Domnanovich A M, Strik D P, Zani L, Pfeiffer B, Karlovits M, Braun R, Holubar P
Institute of Applied Microbiology, BOKU-University of Natural Resources and Applied Life Sciences, Muthgasse 18, A-1 190 Vienna, Austria.
Commun Agric Appl Biol Sci. 2003;68(2 Pt A):215-8.
One of the goals of the EU-Project AMONCO (Advanced Prediction, Monitoring and Controlling of Anaerobic Digestion Process Behaviour towards Biogas Usage in Fuel Cells) is to create a control tool for the anaerobic digestion process, which predicts the volumetric organic loading rate (Bv) for the next day, to obtain a high biogas quality and production. The biogas should contain a high methane concentration (over 50%) and a low concentration of components toxic for fuel cells, e.g. hydrogen sulphide, siloxanes, ammonia and mercaptanes. For producing data to test the control tool, four 20 l anaerobic Continuously Stirred Tank Reactors (CSTR) are operated. For controlling two systems were investigated: a pure fuzzy logic system and a hybrid-system which contains a fuzzy based reactor condition calculation and a hierachial neural net in a cascade of optimisation algorithms.
欧盟项目AMONCO（面向燃料电池沼气利用的厌氧消化过程行为的先进预测、监测与控制）的目标之一是创建一种用于厌氧消化过程的控制工具，该工具可预测次日的容积有机负荷率（Bv），以获得高质量的沼气产量。沼气应含有高浓度的甲烷（超过50%）以及低浓度的对燃料电池有毒的成分，例如硫化氢、硅氧烷、氨和硫醇。为了生成测试该控制工具的数据，运行了四个20升的厌氧连续搅拌槽式反应器（CSTR）。为了进行控制，研究了两种系统：一种是纯模糊逻辑系统，另一种是混合系统，该混合系统包含基于模糊的反应器条件计算以及在优化算法级联中的分层神经网络。
