A novel, repeated fed-batch ethanol production system from corn flour by fully recycling fermented supernatants is demonstrated. With recovery of ethanol by evaporation, consecutive removal of the insoluble and soluble inhibitory substances accumulated either completely or partially by filtration, the concentrations of the soluble inhibitors in the system could be maintained at their equilibriums. As a result, a sustained high concentration ethanol production and significant pollution control performance were obtained.

An on-line adaptive substrate feeding control system based on DO/pH easurements and artificial neural network pattern recognition (ANNPR)model for fed-batch cultivation processes proposed previously was successfully applied for the phytase production with recombinant Pichia pastoris. The control strategy could effectively improve the fermentation performance for both cultivation and induction phases. With the standard ANNPR model-based control strategy, the cultivation time before starting the methanol induction could be shortened for about 30%. By adequately increasing the feeding rate iterative step size of the ANNPR-based control during induction phase, methanol concentration could be automatically controlled within an optimal range, leading to an approximate three-fold stable increase in phytase activity compared with those obtained by the traditional DO-Stat method and the on-line methanol electrode-based on–off control strategy. The effectiveness, universal ability, as well as the operational simplicity of the proposed control system has been further verified in the recombinant P. pastoris fed-batch culture process.

It has been recognized that fuzzy control is quite effective in coping with the uncertain dynamics associated with bioprocesses. However, it is, in general, quite time-consuming to determine the IF-THEN type of rules and membership functions in conventional fuzzy control. In the present study, we consider adjusting the membership function on-line with the aid of neural networks, where the role of the neural networks is to recognize the patterns of changes in the DO concentration (oscillatory/ non-oscillatory), and ethanol concentration (increasing/decreasing/nearly constant) in baker's yeast fed-batch cultivation. This neuro-fuzzy control strategy retains the advantages of both neural nets and fuzzy control. The application to baker's yeast fermentation shows the power of this strategy as compared with conventional fuzzy control.

A novel on-line dual analyzer combined with an automatic sampling device was developed. When immobilized glucose oxidase and lactate oxidase are used in the analyzer, glucose and lactate concentration can be measured on-line. Lactate concentration did not influence the measurement of glucose concentration and vise versa. As an example of the application of the analyzer, on-off control of the glucose concentration in fed-batch lactic acid fermentation was carried out to increase the final lactate concentration as much as possible. When the glucose concentration was controlled at I g/l, the final lactate concentration was 57 g/l, corresponding to a 21% increase over that obtained when the glucose concentration was set at 20 g//. The experimental results show that if the glucose concentration is controlled at a low value, say about I g/l, then the accumulation of by-prod-ucts such as acetate can be repressed and the yield of lactate from glucose increases.

We previously proposed a neuro-fuzzy control strategy for bioreactors, and showed the power of this approach by computer simulation. However, pattern learning and pattern recognition by neural networks was quite time-consuming. This is because many training data must be generated somehow, and this has been considered to be an obstacle to practical applications. We made several experiments using E. coli. It was shown that pattern learning and recognition of the change in the dissolved oxygen concentration by neural networks can be easily made only with experimental data. This may be due to the fact that experimental data contain a variety of patterns in nature.

Fuzzy reasoning was applied to control both ethanol and glucose concentrations in fed-batch cultures of baker's yeast. This fuzzy controller consisted of three membership functions (concentrations of dissolved oxygen (DO), ethanol and glucose) and 18 production rules. Fuzzy inference was carried out by IF {A is a and B is b,…}, THEN {C is c} from the on-line measured concentrations of DO, ethanol and glucose. When "medium" concentrations of ethanol and glucose in fedbatch culture of baker's yeast were set at 2g/1 and 0.2g/l, both ethanol and glucose concentrations were controlled at 2.67 ± 0.35g/l and 0.27± 0.25g/l, respectively, ethanol production was reduced from 26g/l to 34g/l, cell yield increased from 0.38 to 0.53g dry cell/g consumed glucose and ethanol yield decreased from 0.50 to 0.14g. ethanol/g consumed glucose, respectively, as compared with those of the glucose only control at 0.2g/l.

Several on-line optimizing control strategies were proposed and tested by computer simulation for the efficient operation of bioreactors. The control task was divided into two, one of which was to search for the optimal operating point and passed the set point to the lower layer of which task was to make the process output follow the set point as soon as possible. It was shown to be effective for the upper layer to express the objective function as a polynomial with respect to the measurement variable and to make use of it for finding the optimum point. Noting that the major dynamic characteristics of bioreactor system is the time-varying and nonlinear nature, the adaptive type control system is inevitable. It was shown to be quite effective to use discrete type self-tuning PID controller and the optimal controller compensated for the interaction between the control loops.Application was made to the cell recycle system for the production of lactic acid and baker's yeast cultivation. It was found from the former application that the control quality can be significantly improved by incorporating the decoupling strategy into the lower layer closed-loop system. It was also found from the latter application that the initial startup period can be significantly reduced by making use of the rough mathematical model.

Automated substrate supply by three types of pH-mediated control methods for ethanol fermentation using Zymomonas mobilis was investigated. Method I involved continuous substrate feeding with pH control using alkali. The system was stabilized by the growth rate of the microorganisms depending on a limiting substrate concentration; however, the growth rate could not be maintained at the maximum specific growth rate (pmax) value. Method 2 involved pH self-stabilization without alkali. When the system was operated under this method, a stopped-flow situation occurred and the growth rate became substrate-limited due to the extremely low glucose concentration. In Method 3, pH-stat culture with separate inflows of alkali and feed medium was carried out. The rate of medium input in this system was determined by the growth rate of the culture in response to a change of pH and it was possible to culture the microorganism at the maximum growth rate without the risk of washout. In this method, the dilution rate and biomass concentration were influenced by the pH set-points, the control interval time and the ratio of inflow of feed medium to alkali. A small difference between the higher pH set-point limit and the culture pH as well as a short control interval time for a computer-controlled digital set point resulted in a large specific growth rate (p) and higher ethanol productivity. A setup involving a digital set-point control (DSC) system demonstrated advantages over a setup involving a feedback control loop using an analog signal. Under optimum operation conditions of DSC, high ethanol productivity of over of 11gI-1h-1 was obtained.

The adaptive on-line optimization strategy was applied for continuous lactic acid fermentation. The control objective was to maximize the lactate production rate while keeping the lactate concentration at a specified value. The control task was divided into two. The task of the upper layer in the hierarchical control scheme was to search for the optimal operating point for cell concentration, while the task of the lower layer was to make the cell and lactate concentrations follow the respective set points which were either specified or given from the upper layer. The cell and lactate concentrations were either measured on-line or estimated every 2min. The bioreactor was equipped with a hollow-fiber module, and control was implemented by manipulating the draw-off rate through the filter and the bleed stream flow rate in an on-off manner. It was experimentally shown that productivity as high as 20kg/m3. h could be attained and maintained for abgut 40-50h when the lactate concentration was kept at either 20kg/m3 or 30kg/m3.

Mathematical formulation was made for the performance evaluation of extractive fermentation using multiple solvents. Two types of solvent-supplying strategies were considered. One is to add multiple solvents simul-taneously and the product is removed at one time. Another is to add them one by one consecutively. Computer simulation was made for batch, fed-batch, and repeated fed-batch operation of acetone-butanol fermentation to show the power of the approach. The result shows that the significant performance improvement in terms of the productivity and the product concentration is expected when two extractants such as oleyl alcohol and benzyl benzoate are used as compared with the case of using only one solvent.