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MTP_10MT61R20_Rajesh_Jha

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MTP_10MT61R20_Rajesh_Jha

  1. 1. OPTIMIZING CO2 EMISSIONSOF AN OPERATING BLAST FURNACE USING GENETIC ALGORITHMANDGENETIC PROGRAMMING. Presented by Rajesh Jha1 Under guidance of Prodip Kumar Sen2, Nirupam Chakraborti2 1 M. Tech. (10MT61R20), 2 Professor Department of Metallurgical and Materials Engineering, I.I.T. Kharagpur 4/30/2012 1
  2. 2. Global Iron and Steel Industry Figure 1: Contribution of various routes in Iron and Steel making (Peter Schmöle and Hans Bodo Lüngen (2004)).4/30/2012 2
  3. 3. TYPICAL CO2 EMISSIONS IN AN INTEGRATED STEEL PLANT Figure 2. Share of total CO2 emitted from different sources in an Integrated steel plant,(Ariyama and Sato, (2006)) 4/30/2012 3
  4. 4. IRON MAKING BLAST FURNACE The Process Chemistry and Transport Phenomenon in a Blast Furnace are Highly Complex and Non-linear in nature To study the complex and non-linear behavior , a model has to be developed. Figure 3. Schematic diagram of a Blast Furnace4/30/2012 4
  5. 5. DEVELOPMENT Of DATA DRIVEN MODEL ANALYTICAL MODEL BASED ON theoretical knowledge of Chemical reactions, Thermodynamics and Transport phenomenon. LIMITATIONS Collection of data, Random fluctuation, Composition of raw materials, Interaction between input variables etc. DATA DRIVEN MODEL BASED ON Actual production data ADAVANTAGES Flexible, Provides vital information on complex interrelations between input and output variables, Can capture actual behavior of process. 4/30/2012 5
  6. 6. AVAILABLE DATA* INPUT • Hot Blast Temperature(°C) • Average Blast Pressure(Kg/cm²) • Average Blast Volume(Nm³/THM) • Top Pressure(Kg/cm²) • Blast Humidification(gm/Nm³) • Carbon Rate(Kg/THM) • Coke Ash(%) • Coke Moisture(%) • Sinter Ore Ratio OUTPUT • Productivity ((THM/Day)/W.V.) • Net CO2 ((T/Day)/W.V.) in exit gas stream • Silicon content(%) in the Hot Metal * One Year operational data from RSP, BF No. - 44/30/2012 6
  7. 7. DATA PREPROCESSING • Carbon rate recalculation • Top Gas Estimation • Linear Interpolation for filling of missing Data • Removing Outliers 4/30/2012 7
  8. 8. INPUT VARIABLES IN THE DATASHEET 4/30/2012 8
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  11. 11. PROBLEM FORMULATION MULTIOBJECTIVE OPTIMIZATION Maximize Productivity and Minimize net CO2 in the exit gas stream subject to a constraint on the Silicon content in the Hot Metal. Constraint on Silicon : 1. Low Silicon (0.40 – 0.55 %), 2. Medium Silicon (0.55 – 0.70 %) 3. High Silicon (0.70 - 0.80 %) 4/30/2012 11
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  13. 13. MODEL DEVELOPMENT 1. Artificial Neural Network (ANN): • Evolutionary Neural Network (EvoNN) based on Predator- Prey Genetic Algorithm. (Pettersson et al (2007)) • Neural Network (NN) in modeFRONTIER 2. Genetic Programming: • Bi-Objective Genetic Programming (BioGP) based on Predator-Prey Genetic Algorithm. • Evolutionary Designs (ED) in modeFRONTIER. 4/30/2012 13
  14. 14. OPTIMIZATION 1. Evolutionary Neural Network (EvoNN) by Predator-Prey Genetic Algorithm. 2. Bi-Objective Genetic Programming (BioGP) by Predator- Prey Genetic Algorithm. Validation of above results by Optimization Softwares: 1. modeFRONTIER (Esteco) 2. Kimeme (Cyber Dyne S.r.l.) 4/30/2012 14
  15. 15. EVOLUTIONARY ALGORITHM Goals of Ideal Multi Objective Optimization (1.) Converge the solutions on the Pareto-optimal front. (2.) Maintain as diverse a distribution as possible. In EA this can be achieved by (1.) Population approach to find multiple solutions (2.) Niche-preservation methods to find diverse solutions 4/30/2012 15
  16. 16. 16 Features C o s t PARETO FRONT 4/30/2012
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  18. 18. WORKING OF EA (Deb (2001)) 4/30/2012 18
  19. 19. NEURAL NETWORK 4/30/2012 19
  20. 20. NEURAL NETWORK Output layer HIDDEN LAYER INPUT LAYER 4/30/2012 20
  21. 21. Preys are randomly generated solutions Predators are entities whose sole purpose is to kill the weakest prey based on fitness, it’s population remains fixed during the whole process. Both Predators and Preys are allowed to move around A desired Predator–Prey ratio is maintained. PREDATOR-PREY ALGORITHM 4/30/2012 21
  22. 22. EvoNN In this case preys population consists of lower part of Neural Network The objective functions on which GA trains are The training error (f1). The complexity or number of active connections in the lower part of neural net (f2). Due to conflicting nature of objective functions f1 and f2, the tradeoff can be represented as a Pareto-front. Multi-objective optimization is done by Predator-Prey Genetic Algorithm to minimize f1 and f2. 4/30/2012 22
  23. 23. CROSSOVER MUTATION 4/30/2012 23
  24. 24. COMPLEXITY ANALYSIS 4/30/2012 24
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  28. 28. Single Variable Response (SVR) EvoNN 4/30/2012 28
  29. 29. GENETIC PROGRAMMING  It is an extension of Genetic Algorithm  Solutions are represented by randomly generated Computer Programs  It exempts human from designing complex algorithm. Not only designing, but also creating ones that give desired optimal solutions 4/30/2012 29
  30. 30. EVOLUTION REQUIREMENTS Major Preparatory steps /Objective: Find a computer program with several inputs whose output equals the given data 1 Define Terminal set: T = {X, Y, Random-Constants etc.} 2 Define Function set: F = {+, -, *, /, exp, powsq, sin, etc. } 3 Define Fitness Measure: In order to evaluate the Evolved Computer Programs for selection. 4 Define Parameters: Population size, Crossover, Mutation 5 Termination Criterion: No. of Iterations or Convergence 4/30/2012 30
  31. 31. RANDOMLY GENERATING PROGRAMS (+ 2 3 (* X 7) (/ Y 5)) 2 3 * / X 7 5Y + 4/30/2012 31
  32. 32. CROSSOVER Select a random node in each program Swap the two nodes 4/30/2012 32
  33. 33. MUTATION First pick up a random node Delete the node and its children, and replace it with a randomly generated program 4/30/2012 33
  34. 34. BioGP In this case preys population consists of computer programs in form of trees. The objective functions on which GA trains are The training error (f1). The complexity or number of active nodes or depth of the tree (f2). Due to conflicting nature of objective functions f1 and f2, the tradeoff can be represented as a Pareto- front. Multi-objective optimization is done by Predator-Prey Genetic Algorithm to minimize f1 and f2. 4/30/2012 34
  35. 35. PROPOSED GP TREE 4/30/2012 35
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  39. 39. Single Variable Response (SVR) BioGP 4/30/2012 39
  40. 40. modeFRONTIER It is a multidisciplinary and multi-objective optimization software. The objectives and constraint were trained using 1. Neural Network (NN) 2. Evolutionary Designs (ED) 4/30/2012 40
  41. 41. modeFRONTIER 4/30/2012 41
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  46. 46. COMPARISON 4/30/2012 46
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  49. 49. CONCLUSIONS (Training) • Both EvoNN and BioGP were able to capture some of the basic trends and interrelationships. The trained data fitted the plant data well and the errors associated with these techniques were quite low and comparable to some extent. • In modeFRONTIER NN the data almost captured all the trends of the actual plant data and it seemed that it is over fitting. • In modeFRONTIER ED, the trained data was able to catch the basic trends of the actual plant data. The trained data fitted the plant data well and the errors associated with these techniques were quite low and comparable with EvoNN and BioGP. 4/30/2012 49
  50. 50. CONCLUSIONS (contd..) • Direct relation between CO2 and carbon rate, hence any attempt to decrease the CO2 will help in reducing the Carbon (fuel) rate. • Direct relation between Output Silicon and Coke Ash, any attempt to constrain the Silicon will point towards bounding the Coke ash and it will consequently help in maintaining the quality of the raw material (Coke) and thus help in smother operation. • Direct relation between Productivity and Hot Blast temperature, any attempt to increase the Productivity will demand for an increase in Hot blast Temperature. It will help in lowering the carbon (Coke) rate as an additional part of heat is now supplied by the blast. 4/30/2012 50
  51. 51. OPTIMIZATION through EvoNN 4/30/2012 51
  52. 52. OPTIMIZATION through BioGP 4/30/2012 52
  53. 53. modeFRONTIER It is a multidisciplinary and multi-objective optimization software. The trained objectives and constraint were optimized according to problem formulation. It was used for validating the results obtained through EvoNN and BioGP. Multi-objective optimization was performed by 1. Non-dominated Sorting Genetic Algorithm ΙΙ (NSGA ΙΙ ) 2. Evolution Strategies (ES) 3. Multi-Membered (mu+lambda) Evolution Strategies (MMES) 4. Multi Objective Particle Swarm Optimization (MOPSO) 5. Multi Objective Simulated Annealing (MOSA) 4/30/2012 53
  54. 54. WORK FLOW 4/30/2012 54
  55. 55. Comparison (modeFRONTIER NN) 4/30/2012 55
  56. 56. Comparison (modeFRONTIER NN) 4/30/2012 56
  57. 57. Comparison (modeFRONTIER NN) 4/30/2012 57
  58. 58. Comparison (modeFRONTIER ED) 4/30/2012 58
  59. 59. Comparison (modeFRONTIER ED) 4/30/2012 59
  60. 60. Comparison (modeFRONTIER ED) 4/30/2012 60
  61. 61. Kimeme It is a multidisciplinary and multi-objective optimization software. Here, we used the functions evolved from Evolutionary Designs in modeFRONTIER. It was used for validating the results obtained through EvoNN and BioGP. Multi-objective optimization was performed by 1. Non-dominated Sorting Genetic Algorithm ΙΙ (NSGA ΙΙ ) 2. Multi Objective Evolution Strategies (MOES) 3. Multi Objective Differential Evolution (MODE) 4. Multi Objective Particle Swarm Optimization (MOPSO) 5. Strength Pareto Evolutionary Algorithm 2 (SPEA2) 6. Archived Multi Objective Simulated Annealing (AMOSA) 4/30/2012 61
  62. 62. Comparison (Kimeme) 4/30/2012 62
  63. 63. Comparison (Kimeme) 4/30/2012 63
  64. 64. Comparison (Kimeme) 4/30/2012 64
  65. 65. CARBON RATE ANALYSIS 4/30/2012 65
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  68. 68. EvoNN ( % Si 0.70-0.80) HBT Analysis 4/30/2012 68
  69. 69. CONCLUSIONS We were able to get well defined Pareto front by all the techniques. The obtained results by various techniques were in the vicinity of the results found by EvoNN and BioGP. In modeFRONTIER NN there was a case of overfitting. From our model prediction, • We were able to increase Productivity by about 10 - 12 %. • We were able to decrease CO2 in the exit gas by about 8 -10%. • We were able to decrease Carbon rate by about 8 – 10 %. • We were able to constrain Silicon content of Hot Metal within the prescribed bounds that we had set during Multi-Objective Optimization Problem formulation. 4/30/2012 69
  70. 70. FUTURE WORK • In EvoNN we can attempt for ways so as to catch data points with High productivity particularly in the High Silicon region. • We can attempt to decrease the training error of BioGP as it has the largest associated error when compared to other techniques. 4/30/2012 70
  71. 71. REFERENCES • Ariyama, Tatsuro and Sato, Michitaka, (2006), Optimization of Ironmaking Process for Reducing CO2 Emissions in the Integrated Steel Works, ISIJ International, Vol. 46, No. 12, pp. 1736–1744. • Cyber Dyne S.r.l. "Kimeme. A new flexible platform for multi- objective and multi-disciplinary optimization." http://www.kimeme.com/ • Deb, Kalyanmoy, (2001), Multi-Objective Optimization Using Evolutionary Algorithms, John-Wiley, Chicheter. • Esteco, http://www.esteco.com/home/mode_frontier.html 4/30/2012 71
  72. 72. REFERENCES • Li, X., (2003), A real-coded predator–prey genetic algorithm for multiobjective optimization, in: C.M. Fonseca, P.J. Fleming, E. Zitzler, K. Deb, L. Thiele (Eds.), Proceedings of the Second International Conference on Evolutionary Multi-Criterion Optimization, Lecture Notes in Computer Science, vol. 2632, LNCS, pp. 207. • Pettersson,F., Saxen,H., Chakraborti,N., (2007) A genetic algorithm based multiobjective neural net applied to noisy blast furnace data, Applied Soft Computing, vol-7, pp. 387-397. • Schmöle, Peter and Lüngen, Hans Bodo, (2004), Hot metal production in the blast furnace from an ecological point of view, Presented at the 2nd International Meeting on Ironmaking and 1st International Symposium on Iron Ore, Vitoria, Brazil, 4/30/2012 72
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  74. 74. Fitness of prey i as taken by predator j E training error N Number of active networkRandomly generated weight EFFECTIVE FITNESS ASSIGNED NUMBER OF MOVES(M) Target size of prey populationNumber of prey Number of predator 4/30/2012 74
  75. 75. MUTATION Is mutated version of are randomly picked weights User defined mutation constant FONSECA RANKING Rank of any individual Number of individuals dominating i4/30/2012 75
  76. 76. Neural net construction and network training at point A 4/30/2012 76
  77. 77. Neural net construction and network training at point B 4/30/2012 77
  78. 78. Neural net construction and network training at point C 4/30/2012 78
  79. 79. COMPLEXITY ANALYSIS 4/30/2012 79
  80. 80. Productivity EvoNN 4/30/2012 80
  81. 81. CO2 EvoNN 4/30/2012 81
  82. 82. Silicon EvoNN 4/30/2012 82
  83. 83. Productivity BioGP 4/30/2012 83
  84. 84. CO2 BioGP 4/30/2012 84
  85. 85. Silicon BioGP 4/30/2012 85

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