Detailed Action
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is responsive to the communications filed 2/21/2025. As per the claims filed 10/23/2023:
Claims 12-13, 19-20 were amended.
No claims were added/cancelled.
Claims 1-20 are pending.
Claim(s) 1, 12, 13, 14, 19, 20 is/are independent claim(s).
Note Regarding Prior Art
Examiner cites particular columns, paragraphs, figures and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
Note Regarding AIA Status
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-2, 4, 11-13 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Segev E. Wasserkrug et al (US PG Pub No US2017/0061309; Published: 03/02/2017)(hereinafter: Wasserkrug).
Note: Wasserkrug was cited in the IDS filed 10/23/2023.
Claim 1:
As per independent claim 1, Wasserkrug discloses a computer implemented method comprising:
processing, using at least one processor, one or more measurements received from one or more sensors of at least one wastewater treatment facility communicatively coupled to the at least one processor [[0018] there is provided a system for wastewater treatment. The system comprises an interface for receiving: influent readings from a plurality of sensors located along at least one influent stream of a wastewater treatment unit, effluent readings from a plurality of sensors located along at least one effluent stream of the wastewater treatment unit,]] and storing the processed one or more measurements in at least one storage location [[0034] storage medium, [0056] The system 199 further includes or connected to a memory 212 hosting an operational optimization module 211 executed by the processors 203 for calculating recommendations or control instructions(s), for example as described below and depicted in FIG. 1.] see [0018] data values received from sensors must be stored at least temporarily in order to calculate more values;
determining, using the at least one processor, based on the processed one or more measurements, one or more first times [[0067] calculating an operational recommendation(s) for improving the treatment process of the WWTU, for example by taking a decision based on the outcome of estimating a state in the state space according to a combination of values of the reduced set of variables acquired in 101-105. An improvement may be a cost reduction, or effluent quality improvement or both. The operational recommendations are optionally real time instructions to controllers of the equipment of the monitored WWTU, for instance a WWTP. Examples of control instructions are a DO set point instruction, an internal recycle (IR) pump rate change instructions, a WAS flow pump rate instructions and/or the like. [0083] Block 303 depicts a real time control loop where readings and data about the state of the WWTU is received for calculation operational recommendations for the WWTU. Optionally, the recommendations are time-based as derived from a time dependent state]. and
controlling, using the at least one processor, operation of the at least one wastewater treatment facility using the determined one or more first times [[0004], [0067] The operational recommendations are optionally real time instructions to controllers of the equipment of the monitored WWTU, for instance a WWTP. Examples of control instructions are a DO set point instruction, an internal recycle (IR) pump rate change instructions, a WAS flow pump rate instructions and/or the like. [0083] The operational recommendations are used for automatically calculating instructions to the corresponding controllers of the equipment of the WWTU with respect to the inputted policy. Optionally, the optimization problem is minimizing a total cost while complying with the regulatory requirements, see for example above described equation 4.]
Claim 2:
As per claim 2, which depends on claim 1, Wasserkrug discloses wherein the one or more sensors monitor and measure at least one of: one or more operational parameters associated with operation of at least one wastewater treatment facility, one or more external parameters associated with an environment of the at least one wastewater treatment facility, one or more power parameters associated with a power consumption by the at least one wastewater treatment facility for operating of one or more processes of the at least one wastewater treatment facility, one or more power parameters associated with power generation by the at least one wastewater treatment facility from one or more on-site electricity generation resources, one or more parameters associated with at least one of direct and indirect energy storage by the at least one wastewater treatment facility, and any combination thereof. [[0055] The system 199 further includes an interface 106 for receiving readings of sensor(s) located to monitor a value of influent flow variable, for instance sensor(s) 201 and sensor(s) which are located to monitor a value of total nitrogen at effluent variable and a value of a total phosphorus at effluent variable, for instance sensor(s) 202],
Claim 4:
As per claim 4, which depends on claim 2, Wasserkrug discloses wherein the one or more operational parameters include at least one of the following: a flow of wastewater into the at least one wastewater treatment facility, a flow of processed wastewater between one or more unit processes of the at least one wastewater treatment facility; a flow of primary solids out of one or more primary treatment processes of the at least one wastewater facility; a flow of waste activated sludge out of one or more secondary processes of the at least one wastewater treatment facility; a flow of at least one of fats, oils and greases received by the at least one wastewater treatment facility and injected into one or more anaerobic digesters of the at least one wastewater treatment facility; a flow of food and/or organic wastes received by the at least one wastewater treatment facility and injected into one or more anaerobic digesters of the at least one wastewater treatment facility; a concentration of total and volatile solids, chemical oxygen demand, total nitrogen, and ammonia in flows of primary solids out of one or more primary treatment processes of the at least one wastewater facility; a concentration of total and volatile solids, chemical oxygen demand, total nitrogen, and ammonia in flows of waste activated sludge of one or more secondary processes of the at least one wastewater treatment facility; a concentration of total and volatile solids, chemical oxygen demand, total nitrogen, and ammonia in flows of at least one of fats, oils and greases received by the at least one wastewater treatment facility; a concentration of total and volatile solids, chemical oxygen demand, total nitrogen, and ammonia in flows of food and/or organic wastes received by the at least one wastewater treatment facility; a concentration of total and volatile solids, chemical oxygen demand, total nitrogen, and ammonia in the one or more anaerobic digesters of the at least one wastewater treatment facility; an amount of raw wastewater stored at the at least one wastewater treatment facility; an amount of processed wastewater stored at the at least one wastewater treatment facility; a temperature associated with operating one or more processes of the at least one wastewater treatment facility; a temperature associated with operating one or more on-site co-generation units of the at least one wastewater treatment facility to generate at least one of heat and electricity; a pressure associated with operating one or more processes of the at least one wastewater treatment facility; and any combination thereof [[0046] influent flow variable—for example values measured by sensors at the input of liquid line or at the sludge line when centrifuge is on or off, [0055] The system 199 further includes an interface 106 for receiving readings of sensor(s) located to monitor a value of influent flow variable [0004] receiving effluent readings from a plurality of effluent sensors located along at least one effluent stream of the wastewater treatment unit,].
Claim 11:
As per claim 11, which depends on claim 1, Wasserkrug discloses, wherein the controlling includes executing, using the at least one processor, the one or more processes at the determined one or more times [[0067] This allows, as shown at 107, calculating an operational recommendation(s) for improving the treatment process of the WWTU, for example by taking a decision based on the outcome of estimating a state in the state space according to a combination of values of the reduced set of variables acquired in 101-105. An improvement may be a cost reduction, or effluent quality improvement or both].
Claim 12:
As per independent claim 12, it recites a system comprising: at least one programmable processor; and a non-transitory machine-readable medium storing instructions that, when executed by the at least one programmable processor, cause the at least one programmable processor to perform the method of claim 1, therefore it is rejected under the same rationale as claim 1 above.
Claim 13:
As per independent claim 13, it recites a computer program product comprising a non-transitory machine-readable medium storing instructions that, when executed by at least one programmable processor, cause the at least one programmable processor to perform the method of claim 1, therefore it is rejected under the same rationale as claim 1 above.
Claim(s) 14-16, 19-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Tuoyuan Cheng et al (Forecasting of Wastewater Treatment Plant Key
Features Using Deep Learning-Based Models: A Case Study; Published: 10/21/2020)(hereinafter: Cheng).
Claim 14:
As per independent claim 14, Cheng discloses a computer implemented method, comprising: processing, using at least one processor, one or more measurements received from one or more sensors of at least one wastewater treatment facility communicatively coupled to the at least one processor, and storing the processed one or more measurements in at least one storage location [[page 2, Introduction] Therefore, sensors are involved to monitor those parameters and further indicate the status of treatment processes from the plant level to the unit process scale, which is offering voluminous amount of environmental multivariate time-series data [3]]. Processor and storage devices are implicit since data is collected and analyzed.
training, using the at least one processor, at least one model using at least one or more parameters associated with operation of one or more processes of the at least one wastewater treatment facility, the one or more processes being associated with the one or more processed measurements[[abstract]The accurate forecast of wastewater treatment plant (WWTP) key features can comprehend and predict the plant behavior to support process design and controls, improve system reliability, reduce operational costs, and endorse optimization of overall performances. Deep learning technologies as proven data-driven soft-sensors should be developed for WWTP applications to tackle the process of non-linearity and the dynamic nature of environmental data. This study adopts deep learning-based models as soft-sensors to forecast WWTP key features, such as influent flow, influent temperature, influent biochemical oxygen demand (BOD), effluent chloride, effluent BOD, and power consumption [section F. the proposed forecasting workflow] This study is about modeling, and forecasting of WWTP key features using deep learning-based models. The schematic presentation of the proposed forecasting framework is depicted in Figure 5. This forecasting framework is performed in two parts: training and online forecasting. First, we smooth the WWTP key features using the EWMA filter to reduce the effect of noise measurements and outliers. Then, we split the smoothed time-series data into a training sub-data and a testing sub-data. We use the training data to build the investigated deep learning models.]
determining, using the trained model, the one or more times during which the at least one wastewater treatment facility is configured to at least one of: consume energy for operating of the one or more processes, produce energy for operating of the one or more processes, and store energy as a result of operating of the one or more processes [[page 3] we would build deep learning forecasting models as soft-sensors for wastewater treatment key features, including influent flow, influent temperature, influent biochemical oxygen demand (BOD) concentration, effluent
chloride concentration, effluent BOD concentration, and power consumption during the treatment… They are closely monitored and controlled, which is contributing predominantly to energy consumption and operational expenditures]
managing, using the at least one processor, operation of the one or more processes of at least one wastewater treatment facility based on the determining [[page 3] we would build deep learning forecasting models as soft-sensors for wastewater treatment key features, including influent flow, influent temperature, influent biochemical oxygen demand (BOD) concentration, effluent
chloride concentration, effluent BOD concentration, and power consumption during the treatment… They are closely monitored and controlled, which is contributing predominantly to energy consumption and operational expenditures].
Claim 15:
As per claim 15, which depends on claim 14, Cheng discloses wherein the managing includes upgrading operation of the one or processes of the at least one wastewater treatment facility [[conclusion ] Forecasting of WWTP key features can comprehend and predict the plant behavior to support process design and controls, improve system reliability, reduce operational costs and endorse optimization of overall performances. The trained model can be further incorporated into optimization scenarios to offer data-driven strategies for daily optimal WWTP operation, pollutant removal, and cost reduction purposes]. The result of the forecasting is to upgrade operation of the WWTP in order to improve it or make it more efficient.
Claim 16:
As per claim 16, which depends on claim 14, Cheng discloses wherein the managing includes controlling operation of the one or processes of the at least one wastewater treatment facility [[conclusion ] Forecasting of WWTP key features can comprehend and predict the plant behavior to support process design and controls, improve system reliability, reduce operational costs and endorse optimization of overall performances. The trained model can be further incorporated into optimization scenarios to offer data-driven strategies for daily optimal WWTP operation, pollutant removal, and cost reduction purposes]. The result of the forecasting is to upgrade operation of the WWTP in order to improve it or make it more efficient.
Claim 19:
As per independent claim 19, it recites a system comprising: at least one programmable processor; and a non-transitory machine-readable medium storing instructions that, when executed by the at least one programmable processor, cause the at least one programmable processor to perform the method of claim 14, therefore it is rejected under the same rationale as claim 14 above.
Claim 20:
As per independent claim 20, it recites a computer program product comprising a non-transitory machine-readable medium storing instructions that, when executed by at least one programmable processor, cause the at least one programmable processor to perform the method of claim 14, therefore it is rejected under the same rationale as claim 14 above.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 5, 6, 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wasserkrug as applied to claim 1 above in view of Prabhu Raja Subbarayalu et al (US PG Pub No. US2018/0121889; Published: 05/03/2018)(hereinafter: Raja).
Claim 5:
As per claim 5, which depends on claim 2, Wasserkrug failed to disclose the limitations of claim 5.
Raja, in the same field of waste water treatment discloses wherein the one or more external parameters include at least one of the following: an external temperature corresponding to a temperature of air outside of the at least one wastewater treatment facility, an internal temperature corresponding a temperature of air at the at least one wastewater treatment facility, an external humidity corresponding a humidity of air outside of the at least one wastewater treatment facility, an internal humidity corresponding a humidity of air at the at least one wastewater treatment facility, an external precipitation amount corresponding to the local precipitation at a location of the at least one wastewater treatment facility, and any combination thereof. [[0041] the dynamic data 119 are the operational data which vary periodically based on one or more real-time operations and/changes happening at the waste water treatment plant. The dynamic data 119 may be categorized into two groups namely, internal dynamic data 119a and external dynamic data 119b, as shown in FIG. 2B. As an example, the internal dynamic data 119a may include, without limiting to… energy consumption values, amount of energy generated, voltage and current values, valve positions and volume of sludge As an example, the external dynamic data 119b may include, weather related data such as, storm water data, rainfall data, relative humidity, air temperature, air and/or atmospheric pressure and other data like tariff structures from energy distributors etc.].
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the sensors and sensor data of Wasserkrug to include one or more external parameters including air temperature as disclosed by Raja. The motivation for doing so would have been to adapt settings based on this information, since the control system may adjust the water treatment system because of the increase load caused by sunlight and/or temperature.
Claim 6:
As per claim 6, which depends on claim 2, Wasserkrug failed to disclose the limitations of claim 6.
Raja, in the same field of waste water treatment discloses wherein the one or more power consumption parameters include at least one of the following: an amount of electricity consumed for operating one or more treatment processes and/or auxiliary processes of the at least one wastewater treatment facility, an amount of electricity consumed for operating one or more direct and/or indirect energy storage systems of the at least one wastewater treatment facility, an amount of electricity consumed to operate a heat pump of the at least one wastewater treatment facility, and any combination thereof [[0041] the dynamic data 119 are the operational data which vary periodically based on one or more real-time operations and/changes happening at the waste water treatment plant. The dynamic data 119 may be categorized into two groups namely, internal dynamic data 119a and external dynamic data 119b, as shown in FIG. 2B. As an example, the internal dynamic data 119a may include, without limiting to… energy consumption values, amount of energy generated, voltage and current values, valve positions and volume of sludge ].
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the sensors and sensor data of Wasserkrug to include one more power consumption parameters such as an amount of electricity consumed for operating one or more treatment processes as disclosed by Raja. The motivation for doing so would have been identifying an optimal range for operating each of the operational parameters in order to optimize power consumption for the waste water treatment plant(0004).
Claim 10:
As per claim 10, which depends on claim 1, Wasserkrug failed to disclose the limitations of claim 10.
Raja, in the same field of waste water treatment discloses wherein the determining includes determining the one or more times to reduce a power consumption by the at least one wastewater treatment facility [[0028] The one or more inflection points indicate an optimal range of operational data in which each of the one or more operational parameters have to be operated in order to achieve the desired targets and to reduce overall power consumption in the waste water treatment plant. [0046] The real-time threshold values are 125 derived for each of the one or more operational parameters considering the degree of influence of the operational parameters to meet the final objective (quality parameter, power consumption reduction).]
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the times determination of Wasserkrug to include determining the one or more times to reduce a power consumption by the at least one wastewater treatment facility as disclosed by Raja. The motivation for doing so would have been identifying an optimal range for operating each of the operational parameters in order to optimize power consumption for the waste water treatment plant(0004).
Claim(s) 3, 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wasserkrug as applied to claim 1 above in view of Bruno Delahaye et al (US PG Pub No. US 2021/0398140; Priority: 12/13/2018)(hereinafter: Delahaye).
Claim 3:
As per claim 3, which depends on claim 2, Wasserkrug failed to disclose the limitations of claim 3.
Delahaye, in the same field of waste water management discloses wherein the one or more first times include one or more times during which the at least one wastewater treatment facility is configured to at least one of: consume energy for operating of the one or more processes, produce energy for operating of the one or more processes, and directly and/or indirectly store one or more co- dependent types of energy as a result of operating of one or more operationally co-dependent processes, and any combination thereof. [[0018] the collected data related to at least one exploitation scenario comprise data representing a set of on-site processes, the computing including, for at least one indicator value, calculating a respective direct impact of the set of on-site processes and a respective indirect impact of the set of on-site processes; [0019] the data representing the set of on-site processes comprise on-site energy consumption data, on-site].
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the data collection of Wasserkrug to include data about one or more times during which the at least one wastewater treatment facility is configured to consume energy for operating of the one or more processes as disclosed by Delahaye. The motivation for doing so would have been to calculate energy consumption/production over time, resulting in decisions made to offset or efficiently manage energy consumption.
Claim 7:
As per claim 7, which depends on claim 2, Wasserkrug failed to disclose the limitations of claim 7.
Delahaye, in the same field of waste water management discloses wherein the one or more power generation parameters include at least one of the following: a flow of biogas generated by the at least one wastewater treatment facility, a heating value of biogas generated by the at least one wastewater treatment facility, a flow of natural gas consumed by the at least one wastewater treatment facility for operation of one or more processes, an amount of electricity generated by an on-site combustion turbine and/or internal combustion engine of the at least one wastewater treatment facility, an energy generated by an on-site combustion turbine and/or internal combustion engine operating for combined heat and power of the at least one wastewater treatment facility, an amount of electricity generated by an array of on-site solar photovoltaic panels of the at least one wastewater treatment facility, an amount of electricity generated by an on-site wind turbine of the at least one wastewater treatment facility, an amount of electricity generated by a microbial fuel cell of the at least one wastewater treatment facility, an amount of electricity generated by a biogas fuel cell of the at least one wastewater treatment facility, and any combination thereof [[0155] For example, when calculating the energy carbon footprint, the user first collects data on: the consumption/production of electricity on the site, consumption/production of natural gas on the site and/or consumption/production of fuel on the site. In addition, biogas valorization data on-site and/or off-site may also be collected. An indicator corresponding to the carbon footprint of energy is then automatically calculated based on the collected data].
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the data collection of Wasserkrug to collect one or more power generation parameters including a flow of natural gas consumed by the at least one wastewater treatment facility for operation of one or more processes as disclosed by Delahaye. The motivation for doing so would have been to calculate the carbon footprint of energy based on the collected data, resulting in decisions made to offset or efficiently manage energy consumption.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wasserkrug as applied to claim 1 above in view of Jenji Mayuzumi (US PG Pub No. 2010/0200476; Published: 08/12/2010)(hereinafter: Mayuzumi).
Claim 8:
As per claim 8, which depends on claim 2, Wasserkrug failed to disclose the limitations of claim 8.
Mayuzumi, in the same field of waste water management discloses wherein the one or more direct or indirect energy storage parameters include at least one of the following: an amount of energy stored in an on-site battery of the at least one wastewater treatment facility, an amount of electric power flowing into or out of an on-site battery of the at least one wastewater treatment facility, a state of health of an on-site battery of the at least one wastewater treatment facility, a state of charge of a non-site battery of the at least one wastewater treatment facility, a volume stored in a raw wastewater storage tank of the at least one wastewater treatment facility, a volume stored in a primary effluent storage tank of the at least one wastewater treatment facility, a volume stored in a secondary effluent storage tank of the at least one wastewater treatment facility, a volume stored in a low pressure membrane biogas holder of the at least one wastewater treatment facility, a volume stored in a medium pressure biogas storage tank of the at least one wastewater treatment facility, a volume stored in liquefied biogas storage tank of the at least one wastewater treatment facility, a volume stored in an anaerobic digester's headspace and available piping volume capacity preceding a co- generation fuel inlet valve of the at least one wastewater treatment facility, an oxygen volume or pressure stored of an oxygen tank of the at least one wastewater treatment facility, a volume of flows into and out of all of the storage tanks specified above of the at least one wastewater treatment facility, a dissolved oxygen concentration in an activated sludge basin, trickling filter, or aerobic membrane bioreactor of the at least one wastewater treatment facility, an amount of oxygen flowing into an activated sludge basin, trickling filter, or aerobic membrane bioreactor of the at least one wastewater treatment facility, a volume and a total and volatile solids concentration stored in a sludge holding tank, a volume and a total and volatile solids concentration stored in a fats oils and greases holding tank, a volume and a total and volatile solids concentration stored in a food waste tank of the at least one wastewater treatment facility, a volume and a total and volatile solids concentration stored in an organic wastes tank of the at least one wastewater treatment facility, a flow into or out of any of the biosolids holding tanks of the at least one wastewater treatment facility, and any combination thereof. [[0011] The wastewater treatment apparatus of the present disclosure may also be provided with a water volume sensor for monitoring the water volume in the wastewater tank so that the wastewater in the tank is supplied to the combustion pot when it is determined by this water volume sensor that the water volume in the wastewater tank has reached a prescribed level.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the data collection of Wasserkrug to monitor one or more direct or indirect energy storage parameters including a volume stored in a raw wastewater storage tank of the at least one wastewater treatment facility as disclosed by Mayuzumi. The motivation for doing so would have been to monitor tank levels and guarantee wastewater in the wastewater tank never overflows, and the vicinity of the apparatus can be kept clean (0012).
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wasserkrug as applied to claim 1 above in view of Cheng.
Claim 9:
As per claim 9, which depends on claim 2, Wasserkrug failed to disclose the limitations of claim 9.
Cheng, in the same field of wastewater treatment and model based forecasting discloses
wherein the determining includes training at least one model using at least one of: the one or more operational parameters, the one or more external parameters, the one or more power consumption parameters, the one or more power generation parameters, the one or more direct or indirect energy storage parameters, and any combination thereof [[abstract]The accurate forecast of wastewater treatment plant (WWTP) key features can comprehend and predict the plant behavior to support process design and controls, improve system reliability, reduce operational costs, and endorse optimization of overall performances. Deep learning technologies as proven data-driven soft-sensors should be developed for WWTP applications to tackle the process of non-linearity and the dynamic nature of environmental data. This study adopts deep learning-based models as soft-sensors to forecast WWTP key features, such as influent flow, influent temperature, influent biochemical oxygen demand (BOD), effluent chloride, effluent BOD, and power consumption [section F. the proposed forecasting workflow] This study is about modeling, and forecasting of WWTP key features using deep learning-based models. The schematic presentation of the proposed forecasting framework is depicted in Figure 5. This forecasting framework is performed in two parts: training and online forecasting. First, we smooth the WWTP key features using the EWMA filter to reduce the effect of noise measurements and outliers. Then, we split the smoothed time-series data into a training sub-data and a testing sub-data. We use the training data to build the investigated deep learning models.]
forecasting, using the trained model, the one or more first times during which the at least one wastewater treatment facility is configured to at least one of: consume energy for operating of the one or more processes, produce energy for operating of the one or more processes, and store energy as a result of operating of the one or more processes [[page 3] we would build deep learning forecasting models as soft-sensors for wastewater treatment key features, including influent flow, influent temperature, influent biochemical oxygen demand (BOD) concentration, effluent
chloride concentration, effluent BOD concentration, and power consumption during the treatment… They are closely monitored and controlled, which is contributing predominantly to energy consumption and operational expenditures].
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the data collection and data analysis teachings of Wasserkrug to train at least one model using the one or more operational parameters and to forecast, using the trained model, the one or more first times during which the at least one wastewater treatment facility is configured to consume energy for operating of the one or more processes as disclosed by Cheng. The motivation for doing so would have been to improve system reliability, reduce operational costs and endorse optimization of overall performances (conclusion).
Allowable Subject Matter
Claims 17-18 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: none of the cited prior art of record discloses the subject matter of claim 17. More specifically, determining using the at least one processor, one or more optimal future direct and/or indirect energy storage upgrades and one or more energy generation upgrades for implementation at the at least one wastewater treatment facility in accordance with the determined one or more times. The prior art of record, including Cheng as cited above discloses developing machine learning models to increase efficiency of WWTPs but do not disclose a processor determining one or more optimal future direct and/or indirect energy storage upgrades and one or more energy generation upgrades for implementation at the at least one wastewater treatment facility.
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HOWARD CORTES whose telephone number is (571)270-1383. The examiner can normally be reached on M-F, 8:00 am - 5:00 pm EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Scott T Baderman can be reached on (571)272-3644. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/HOWARD CORTES/ Primary Examiner, Art Unit 2118