Institute of Automation & Industrial IT, Cologne University of Applied Sciences, Steinmüllerallee 1, 51643 Gummersbach, Germany.
Water Sci Technol. 2012;66(5):1088-95. doi: 10.2166/wst.2012.286.
The optimization of full-scale biogas plant operation is of great importance to make biomass a competitive source of renewable energy. The implementation of innovative control and optimization algorithms, such as Nonlinear Model Predictive Control, requires an online estimation of operating states of biogas plants. This state estimation allows for optimal control and operating decisions according to the actual state of a plant. In this paper such a state estimator is developed using a calibrated simulation model of a full-scale biogas plant, which is based on the Anaerobic Digestion Model No.1. The use of advanced pattern recognition methods shows that model states can be predicted from basic online measurements such as biogas production, CH4 and CO2 content in the biogas, pH value and substrate feed volume of known substrates. The machine learning methods used are trained and evaluated using synthetic data created with the biogas plant model simulating over a wide range of possible plant operating regions. Results show that the operating state vector of the modelled anaerobic digestion process can be predicted with an overall accuracy of about 90%. This facilitates the application of state-based optimization and control algorithms on full-scale biogas plants and therefore fosters the production of eco-friendly energy from biomass.
大规模沼气厂运行的优化对于使生物质成为可再生能源的竞争来源非常重要。实施创新的控制和优化算法，如非线性模型预测控制，需要在线估计沼气厂的运行状态。这种状态估计允许根据工厂的实际状态进行最佳控制和运行决策。本文使用基于厌氧消化模型 1 的全规模沼气厂校准模拟模型开发了这样的状态估计器。使用先进的模式识别方法表明，可以根据沼气产量、沼气中甲烷和二氧化碳含量、pH 值和已知底物的底物进料量等基本在线测量值来预测模型状态。使用的机器学习方法使用使用沼气厂模型创建的合成数据进行训练和评估，该模型模拟了广泛的可能工厂运行区域。结果表明，所模拟的厌氧消化过程的运行状态向量可以以约 90%的整体精度进行预测。这便于在全规模沼气厂应用基于状态的优化和控制算法，从而促进了生物质的环保能源生产。
