Water and wastewater systems in the era of data explosion

Transcription

1 Water and wastewater systems in the era of data explosion Michela Mulas Water and Environmental Engineering Dept. of Civil and Environmental Engineering Aalto University

2 Motivations Water treatment facilities are continuously challenged to satisfy new constraints in terms of quality of the effluents for compliance with stringent environmental regulations, sustainable reuse and cost optimization. Data explosion The facilities are becoming modern with a vast amount of on-line and off-line measurements collected routinely and the operators and process engineers are becoming more experienced in instrumentation and automation concepts. Data are used for decision support, for early warning of disturbances and process changes, for tracking interesting and relevant parameters as well as for the basis of control actions. Objective Discuss the potential of encapsulating data-enhanced process knowledge and modelling capability in automation systems.

3 Motivations Water treatment facilities are continuously challenged to satisfy new constraints in terms of quality of the effluents for compliance with stringent environmental regulations, sustainable reuse and cost optimization. Data explosion The facilities are becoming modern with a vast amount of on-line and off-line measurements collected routinely and the operators and process engineers are becoming more experienced in instrumentation and automation concepts. Data are used for decision support, for early warning of disturbances and process changes, for tracking interesting and relevant parameters as well as for the basis of control actions. Objective Discuss the potential of encapsulating data-enhanced process knowledge and modelling capability in automation systems.

4 Motivations Water treatment facilities are continuously challenged to satisfy new constraints in terms of quality of the effluents for compliance with stringent environmental regulations, sustainable reuse and cost optimization. Data explosion The facilities are becoming modern with a vast amount of on-line and off-line measurements collected routinely and the operators and process engineers are becoming more experienced in instrumentation and automation concepts. Data are used for decision support, for early warning of disturbances and process changes, for tracking interesting and relevant parameters as well as for the basis of control actions. Objective Discuss the potential of encapsulating data-enhanced process knowledge and modelling capability in automation systems.

5 Outline Introduction Drinking water network Sävel research project EfeSus research project Diamond research project

6 Sävel research project Water mains management The effect of aging on the water distribution network are very hard to detect, because pipes are buried below the level of soil frost penetration. Sävel project Funded by Tekes Research parties Aalto University Riku Vahala and Kia Aksela National Institute of Health and Welfare Water and Health Unit Ilkka Miettinen Water companies Companies and associations Project duration: 1 July June 2012 Goals Develop a system that detects network malfunctions more effectively and respond to them with greater precision. Detect disturbances by analyzing continuous measurements, water-use forecast and network model. Improve the management of the network asset.

7 EfeSus research project renovation Sewer are valuable assets. There is the need to systematic approach to renovation planning as well as for comprehensive data collection and automated treatment of data. EfeSus project Funded by Tekes Research parties Aalto University: Riku Vahala and Tuija Laakso Finnish Meteorological Institute: Jarmo Koistinen University of Exeter (UK): Dragan Savic Companies Wastewater utilities Project duration: 1 June June 2014 Goals Improve the prioritization of renovation and repair activities - what to renovate and when. Develop the use and the processing of existing data resources. Create a software prototype for assessing the different network areas.

8 Diamond research project The lack of appropriate data management tools is clearly limiting a broader implementation and efficient use of new sensors, monitoring systems and advanced process controllers. Diamond project Funded by FP7 Capacities: Research for the benefits of SMEs RTD performers: CEIT (Spain) Uppsala Universitet (Sweden) IVL Svenska Miljoe Institutet AB (Sweden) Aalto University (Finland) Small and Medium Enterprises: Mondragón Sistemas de Información (Spain) Cerlic Controls AB (Sweden) Mipro Oy (Finland) End-Users: Aguas de Gipuzkoa S.A (Spain) Stockholm Vatten AB (Sweden) Project duration: 1 September August 2014

9 Diamond research project DIAMOND AdvanceD data management and InformAtics for the optimum operation and control of wastewater treatment plants Processing, centralising, synthesizing, correcting and completing all the heterogeneous data available in a WWTP. Interpreting and extracting the maximum information from these plant data. Constructing new information from existing measurable variables. Facilitating the decision-making process of the operators of the WWTP. Facilitating the design and implementation of plant-wide operational strategies and automatic controllers. Optimize the operation of wastewater systems by adequately managing and using all the information available in the plant.

10 : Funded by MVTT Aalto University: Viikinmäki project Water and Environmental Engineering Environmental and Industrial Machine Learning Viikinmäki s personnel The wastewater treatment line consists of: bar screening and grit removal pre-aeration and primary sedimentation activated sludge process (8 lines) secondary sedimentation biological post-filtration (post-denitrification) The sludge treatment line has mesophilic digesters and dewatering systems Retrieved from Monitoring nitrate concentrations in the denitrifying post-filtration unit.

11 : Funded by MVTT Aalto University: Viikinmäki project Water and Environmental Engineering Environmental and Industrial Machine Learning Viikinmäki s personnel The wastewater treatment line consists of: bar screening and grit removal pre-aeration and primary sedimentation activated sludge process (8 lines) secondary sedimentation biological post-filtration (post-denitrification) The sludge treatment line has mesophilic digesters and dewatering systems Retrieved from Monitoring nitrate concentrations in the denitrifying post-filtration unit.

12 Process description: The denitrifying post-filtration unit Overall a total nitrogen removal of 90% Ten Biostyr R filters arranged in parallel The influent wastewater is equally distributed Before each cell, the incoming flow is split in two Attached biomass tends to clog the cell Periodic backwashes with effluent wastewater and a counter-current air flow To favor the removal, methanol is dosed with a feedback loop policy Dosing according to the nitrate concentration in the filters, measured on-line with analytical instruments Treated wastewater is discharged into a common effluent channel

13 Process description: The denitrifying post-filtration unit Overall a total nitrogen removal of 90% Ten Biostyr R filters arranged in parallel The influent wastewater is equally distributed Before each cell, the incoming flow is split in two Attached biomass tends to clog the cell Periodic backwashes with effluent wastewater and a counter-current air flow To favor the removal, methanol is dosed with a feedback loop policy Dosing according to the nitrate concentration in the filters, measured on-line with analytical instruments Treated wastewater is discharged into a common effluent channel

14 Process description: The denitrifying post-filtration unit Overall a total nitrogen removal of 90% Ten Biostyr R filters arranged in parallel The influent wastewater is equally distributed Before each cell, the incoming flow is split in two Attached biomass tends to clog the cell Periodic backwashes with effluent wastewater and a counter-current air flow To favor the removal, methanol is dosed with a feedback loop policy Dosing according to the nitrate concentration in the filters, measured on-line with analytical instruments Treated wastewater is discharged into a common effluent channel

15 Process description: The denitrifying post-filtration unit Proper functioning of the nitrate sensors is of crucial importance from an environmental and an economical point of view but harsh environmental conditions expose these instruments to malfunction One big problem is vicinity of the analyzers to the effluent channel Methanol control is compromised Need for a back-up instrument Retrieved from Main goal Develop an array of soft-sensors that estimate in real-time the nitrate concentration in the cells Accurate and computationally light models are the priority Starting from easy to measure process variables

16 Process description: The denitrifying post-filtration unit Proper functioning of the nitrate sensors is of crucial importance from an environmental and an economical point of view but harsh environmental conditions expose these instruments to malfunction One big problem is vicinity of the analyzers to the effluent channel Methanol control is compromised Need for a back-up instrument Retrieved from Main goal Develop an array of soft-sensors that estimate in real-time the nitrate concentration in the cells Accurate and computationally light models are the priority Starting from easy to measure process variables

17 Soft-sensors based on unstructured models Based on a more-or-less accurate description of relationships between data An unstructured software sensor is often seen as input-output model the inputs X are easy to measure the output y are hard to measure Assuming the existence of a functional relationship between the inputs and the outputs (y = f(x)+ε), the model is calibrated to reconstruct it (ˆf ) Unstructured models rely on methods for data analysis: Techniques for sample selection Techniques for variable selection Techniques for regression KISS: Start with a simple model type

18 Soft-sensor design A set of process measurements has been collected: 3 years of continuous operations ( ), hourly averages Sample selection The overall number of available process variable relevant to the task is 142: 7 for the influent 5 for the effluent (12+1) 10 for the filters Variable selection Simplicity is on of the main requirements to allow a direct implementation in the plant s control system. TAG Description Units I-NO 3-1 Influent Nitrate-Nitrogen (sensor 1) mg/l I-NO 3-2 Influent Nitrate-Nitrogen (sensor 2) mg/l I-SS-1 Influent Suspended Solids (sensor 1) mg/l I-SS-2 Influent Suspended Solids (sensor 2) mg/l I-O 2 Influent Dissolved Oxygen mg/l I-PO Influent Phosphate-Phosphorus mg/l I-TP Influent Total Phosphorus mg/l E-NO 3 Effluent Nitrate-Nitrogen mg/l E-TOC Effluent Total Organic Carbon mg/l E-PO Effluent Phosphate-Phosphorus mg/l E-TP Effluent Total Phosphorus mg/l E-T Effluent Temperature C Fi-QWW i-th Filter Backwashing water flowrate m 3 /s Fi-QWA i-th Filter Backwashing air flowrate m 3 /s Fi-QW -1 i-th Filter Wastewater flowrate (line 1) m 3 /s Fi-QW -2 i-th Filter Wastewater flowrate (line 2) m 3 /s Fi-QM-1 i-th Filter Methanol flowrate (line 1) m 3 /h Fi-QM-2 i-th Filter Methanol flowrate (line 2) m 3 /h Fi-P-1 i-th Filter Pressure at the bottom kpa Fi-P-2 i-th Filter Pressure at the top kpa Fi-NO 3 i-th Filter Nitrate-Nitrogen mg/l Fi-HL i-th Filter Head-Loss m Fi-CR i-th Filter Clogging rate % Fi-HRU i-th Filter Hour in use Fi-ITW i-th Filter Intermediate time of backwash Regression models

19 Soft-sensor design A set of process measurements has been collected: 3 years of continuous operations ( ), hourly averages Sample selection The overall number of available process variable relevant to the task is 142: 7 for the influent 5 for the effluent (12+1) 10 for the filters Variable selection Simplicity is on of the main requirements to allow a direct implementation in the plant s control system. TAG Description Units I-NO 3-1 Influent Nitrate-Nitrogen (sensor 1) mg/l I-NO 3-2 Influent Nitrate-Nitrogen (sensor 2) mg/l I-SS-1 Influent Suspended Solids (sensor 1) mg/l I-SS-2 Influent Suspended Solids (sensor 2) mg/l I-O 2 Influent Dissolved Oxygen mg/l I-PO Influent Phosphate-Phosphorus mg/l I-TP Influent Total Phosphorus mg/l E-NO 3 Effluent Nitrate-Nitrogen mg/l E-TOC Effluent Total Organic Carbon mg/l E-PO Effluent Phosphate-Phosphorus mg/l E-TP Effluent Total Phosphorus mg/l E-T Effluent Temperature C Fi-QWW i-th Filter Backwashing water flowrate m 3 /s Fi-QWA i-th Filter Backwashing air flowrate m 3 /s Fi-QW -1 i-th Filter Wastewater flowrate (line 1) m 3 /s Fi-QW -2 i-th Filter Wastewater flowrate (line 2) m 3 /s Fi-QM-1 i-th Filter Methanol flowrate (line 1) m 3 /h Fi-QM-2 i-th Filter Methanol flowrate (line 2) m 3 /h Fi-P-1 i-th Filter Pressure at the bottom kpa Fi-P-2 i-th Filter Pressure at the top kpa Fi-NO 3 i-th Filter Nitrate-Nitrogen mg/l Fi-HL i-th Filter Head-Loss m Fi-CR i-th Filter Clogging rate % Fi-HRU i-th Filter Hour in use Fi-ITW i-th Filter Intermediate time of backwash Regression models

20 Soft-sensor design A set of process measurements has been collected: 3 years of continuous operations ( ), hourly averages Sample selection The overall number of available process variable relevant to the task is 142: 7 for the influent 5 for the effluent (12+1) 10 for the filters Variable selection Simplicity is on of the main requirements to allow a direct implementation in the plant s control system. TAG Description Units I-NO 3-1 Influent Nitrate-Nitrogen (sensor 1) mg/l I-NO 3-2 Influent Nitrate-Nitrogen (sensor 2) mg/l I-SS-1 Influent Suspended Solids (sensor 1) mg/l I-SS-2 Influent Suspended Solids (sensor 2) mg/l I-O 2 Influent Dissolved Oxygen mg/l I-PO Influent Phosphate-Phosphorus mg/l I-TP Influent Total Phosphorus mg/l E-NO 3 Effluent Nitrate-Nitrogen mg/l E-TOC Effluent Total Organic Carbon mg/l E-PO Effluent Phosphate-Phosphorus mg/l E-TP Effluent Total Phosphorus mg/l E-T Effluent Temperature C Fi-QWW i-th Filter Backwashing water flowrate m 3 /s Fi-QWA i-th Filter Backwashing air flowrate m 3 /s Fi-QW -1 i-th Filter Wastewater flowrate (line 1) m 3 /s Fi-QW -2 i-th Filter Wastewater flowrate (line 2) m 3 /s Fi-QM-1 i-th Filter Methanol flowrate (line 1) m 3 /h Fi-QM-2 i-th Filter Methanol flowrate (line 2) m 3 /h Fi-P-1 i-th Filter Pressure at the bottom kpa Fi-P-2 i-th Filter Pressure at the top kpa Fi-NO 3 i-th Filter Nitrate-Nitrogen mg/l Fi-HL i-th Filter Head-Loss m Fi-CR i-th Filter Clogging rate % Fi-HRU i-th Filter Hour in use Fi-ITW i-th Filter Intermediate time of backwash Regression models

21 Soft-sensor design A set of process measurements has been collected: 3 years of continuous operations ( ), hourly averages Sample selection The overall number of available process variable relevant to the task is 142: 7 for the influent 5 for the effluent (12+1) 10 for the filters Variable selection Simplicity is on of the main requirements to allow a direct implementation in the plant s control system. TAG Description Units I-NO 3-1 Influent Nitrate-Nitrogen (sensor 1) mg/l I-NO 3-2 Influent Nitrate-Nitrogen (sensor 2) mg/l I-SS-1 Influent Suspended Solids (sensor 1) mg/l I-SS-2 Influent Suspended Solids (sensor 2) mg/l I-O 2 Influent Dissolved Oxygen mg/l I-PO Influent Phosphate-Phosphorus mg/l I-TP Influent Total Phosphorus mg/l E-NO 3 Effluent Nitrate-Nitrogen mg/l E-TOC Effluent Total Organic Carbon mg/l E-PO Effluent Phosphate-Phosphorus mg/l E-TP Effluent Total Phosphorus mg/l E-T Effluent Temperature C Fi-QWW i-th Filter Backwashing water flowrate m 3 /s Fi-QWA i-th Filter Backwashing air flowrate m 3 /s Fi-QW -1 i-th Filter Wastewater flowrate (line 1) m 3 /s Fi-QW -2 i-th Filter Wastewater flowrate (line 2) m 3 /s Fi-QM-1 i-th Filter Methanol flowrate (line 1) m 3 /h Fi-QM-2 i-th Filter Methanol flowrate (line 2) m 3 /h Fi-P-1 i-th Filter Pressure at the bottom kpa Fi-P-2 i-th Filter Pressure at the top kpa Fi-NO 3 i-th Filter Nitrate-Nitrogen mg/l Fi-HL i-th Filter Head-Loss m Fi-CR i-th Filter Clogging rate % Fi-HRU i-th Filter Hour in use Fi-ITW i-th Filter Intermediate time of backwash Regression models

22 Soft-sensor design A set of process measurements has been collected: 3 years of continuous operations ( ), hourly averages Sample selection The overall number of available process variable relevant to the task is 142: 7 for the influent 5 for the effluent (12+1) 10 for the filters Variable selection Simplicity is on of the main requirements to allow a direct implementation in the plant s control system. TAG Description Units I-NO 3-1 Influent Nitrate-Nitrogen (sensor 1) mg/l I-NO 3-2 Influent Nitrate-Nitrogen (sensor 2) mg/l I-SS-1 Influent Suspended Solids (sensor 1) mg/l I-SS-2 Influent Suspended Solids (sensor 2) mg/l I-O 2 Influent Dissolved Oxygen mg/l I-PO Influent Phosphate-Phosphorus mg/l I-TP Influent Total Phosphorus mg/l E-NO 3 Effluent Nitrate-Nitrogen mg/l E-TOC Effluent Total Organic Carbon mg/l E-PO Effluent Phosphate-Phosphorus mg/l E-TP Effluent Total Phosphorus mg/l E-T Effluent Temperature C Fi-QWW i-th Filter Backwashing water flowrate m 3 /s Fi-QWA i-th Filter Backwashing air flowrate m 3 /s Fi-QW -1 i-th Filter Wastewater flowrate (line 1) m 3 /s Fi-QW -2 i-th Filter Wastewater flowrate (line 2) m 3 /s Fi-QM-1 i-th Filter Methanol flowrate (line 1) m 3 /h Fi-QM-2 i-th Filter Methanol flowrate (line 2) m 3 /h Fi-P-1 i-th Filter Pressure at the bottom kpa Fi-P-2 i-th Filter Pressure at the top kpa Fi-NO 3 i-th Filter Nitrate-Nitrogen mg/l Fi-HL i-th Filter Head-Loss m Fi-CR i-th Filter Clogging rate % Fi-HRU i-th Filter Hour in use Fi-ITW i-th Filter Intermediate time of backwash Regression models

23 Soft-sensor design A set of process measurements has been collected: 3 years of continuous operations ( ), hourly averages Sample selection The overall number of available process variable relevant to the task is 142: 7 for the influent 5 for the effluent (12+1) 10 for the filters Variable selection Simplicity is on of the main requirements to allow a direct implementation in the plant s control system. TAG Description Units I-NO 3-1 Influent Nitrate-Nitrogen (sensor 1) mg/l I-NO 3-2 Influent Nitrate-Nitrogen (sensor 2) mg/l I-SS-1 Influent Suspended Solids (sensor 1) mg/l I-SS-2 Influent Suspended Solids (sensor 2) mg/l I-O 2 Influent Dissolved Oxygen mg/l I-PO Influent Phosphate-Phosphorus mg/l I-TP Influent Total Phosphorus mg/l E-NO 3 Effluent Nitrate-Nitrogen mg/l E-TOC Effluent Total Organic Carbon mg/l E-PO Effluent Phosphate-Phosphorus mg/l E-TP Effluent Total Phosphorus mg/l E-T Effluent Temperature C Fi-QWW i-th Filter Backwashing water flowrate m 3 /s Fi-QWA i-th Filter Backwashing air flowrate m 3 /s Fi-QW -1 i-th Filter Wastewater flowrate (line 1) m 3 /s Fi-QW -2 i-th Filter Wastewater flowrate (line 2) m 3 /s Fi-QM-1 i-th Filter Methanol flowrate (line 1) m 3 /h Fi-QM-2 i-th Filter Methanol flowrate (line 2) m 3 /h Fi-P-1 i-th Filter Pressure at the bottom kpa Fi-P-2 i-th Filter Pressure at the top kpa Fi-NO 3 i-th Filter Nitrate-Nitrogen mg/l Fi-HL i-th Filter Head-Loss m Fi-CR i-th Filter Clogging rate % Fi-HRU i-th Filter Hour in use Fi-ITW i-th Filter Intermediate time of backwash Regression models

24 Estimation performance The hardware sensor in Filter 9 is not returning any measurement. 0.8 F9 QW Oct Oct Oct Oct Oct F3 QW Oct Oct Oct Oct Oct.09 The missing measurements can be replaced by the soft-sensor estimates

25 Estimation performance The hardware sensor in Filter 9 is not returning any measurement. F9 NO Measured F9 QW Oct Oct Oct Oct Oct.09 F3 NO Measured F3 QW Oct Oct Oct Oct Oct.09 The missing measurements can be replaced by the soft-sensor estimates

26 Estimation performance The hardware sensor in Filter 9 is not returning any measurement. F9 NO k NN Measured F9 QW LLR OLSR Oct Oct Oct Oct Oct.09 F3 NO k NN Measured F3 QW LLR OLSR Measured Oct Oct Oct Oct Oct.09 The missing measurements can be replaced by the soft-sensor estimates

27 Estimation performance The hardware sensor in Filter 9 is not returning any measurement. F9 NO k NN Measured F9 QW LLR OLSR Oct Oct Oct Oct Oct.09 F3 NO k NN Measured F3 QW LLR OLSR Measured Oct Oct Oct Oct Oct.09 The missing measurements can be replaced by the soft-sensor estimates

28 Estimation performance Erroneous measurements are returned by the hardware sensor in Filter F6 QW F6 QW Mar Mar Mar Mar F3 QW F3 QW Mar Mar Mar Mar.10 The soft-sensor is capable of recovering the nitrate concentration

29 Estimation performance Erroneous measurements are returned by the hardware sensor in Filter 6. F6 NO3 F6 QW Measured F6 QW Mar Mar Mar Mar Measured F3 QW F3 NO3 F3 QW Mar Mar Mar Mar.10 The soft-sensor is capable of recovering the nitrate concentration

30 Estimation performance Erroneous measurements are returned by the hardware sensor in Filter 6. F6 NO3 F6 QW k NN Measured F6 QW LLR OLSR Measured Mar Mar Mar Mar k NN Measured F3 QW LLR OLSR Measured F3 NO3 F3 QW Mar Mar Mar Mar.10 The soft-sensor is capable of recovering the nitrate concentration

31 Viikinmäki project Potential benefits for the WWTP supervision and monitoring Back-up system to conventional analytical equipment for replacing out-of-order components. Validation tools for existing field measurements. We discussed the potential of encapsulating data-enhanced process knowledge and modelling capability in automation systems through different project examples: Sävel: Water supply real-time management Efesus: Effective sewer condition management using online sensor information Diamond: Advanced data management and informatics for the optimum operation and control of wastewater treatment plants

32 Viikinmäki project Potential benefits for the WWTP supervision and monitoring Back-up system to conventional analytical equipment for replacing out-of-order components. Validation tools for existing field measurements. We discussed the potential of encapsulating data-enhanced process knowledge and modelling capability in automation systems through different project examples: Sävel: Water supply real-time management Efesus: Effective sewer condition management using online sensor information Diamond: Advanced data management and informatics for the optimum operation and control of wastewater treatment plants