Swarm intelligence based self-monitoring of bioprocesses

基于群体智能的生物过程自我监控

• 批准号: 383534822
• 负责人: Professor Dr.-Ing. Thomas Becker
• 金额: --
• 依托单位: Lehrstuhl für Brau- und Getränketechnologie
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2017
• 资助国家: 德国
• 起止时间: 2016-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/383534822?language=en
• 关键词: Swarm; intelligence; based; self; monitoring

This project addresses a novel methodic concept in the area of bioprocess monitoring. Not a single sensor is in focus but a network of sensors. This sensor network will acquire intelligence by communication among the sensors. The project goal is the realization of a self-monitoring sensor network based on swarm intelligence. Particle Swarm Optimization (PSO) is the used algorithm. The intelligent sensor network will be capable of identifying faulty sensors and signals by means of multivariate linear and non-linear modeling approaches in comparison with other measurements of the sensor network and process information. Additionally, it will be able to replace these faulty sensor values. By this auto-compensation process control can be carried on independent of sensor failures and batches that would be lost otherwise can be finished successfully. By using the swarm intelligence idea, the functionality of each physically built-in sensor (temperature, pressure, pH, pO2, offgas CO2/O2, methanol, turbidity) can automatically be examined. This allows continuous monitoring and evaluation of process data, as well as the creation of a learning, autonomous, intelligent system with memory.Intended research results:- Establishment of a network of process-relevant sensors and information technology networking of the sensors (AP 1)- Setting up of a fully automated reactor system (AP 2), which is necessary for the application of the swarm intelligence concept- Construction of the individuals of a swarm: For each individual, a defined partial model is formulated that maps the different inputs (measured values of the sensors) to one another (AP 3)- Analysis of the suitability of the submodels used in a swarm (AP 3)- Development of a functionality that uses historical data for the automatic parameterization of a submodel (online parameter adjustment) (AP 3)- Online prediction of the trajectory of each sensor value via the whole sensor network (AP 4)- Evaluation of the measured values of each process-relevant sensor in comparison with the sensor network (AP 4)- Identification of faulty sensors and assignment of the correct error category (systemic error, drift, noise, partial / complete failure of one or more sensors) (AP 4)- Reconstruction of faulty sensor values (AP 5)- Validation of the functionality of the network in the present process by varying the sensor inputs and process conditions (AP 6 and AP 7)- Assessment of information redundancy within the sensor network (AP 8)- Analysis of the transferability of the system to other fermentations (AP 8)

该项目提出了生物过程监测领域的一个新颖的方法概念。焦点不是单个传感器，而是传感器网络。该传感器网络将通过传感器之间的通信来获取情报。该项目的目标是实现基于群体智能的自我监控传感器网络。使用的算法是粒子群优化（PSO）。智能传感器网络将能够通过多元线性和非线性建模方法与传感器网络的其他测量和过程信息进行比较来识别故障传感器和信号。此外，它将能够替换这些有故障的传感器值。通过这种自动补偿过程控制，可以独立于传感器故障进行，并且可以成功完成否则会丢失的批次。通过使用群体智能理念，可以自动检查每个物理内置传感器的功能（温度、压力、pH、pO2、废气 CO2/O2、甲醇、浊度）。这允许对过程数据进行连续监控和评估，以及创建一个具有记忆功能的学习、自主、智能系统。预期研究成果：- 建立过程相关传感器网络和传感器信息技术网络（AP 1 )- 建立全自动反应堆系统 (AP 2)，这是应用群体智能概念所必需的 - 群体个体的构建：对于每个个体，制定一个定义的部分模型来映射不同的输入（测量值传感器）相互之间（AP 3） - 分析群体中使用的子模型的适用性（AP 3） - 开发一种使用历史数据自动参数化子模型的功能（在线参数调整）（AP 3） - 通过整个传感器网络在线预测每个传感器值的轨迹 (AP 4) - 与传感器网络比较评估每个过程相关传感器的测量值 (AP 4) - 识别故障传感器并分配故障传感器纠正错误类别（系统错误、漂移、噪声、一个或多个传感器的部分/完全故障）（AP 4）- 重建故障传感器值（AP 5）- 通过改变传感器输入来验证当前过程中的网络功能和工艺条件（AP 6 和 AP 7）- 传感器网络内信息冗余的评估（AP 8）- 系统向其他发酵的可转移性分析（AP 8）