Thesis (Ph.D.) - University of Queensland, 2003. / Includes bibliography.

Thesis (MTech (Electrical Engineering))--Peninsula Technikon, 2002. / Word processed copy. Summary in English. Includes bibliographical references (leaves 147-155). Also available online.

Thesis (MTech (Electrical Engineering))--Peninsula Technikon, Cape Town, 2002 / Fermentation is the process that results in the formation of alcohol or organic acids on the basis of growth of bacteria, moulds or fungi on different nutritional media (Ahmed et al., 1982). Fermentation process have three modes of operation i.e. batch, fed-batch and continuous mode ofoperation. The process that interests a lot of control engineers is the fed-batch fe=entation process (Johnson, 1989). The Fed-batch process for the production ofyeast is considered in the study. The considered yeast in the study is the Saccharomyces cerevisiae. It grows in both aerobic and anaerobic environmental conditions with maximum product in the aerobic conditions, also at high concentration of glucose (Njodzi, 2001). Complexity of fedbatch fe=entation process, non-linearity, time varying characteristics, application of conventional analogue controllers provides poor control due to problems in tuning individual loops and the process characteristics. The problem for control of the fedbatch process for the production of yeast is further complicated by the lack of on-line sensors, lack of adequate models as a result of poorly understood dynamics. The lack of on-line sensors results in the impossibility of tuning the analogue controllers in real time. The process for propagation of yeast in aerobic conditions is considered in the dissertation. The experiments are conducted at the University of Cape Town (VCT), Department of Chemical Engineering with a bioreactor and bio-controller are combined in a Biostat ® C lab scale plant (B. Braun Biotech International, 1996). The bio-controller has built in PID controller loops for control variables, with the ability to adjust the controller parameters i.e. P, D and I through the serial interface (Seidler, 1996).

Submitted in fulfillment ofthe requirement for Masters degree oftechnology (Mtech): Electrical engineering, 2002 / Fermentation is the process that results in the formation of alcohol or organic acids on the basis of growth of bacteria, moulds or fungi on different nutritional media (Ahmed et al., 1982). Fermentation process have three modes of operation i.e. batch, fed-batch and continuous mode ofoperation. The process that interests a lot of control engineers is the fed-batch fe=entation process (Johnson, 1989). The Fed-batch process for the production ofyeast is considered in the study. The considered yeast in the study is the Saccharomyces cerevisiae. It grows in both aerobic and anaerobic environmental conditions with maximum product in the aerobic conditions, also at high concentration of glucose (Njodzi, 2001). Complexity of fedbatch fe=entation process, non-linearity, time varying characteristics, application of conventional analogue controllers provides poor control due to problems in tuning individual loops and the process characteristics. The problem for control of the fedbatch process for the production of yeast is further complicated by the lack of on-line sensors, lack of adequate models as a result of poorly understood dynamics. The lack of on-line sensors results in the impossibility of tuning the analogue controllers in real time. The process for propagation of yeast in aerobic conditions is considered in the dissertation. The experiments are conducted at the University of Cape Town (VCT), Department of Chemical Engineering with a bioreactor and bio-controller are combined in a Biostat ® C lab scale plant (B. Braun Biotech International, 1996). The bio-controller has built in PID controller loops for control variables, with the ability to adjust the controller parameters i.e. P, D and I through the serial interface (Seidler, 1996). Even though the used lab scale bio-controller has the ability to monitor certain variables, the automation of the industrial bioreactors is still developing slowly (Dochan and Bastin, 1990) with major problems experienced in modelling and measuring important control variables on-line. This existing situation is due to the characteristics of the fermentation processes as an object of control with highly non-linear, non-stationary, slow dynamics and complex relationships between variables. The existing control strategies on industry are based only of local PID control of some easy for measuring variables. No computer systems for monitoring and optimisation of the process (Morari and Stephanopoulos, 1980). The dissertation is overcoming the mentioned above drawbacks by developing methods, algorithms and programmes for building of a two layer system for optimal control of the Biostat ® C pilot plant with the following subsystems: ~ Data acquisition, ~ Modelling and simulation, ~ Model parameter estimation, ~ Process optimisation, ~ PlO controller parameter tuning, ~ Real time control implementation The system is based on LabVIEW™ and serial communication protocol. The interface between the Bio-start and the host computer is through a standard communication serial port. The development in the dissertation are described as follows: Chapter 1 describes the necessity of the research discussed in the dissertation and highlights comparison between the different approaches for modelling and control of fed-batch processes for the production of yeast, (Johansson, 1993). The aim and the objectives ofthe dissertation are stated and explained. Chapter 2 describes the process as an object of control looking precisely at the influence of the physiochemical variables on the biological variables. The relationship is identified through the enzymes. The results from previous experiments are discussed to illustrate the constraints associated with the control of the process under study Chapter 3 describes the different types of models as applicable to the dissertation. The comparison between different types is highlighted. The derivation of the developed yeast model using mass balance equations and rate laws is discussed and presented in the chapter. The problem for simulation of the model is solved using Matlab and LabVIEW™ programs. Chapter 4 describes the formulation of the problem for estimation of the fed-batch model coefficients and the method, algorithm and programme developed to solve this problem. In chapter 5 the optimal control problem is formulated and solved using optimal control theory, the approach of the functional of Lagrange is used. The optimisation layer problems are determined and based on the solutions of the previous upper layer i.e. the model parameters from the adaptation layer. The optimal operation of the process or yield of the yeast is based on some criteria for the production of biomass, and some constraints over minimal and maximal values of the variables. Decomposition method to solve the optimal control problem is developed on the bases of an augmented functional of Lagrange and decomposition in time domain. Algorithm of the method and program in Matlab are developed. Tuning of PID parameters for the controllers in Biostat ® C is described based on the optimal trajectories for the physiochemical variables obtained from the optimal control problem solution. Chapter 6 presents algorithms and programmes for monitoring and real time control of fed-batch fermentation process for the production of yeast, using Personal Computer, B Braun Biostat ® C Lab scale fermentation unit and LabVIEW™ as driver software. Hardware and software parts of the control systems are described and discussed. LabVIEW™ code is described. Chapter 7 presents the users manual. The mam functionality of the developed application and programmes is described and discussed. The source code ofthe developed programmes is presented in chapter 8. Chapter 9 presents the conclusion highlighting the developments in the dissertation as well as the future work on the topic and the possible application of the developed work in industry on the bigger scale fermentors. The positive characteristics of the developed methods algorithms and programmes are: -7 The developed model incorporates the physiochemical variables in the biological mass balance equations. In this way: the influence of the enzymes over the biological variables is utilised and possibilities for process optimisation is created. The process can be optimised m both physiochemical and biological variables. The physiochemical variables can be used as control inputs to reach the process optimisation. Data acquisition system gIves good possibilities for analysis and simulation ofthe process. The optimal control of the process is achieved without using expensive online sensors for measurement of the biological variables. This is why the developed system is applicable to the existing hardware and software control and measurement systems in industry. The control system automates the operation of the lab scale fermentation unit. It is safe, stable and operational.

Thesis (Ph.D.) - University of Queensland, 2005. / Includes bibliography.

The start-up and step-up operation of two 16-liter, continuously operated, upflow anaerobic reactors receiving baker's yeast wastewater is presented in this thesis. The two reactors (A and B) were almost identical in construction. Reactor A was a conventional upflow anaerobic sludge blanket (UASB) reactor, and reactor B was a hybrid reactor. In addition to all the features of a UASB reactor, a fixed-film structure was installed in the mid section of the reactor B. Both reactors were operated at 35 °C and at a constant hydraulic retention time of 7 days. The waste strength, expressed in chemical oxygen demand (COD), was varied from 8 g COD liter⁻¹ (during the start-up) to 58 g COD liter⁻¹. The organic loading rate ranged from 1.1 to 9.4 g COD liter⁻¹ day⁻¹. The start-up lasted for the first 46 days. Towards the end of the start-up, methane production rates of 0.23 and 0.28 liter CH₄ liter⁻¹ day⁻¹ and COD reductions of 62.2% and 67.2% were achieved at organic loading rates of 1.1 and 1.3 g COD liter⁻¹ day⁻¹ for reactors A and B respectively. During the step-up operation, maximum methane production rates were, for reactors A and B respectively, 0.91 and 0.95 liter CH₄ liter⁻¹ day⁻¹ at organic loading rates of 5.8 and 6.4 g COD liter⁻¹ day⁻¹. In addition, reactor profiles for sludge concentration, pH, volatile fatty acids, and COD are also presented. / Applied Science, Faculty of / Chemical and Biological Engineering, Department of / Graduate