Computational Intelligence in the Chemical Industry

Transcription

1 Computational Intelligence in the Chemical Industry Elsa Jordaan The Dow Chemical Company Core R&D, Engineering & Process Sciences Modelling EMJ/March 30,2007 University of Brussels Page 1

2 About Dow Leading science and technology company employees (5 600 in Global R&D) 2005 Record Sales of $46.3 billion Customers in more than 180 countries Wide range of markets: food, transportation, health and medicine, personal and home care, and building and construction Sales by Operating Segment (dollars in millions) $3,364 $7,713 $11,815 $11,388 $5,660 $306 $6, Sales by Geographic Area (dollars in millions) Performance Plastics Performance Chemicals Agricultural Sciences Plastics Chemicals Hydrocarbons & Energy Unallocated & Other $17,524 Global R&D: Business R&D dedicated to an operating segment Core R&D supports all operating segments and functions $12,159 $16,624 North America Europe Rest of the World * 2005 Corporate Report EMJ/March 30,2007 University of Brussels Page 2

3 Before 1990 Fundamental modelling a priori knowledge of processes years of research Statistical modelling and data analysis (chemometrics) availability of clean data low-dimensional linearizable EMJ/March 30,2007 University of Brussels Page 3

4 After 1990 Highly nonlinear processes High-dimensional data Noisy data Fast development of models New set of tools Neural Networks Genetic Algorithms Genetic Programming Support Vector Machines Particle Swarm Optimization EMJ/March 30,2007 University of Brussels Page 4

5 Computational Intelligence at Dow Application Domains Material Design Materials Research Production Design Production Monitoring & Analysis Business Modeling Color Matching Appearance Engineering Polymer Design Synthetic Leather Examples Diverse Chemical Library Selection Fundamental Model Building Reaction Kinetics Modeling Combi-Chem Catalyst Exploration Combi-Chem Data Analysis Nonlinear DOE Bioreactor Optimization Critical Parameter Monitoring Calibration Variable Selection Intelligent Alarm Processing Emulator for Online Optimization Emissions Monitoring Diffusion of Innovation Hydrocarbon Trading & Energy Systems Optimization Scheduling Heuristics Plant Capacity Drivers EMJ/March 30,2007 University of Brussels Page 5

6 Specific Examples Inferential Sensors (NN, SVM, GP) NOx-Emissions predictions Biomass concentration prediction in batch fermentation processes Distillation impurity estimation Fault and Drift Detection of inline GC Knowledge discovery New rheological insights Optimization Multi-objective of plastic properties EMJ/March 30,2007 University of Brussels Page 6

7 Inferential Sensor grab sample lab test Quality output (NOx,C3,Biomass) control time delay Process inputs (Temp, Pressure)? Quality prediction Key objective: To predict difficult-to-measure parameters (NOx, Hg, Melt Index) from easy-to-measure data (temperature, pressure, flow, etc.) EMJ/March 30,2007 University of Brussels Page 7

8 Inferential Sensor Development Full data set Nonlinear sensitivity analysis Time delay influence Analytical Neural Networks Reduced inputs data Outliers detection Condensed data set generation Support Vector Machines Condensed data Symbolic regression Functional solutions selection Genetic Programming Y = f(x) EMJ/March 30,2007 University of Brussels Page 8

9 Stacked Analytical Neural Networks X1 X2 X3 B X1 X2 X3 B X1 X2 X3 B Z1 Z2 Z3 Z4 Z1 Z2 Z3 Z4 Y Y Y1 Y2 Y3 B Y1 Y2 or Z1 Z2 Z3 Y Y Fast development Diverse subnet consensus indicator of model output quality Allows explicit calculations of input/output sensitivity Can handle time-delayed inputs by convolution functions X1 X2 X3 Z2 Z3 Z4 Y Y3 B Z4 Gives more reliable estimates based on multiple models statistics B EMJ/March 30,2007 University of Brussels Page 9

10 Nonlinear Sensitivity Analysis Iteratively eliminate input variables Sensitivity of a variable is the impact to the quality of the total model Sequence of inputs elimination C/212A may 30NN 40 Input number Number of eliminated inputs EMJ/March 30,2007 University of Brussels Page 10

11 Support Vector Machines Only 3 support vectors needed to define optimal hyperplane y i =+1 Solid theoretical basis => Statistical Learning Theory Model building is based on global optimum Explicit control over model complexity yi = 1 w Support vector optimal hyperplane ad hoc Kernel selection Complex theory No commercial software Computationally intensive Key to robust modeling EMJ/March 30,2007 University of Brussels Page 11

12 Kernel functions in SVM EMJ/March 30,2007 University of Brussels Page 12

13 Outlier detection using SVM Support vectors are unusual data points Set of sv s contain also the outliers Inversed reasoning can be used for data compression EMJ/March 30,2007 University of Brussels Page 13

14 Genetic Programming a * Parent 1 Parent 2 / + - * - sqr sqr a a b sqr b / * a a b b b a b a Offspring 1 Offspring 2 / + - sqr - sqr How does it work? Based on artificial evolution of millions of potential nonlinear functions Start with population of basic operators and functions: + - / * sqrt(x) exp(x) sin(x) x a Let new functions (offspring) evolve through mutation, cross-over, etc. Next generation => survival of the fittest a sqr a + * - a a - * b / * a a b a b b b b Example of GP-generated function y = *((x2 + x3/x4)) What is the result? Many (thousands) possible solutions with different levels of complexity The final result is an explicit nonlinear function EMJ/March 30,2007 University of Brussels Page 14

15 NOx-Emissions via NN Environmental laws limits amount yearly emitted by a production site. US/EU requires an accurate estimate of the emissions. The hardware solution CEMS available at a cost of $ per unit. May use predictive model accuracy yearly audited Commercial system based on NN Dow-internally developed model (based on statistical model) EMJ/March 30,2007 University of Brussels Page 15

16 Training & Performance Commercial Inferential sensor Model Neural network with 14 inputs GP-model with 5 inputs Maintenance On-line implementation RATA 2003 RATA 2004 Complete model re-training done by vendor. Requires specialized run-time licenses 1.8 % 1.3 % 5.8 % 3.6 % Better accuracy Low maintenance cost No software licenses Estimated savings ~ $100M/sensor Parameter fit in statistical software Direct implementation in control EMJ/March 30,2007 University of Brussels Page 16

17 [Biomass] via GP-ensemble Bacterial fermentation processes in batch reactors Need to know when to stop growth-phase and go into production phase Biomass-concentration indicates when enough organisms are present for production Concentrations are measured by weighing the dried biomass taken from samples every 2-4 hours Online hardware equipment (e.g. optical density) is very expensive ($ per unit) Typical difficulties for modeling High batch-to-batch variation New cultures requires retraining EMJ/March 30,2007 University of Brussels Page 17

18 Experimental Data Input variables: 18 process variables Output variable: measured OD (proportional to biomass concentration) 8 batches for training 3 batches for validation EMJ/March 30,2007 University of Brussels Page 18

19 Performance of soft sensor Robust w.r.t batch-to-batch variation Accuracy Detection of inconsistencies EMJ/March 30,2007 University of Brussels Page 19

20 Impact: $ and quality Pseudo Real Time Medium Time Low Time Multiple Instruments (seconds, minutes) (hours) (daily/weekly) (capital cost) Temperature Flow Lab measurements Hardware analyzer 1 Hardware analyzer 2 Pressure Inferential Sensor Calibration Lab measurements Calibration Hardware analyzer 3 Inferential Sensor Improved controllability Fewer lab measurements Lower capital costs EMJ/March 30,2007 University of Brussels Page 20

21 C3-estimation using GP Raw material (long-chain polymers) are broken in crackers under high T and high P. C 8 (octane) Shorter chain polymers needed for other processes C 3 (propane) C 2 (ethane) C 4 (buthane) Need estimation of fraction of C3 Measured by Gas-chromatograph Problems: GC-measurement frequently unavailable (drifting, faulty measurements) EMJ/March 30,2007 University of Brussels Page 21

22 x1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 X16 X17 X18 X19 X20 X21 X22 x23 GP model development Model 1 (GP selection based on x1 to x23) : x EMJ/March 30,2007 University of Brussels Page x x x8 x x x x x Model 3 (GP selection based on x1 to x7, x9 to x23): x 2 21 R 2 = RMSE = Model 2 (GP selection based on x1 to x23 and x8-x11) R 2 = RMSE = Strongly supported by the HC engineers R 2 = RMSE = Model 4 (GP selection based on x1 to x23, x8-x11, and log y) exp x x 8 11 x + x 12 8 R 2 = RMSE =

23 In operation since May 2004 Online Performance In parallel with GC Good long-term performance Avg error = Selected models Prediction error upper limit lower limit When GC is down, -0.3 we get a good prediction from the models /25 09/04 09/14 09/24 10/04 10/14 10/24 EMJ/March 30,2007 University of Brussels Page 23

24 Fault & Drift Detection (1) Detect abnormal process conditions EMJ/March 30,2007 University of Brussels Page 24

25 Fault & Drift Detection (2) Model Prediction when Variable 18 was Fixed Between Samples 1100 and Observed Y 1.8 Predicted Y Model Disagreement Indicator Y Robust to faulty measurement Critical Limit Sample Number EMJ/March 30,2007 University of Brussels Page 25

26 Fault & Drift Detection (3) Predicted Y - Observed Y Y (a) Error Between Analytical Instrument and Model Upper Limit Lower Limit (b) Model Prediction on Testing Data Predicted Y Observed Y Model Disagreement Analytical Instrument Drift Simulation Detect drift of analytical instrument Sample Number EMJ/March 30,2007 University of Brussels Page 26

27 Knowledge discovery via GP Understanding the complex connection between molecular structure and rheological properties is key to new product development. (MWD rheology) Empirical Models Measure MWD indirectly Moments: Mw, Mn and Mw/Mn rheology = f(moments) Only valid for limited distribution parameter ranges Fundamental Models Theoretically derived MWD theoretically determined rheology = f(mwd) Valid for large ranges However, MWD not known in practice Desire: rheology = f(moments), but also valid for large ranges EMJ/March 30,2007 University of Brussels Page 27

28 Virtual Experimental Design Use fundamental models to generate rheology data (output) Set I: Monodisperse systems Set II: Polydisperse systems - unimodal Set III: Polydisperse systems bimodal Derive moments data from theoretical distributions (inputs) Use GP to develop large range model EMJ/March 30,2007 University of Brussels Page 28

29 Performance New Model sets I - III low Mw test points high Mw unimodal test points high Mw/Mn unimodal test points log J rel GP fit EMJ/March 30,2007 University of Brussels Page 29

30 Particle Swarm Optimization An efficient technique to find the global optimum for model inversion and non-linear parameter estimation At each time step t For each particle i Update the position change (velocity) Vi ( t + 1) = χ ( Vi ( t) + c1 rand(0,1) ( Pi ( t) Xi( t)) + c rand(0,1) ( P ( t) X ( )) Then move X i ( t + 1) = X ( t) + V ( t + 1) i i 2 g i t Global best Local best Local best Note: - stochastic component c, c, 1 2 χ - parameters default values (2.05, 2.05, 0.73) EMJ/March 30,2007 University of Brussels Page 30

31 Optimized color spectrum of plastics Real-time optimization in 2-3 seconds ColourPro Formulation Optimization PSO and GA convergence 40 Multiple-objective PSO with 15 variables Frequency More Residual error of match Frequency Swarm GA EMJ/March 30,2007 University of Brussels Page 31

32 Requirements for success Accuracy of prediction Robustness in the production environment where the quality of the input data is not guaranteed Extrapolation (generalization) outside the known operating conditions Self-assessment capabilities to warn operators of low confidence of the model Fault and drift detection of hardware sensors (analytical instruments) Interpretability of the model by the process engineers Ease of implementation in the current control systems Cheap and fast to development and maintain EMJ/March 30,2007 University of Brussels Page 32

33 Key messages to take home (Chemical) Industry needs a broad toolset (no silver bullet technique) different techniques are complementary hybrid solutions are the most effective Accuracy of a model is not the only requirement for successful applications interpretability ease of implementation low cost of maintenance EMJ/March 30,2007 University of Brussels Page 33

34 External Publications of Applications Application Initial data size Reduced data size Model structure Reference Inferential sensors Interface level prediction Interface level prediction (25 inputs x 6500 data pts) (28 inputs x 2850 data pts) (2 inputs x 2000 data pts) 3 models, 2 inputs Kordon and Smits, 2001 (5 inputs x 2850 data pts) One model, 3 inputs Kalos et al, 2003 Emissions prediction (8 inputs x 251 data pts) (4 inputs x 34 data pts) Two models, 4 inputs Kordon et al, 2003b Biomass prediction (10 inputs x 705 data pts) (10 inputs x 705 data pts) 9 models ens, 2-3 inputs Jordaan et al, 2004 Propylene prediction Emulators (23 inputs x 6900 data pts) (7 inputs x 6900 data pts) 4 models ens, 2-3 inputs Jordaan et al, 2004 Chemical reactor (10 inputs x 320 data pts) (10 inputs x 320 data pts) 5 models, 8 inputs Kordon et al, 2003a Accelerated modeling Structure-property (5 inputs x 32 data pts) (5 inputs x 32 data pts) One model,4 inputs Kordon et al, 2002 Structure-property (9 inputs x 24 data pts) (9 inputs x 24 data pts) 7 models, 3-5 inputs Kordon and Lue, 2004 Structure-property (4 inputs x 289 data pts) one model, 3 inputs Jordaan et al Linearized transforms Chemical reactor model (4 inputs x 19 data pts) (4 inputs x 19 data pts) 3 transforms Castillo et al, 2002 EMJ/March 30,2007 University of Brussels Page 34