, ionic content, inorganic contaminant, organic contaminant.

8. The system of claim 1, wherein the mechanistic simulator includes an activated sludge model (ASM).

9. A method of predicting an effluent parameter associated with a wastewater process including the steps of: — (a.) receiving at a predictor module a first input dataset comprising a plurality of wastewater inflow parameters; (b.) correlating the plurality of wastewater inflow parameters with a biodegradable group; (c.) predicting the biodegradable group based on the correlation with the wastewater inflow parameters; (d.) combining the first input dataset and the biodegradable group of to form a second input dataset; and (e.) receiving at a mechanistic simulator the second input dataset to provide a simulated effluent parameter.

10. The method of claim 9, further including the step of receiving at the mechanistic simulator sludge characteristics of the wastewater treatment process as part of the second input dataset.

11. The method of claim 9, further including the step of correlating the plurality of wastewater inflow parameters with at least one industry type.

12. The method of claim 9, wherein the first input dataset is obtained from at least one physical sensor and at least one soft sensor.

13. The method of claim 9, wherein the predictor module includes a machine learning module configured to learn the correlation between the plurality of wastewater inflow parameters with at least one biodegradable group.

14. The method of claim 9, wherein the biodegradable group is one of the following groups: —(i.) a biodegradable soluble group, (ii.) a non-biodegradable soluble group, (iii.) a slowly biodegradable colloidal group, (iv.) a slowly biodegradable particulates group and (v.) a non-biodegradable particulates group.

15. The method of claim 9, wherein the plurality of wastewater inflow parameters include at least two of the following: —input chemical oxygen demand (COD), total organic carbon (TOC), solids content, ionic content, inorganic contaminant, organic contaminant.

16. A non-transitory computer readable medium containing executable software instructions thereon wherein when executed performs the method of predicting an effluent parameter associated with a wastewater process including the steps of: —receiving a first input dataset comprising a plurality of wastewater inflow parameters; correlating the plurality of wastewater inflow parameters with a biodegradable group; predicting the biodegradable group associated with the wastewater inflow parameters; combining the first input dataset and the biodegradable group to form a second input dataset; and receiving at a mechanistic simulator the second input dataset to provide a simulated effluent parameter.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In the figures, which illustrate, by way of example only, embodiments of the present disclosure,

[0036] FIG. 1: is a system diagram of a system for predicting a parameter associated with wastewater treatment according to some embodiments of the invention.

[0037] FIG. 2: is a flow chart illustrating a method of predicting a parameter associated with wastewater treatment according to some embodiments of the invention.

[0038] FIG. 3: illustrates possible inputs and outputs associated with a machine learning module. As an example, the machine learning will then predict the outputs associated with different groups of biodegradability of the wastewater.

[0039] FIG. 4a and FIG. 4b: show the result where the system is utilized for prediction of effluent chemical oxygen demand (COD) of two wastewater treatment processes.

DETAILED DESCRIPTION

[0040] Throughout this document, unless otherwise indicated to the contrary, the terms “comprising”, “consisting of”, “having” and the like, are to be construed as non-exhaustive, or in other words, as meaning “including, but not limited to”.

[0041] Furthermore, throughout the specification, unless the context requires otherwise, the word “include” or variations such as “includes” or “including” will be understood to imply the inclusion of a stated integer or group of integers but not the exclusion of any other integer or group of integers.

[0042] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as is commonly understood by a skilled person to which the subject matter herein belongs.

[0043] In accordance with an aspect there is a system for predicting one or more parameters associated with a wastewater treatment process including a predictor module and a mechanistic simulator, the predictor module operable to receive a plurality of wastewater inflow parameters as a first input dataset to predict a biodegradable type of effluent wastewater; and the mechanistic simulator operable to receive the biodegradable type of effluent wastewater and the plurality of wastewater inflow parameters as a second input dataset to predict an effluent parameter.

[0044] Depending on industry, the second input dataset may also include additional data such as a sludge characterization data.

[0045] It is appreciable that the plurality of wastewater inflow parameters or dataset can include two or more of the following list of dataset obtained over to specified time horizon: —chemical oxygen demand (COD), total organic carbon (TOC), solids content, ionic content, inorganic contaminant, organic contaminant. Sensors (both physical and soft sensors) may be positioned around one or more wastewater treatment plants to facilitate the data collection process.

[0046] FIG. 1 shows a system 10 for predicting one or more parameters associated with a wastewater treatment process. The system 10 includes a characterization module 12, a predictor module 14, and a mechanistic simulator 16. Each of the modules 12, 14 and 16 may include one or more computer processors, one or more centralized or distributed computer networks, such computer networks may include a cloud network. The computer networks may be arranged in data communication with industry-specific wastewater treatment plants to receive the required dataset, via physical or software (soft) sensors deployed at desired locations on the wastewater treatment plants.

[0047] The characterization module 12 may include hardware components such as server computer(s) arranged in a distributed or non-distributed configuration to implement characterization databases. The hardware components may be supplemented by a database management system configured to compile one or more industry-specific characteristic databases. In some embodiments, such industry-specific characteristic databases may include one or more correlation table between biodegradability data of effluent wastewater obtained from at least one specific industry and its corresponding inflow wastewater data or parameters. In some embodiments, the industry-specific characteristic databases may include analysis modules to correlate one or more dataset with an industry. Such analysis modules may include an expert rule database, a fuzzy logic system, or any other artificial intelligence module.

[0048] In some embodiments, the at least one specific industry includes one of the following: —petrochemical industry, pharmaceutical industry, pulp and paper industry, sewage treatment industry etc.

[0049] It is appreciable that for different industries, different characteristic databases are required, as the characteristic and constituents of the wastewater can differ greatly between different industries. Hence, a database for petrochemical industry may not be applicable or suitable to the pharmaceutical industry. Inflow parameters such as chemical oxygen demand (COD), carbon content, nitrogenous content, dissolved solids, ion concentrations, and chemical composition can be included into the database.

[0050] Over time, it is appreciable that different characteristic databases corresponding to different industries are generated and populated. These data are grouped according to the type of industry for subsequent learning and operation of the predictor module 14. The characterization module 12 can also be updated as and when new data is received by the system 10.

[0051] In some embodiments, the biodegradability group/classification of different samples of wastewater can be based on biodegradability data obtained from a bioassay method (e.g. respirometry), a simple jar test (lab scale test) or historical data of the specific wastewater treatment plant. In some embodiments, multiple classification methods may be used and the average results, or weighted average results across different classification methods obtained.

[0052] Once the characteristic databases are generated, a selected dataset relevant to an industry (also referred to as a first input dataset), can be fed into the predictor module 14. The predictor module 14 is configured to learn based on the first input dataset. The predictor module 14 may include one or more machine learning algorithms, such as an artificial neural network and/or decision tree regression to learn or correlate the biodegradability data to its corresponding inflow parameter data. The machine learning algorithm then generates an output according to the inflow parameter data. The output can be the biodegradability data corresponding to the inflow parameter data. In some embodiments, the biodegradability data may be grouped as follows: —(i.) biodegradable soluble, (ii.) non-biodegradable soluble, (iii.) slowly biodegradable colloidal, (iv.) slowly biodegradable particulates and (v.) non-biodegradable particulates.

[0053] In some embodiments, further sub-groups from the above five groups may be formed. For example, in the case where the inflow parameter is a total organic carbon (tCOD) parameter of the wastewater, the biodegradable soluble group can be further sub-divided into readily biodegradable and soluble inert in addition to slowly biodegradable colloidal.

[0054] The non-biodegradable particulates group may include particulate inert, which is an insoluble portion of COD unaffected by biological activity and is thus retained in the system without being biodegraded. The particulate inert parameter affects mixed liquor parameter and can be estimated by comparison with real plant data, which can be based on simulation studies to real plant data and/or trial and error studies.

[0055] In the case of a non-biodegradable soluble COD, it may be referred to as inert soluble COD. The inert soluble COD is a soluble portion of COD which is unaffected by biological activity. The inert soluble COD parameter affects effluent COD concentration/sludge growth, and may be determined by direct plant/lab scale reactor measurement. The inert soluble COD may be equivalent or correlated to a filtered effluent COD. The inert soluble COD parameter may be obtained via historical data.

[0056] Slowly biodegradable COD (sbCOD) may refer to colloidal and particulate materials, which may correspond to extracellular enzymatic breakdown prior to adsorption and consumption. In some embodiments, the sbCOD parameter may be expressed mathematically in equation (1) as follows: —

sbCOD=COD.sub.total−COD.sub.readily biodegradable−COD.sub.soluble inert−COD.sub.particulate inert  (1)

[0057] In relation to readily biodegradable COD (rbCOD), the rbCOD group refers to small soluble molecules that can be readily adsorbed and consumed by microbes. The rbCOD parameter can be derived or obtained from a bioassay method mathematically expressed as equation (2).

[00001]$\begin{array}{cc}\mathrm{rbCOD}=\frac{\int r_{o2}-\int r_{O2,\mathrm{Baseline}\mathrm{respiration}}}{1-Y_H}{Y}_H=1-\frac{\mathrm{CO}}{{\mathrm{COD}}_{\mathrm{sodium}\mathrm{acetate}}}& \left(2\right)\end{array}$

Wherein the various parameters include r.sub.o2 refers to respiration rate, CO refers to consumed oxygen.

[0058] In general, organics in wastewater can be differentiated into biodegradability and solubility. These organics affects the overall performance of the wastewater treatment.

[0059] It is appreciable that in some embodiments, the machine learning module may be based on either a supervised learning or unsupervised learning.

[0060] Different machine learning model may be used, depending on the suitability of such machine learning model with the different industries. For example, neural networks typically used for machine learning may be combined with other algorithms for tuning of the applicable weights of each neuron in the neural network.

[0061] Once the machine learning model is trained and validated, a new inflow wastewater (typically not a data entry within the classification database) may be utilized to predict its biodegradability group according to FIG. 3. The predicted biodegradability group, together with the wastewater inflow parameter(s) and sludge characterization data, if any, is combined as a second input dataset and fed into the mechanistic simulator 16. The mechanistic simulator 16 is used to mimic a biochemical processes in wastewater treatment to predict an outflow or effluent parameter.

[0062] In some embodiments, the mechanistic simulator 16 may be an Activated Sludge Model (ASM).

[0063] The mechanistic simulator 16 is operate to simulate the outflow parameter so as to predict and/or determine a final output effluent wastewater parameter. Effluent wastewater parameters such as chemical oxygen demand COD, nitrogenous content, mixed liquor volatile suspended solids can be predicted as the final output effluent wastewater parameter. The overall process of the model is illustrated in FIG. 1.

[0064] According to another aspect of the invention/disclosure there is a method for predicting an effluent parameter associated with a wastewater process including the steps of: —(a.) receiving at a predictor module a first input dataset comprising a plurality of wastewater inflow parameters; (b.) predicting a biodegradable group associated with the effluent wastewater; (c.) combining the first input dataset and the biodegradable type of effluent wastewater to form a second input dataset; and (d.) receiving at a mechanistic simulator the second input dataset to provide a simulated effluent parameter.

[0065] FIG. 2 is a flow chart illustrating a method of predicting a parameter associated with a wastewater process 200. The method 200 suitably works with the system 10 and may be used an effluent wastewater treatment parameter such as the COD of an aerobic wastewater treatment plant. The method 200 includes the steps of: —receiving at a predictor module a first input dataset comprising a plurality of wastewater inflow parameters (step s202); predicting a biodegradable type of effluent wastewater (step s204); combining the first input dataset and the predicted biodegradable type of effluent wastewater to form a second input dataset (step s206); and receiving at a mechanistic simulator 16 the second input dataset to predict a resultant effluent parameter (step s208).

[0066] An example of the first input dataset and biodegradable type of effluent wastewater may be illustrated as shown in Table 1. Table 1 shows the input parameters in the form of COD, soluble COD (sCOD), Dissolved Organic Carbon (DOC), Total nitrogen (TN), Bromine (Br), Total Dissolved Solids (TDS), and the predicted inert sCOD fraction which is output from the predictor module 14.

TABLE-US-00001 TABLE 1 Example inputs and output parameter (sCOD) obtained from the predictor module 14. Predicted Inert Days COD SCOD DOC TN Br TDS sCOD fraction 0 111.3121 79.525 80.465 19.61 0 2910 0.485859217 10 361.25 293.5533 104.7 13.31266 941.574 8720 0.362281253 22 428.7667 372.3667 90.08667 16.024 1607.567 5965 0.348598022 35 291.6 274.77 66.37 12.059 471.096 3920 0.423322733 43 249.54 178.6913 35.06 33.7 64.264 3633.33 0.245924686 55 499.77 472.31 67.94 25.52 1221.8 5675 0.272245125 68 232.225 155.895 39.73 28.3 66.525 2880 0.246497874 77 593.217 479.2473 83.4625 10.17125 1159.793 2000 0.259749755 92 447.51 393.06 58.47 12.1305 1386.227 2830 0.331460455 101 125.22 95.08 25.7 23.29 40 1600 0.413202803 115 533.58 467.33 63.12 27.685 1007.663 5045 0.23644336 126 69.03 58.7 15.44 30.69 12.78825 4126.7 0.21303065 136 79.37 78.34 24.84 29.96 5 2779.167 0.375865261 149 359.29 310.58 38.3 25.76 1338.575 4705 0.246553386 162 509.02 428.665 55.91 48.1533 1227.612 3253 0.21167823 168 418.82 337.97 36.1 38.29 1245.62 3540 0.202502789

[0067] In some embodiments, the method 200 also include the step of receiving sludge characterization data as part of the second input dataset (step s210) after step s206. The sludge characterization data may then be fed into the mechanistic simulator (step s208).

[0068] In some embodiments, the first input dataset may be obtained from physical and/or soft sensors displaced in or on suitable locations of an aerobic wastewater treatment plant. The aerobic wastewater treatment plant may be arranged upstream of subsequent wastewater treatment processes such as anaerobic wastewater treatment (not shown).

[0069] In some embodiments, the method 200 can be installed as executable software codes in a non-transitory computer readable medium. Such computer readable medium may be in the form of memory units, such as random access memory (RAM), read only memory (ROM), hard disks, application specific integrated circuit chip (ASIC), and/or field-programmable gate array (FPGA). In some embodiments, the non-transitory computer readable medium can be integrated with the physical and/or soft sensors for detecting inflow and outflow parameters.

[0070] FIG. 3 shows an example of the predictor module 14 in the form of a machine learning or artificial intelligence module 300. The machine learning module 300 is configured to receive an input set 302 to generate an output or output set 304.

[0071] The input set 302 may include a list of wastewater inflow data such as chemical oxygen demand 312, total organic carbon 314, solids content 316, ionic content 318, inorganic contaminant 320, and organic contaminant 322. The output 304 generated may be in the form of the following biodegradable classifications or groups, including, but not limited to, a soluble biodegradable group, a soluble non-biodegradable group, a slowly biodegradables group, and a particulates non-biodegradable group.

[0072] FIG. 4a is a result showing the application of the system 10 and method 200 to the prediction of an output effluent COD (in milligrams per litre mg/L) of the effluent wastewater in an aerobic wastewater treatment process. The graph in FIG. 4a plots the COD concentration of influent wastewater and treated effluent wastewater. The predictor algorithm based on a neural work was utilized. The results were compared with the actual measured effluent discharge and demonstrated reasonable accuracy. The inflow COD level is also plotted to demonstrate the efficacy and effectiveness of the aerobic wastewater treatment process in reducing the amount of COD level after wastewater treatment.

[0073] FIG. 4b is a result showing the application of the system 10 and method 200 to the prediction of an output effluent COD (in mg/L) of the effluent wastewater in another aerobic wastewater treatment process. The comparison shows a good tracking of the simulated effluent COD with respect to the actual measured effluent COD having an average relative deviation of less than 7% and normalised root mean square error of less than 0.1.

[0074] The use of machine learning to correlate the inflow parameter to biodegradability of inflow wastewater is advantageous in that it results in significant savings in labour and time required to determine a biodegradability of inflow wastewater. In addition, as the system 10 takes into account various industries via the characterization module, new wastewater stream can be easily incorporated and biodegradability correlated by training of the neural network. In addition, the system 10 is capable of predicting one or more outflow parameter regardless of flowrate of inflow wastewater streams. This is contrasted to existing systems where new wastewater stream are required to undergo treatability study to determine the associated industry. With the characterization module 12, any new wastewater stream may be quickly characterized or classified into an industry source.

[0075] In some embodiments, the obtainment of the first input dataset, second input dataset may be achieved remotely or separately from the computer systems utilized for processing them.

[0076] In some embodiments, two or more of the characterization module 12, the predictor module 14, and the mechanistic simulator 16 may be integrated in a single processor, computer, or server. In these embodiments, which may form another aspect, there may include a non-transitory computer readable medium containing executable software instructions thereon wherein when executed performs a method of predicting an effluent parameter associated with a wastewater process including the steps of: —receiving a first input dataset comprising a plurality of wastewater inflow parameters; predicting a biodegradable group associated with the effluent wastewater; combining the first input dataset and the biodegradable type of effluent wastewater to form a second input dataset; and receiving at a mechanistic simulator the second input dataset to provide a simulated effluent parameter.

[0077] It should be appreciated by the person skilled in the art that the above invention is not limited to the embodiment described. In particular, various embodiments may be applied to anaerobic wastewater treatment. It is appreciable that modifications and improvements may be made without departing from the scope of the present invention.

[0078] It should be further appreciated by the person skilled in the art that one or more of the above modifications or improvements, not being mutually exclusive, may be further combined to form yet further embodiments of the present invention.