An Approach to Continuous Approximation of Pareto Front Using Geometric Support Vector Regression for Multi-objective Optimization of Fermentation Process

The approaches to discrete approximation of Pareto front using multi-objective evolutionary algorithms have the problems of heavy computation burden, long running time and missing Pareto optimal points. In order to overcome these problems, an approach to continuous approximation of Pareto front using geometric support vector regression is presented. The regression model of the small size approximate discrete Pareto front is constructed by geometric support vector regression modeling and is described as the approximate continuous Pareto front. In the process of geometric support vector regression modeling, considering the distribution characteristic of Pareto optimal points, the separable augmented training sample sets are constructed by shifting original training sample points along multiple coordinated axes. Besides, an interactive decision-making(DM)procedure, in which the continuous approximation of Pareto front and decision-making is performed interactively, is designed for improving the accuracy of the preferred Pareto optimal point. The correctness of the continuous approximation of Pareto front is demonstrated with a typical multi-objective optimization problem. In addition,combined with the interactive decision-making procedure, the continuous approximation of Pareto front is applied in the multi-objective optimization for an industrial fed-batch yeast fermentation process. The experimental results show that the generated approximate continuous Pareto front has good accuracy and completeness. Compared with the multi-objective evolutionary algorithm with large size population, a more accurate preferred Pareto optimal point can be obtained from the approximate continuous Pareto front with less computation and shorter running time. The operation strategy corresponding to the final preferred Pareto optimal point generated by the interactive DM procedure can improve the production indexes of the fermentation process effectively.

On-line Scheduling Algorithm for Penicillin Fed-batch Fermentation

An on-line scheduling algorithm to maximize gross profit of penicillin fed-batch fermentation is proposed. According to the on-line classification method, fed-batch fermentation batches are classified into three categories. Using the scheduling strategy, the optimal termination sequence of batches is obtained. Pseudo on-line simulations for testing the proposed algorithm with the data from industrial scale penicillin fermentation are carried out.

林可霉素工业生产发酵工艺优化

为了提高林可霉素工业生产发酵效价,该研究对林可霉素发酵工艺进行了优化。结果表明,林可霉素最佳发酵条件为二级种子罐发酵培养基中淀粉和黄豆饼粉添加量分别为0.97%和1.43%、延长二级种子移种菌龄至48 h及四级发酵罐基础料计料体积为8 m3。在此优化发酵工艺条件下,林可霉素发酵四级放罐效价高达7 505 U/m L,比原始工艺四级放罐效价提高了16%。初步优化了林可霉素工业生产发酵工艺,提高了林可霉素发酵效能和产量。

洁霉素发酵平衡生产期的糖耗速率预测

本文研究了工业规模的批式洁霉素发酵过程中平衡生产期中有效的糖耗速率预测模型，在我国洁霉素的批式发酵过程中首次设计并实现了流加补料的计算机系统。洁霉素产量可提高５％。

林可霉素生产中三级种子罐的发酵工艺优化

【背景】林可霉素是一种在临床应用上占有重要地位的林可酰胺类抗生素,关于调控发酵生产中三级种子罐相关参数优化林可霉素发酵工艺的研究较少。【目的】优化林可霉素发酵工艺,提高林可霉素发酵效价及市场竞争力。【方法】对林可霉素生产中三级种子罐的培养基和接种量及三级种子移种菌龄进行优化。【结果】在三级种子罐培养基中葡萄糖、淀粉、玉米浆、黄豆饼粉和硫酸铵浓度分别为64.0、5.0、15.0、14.5和3.5g/L,三级种子罐接种量为25%及三级种子移种菌龄为60h的优化条件下,林可霉素四级发酵效价高达7883U/mL,比优化前效价提高了10%。【结论】对林可霉素生产中三级种子罐相关参数进行调控,初步优化了林可霉素发酵工艺,提高了发酵效价,为优化林可霉素发酵工艺提供了新思路。