Prediction Of State Variables And The Optimal Scheduling For Industrial 2-keto-L-gulonic Acid Fermentation Processes

L-ascorbic acid (L-AA) also known as vitamin C is an essential nutrient for human being. In the 1970s, an innovative bioconversion process was developed for the production of 2-keto-L-gulonic acid (2-KGA), a key precursor for L-ascorbic acid synthesis. With this process, 2-KGA is produced via the two-stage fermentation, and then converted to L-AA by catalytic reactions. Since then, fermentation became the dominant process in the L-AA production. In the last decades, the equipment and production scale in fermentation industries are rapidly expanded with the development of biotechnology, then the higher automation, stability and reliability of the process are required. However, it is difficult to obtain the kinetic modeling for 2-KGA fermentation processes because the complex interactions of the two involved microorganisms are not well known yet. On the other hand, a large amount of process data are collected from industrial 2-KGA cultivation and the data contain abundant information of processes. Therefore, data-driven improvement of the process performance is focused on in this dissertation.Based on the on-line measurements and off-line assay data, key technologies are studied in this dissertation for optimizing industrial 2-KGA fermentation processes, such as on-line prediction of state variables, optimal scheduling for multi-bioreactor workshop, process monitoring and fault detection, where artificial intelligence technologies (e.g., support vector machines, intelligent database and fuzzy logic) and statistical analysis methods are applied. The main contents are as follow:1. Prediction of state variables in industrial 2-KGA fermentation processes The state variables such as 2-KGA formation and 2-KGA concentration could provide important information for the optimization of fermentation processes. Data-driven prediction of the state variables is presented in this paper. The 2-KGA formation is on-line predicted by integrating SVM-based rolling learning-prediction technology with the AdaBoost algorithm. The AdaBoost algorithm is used to adaptively boost the performance of SVM predictors, which is demonstrated to be beneficial to improve the prediction accuracy and the robustness. The validation results by using the data from commercial scale 2-KGA cultivation show good generalization performance and noise tolerance of the prediction approach. According to the estimation of future medium volume by statistical analysis and the prediction of total product, 2-KGA concentration is predicted.Due to the risk of contamination and other disturbances, the abnormality of fermentation processes often arises. The on-line fault detection method based on the prediction of product formation is presented for industrial 2-KGA cultivation. The results demonstrate that the proposed method could rapidly detect abnormalities of the processes.2. On-line computation and prediction of the profit functionThe profit function is an integrated index to describe the cost-effect of the 2-KGA fermentation processes, which is defined as the gross profit of a batch over its production time. The profit function is online calculated according to the mass balance and further predicted with the SVM-based rolling learning-prediction technique, which is potentially applicable for optimal scheduling. In the dissertation, it is also discussed how to establish and update the historical training database. Two updating database methods are proposed according to the profit category and the K-NN algorithm, respectively. The prediction results using the data from industrial scale 2-KGA cultivation indicate the advantage of the K-NN algorithm-based updating method and that may be independent on the inoculation sequences. 3. Optimal scheduling for 2-KGA cultivationAn optimal scheduling approach for 2-KGA fermentation process is proposed to improve allocation of L-sorbose resource with the aim of maximizing the economic profit of the multi-bioreactor workshop. The empirical operation in 2-KGA cultivation under study is to assign the same quantity of L-sorbose to each batch regardless of the batch-to-batch variations, while the optimal scheduling approach presented will determine the L-sorbose feeding according to the evaluation of the profit-making ability of the individual batch. Each batch is on-line evaluated based on the batch classification and its scheduling function. The batches of high profit-making ability will be fed more L-sorbose to lengthen the cultivation of these batches and exploit their profit contribution, while the poorly performed batches will be decreased the quantity of fed L-sorbose and terminated earlier to avoid profit loss. As a result, the scheduling strategy will make use of the L-sorbose resource more effectively and yield higher overall profit. Pseudo-on-line scheduling is carried out by using the data of industrial 2-KGA batches. The total profit increase of ca. 7% is demonstrated in comparison with the empirical operation.4. Developing on-line prediction and optimal scheduling software for 2-KGA fermentation processesFinally, the software is developed for 2-KGA fermentation processes based on the theoretic researches in this dissertation. The software real-time collects data from 2-KGA industrial cultivation. It is able to realize on-line prediction of the key state variables such as total product and product concentration, computation and prediction of the profit function, on-line evaluating the profit-making potential of the current batches, and optimal scheduling for multi-bioreactor system. The software has been installed and run successfully in the 2-KGA fermentation workshop.