Swarm intelligence based self-monitoring of bioprocesses

该项目在生物过程监测领域提出了一种新的方法概念。我们关注的不是单个传感器，而是一个传感器网络。该传感器网络通过传感器之间的通信来获取智能。项目目标是实现基于群体智能的自监测传感器网络。粒子群优化算法（PSO）是常用的算法。与传感器网络和过程信息的其他测量相比，智能传感器网络将能够通过多元线性和非线性建模方法识别故障传感器和信号。此外，它将能够替换这些故障传感器值。通过这种自动补偿过程控制可以独立于传感器故障进行，否则会丢失的批次可以成功完成。通过使用群体智能的想法，每个物理内置传感器（温度，压力，pH, pO2，废气CO2/O2，甲醇，浊度）的功能可以自动检查。这允许对过程数据进行持续监测和评估，以及创建具有记忆的学习、自主、智能系统。预期的研究成果：-建立与过程相关的传感器网络和传感器的信息技术网络(AP 1)-建立一个完全自动化的反应堆系统（AP 2），这是应用群体智能概念所必需的-构建群体中的个体：对每个人来说，定义局部模型制定映射不同的输入传感器(测量值)彼此(美联社3)——用于分析的适用性的子群(美联社3)——开发的功能,使用历史数据的自动参数化子模型(在线参数调整)(美联社3)——在线预测的轨迹每个传感器值通过整个传感器网络(美联社4)——评估每个中与传感器的测量值与传感器网络的比较(AP 4)-故障传感器的识别和正确错误类别的分配(系统误差、漂移、噪声、一个或多个传感器的部分/完全故障(ap4)-故障传感器值的重建(ap5)-通过改变传感器输入和工艺条件（ap6和ap7）验证当前工艺中网络的功能-传感器网络内信息冗余的评估(ap8)-系统可转移性的分析（ap8）

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)