Data Mining and Evolutionary Computation Algorithms for Process Modeling and Optimization

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1 Data Mining and Evolutionary Computation Algorithms for Process Modeling and Optimization Zhe Song, Andrew Kusiak 2139 Seamans Center Iowa City, Iowa Tel: Fax: Outline Introduction to process modeling Data mining and evolutionary computation algorithms Case studies in power plant optimization 1

2 Process Modeling Physics based (analytical) modeling Energy, mass conservation Data-driven modeling Data mining, e.g. neural networks, decision tree Statistical learning Grey-box modeling Part of physical modeling, part of data driven Fuzzy modeling Physics Based Modeling Advantages Generalize and extrapolate well Make sense Disadvantages May not be accurate enough Cumbersome and hard to set up Need deep knowledge 2

3 Advantages Data-Driven Modeling Quick, easy and simple Accurate and flexible Disadvantages Sometimes not generalizable Black-box Depend on data Grey-Box Modeling Advantages Partially understandable Accurate and generalizable Disadvantages More complex Datadriven Physical models Grey-box 3

4 Data Mining for Process Modeling (1) Select appropriate process variables Prepare the data into desired formats Preprocess the data if necessary Select different algorithms based on application or domain expertise Evaluate the results and repeat experiments, if necessary Data Mining in Process Modeling (2) y = f( x, v) 4

5 Data Mining in Process Modeling (3) Sample data for training numeric Data Mining in Process Modeling (4) Sample data for training discrete e 5

6 Evolutionary Computation Algorithms Genetic algorithms Evolutionary strategy Single objective Multiple objective Evolutionary Computation Algorithms Advantages No gradient information is needed Tends to find global optima Easy to implement Disadvantages Computationally expensive No guarantee for true global optima Needs to tuned 6

7 Process Optimization and Evolutionary Computation Algorithms Why use evolutionary computation algorithms? max/min y x s.. t g( x) S y = f( x, v) Constraints Evolutionary Strategy Algorithm Individual i i ( x, σ ) = ( x1,..., x k ) x i i i T σ i = ( i 1,..., i ) T σ σ k 7

8 Mutation σ i i Mutate the standard deviation vector first N 1 (0, ') (0, ) ( (0, τ ') + N (0, τ ),, N τ + N e e ) k τ = σ i i i x = x + N(, 0 σ ) Selection and Recombination of Parents i SeletedParents x i i SeletedParents σ (, ) 2 2 i Could be more than 2? 8

9 Children Selection Select only from children pool, converge slowly, needs more generations to find the optima Put children and parents together for selection, tends to converge fast and be trapped in local optima Combustion Process Modeling MidAmerican Energy Power Plant 9

10 Testing Results of Different Algorithms Neural Network Boosting Tree Random Forest CART SVM MAE Std MAE Std MAE Std MAE Std MAE Std Jan Oct Feb Mar Apr May Jun Jul Aug Average Testing Results of Different Algorithms (1) Average MAE for five data mining i algorithms MAE 10

11 Test Results of Different Algorithms (2) Average Std dfor five data mining i algorithms Std Model Performance Prediction error: Predicted vs observed values 11

12 Evolutionary Strategy Algorithm Software implemented at UI Intelligent Systems Lab MW Load 12

13 Observed and Predicted Efficiency Boiler Oxygen: Observed and Recommended 13

14 Combustion Process Modeling University of Iowa Power Plant No of obs UI Power Plant Load Distribution

15 Data Bias Some power plant runs at several load scenarios most of the time Data is most generated from a fixed number of loads It is better to build a separate model for each load scenario Model Accuracy for UI PP 170 klb/hr Load Boiler Efficiency Observed Boiler Efficiency Predicted 15

16 Model Accuracy for UI PP 160 klb/hr Load Boiler Efficiency Observed Boiler Efficiency Predicted Model Accuracy for UI PP on Other Load Scenarios Boiler Efficiency Observed Boiler Efficiency Predicted 16

17 Field Tests: Time Periods 5/21/ :30:43 AM to 5/29/ :30:43 AM, supervisory control OFF, 1491 data points collected during this time period 5/29/2008 2:22:27 PM to 5/31/2008 3:55:32 AM, supervisory control ON, 1639 data points collected during this time period Difference in Air Flows Air flow SA OFF ON PA 17

18 UCL= Efficiency Shift OFF mean = std = 1.76 ON mean = 82.71, std = No of obs Boiler efficiency 18

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