发酵过程多目标协同优化控制方法及系统研究
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摘要
生物发酵是生化工程和现代生物技术及其产业化的基础。随着现代生物技术的快速发展，发酵工业生产规模的不断扩大，迫切需要通过优化控制方法提高发酵过程的生产率和生产水平。早期的发酵过程单目标优化控制无法同时考虑发酵过程的产品产量、底物消耗、发酵时间等多个生产指标。发酵过程多目标优化控制是提高发酵过程生产水平和经济效益的有效途径。现有的基于线性加权的发酵过程多目标优化控制方法需人为设定目标权值，因人为因素影响优化效果，仅适用于不存在冲突关系的多目标问题。多目标进化算法（MOEA）基于较大尺寸种群寻优，且需较大进化代数，存在大量目标函数评估计算，难以处理包含大规模数据集的发酵过程多目标优化控制问题。此外，在发酵过程多目标优化控制中，多目标优化问题的求解包括产生近似Pareto前沿的多目标优化和选择偏好解的决策。已有的多目标优化与决策集成方法存在计算量大、运算时间长、易造成最优解遗漏等问题。因此，研究更为高效的多目标优化与决策集成方法，进而实现发酵过程的多目标协同优化控制，对于进一步提高发酵过程的生产水平具有重要的理论意义和应用价值。
本文在详细分析发酵过程优化控制及多目标优化问题求解方法研究现状的基础上，对发酵过程多目标协同优化控制方法及系统进行研究。
提出了一种基于交互式多目标优化与决策和多控制器协同控制的发酵过程多目标协同优化控制方法。通过Pareto前沿离散、连续近似和多属性决策的交互式调用，实现了在仅需较小计算量和较短运算时间的前提下，基于完整近似连续Pareto前沿产生具有较高精度的偏好解，并通过基于切换策略的发酵过程多控制器协同控制，有效地提高了发酵过程优化控制的控制精度、灵活性、适应性；在此基础上，给出了发酵过程多目标协同优化控制系统结构及各功能模块实现流程。
提出了一种基于几何支持向量回归（GSVR）的Pareto前沿连续近似方法。首先，给出一种基于群能量守恒多目标粒子群优化（SEC-MOPSO）的Pareto前沿离散近似方法，通过引入群能量守恒机制提高种群的搜索能力，使小尺寸近似离散Pareto前沿具有较好的分布性和多样性；进而，通过建立小尺寸离散Pareto前沿的GSVR模型，实现了Pareto前沿的连续近似；结合Pareto最优点的分布特征，给出一种沿多轴平移的扩展训练样本集构建方法，解决了沿单轴平移无法获得可分扩展训练样本集问题。结合典型测试函数的实验结果表明，基于SEC-MOPSO的Pareto前沿离散近似方法能产生具有较好分布性和多样性的小尺寸近似离散Pareto前沿，基于GSVR的Pareto前沿连续近似方法能产生较为完整、精确的近似连续Pareto前沿。
针对发酵过程的优化控制问题，结合发酵过程的时变性、不确定性，提出了一种基于预测切换策略的发酵过程多控制器协同控制方法。通过提出一种基于非线性二次高斯(NLQG)的发酵过程预测控制方法，将扩展Kalman滤波器（EKF）与非线性二次调节器（NLQR）串联构建NLQG控制器；根据控制性能指标预测多个控制器的切换规律，实现多控制器协同控制。结合典型非线性系统的实验结果表明，所提出的发酵过程预测控制方法对于设定值变化具有良好的跟踪效果，对不同噪声环境所引起的干扰具有较强的鲁棒性，采用多控制器协同控制方法可以有效地提高控制精度。
结合工业酵母分批补料发酵过程和青霉素分批补料发酵过程，对发酵过程多目标协同优化控制方法及系统进行了实验研究。实验结果表明，本文所提出的发酵过程多目标协同优化控制方法解决了发酵过程多目标优化控制所存在的计算量大、运算时间长、易造成最优解遗漏等问题，实现了发酵过程多个生产指标的最优权衡，获得了良好的优化控制效果，在发酵过程优化控制领域有着广泛的应用前景。
Fermentation is the basis of bioengineering, modern biotechnology andtheir industrialization. With the rapid development of modern biotechnologyand the continually expanding of production scale of fermentation industry, itis urgent to improve the productivity and production quality of fermentationprocess by optimization control methods. The early single objectiveoptimization control for fermentation process can not deal with a number ofproduction indexes such as production output, substrate cost, fermentationtime in fermentation process at the same time. The Multi-ObjectiveOptimization (MOO) control for fermentation process is an effect way toimprove the production quality and benefits of fermentation process. Theexisting MOO control method for fermentation process based on linearweighted summation has the problem that the artificial weights of objectivesmay have negative impact on the results of optimization and only beappropriate for the case that the objectives are not competing with each other.Multi-Objective Evolutionary Algorithm (MOEA) applies large size population to search optimal solutions and needs a large number ofevolutionary generations, which lead to require substantial evaluations ofobjective functions and can not deal with MOO control problem which haslarge-scale data in fermentation process. Beside, in MOO control forfermentation process, the MOO which generates approximate Pareto front andthe Decision-Making (DM) which selects the preferred solution are two mainsub-tasks of solving MOO problem. However, the existing integrationmethods of MOO and DM have the problem of heavy computation burden,long running time and omitting optimal solutions. Therefore, the research ofmore efficient method for integrating MOO and DM in order to realize MOOcontrol for fermentation process has important theoretical significance andpractical applications value for further improving the production levels offermentation process.
On the basis of analyzing the review and the current state of research inoptimization control for fermentation process and methods for solving MOOproblem, a multi-objective cooperative optimization control method andsystem for fermentation process is studied in this paper.
A multi-objective cooperative optimization control method forfermentation process combining interactive MOO and DM withmulti-controller cooperative control is proposed. The discrete, continuousapproximation of Pareto front and Multi-Attribute Decision-Making (MADM)are invoked interactively, then a preferred solution which has high accuracy can be generated based on complete approximate continuous Pareto front withsmall computation and short running time; the control precision, flexibilityand adaptability of optimization control for fermentation process is improvedby multi-controller cooperative control based on switch strategy. On that basis,the architecture and each function module’s realization of multi-objectivecooperative optimization control system are given.
A method for continuous approximation of Pareto front based onGeometric Support Vector Regression (GSVR) is proposed. At first, a methodfor discrete approximation of Pareto front based on Swarm EnergyConservation Multi-Objective Particle Swarm Optimization (SEC-MOPSO) isgiven. The swarm energy conservation mechanism is introduced for boostingthe exploration capability of swarm and improving the distribution anddiversity of small size discrete Pareto front. Then the continuousapproximation of Pareto front is realized by establishing the GSVR model ofsmall size discrete Pareto front. Considering the distribution characteristic ofPareto optimal points, a method for generating the augmented training samplesets by shifting the original training samples along multiple coordinated axis isgiven to solve the problem of unable to generate separable augmented trainingsample sets by shifting original training samples only along single coordinatedaxis. The experiments are carried out to classical test functions, the resultsshow that the small size approximate discrete Pareto front generated bySEC-MOPSO has good distribution and diversity. The method for continuous approximation of Pareto front based on GSVR can generate complete andaccurate approximate continuous Pareto front.
For the optimization control problem, considering the time variety anduncertainty of fermentation process, a multi-controller cooperative controlmethod for fermentation process based on a predictive switching strategy isproposed. A predictive control method for fermentation process based onNon-Linear Quadratic Gaussian (NLQG) is proposed, the NLQG controller iscomposed of a Extended Kalman Filter (EKF) and a Non-Linear QuadraticRegulator (NLQR) which are connected in series; the switched law of anumber of controllers is predicted according to a control performance indexand the multi-controller cooperative control can be realized. The experimentsare carried out to a classical non-linear system, the results show that theproposed fermentation process predictive control method has good trackingeffect for the change of set value and strong robustness in different noisyenvironment. The multi-controller cooperative control method forfermentation process can enhance the control precision effectively.
An industrial fed-batch yeast fermentation process and a penicillinfed-batch fermentation process are introduced to carry out the experimentalresearch on the multi-objective cooperative optimization control method andsystem for fermentation process. The results show that, the proposedmulti-objective cooperative optimization control method for fermentationprocess can solve the problem of large computation amount, long running time, omitting optimal solutions. The method can realize the optimal tradeoff amonga number of production indexes in fermentation process, obtain good controleffect and has wide application prospects in optimization control forfermentation process.
本文在详细分析发酵过程优化控制及多目标优化问题求解方法研究现状的基础上，对发酵过程多目标协同优化控制方法及系统进行研究。
提出了一种基于交互式多目标优化与决策和多控制器协同控制的发酵过程多目标协同优化控制方法。通过Pareto前沿离散、连续近似和多属性决策的交互式调用，实现了在仅需较小计算量和较短运算时间的前提下，基于完整近似连续Pareto前沿产生具有较高精度的偏好解，并通过基于切换策略的发酵过程多控制器协同控制，有效地提高了发酵过程优化控制的控制精度、灵活性、适应性；在此基础上，给出了发酵过程多目标协同优化控制系统结构及各功能模块实现流程。
提出了一种基于几何支持向量回归（GSVR）的Pareto前沿连续近似方法。首先，给出一种基于群能量守恒多目标粒子群优化（SEC-MOPSO）的Pareto前沿离散近似方法，通过引入群能量守恒机制提高种群的搜索能力，使小尺寸近似离散Pareto前沿具有较好的分布性和多样性；进而，通过建立小尺寸离散Pareto前沿的GSVR模型，实现了Pareto前沿的连续近似；结合Pareto最优点的分布特征，给出一种沿多轴平移的扩展训练样本集构建方法，解决了沿单轴平移无法获得可分扩展训练样本集问题。结合典型测试函数的实验结果表明，基于SEC-MOPSO的Pareto前沿离散近似方法能产生具有较好分布性和多样性的小尺寸近似离散Pareto前沿，基于GSVR的Pareto前沿连续近似方法能产生较为完整、精确的近似连续Pareto前沿。
针对发酵过程的优化控制问题，结合发酵过程的时变性、不确定性，提出了一种基于预测切换策略的发酵过程多控制器协同控制方法。通过提出一种基于非线性二次高斯(NLQG)的发酵过程预测控制方法，将扩展Kalman滤波器（EKF）与非线性二次调节器（NLQR）串联构建NLQG控制器；根据控制性能指标预测多个控制器的切换规律，实现多控制器协同控制。结合典型非线性系统的实验结果表明，所提出的发酵过程预测控制方法对于设定值变化具有良好的跟踪效果，对不同噪声环境所引起的干扰具有较强的鲁棒性，采用多控制器协同控制方法可以有效地提高控制精度。
结合工业酵母分批补料发酵过程和青霉素分批补料发酵过程，对发酵过程多目标协同优化控制方法及系统进行了实验研究。实验结果表明，本文所提出的发酵过程多目标协同优化控制方法解决了发酵过程多目标优化控制所存在的计算量大、运算时间长、易造成最优解遗漏等问题，实现了发酵过程多个生产指标的最优权衡，获得了良好的优化控制效果，在发酵过程优化控制领域有着广泛的应用前景。
Fermentation is the basis of bioengineering, modern biotechnology andtheir industrialization. With the rapid development of modern biotechnologyand the continually expanding of production scale of fermentation industry, itis urgent to improve the productivity and production quality of fermentationprocess by optimization control methods. The early single objectiveoptimization control for fermentation process can not deal with a number ofproduction indexes such as production output, substrate cost, fermentationtime in fermentation process at the same time. The Multi-ObjectiveOptimization (MOO) control for fermentation process is an effect way toimprove the production quality and benefits of fermentation process. Theexisting MOO control method for fermentation process based on linearweighted summation has the problem that the artificial weights of objectivesmay have negative impact on the results of optimization and only beappropriate for the case that the objectives are not competing with each other.Multi-Objective Evolutionary Algorithm (MOEA) applies large size population to search optimal solutions and needs a large number ofevolutionary generations, which lead to require substantial evaluations ofobjective functions and can not deal with MOO control problem which haslarge-scale data in fermentation process. Beside, in MOO control forfermentation process, the MOO which generates approximate Pareto front andthe Decision-Making (DM) which selects the preferred solution are two mainsub-tasks of solving MOO problem. However, the existing integrationmethods of MOO and DM have the problem of heavy computation burden,long running time and omitting optimal solutions. Therefore, the research ofmore efficient method for integrating MOO and DM in order to realize MOOcontrol for fermentation process has important theoretical significance andpractical applications value for further improving the production levels offermentation process.
On the basis of analyzing the review and the current state of research inoptimization control for fermentation process and methods for solving MOOproblem, a multi-objective cooperative optimization control method andsystem for fermentation process is studied in this paper.
A multi-objective cooperative optimization control method forfermentation process combining interactive MOO and DM withmulti-controller cooperative control is proposed. The discrete, continuousapproximation of Pareto front and Multi-Attribute Decision-Making (MADM)are invoked interactively, then a preferred solution which has high accuracy can be generated based on complete approximate continuous Pareto front withsmall computation and short running time; the control precision, flexibilityand adaptability of optimization control for fermentation process is improvedby multi-controller cooperative control based on switch strategy. On that basis,the architecture and each function module’s realization of multi-objectivecooperative optimization control system are given.
A method for continuous approximation of Pareto front based onGeometric Support Vector Regression (GSVR) is proposed. At first, a methodfor discrete approximation of Pareto front based on Swarm EnergyConservation Multi-Objective Particle Swarm Optimization (SEC-MOPSO) isgiven. The swarm energy conservation mechanism is introduced for boostingthe exploration capability of swarm and improving the distribution anddiversity of small size discrete Pareto front. Then the continuousapproximation of Pareto front is realized by establishing the GSVR model ofsmall size discrete Pareto front. Considering the distribution characteristic ofPareto optimal points, a method for generating the augmented training samplesets by shifting the original training samples along multiple coordinated axis isgiven to solve the problem of unable to generate separable augmented trainingsample sets by shifting original training samples only along single coordinatedaxis. The experiments are carried out to classical test functions, the resultsshow that the small size approximate discrete Pareto front generated bySEC-MOPSO has good distribution and diversity. The method for continuous approximation of Pareto front based on GSVR can generate complete andaccurate approximate continuous Pareto front.
For the optimization control problem, considering the time variety anduncertainty of fermentation process, a multi-controller cooperative controlmethod for fermentation process based on a predictive switching strategy isproposed. A predictive control method for fermentation process based onNon-Linear Quadratic Gaussian (NLQG) is proposed, the NLQG controller iscomposed of a Extended Kalman Filter (EKF) and a Non-Linear QuadraticRegulator (NLQR) which are connected in series; the switched law of anumber of controllers is predicted according to a control performance indexand the multi-controller cooperative control can be realized. The experimentsare carried out to a classical non-linear system, the results show that theproposed fermentation process predictive control method has good trackingeffect for the change of set value and strong robustness in different noisyenvironment. The multi-controller cooperative control method forfermentation process can enhance the control precision effectively.
An industrial fed-batch yeast fermentation process and a penicillinfed-batch fermentation process are introduced to carry out the experimentalresearch on the multi-objective cooperative optimization control method andsystem for fermentation process. The results show that, the proposedmulti-objective cooperative optimization control method for fermentationprocess can solve the problem of large computation amount, long running time, omitting optimal solutions. The method can realize the optimal tradeoff amonga number of production indexes in fermentation process, obtain good controleffect and has wide application prospects in optimization control forfermentation process.
引文
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