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Parameter Optimization of a Bioprocess Model using Tabu Search Algorithm

Parameter Optimization of a Bioprocess Model using Tabu Search Algorithm. Olympia Roeva, Kalin Kosev Institute of Biophysics and Biomedical Engineering Bulgarian Academy of Sciences 105 Acad. G. Bonchev Str., Sofia 1113, Bulgaria E-mail: olympia@clbme.bas.bg. 1. Introduction

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Parameter Optimization of a Bioprocess Model using Tabu Search Algorithm

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  1. Parameter Optimization of a Bioprocess Model using Tabu Search Algorithm Olympia Roeva, Kalin Kosev Institute of Biophysics and Biomedical Engineering Bulgarian Academy of Sciences 105 Acad. G. Bonchev Str., Sofia 1113, Bulgaria E-mail: olympia@clbme.bas.bg

  2. 1. Introduction 2. Outline of the TS algorithm 3. Test problem 4. Results and discussion Bioprocesses → complex → highly nonlinear Mathematical descriptions → hard simplifications Metaheuristic methods →new, more adequate modeling concepts

  3. 1. Introduction 2. Outline of the TS algorithm 3. Test problem 4. Results and discussion

  4. 1. Introduction 2. Outline of the TS algorithm 3. Test problem 4. Results and discussion with Stefka Fidanova

  5. 1. Introduction 2. Outline of the TS algorithm 3. Test problem 4. Results and discussion Tabu Search (TS) → Fred Glover, 1986

  6. 1. Introduction 2. Outline of the TS algorithm 3. Test problem 4. Results and discussion A pseudo code of a TS is presented as: Step 1. Initialization Step 3. Next iteration Set k = 1 Set k = k + 1 Generate initial solution S0 IF k = N THEN Set S1 = S0, then G(S1) = G(S0) STOP Step 2. Moving ELSE Select Sc from neighborhood of SkGOTO Step 2 IF move from Skto Scis already in TL THEN END IF Sk+1 = Sk GOTO Step 3 END IF IF G(Sc) = G(S0) THEN S0 = Sc END IF Delete the TL move in the bottom of TL Add new Tabu Move in the top of TL GOTO Step 3

  7. 1. Introduction 2. Outline of the TS algorithm 3. Test problem 4. Results and discussion Parameter identification of E. coli MC4110 fed-batch cultivation model Real experimental data of the E. coli MC4110 fed-batch cultivationare used.

  8. 1. Introduction 2. Outline of the TS algorithm 3. Test problem 4. Results and discussion Case 1 Objective function

  9. 1. Introduction 2. Outline of the TS algorithm 3. Test problem 4. Results and discussion Case 2

  10. 1. Introduction 2. Outline of the TS algorithm 3. Test problem 4. Results and discussion

  11. 1 Experimental data Model data (TS) 0.8 0.6 Substrate, [g/l] 0.4 0.2 0 6 7 8 9 10 11 12 Time, [h] Results from optimization 0.14 0.12 0.1 0.08 Acetate, [g/l] 0.06 0.04 0.02 6 7 8 9 10 11 12 Time, [h] 1. Introduction 2. Outline of the TS algorithm 3. Test problem 4. Results and discussion Time profiles of the process variables

  12. 10 Experimental data Model data (TS) 8 6 Biomass, [g/l] 4 2 0 6 7 8 9 10 11 12 Time, [h] 21.2 21 20.8 20.6 Dissolved oxygen, [%] 20.4 20.2 20 6 7 8 9 10 11 12 Time, [h] 1. Introduction 2. Outline of the TS algorithm 3. Test problem 4. Results and discussion Time profiles of the process variables

  13. 5. Conclusion • TS performs • equal that GA and SA in terms of solution quality and • betterthat GA and SAin terms ofcomputation time • for considered here problem. • Summarized: • TS avoids entrapment in local minima and continues the search to give a near-optimal final solution; • TS is very general and conceptually much simpler than either SA or GA; • TS has no special space requirement and is very easy to implement (the entire procedure only occupies a few lines of code); • TS is a flexible framework of a variety of strategies originating from artificial intelligence and is therefore open to further improvement.

  14. 6. Future work

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