Doctorado en Biociencias · 2017
Application of strategies of advanced control under the active disturbance rejection control, to produce lipids from microalgae
En esta investigación se diseñaron estrategias de control avanzado bajo el enfoque del rechazo activo de perturbaciones (ADRC, Active Disturbance Rejection Control) para incrementar la producción de biomasa en cultivos de microalgas. Para lo anterior, desde el punto de vista del control, esta investigación se planeó en dos etapas: control y optimización. La primera etapa resultó en tres diseños diferentes de controladores: dos estrategias ADRC asistida por observador y un control libre de modelo (MFC, Model-Free Control). En cada caso, el objetivo fue garantizar el seguimiento de la señal de referencia. En la segunda etapa, se realizaron dos diseños de estrategias de optimización con el fin de incrementar la producción de biomasa, una fuera de línea y una en línea. Al comparar, a nivel de simulación, estas estrategias con otras propuestas ya existentes, se encontró que: 1) las estrategias ADRC asistidas por observador tienen poca dependencia del modelo, permitiendo trabajar con un modelo aproximado que solo requiere conocer el orden del sistema y la ganancia de entrada; 2) la optimización fuera de línea aunque logra maximizar la producción de biomasa requiere conocer el modelo y 3) la propuesta que combina MFC con la optimización en línea, puede actuar sobre cualquier cultivo de microalgas ya que no necesita de un modelo. Todas las propuestas son robustas frente a perturbaciones permitiendo incrementar la producción de biomasa cuando se hace uso de una estrategia de optimización.
Texto completo 40 páginas con texto
Leer la tesis completa Ficha en el repositorio
Contenido
- INTRODUCTIONp. 15
- Referencesp. 16
- OBJECTIVESp. 18
- 2.1 General Objectivep. 18
- 2.2 Specific Objectivesp. 18
- 2.3 Objectives-articles relationshipp. 18
- 2.4 Status of written articlesp. 20
- 2.5 Individual’s contributionsp. 21
- RESULTSp. 24
- Culturep. 8
- Graphic abstractp. 24
- Highlightsp. 9
- Abbreviationsp. 9
- Referencesp. 16
- microalgae photobioreactorp. 20
- Abstractp. 24
- Graphic abstractp. 24
- Highlightsp. 25
- Abbreviationsp. 25
- with productivityp. 30
- Referencesp. 16
- in a batch culture53
- Abstract53
- Graphic abstractp. 24
- Highlights53
- Abbreviations54
- Referencesp. 16
- microalgae in a batch culturep. 21
- Abstract73
- Graphic abstractp. 24
- Highlights73
- Abbreviations74
- Referencesp. 16
- obliquus93
- Abstract93
- Graphic abstractp. 24
- Highlights93
- Abbreviations94
- Referencesp. 16
- of Acutodesmus obliquusp. 23
- Abstract108
- Graphic abstractp. 24
- Highlights108
- Abbreviations109
- Referencesp. 16
- CONCLUSION AND FUTURE WORK125
- 5.1 Conclusionp. 37
- 5.2 Future work125
- Figure 2.1. Structure of the document and published papersp. 20
- Figure 3.1.1. General diagram of the control proposalp. 14
- Figure 3.1.2. Determination of μoptp. 17
- Figure 3.1.3. Signal tracking reference (μopt)p. 18
- Figure 3.1.4. Control signal behavior (q0(t))p. 18
- Figure 3.1.5. Biomass concentration (CX) obtained in 150 hp. 18
- Figure 3.1.6. Behavior of reference signal versus changes in KIp. 19
- Figure 3.1.7. Biomass concentration (CX) versus changes in KIp. 20
- Figure 3.1.8. Biomass of reference signal versus changes in KIIp. 20
- Figure 3.1.9. Biomass concentration (CX) versus changes in KIIp. 20
- Figure 3.2.1. Schematic representation of a generic bubble column photobioreactorp. 28
- steady statep. 31
- Figure 3.2.3. Conceptual diagram of the ADRC structure and signals flowp. 35
- (D(t)) and the specific growth rate (μ(t)) under nominal conditionsp. 38
- Figure 3.2.5. Conceptual diagram of the signals flow when the disturbance is presentp. 39
- disturbance. Right column (c,d,e), smooth disturbancep. 40
- (D(t)) and the specific growth rate (μ(t)) whit variations in CX*(t)p. 40
- the positive and negative 30% variation of the nominal value44
- and negative 30% variation of the nominal value44
- numerals. The arrows indicate the direction of time47
- have been labelled with roman numerals. The arrows indicate the direction of time47
- Figure 3.3.1. Schematic representation of PBR57
- Figure 3.3.2. Variation of μp(q0) with values of CX constant60
- Figure 3.3.3. Behavior of q0MAX(CX) and q0*(CX)61
- Figure 3.3.4. Behavior of μMAX(CX)61
- Figure 3.3.5. Optimal decision curve for intensity of q0*62
- Figure 3.3.6. Flowchart of the proposed algorithm62
- Figure 3.3.7. System block diagram62
- curve in nominal conditions65
- curve with perturbations67
- curve with variation of parameters68
- Figure 3.4.1. Schematic representation of a generic flat rectangular PBR77
- first stage. Right figure (b), reference signal in the second stage80
- Figure 3.4.4. Flowchart of the heuristic optimization proposed81
- Figure 3.4.5. Optimal generated reference, μ*(t)81
- according to tracking of the reference value84
- under nominal conditions85
- negative disturbance87
- with variation of parameters88
- Human-Machine Interface (HMI) and E) production system95
- nitrogen concentrations99
- (F) Time 5 (15th day of cultivation)99
- (-). Lipid vacuoles (-) produced during 15 days of experimentation100
- Figure 3.5.5. Lipid extraction efficiency according to method101
- Classical (Orange Line), Microwave (Blue Line), Soxhlet (Black Line)103
- Human-Machine Interface (HMI) and E) production system111
- Figure 3.6.2. Cell concentration according to intensity and color of the light114
- Figure 3.6.3. Lipids production according to intensity and color of the light114
- light115
- Figure 3.6.5. Methyl esters production with violet light at different intensities116
- Figure 3.6.6. Methyl esters production with yellow light at different intensities116
- Figure 3.6.7. Methyl esters production with green light at different intensities117
- Figure 3.6.8. Methyl esters production with red light at different intensities118
- Table 2.1. Status of written articlesp. 20
- Table 2.2. Individual’s contributionp. 21
- Table 3.1.1. Model parametersp. 13
- Table 3.1.2. Comparison NMPC and ADRC strategiesp. 19
- Table 3.2.1. Model parametersp. 19
- Table 3.2.2. Values of D(t) = μ(t) and TIC as time → ∞, for the different values of Iin46
- Table 3.3.1. Model parameters63
- curve in nominal conditions66
- curve with perturbations67
- curve with variation of μ068
- Table 3.4.1. Model parameters84
- under nominal conditions86
- perturbations87
- variation of parameters89
- Table 3.5.1. Statistical probability for comparisons of treatments100
- Table 3.5.2. Biomass and lipids extracted from different types of extractions101
- Table 3.5.3. Relative amounts of acids obtained with the different extraction methods104
- Table 3.6.1. Methyl esters in major percentage giving to intensity and color104
- Table A.1. Physical variables that affecting the growth of microalgae126