The research objective of this case study was to develop an appropriate control method for a bioprocess and to implement it on a laboratory plant, namely the control of the fed batch cultivation of Hansenula polymorpha yeast for alcoholoxydase-containing biomass. At first, the process is described and a mathematical model is proposed and then the control strategy is defined and the intelligent control structure is designed. Finally, the control performances are tested through real data.

A discontinuous fed-batch bioprocess for alcoholoxydase-containing biomass with the methylotrophic yeast Hansenula polymorpha CBS — 4732 was operated in an airlift lab — bioreactor The intracellular enzyme, to be separated further on, is used for obtaining a high — specialized kit for methanol/ethanol determination. The yeast was cultivated on a complex medium with (NHi)2SO4, KH2PO4, Na2HPO4, MgSO4*7H2O, CaCh, yeast extract or autolysed residual beer yeast as organic N source and microelements (Fe, B, Cu, I,

)

where: Es and Em are the substrate and medium loss by evaporation [g/h]; ps and рм are the substrate and medium densities [g/L]; Yx/s is the substrate conversion yield referred to the biomass [g dry matter/ g substrate]; q is the specific growth rate [1/h]; V is the volume of the cultivation medium in the bioreactor [L]; x and S are the biomass and substrate concentrations [g/L] and t is the time [h], qmax represents the maximum specific growth rate [1/h] and Ks is the saturation constant [g/g]. The main process parameters were: continuous temperature control 37oC; a minimal level of pO2 — 10% from the saturation concentration was maintained during the exponential growth; continuous pH control between 4.5 — 5.0 by addition of NH4OH (12.5%); no foam control, if the main parameters are optimally controlled. The unique C source, the methanol was introduced function of the yeast growth rate in connection with the substrate consumption rate for avoiding the growth inhibition by substrate concentration. The developed model (1) is based on the mass-balance principle and on the hypothesis of a non-inhibitive substrate effect (i. e. the specific growth rate is defined by
the Monod equation). In line with the operation mode (fed-batch with discontinuous substrate feeding), there are discontinuous variations of the main variables due to: substrate feeding, medium feeding (to overcome the loss by evaporation or sample collection) or samples withdraws. That is why the following mass-balance equations are to be added to express each discontinuous modification for volume, and substrate or biomass concentrations:

Vk + ASk + AMk = PMk + Vk +1

SkpMVk + ASkpS = PMkpMSk + Sk+1pMVk +1 (32)

XkVk = PMkXk + Xk+1Vk+1

where: Vk, Vk+1=volume before / after modification [L]; ASk, AMk=substrate volume and respectively medium volume adding [L]; PMk=sample withdraw [L]. The same notations are used for Sk, Sk+1 and Xk, Xk+1. We use: ps = 800[g/L]., respectively qm = 1000[g/L]. The identification of the model parameters was carried out based on measured values in order to minimize the modeling error. The identification procedure (i. e. Nelder-Mead algorithm) determines the optimum values for the following process parameters: Es, Em, pmax, Ks and Yx/s.

For this bioprocess, the overall control objective is to obtain large biomass quantities, based on the assumption that high biomass concentration will assure the obtaining of important alcoholoxydase-active biomass. In this paper a control system based on fuzzy logic is proposed. It is well known that Fuzzy Control Systems (FCS) can manipulate incomplete and uncertain information about the process assuring high control performances [6-8]. The proposed FCS receives information about the state of the bioprocess expressed by the biomass and substrate concentrations. Based on this information, FCS computes the quantity of substrate to be added into the reactor. According to these observations the inputs of FCS are the biomass (X) and substrate (S) concentrations, and the output is the correction to be applied on the substrate addition. The rules of FCS are presented in Table 1.

Rules evaluation by the inference engine is made according to the min-max inference rule and the output defuzzyfication is made based on the centroid defuzzyfication method.

Table 6. The rule base

4. Results & discussions

The control loop was implemented in MATLAB, version 7.5. For control loop simulation the proposed mathematical model was used and the simulation results were compared with the experimental data.

The simulation results show that the proposed fuzzy control system is capable of computing the substrate feedings needed for cell growth according to the biomass concentration increase. The evolution of the substrate concentration marks the substrate consumption and additions, as well as the increase of the additions along with cell growth. The biomass concentration obtained by simulation follow closely the experimental data. As a conclusion of this case-study, it can be accepted that the success of such a control implementation is critically dependent upon the technical operating conditions of the process.

5. Conclusions

The overview on the current status of bioprocess modeling and control focuses on three main topics: (i) unstructured versus structured and metabolic modeling; (ii) control based on common technique (model based control and adaptive control); (iii) control based on artificial intelligence.

It is finally to underline that the framework of bioprocess modeling & control still offers interesting perspectives to obtain robust control solutions for the aerobic bioprocess. Moreover the future of bioprocesses’ optimal control will rely on applying the same concept: the use of different modeling methods in conjunction with intelligent control techniques. If a simplified representation of the bioprocess exists (i. e. an a priori model), this optimal profile can serve as an initial trajectory for intelligent control algorithms when the complexity of the process representation is described in a subjective mode (by human expert).