Quantifying and Evaluating Sustainable Remediation: What About the Use of Neural Networks?

Transcription

1 Quantifying and Evaluating Sustainable Remediation: What About the Use of Neural Networks? Case Study Decision tool for prediction of alternative remedial technologies: Arsenic removal from drinking water sources Brian J. Yates Battelle Environmental Restoration and Infrastructures 1

2 Outline Neural Networks Overview Neural Networks Potential in Sustainable Remediation EPA Arsenic Demonstration Project and Prediction With Neural Networks (case study) Moving Forward: What is needed 2

3 What are Neural Networks (NN)? Neural networks may be defined as a set of mathematical methods and computational algorithms designed to simulate the information processing and the acquisition of knowledge in the human brain (Guimares, 2008) 3

4 What are Neural Networks? (cont d) Analogous to the human nervous system which provides: Associative memory Memory recall Predictive capabilities Brown University, Division of Biology and Medicine 4

5 What are Neural Networks? (cont d) Mathematical (Artificial) Neural Networks (ANN) A single state variable is associated with each node A weight is associated with each link between two nodes (neurons) A bias is associated with each neuron A transfer function is defined for each neuron Weight Transfer Function x 1 x 2 w 1 Input Nodes x 3 x m-1 w m b 1 v Σ ϕ y Output Neuron x m Input Hidden Output Bias 5

6 What are Neural Networks? (cont d) The Learning Process Neural network output compared to known output (feed-forward) Weights and bias are updated (back-propagation) Neural network output compared to known output (feed-forward; one epoch) Weights and bias are. Learning process is terminated when error reaches LOCAL minimum (GLOBAL minimum indicates overtraining) Neural Networks in: Physics of Neural Networks 1990 Springer-Verlag, New York 6

7 Neural Networks in Water Treatment Neural Networks have been used to model/predict many different unit processes in water treatment Adsorption (Basheer, et. al., 1996; Brasquet et. al., 1999; Kumar, et. al., 1999; Aghav et. al., 2011) Coagulation (Daneshvar, et. al., 2006; Wu, et. al., 2010) Membrane Filtration (Shetty, et. al., 2003; Hwang, et. al., 2009) (Advanced) Oxidation Processes (Slokar, et. al., 1999; Salari, et. al., 2005; Dutta, et. al., 2010) Phase Transfer (Djebbar, et. al., 2002) Activated Sludge Process (Pai, et. al., 2007, 2008, 2009, 2011; Hamed, 2010; Yilmaz, 2011) 7

8 Neural Networks in Sustainability: A Gap in the Literature Neural Networks have only recently been considered for sustainability applications and the literature are scarce Hydroelectric power and energy strategy reconsiderations in Turkey (Cinar, et. al., 2010) Wind turbine placement and cost/benefit evaluation on the overall reduction of CO 2 emissions in Morocco (Ouammi, et. al., 2010) Prediction and classification of the ecological footprint of 140 nations (Mostafa and Nataraajan, 2009) 8

9 How Can NNs be Used to Predict Sustainable Solutions? Prediction of remedial activity performance Reduction in chemical use Reduction in material use Optimum operational conditions for quickest clean-up (reduction in remedial timeframe) Prediction of sustainability metrics Tangible metrics (GHG emissions, water consumption, etc.) Intangible metrics (public perception, ecological disruption) 9

10 How Can NNs be Used to Predict Sustainable Solutions? (cont d) Feed-Forward Activities/Modules Inputs Impacts Remedial InvestigationRemedial Investigation GHG Emissions Energy Consumption Remedial Effort Remedial Action Construction Remedial Action Operation Activities Criteria Air Pollutants Water Consumption Resource Consumption Long Term Monitoring Worker Safety SiteWise TM GSR Tool The tool is available for free download at: Version 2.0 will be available at the same location in June, 2011 Back-Propagation 10

11 Case Study Decision tool for prediction of alternative remedial technologies: Arsenic removal from drinking water sources 11

12 EPA Arsenic Removal Demonstration Program 1975: Safe Drinking Water Act establishes a Maximum Contaminant Level (MCL) of 0.05 mg/l for arsenic in drinking water January 2001: EPA lowers the MCL for arsenic to 0.01 mg/l October 2001: EPA announces the arsenic rule implementation program (43 sites nationwide) 12

13 EPA Arsenic Removal Demonstration Program (cont d) Removal Technologies included: Absorptive Media* Coagulation/Filtration Ion Exchange Reverse Osmosis System Retrofit 13

14 ANN for Prediction of Arsenic Breakthrough Data Manipulation Data compiled from Arsenic Removal Demonstration Final Reports ( 16 Sites 36 Separate Studies (~2 vessels each site) Missing data was linearly interpolated Constant for geochemical parameters Linearly increasing for influent to second vessels in series Outliers were identified but not removed Breakthrough curves defined as As out /As in vs. Bed Volumes Stewart, MN Vessel A 14

15 ANN for Prediction of Arsenic Breakthrough Network Architecture 13 INPUT Nodes - Cumulative Bed Volumes - ph - ORP - Total As - Soluble As - As 5+ - Particulate As - Total Fe - Total Mn - Soluble Mn - Mg Hardness - Total P - Nitrate No transfer function x 1 x 2 x 3 x m-1 x m 2 HIDDEN Layers 15 HIDDEN neurons in first hidden layer 55 HIDDEN neurons in second hidden layer Tangent Sigmoid Transfer Function w 1 w m 1 b v Σ ϕ y Input Hidden Output 1 OUTPUT Node Total Effluent As Linear Transfer Function 15

16 Data Spread What is Needed for Robust Predictions Good Spread Leads to Robust Predictions Poor Spread Leads to Feeble Predictions 16

17 ANN for Prediction of Arsenic Breakthrough Data Set 1 Training data divided by site Test data hand-picked Stevensville, MD Wellman, TX Stewart, MN 17

18 ANN for Prediction of Arsenic Breakthrough Data Set 2 Training data divided by site Average of 10 ANNs with different starting weights Test data hand-picked Stevensville, MD Wellman, TX Stewart, MN 18

19 ANN for Prediction of Arsenic Breakthrough Data Set 3 Training data divided by vessel Two Vessels from the same site allowed to be mixed in training and testing Test data randomly selected 19

20 ANN for Prediction of Arsenic Breakthrough Data Set 4 Training data divided by observation Two observations from the same site allowed to be mixed in training and testing Test data randomly selected 20

21 ANN for Prediction of Arsenic Breakthrough Data Set 4 (cont d) 21

22 Moving Forward: What is Needed Comprehensive database of sustainability metrics able to be used as training dataset Optimization of Neural Network architectures for evaluation of specific sustainable remediation projects Continuous and complete data collection from new and on-going remediation projects for validation and further training Battelle is currently pursuing these opportunities and identifying clients needs through internally funded projects 22

23 Acknowledgements Melissa Kennedy (Battelle Environmental Restoration and Infrastructures) Douglas Mooney (Battelle Statistics and Information Analysis) Elizabeth Slone (Battelle Statistics and Information Analysis) Tom Sorg (USEPA ORD, Cincinnati) Jennifer Wightman (Battelle Statistics and Information Analysis) 23

24 Discussion Questions? Contact Info: