Modeling And Optimization Of The Rice Wine Fermentation Process

Rice wine is one of the oldest alcoholic beverages in the world. The primary phase ofrice wine fermentation is a typical simultaneous saccharification and fermentation (SSF)process and is also referred to as a semi-solid state and semi-liquid state fermentation process.This process is critical to rice wine quality control, but there has been little published work onkinetic modeling and control of glutinous rice saccharification and rice wine fermentation. Inthis research, glutinous rice saccharification and fermentation were studied experimentallyand theoretically to develop process models and control strategies. To gain insights into theinfluential system variables, a sequence of experimental studies were carried out to determinethe influence of fermentation temperature and source of enzymes on the ecologicalcharacteristics of rice wine, and the effect of temperature on Chinese rice wine brewing withhigh-concentration pre-steamed whole sticky rice. After kinetic models were developedseparately for glutinous rice saccharification and fermentation, a SSF process model wasdeveloped for the rice wine production process.Rice wine samples were produced with four sources of enzymes at three fermentationtemperatures. Enological variables, including ethanol, main sugars, glycerol, and organicacids, were measured by HPLC at the end of primary fermentation (4days) and at the end ofpost fermentation (40days). The data showed that both source of enzymes and temperaturehad significant effects on the concentrations of the measured variables. The results provideinsights into the rice wine fermentation process as affected by different enzymes andfermentation temperatures.The effects of fermentation temperatures on Chinese rice wine quality were investigated.The compositions and concentrations of ethanol, sugars, glycerol, and organic acids in themash of Chinese rice wine samples were determined by HPLC. The experimental resultsindicated that temperature contributed significantly to ethanol production, acid flavor contents,and sugar contents in the fermentation broth of the Chinese rice wines.Glutinous rice saccharification was performed by using α-amylase, glucoamylase, two-enzyme combination, or wheat qu. Experiments were carried out at two different locations,with rice from different sources, and in varied fermentation temperatures. The main productswere identified and measured by HPLC. Low-order kinetic model structures (forms orconstructs of model with adjustable parameters) were proposed based on the major chemicalreactions brought about by different enzymes. The model structures were then tested for theirabilities to capture the main kinetic variations after parameter optimization by a least-squaresalgorithm. The proposed model structures were found useful in representing measured kineticvariations except those in maltotriose produced with wheat qu. The estimated reactions ratescorrectly reflected the variations observed from the experiments and provided insights into thereaction processes in terms of reaction speeds, dominant variations, and primary products. Theactions of α-amylase and wheat qu differed from findings in prior research. The proposedmodel structures show promise for describing the saccharification process of glutinous rice.A kinetic model structure was developed for the fermentation process by Su-25based on the biochemical reactions involved. Experiments with the Chinese rice wine yeast underdifferent conditions were performed and used to validate the model structure. It was foundthat the model structure could decribe the ferementation process. The developed modelstructure can be used to control or optimize rice wine production.Rice wine fermentation was performed by using pre-steamed rice, Chinese wheat qu andrice wine yeast strains Saccharomyces cerevisiae Su-25. Experiments were carried out withdifferent conditions, and the main products were identified and measured by HPLC. A low-order kinetic model structure was proposed based on the major chemical reactions in the ricewine fermentation process. The model structure was then tested for its abilities to capture themain kinetic variations after parameter optimization by a least-squares algorithm. Theproposed model structure showed promise for describing the fermentation process of ricewine.Another kinetic model structure was developed for the fermentation process of Su-25according to the involved biochemical reactions. The model structure can be used for differenttemperatures and different initial substrate concentrations. Experiments with Chinese ricewine yeast under different conditions were performed and used to validate the model structure.The model structure was verified with experiments in both the lab scale and the plant scale.Wine fermentation is a batch processes, for which conventional feedback controltechniques are either ineffective or inapplicable. Based on the framework of GeneralizedPredictive Control (GPC), a predictive control strategy was formulated to control the batchfermentation process by varying the initial process conditions. The formulation is equivalentto GPC with a unit control horizon but a long predictive horizon and its implemented boilsdown to least-squares optimization of an initial process condition vector for a given processoutput target under variaous constraints. Simulations were performed to demonstrate that thetechnique could drive the process towards desired output targets.