Suna Ertunç

New Tuning Method for Generalized Predictive Control of the Production of S. cerevisiae

In this study, S. cerevisiae was produced in a batch bioreactor in aerobic conditions and the growth medium temperature was controlled at its optimal value. In order to control the growth medium temperature, the Generalized Predictive Control (GPC) method was used. The process was described using an Auto Regressive Integrated Moving Average eXogenous (ARIMAX) model. Model parameters were determined by applying Pseudo Random Binary Sequence (PRBS) signals to the process and using Biermans U-D Factorization Algorithm. By using statistical experimental design and Box-Wilson optimization methods, optimal values of the sampling time and weighting factor were determined. A two-level factorial experimental design technique was used to identify a statistical model. The predicted maximum, minimum and the base levels of sampling time and weighting factor were determined on the basis of the previous knowledge about the plant. It was proposed to determine the values of sampling time and weighting factor giving the best control performance. Integral Square Error (ISE) was selected as the optimization criterion. Growth medium temperature was controlled with very small levels of oscillation around the set point by using optimal controller parameters with GPC.

Generalized Minimum Variance Control of Growth Medium Temperature of Baker's Yeast Production

The control of optimum growth medium temperature of a batch bioreactor in which S.cerevisiae was grown under aerobic conditions has been studied. The generalized minimum variance (GMV) algorithm was applied for on-line computer control. A controlled autoregressive moving average model relating the bioreactor temperature and heat input was used to show the dynamic behaviour of the system. The heat input to the bioreactor was chosen as a manipulated variable. A pseudo-random binary sequence signal was applied to the system and the model parameters were determined using the Bierman algorithm. More suitable values of the GMV controller parameters were determined using a total simulation program. These control parameters were used in experimental and theoretical work under several disturbances.

Adaptive control of S.cerevisiae production

The growth medium temperature of a bioreactor, in which S.cerevisiae was produced at aerobic condition, was controlled applying adaptive PID technique. Glucose was used as an energy and carbon source in the growth medium, and an exothermic reaction took place as a result of the glucose consumption of the microorganism under aerobic condition. Then the specific growth rate of the microorganism was inhibited by the increasing growth medium temperature. To maintain the growth medium temperature at its set point which ensures maximumyeast productivity and quality in the bioreactor, non-linear adaptive PID and linear adaptive PID control systems were employed numerically. The bioreactor in which S.cerevisiae yeast was produced under the aerobic condition was considered as a batch system with respect to mass balance. The heat balance between the cooling jacket and the growth medium in the bioreactor was considered as a continuous system with respect to energy. These approximationswere taken as a basis to develop the simulation work. In the simulation studies, models for the bioprocess were developed by using mass and energy balance equations and solved by utilizing the Runge–Kutta–Felhberg method. The control algorithm developed was added to the simulated model. For the adaptive PID control system, Non-linear Auto Regressive Moving Average with eXogenous (NARMAX) and Auto Regressive Moving Average with eXogenous (ARMAX) models were used, and relevant algorithms were developed on the basis of these black box models. The coefficients of the polynomials for both type of models were estimated from the relevant identification method. It was observed that non-linear adaptive PID control results were better than the results of linear adaptive PID and conventional PID control systems.

Application of generalized predictive control to baker's yeast production

Generalized Predictive Control (GPC) was applied in the production of bakers yeast. The bioreactor was modeled with the autoregressive integrated moving average exogenous (ARIMAX) parametric difference equation model.A 2 L bioreactor with a cooling jacket was used for collecting input-output data. In order to measure pH, temperature, and dissolved oxygen in the bioreactor growth medium, suitable sensors were placed in the bioreactor. Medium temperature and the heat of the immersed heater were selected as output and manipulated variable, respectively. Square wave and a pseudo-random binary sequence (PRBS) signal were used as disturbance. Model parameters were calculated by using the recursive least square parameter estimation method. Bioreactor temperature was controlled theoretically using the GPC algorithm. The control performance was investigated by giving positive and negative step responses to the set point. The GPC algorithm holds the bioreactor temperature succesfully at the optimal set point. Optimum values of the maximum costing horizon ( N 2 ), control horizon ( N U ), and control weighting ( u ) were found to be 10, 1, and 0.005, respectively.

Self-Tuning GMV Control of Glucose Concentration in Fed-Batch Baker’s Yeast Production

A detailed system identification procedure and self-tuning generalized minimum variance (STGMV) control of glucose concentration during the aerobic fed-batch yeast growth were realized. In order to determine the best values of the forgetting factor (λ), initial value of the covariance matrix (α), and order of the Auto-Regressive Moving Average with eXogenous (ARMAX) model (na, nb), transient response data obtained from the real process wereutilized. Glucose flow rate was adjusted according to the STGMV control algorithm coded in Visual Basic in an online computer connected to the system. Conventional PID algorithm was also implemented for the control of the glucose concentration in aerobic fed-batch yeast cultivation. Controller performances were examined by evaluating the integrals of squared errors (ISEs) at constant and random set point profiles. Also, batch cultivation was performed, and microorganism concentration at the end of the batch run was compared with the fed-batch cultivation case. From the system identification step, the best parameter estimation was accomplished with the values λ = 0.9, α = 1,000 and na = 3, nb = 2. Theoretical control studies show that the STGMV control system was successful at both constant and random glucose concentration set profiles. In addition, random effects given to the set point, STGMV control algorithm were performed successfully in experimental study.

The effect of the tuning parameters on the performance of the parametric and nonparametric model based control methods for growth medium temperature of Baker's yeast production

In this study, nonparametric and parametric model based control methods were applied in the control line in order to control the growth medium temperature of aerobic bakers yeast production in a batch bioreactor, and the performance was compared experimentally and theoretically. For a non-parametric model, an experimental reaction curve was found by performing the open-loop step test. The reaction curve was presented by the first order dead time model to represent the dynamic behaviour of the reactor. An Internal Model Control (IMC) system based on a non-parametric model, has been used to track the temperature of the reactor mixture. The simulation program is used to calculate the parameters of the parametric Controlled Auto Regressive Moving Average (CARMA) model for Self-Tuning PID (STPID) control. The parameters of the CARMA model are calculated using Bierman algorithm by applying Pseudo Random Binary Sequence (PRBS). In the first part of the control work, a simulation program was used to observe the performance of DVIC and STPID control systems by calculating the Integral Square Error (ISE) values. Two different kinds of operating conditions, such as load and set point effects were applied to test the control performance for servo and regulatory behaviours. The filter time τ f for IMC and the first parameter of Tailoring polynomial for STPID were used as tuning parameters. In addition, PID control system performance was compared with both parametric and non-parametric control systems. In the second part of the control work, the growth medium temperature of aerobic bakers yeast production was controlled experimentally by using IMC and STPID systems in an on-line computer controlled batch bioreactor. Non-parametric and parametric models for both control systems were tested by considering the reaction heat as a load effect. The heat input given from the immersed heater was chosen as the manipulated variable. It was observed that IMC was more effective than STPID and PID methods.

Biological hydrogen production: effects of inoculation and production media

In this work inoculation media are selected as potato, glycerine and milk to investigate the best storage condition that is important to maintain microorganism activity and stable hydrogen production performance. Cheese whey, potato and enriched potato (with additional glucose and CaCO3) are used as the production media and their hydrogen production results are compared in order to determine the best biohydrogen production media. Batch growth of anaerobic Clostridium butyricum for biohydrogen production is performed in 500 ml screw-capped bottles at 28°C. It is noted that both the inoculation and the production media are highly effective on hydrogen production. Results show that the highest hydrogen production yield is obtained as 50.50 ml H2/g COD using milk as inoculation medium and enriched potato as production medium. Another important production factor is the production rate and when the results of three different inoculation media are compared for different production media, milk inoculation and enriched potato production media give the highest results as 41.9291 mL/L.h.

Fuzzy control with genetic algorithm in a batch bioreactor.

In this study, the growth medium temperature in a batch bioreactor was controlled at the set point by using fuzzy model-based control method. Fuzzy control parameters which are membership functions and relation matrix were found using genetic algorithm. Heat input given from the immersed heater and the cooling water flow rate were selected as the manipulated variables in order to control the growth medium temperature in the bioreactor. Controller performance was tested in the face of different types of input variables. To eliminate the noise on the temperature measurements, first-order filter was used in the control algorithm. The achievement of the temperature control was analyzed in terms of both microorganism concentration which was reached at the end of the stationary phase and the performance criteria of Integral of the Absolute Error. It was concluded that the cooling flow rate was suitable as manipulated variable with regard to microorganism concentration. On the other hand, performance of the controller was satisfactory when the heat input given from the immersed heater was manipulated variable.

Optimization of electrochemical treatment of textile dye wastewater

Purpose – The purpose of this paper is to investigate the electrochemical treatment of textile dye wastewater in the presence of NaCl electrolyte by using aluminium electrodes.Design/methodology/approach – The electrochemical treatment of textile dye wastewater was optimized using response surface methodology (RSM). RSM‐based D‐optimal design was employed to construct statistical models relating turbidity and designed effective parameters known as current density, electrolyte concentration and electrolysis time. The experimental plan consists of a three‐factor (three numerical) matrix.Findings – The results show that the current density has significant effect on the reduction of turbidity. Besides, electrolysis time is the most influential factor on the turbidity. In order to enhance the electrochemical treatment performance, no coagulant addition or further physicochemical processes were employed.Originality/value – Industrial certain textile dye wastewater in Turkey is used to determine optimal values.

A new computer algorithm approach to identification of continuous-time batch bioreactor model parameters

The performance of a continuous-time Recursive Least Squares (CRLS) and a discrete-time Recursive Least Squares (DRLS) algorithms are examined for the growth medium temperature control of a cooling batch bioreactor in which Saccharomyces cerevisiae growth at aerobic condition by using Continuous-time Generalised Predictive Control (CGPC) algorithm. MATLAB programme was utilized for recursive parameter identification algorithms (CRLS and DRLS). The success or otherwise of these algorithms are estimated using parameter norm criterion for the various order of models and several input signals. There is a considerable improvement of identification algorithms with the reduced order of models. It has been shown that the performance of a DRLS algorithm is as successful as the other recursive parameter identification of a continuous-time system model.