Water treatment monitoring system

• US 6,845,336 B2
• Filed: 06/25/2002
• Issued: 01/18/2005
• Est. Priority Date: 06/25/2002
• Status: Expired due to Term

• Current Assignee: Neochloris Incorporated
• Original Assignee: Charles R. Stack, Prasad S. Kodukula
• Inventors: Kodukula, Prasad S., Stack, Charles R.
• Primary Examiner(s): Hoff, Marc S.
• Assistant Examiner(s): Desta, Elias
• Application Number:
  US10/179,436
• Publication Number:
  US 20030236649A1
• Time in Patent Office: 938 Days
• Field of Search: 702/47, 702/50, 702/59, 702/91, 702/104, 702/188, 702/189, 702193-194, 702/FOR.103, 702/123, 702/127, 702/128, 700/48, 700/51, 700/67, 706/6, 706/15
• US Class Current: 702/118
• CPC Class Codes: C02F 1/008 Control or steering systems not provided for elsewhere in subclass C02F
• CPC Class Codes: C02F 2209/008 comprising telecommunication features, e.g. modems or antennas
• CPC Class Codes: C02F 2209/08 Chemical Oxygen Demand [COD]; Biological Oxygen Demand [BOD]
• CPC Class Codes: C02F 2209/40 Liquid flow rate
• CPC Class Codes: G01N 33/18 Water

Abstract

A computer system linked by the internet to various remote waste water treatment facilities. The system receives real-time data from the facilities and analyzing the data to determine likely operational upsets and future effluent water quality. The computer system sends signals to a hierarchy of parties depending on the severity of predicted upsets problems and events. The computer also provides a probability distribution of such upsets and water quality and recommendations as how to adjust facility operating parameters to avoid or reduce the upsets to acceptable parameters and maintain effluent water quality parameters within preselected limits.

Claims

1. In a treatment facility adapted for use with water wherein pollutants are removed from the water and wherein water exiting from the facility has various acceptable and predetermined effluent quality parameters;

said facility including a plurality of operational sensors for operably determining process water quality conditions while the water is in the facility;

the improvement comprising;

a) a monitoring computer at a site remote from said facility and including software to receive data from said sensors, analyze the water quality conditions inputted by said sensors and predict effluent water quality and process upsets;

said monitoring computer further including an artificial neural network module to determine solutions to actual and potential water quality and process upsets; and

b) an internet interface operably connecting said computer to said sensors for transferring said process water quality conditions from said sensors to said monitoring computer and transferring said solutions form said monitoring computer to said facility.

2. The facility according to claim 1 including:

a) an alarm system operably connected to said monitoring computer and receiving alarm messages when future water effluent quality as predicated by said monitoring computer is not within acceptable effluent water quality parameters.

3. The facility according to claim 2 wherein:

a) said alarm system is linked to alarms directed to personnel associated with said facility.

4. The facility according to claim 3 wherein:

a) said facility personnel are grouped in at least first and second hierarchal groups;

said alarm system having at least first and second alarm trigger thresholds such that, if said monitoring computer predicts process upsets or process failure events of a first preselected severity, then a first alarm notice is sent to said first hierarchal group and, if said monitoring computer predicts process upsets or process failure events of a second preselected severity, then a second alarm notice is sent to said second hierarchal group.

5. The facility according to claim 2 wherein:

a) said facility is a first facility; and

b) linking a plurality of additional facilities to said monitoring computer such that said monitoring computer evaluates data from each additional facility and sends alarm signals to each facility when such a facility has a potential future process upset or process failure event.

6. The facility according to claim 1 including:

a) a process computer linked to and receiving data from said process sensors;

b) said process computer including an encryption software module for encrypting data sent to said monitoring computer; and

c) said monitoring computer including a decryption module for operably securely receiving data from said facility.

7. The facility according to claim 1 wherein said monitoring computer includes:

a) an artificial neural network module for analyzing incoming process data and adjusting such data where necessary; and

b) said artificial neural network module maintaining historic database of past facility operation and using said database with algorithms to learn how to better prevent or minimize future process upsets and process failure events.

8. The facility according to claim 7 wherein said monitoring computer includes:

a) a statistical module;

b) a pattern recognition module; and

c) a search function module;

d) such that each module of steps a through c integrate with said artificial neural network module to study incoming data and find likely process upsets and process failure events.

9. The facility according to claim 8 wherein said monitoring computer includes:

a) an expert system module to provide recommendations as to how to avoid or limit said process upsets and process failure events.

10. The facility according to claim 9 wherein said monitoring computer includes:

a) an optimization module to propose recommendations to optimize process operation and reduce process operation cost.

11. The facility according to claim 1 wherein:

a) said facility is a waste water treatment facility.

12. The facility according to claim 1 wherein:

a) said facility is a fresh water treatment facility.

25. The facility according to claim 1 wherein:

a) said monitoring computer includes a statistical module;

said statistical module utilizing data received by said monitoring computer to predict effluent water quality and process upsets and failures.

26. The facility according to claim 25 wherein:

a) said statistical module cooperates with said artificial neural network to predict process upsets and failures.

13. A process for real-time monitoring of a water treatment facility comprising the steps of:

a) collecting operational data from said facility;

b) providing a monitoring computer at a remote location from the facility;

c) transferring said data over internet communication lines to the computer;

d) providing software with the monitoring computer to operably analyze the data and to detect ongoing and predict future waste water treatment process failure events; and

e) sending an alarm signal from the monitoring computer to the facility to provide warning of the process failure events.

14. The process according to claim 13 wherein said facility is a first and providing at least one additional facility that is monitored by said monitoring computer.

15. The process according to claim 13 including the step of:

a) linking said computer directly to at least one piece of operating equipment in said facility such that said computer can operably operate said piece of equipment to prevent a process failure event.

16. The process according to claim 13 including the steps of:

a) providing said computer with a historical memory of facility operations;

b) providing said computer with an artificial neural network; and

c) linking said historical memory to said artificial neural network to allow said computer to learn from the historic memory so as to better detect and predict process failure events.

17. A process for real-time monitoring of a water treatment facility comprising the steps of:

a) collecting operational data from said facility;

b) providing a monitoring computer at a remote location from the facility;

c) transferring said data over communication lines to the computer;

d) providing software with the computer to operably analyze the data and predict waste water treatment process upsets and process failure events; and

e) sending a hierarchal alarm signal from the computer to the facility to provide warning of the process upsets and failure events;

said alarm signal having a first hierarchy alarm that is sent to a first party in response to an upset or event having a lower degree of severity and a second hierarchy alarm that is sent to a second party in response to an upset or event having a higher severity.

18. The process according to claim 17 including:

a) transmitting said hierarchal alarm signal throughout a wide variety of communications systems to receivers of such signals.

19. A water treatment system including:

a) a plurality of waste water treatment facilities;

at least some of said facilities being located at sites remote from one another;

b) each of said facilities having a sensor group having multiple sensors, each of said sensors providing real-time data of parameters associated with operation of each facility and surrounding environment thereof;

c) a monitoring computer located at a site remote from at least some of said facilities;

d) a computer and communication interface adapted respectively for each facility to operably continuously connect with the internet;

e) each of said communication interfaces including encryption software such that transmissions passing through the interfaces into the internet are encrypted for security purposes;

f) said monitoring computer including operational software having an artificial neural network module that receives said data from each facility;

said operational software evaluating the received data to determine quality of the received data and using the received data from each facility to predict future operational conditions within the facility;

g) said operational software having a pattern recognition module that cooperates with the neural artificial network module to locate common patterns with historic operation and predict process failure events at each facility;

h) said operational software having a statistical module cooperating with said artificial neural network module to evaluate the data from each of the facilities and predicted future operational conditions of each facility to calculate a probability distribution of parameters that are to be expected in future operational conditions, including effluent water conditions;

i) said computer software including a search module that reviews the probability distributions and compares such to thresholds to see if such thresholds are likely to be exceeded;

said search module generating an alarm signal each time a threshold is predicted to be exceeded and with respect to each predicted process failure event; and

j) a communication link between said computer and parties associated with each of said facilities;

said computer transmitting each alarm signal to a respective party so as to warn the party that either a threshold is likely to be exceeded or a process failure event is likely to occur.

20. The system according to claim 19 including wherein:

a) each of said facilities has associated therewith a hierarchy of personnel;

said computer search function module determining the severity that a threshold is predicted to be exceeded and sending different alarm signals to different personnel in the hierarchy depending on the severity of the threshold that is predicted to be exceeded.

21. The system according to claim 19 wherein:

a) said computer software includes an optimization module that continuously compares predicted facility effluent quality and provides recommended steps to be taken to maintain effluent quality within preselected quality parameters while minimizing facility operational expenses.

22. The system according to claim 19 wherein:

a) said computer software including an expert system module that compares predicted operational upsets and events within each facility to historic operational information so as to identify and propose operational strategies that may be used to prevent such upsets and events and sends such strategies to a facility operator.

23. The system according to claim 19 wherein:

a) said sensors include both soft sensor determination made by said computer and hard sensor measurements made by sensor devices directly measuring some parameters.

24. A process for real-time monitoring of a water treatment facility comprising the steps of:

a) collecting operational data from said facility;

b) providing a monitoring computer at a remote location from the facility;

c) transferring said data over internet communication lines to the computer;

d) providing software with the computer to operably analyze the data, to determine where the facility operation is not being optimized and to develop steps to optimize operation of the facility; and

e) sending a message from the computer to the facility personnel providing steps to take to optimize operation of the facility.