基于停留时间分布的工业规模沼气发酵罐分析

András Tankovics, D. Takács, J. Szendefy, B. Csukás, M. Varga
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引用次数: 1

摘要

Magyar Cukor Zrt Kaposvar糖厂工业规模(13000)厌氧消化池压榨甜菜片混合条件的停留时间分布(RTD)测量分析进行了研究。采用锂盐示踪技术,通过初步研究的混合模拟模型设计氯化锂示踪剂用量和出水采样。用电感耦合等离子体发射光谱(ICP-OES)分析了出口锂离子浓度。应用可编程过程结构的方法,综合考虑几何布置、沼气产生流和循环变化的循环流,生成了具有不同分区和流动结构的混合模型。基于仿真的混合模型近似辨识采用了一种考虑混合流变化的多结构的启发式方法。寻求一种具有较少的室数和参数的模型,以满足测量的RTD。结果表明,上层混合强烈,下层混合不佳,导致了RTD的长尾现象。实际设置支持糖甜菜片和微生物生物量的良好水平分布,而有限程度的垂直混合有助于避免有益微生物组的洗脱。建议的混合模型将与先前阐述的涉及9个细菌群的模型相结合。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Residence Time Distribution-Based Analysis of an Industrial-Scale Biogas Fermenter
Residence Time Distribution (RTD) measurement-based analysis of mixing conditions on an industrial-scale (13,000) anaerobic digester of pressed sugar-beet slices at Kaposvar Sugar Factory of Magyar Cukor Zrt. was studied. The lithium salt tracing technique was applied, while the quantity of the lithium chloride tracer and the sampling of the effluent were designed by a preliminarily studied simulation model of mixing. The lithium concentration at the outlet was analysed by Inductively coupled plasma–optical emission spectroscopy (ICP-OES). Taking into account the geometrical arrangement, the biogas flow produced and the cyclically changing recycle flow, various mixing models were generated with different compartmentalization and flow structures by applying the method of Programmable Process Structures. The simulation-based approximate identification of the mixing model was accomplished by a heuristic approach that took into consideration multiple structures with changing mixing flows. A model with an advantageously small number of compartments and parameters was sought which satisfies the measured RTD. The results suggest the intensive mixing of upper levels with a poorly mixed lower level, which contributes to the long tail in the RTD. The actual set-up supports a good horizontal distribution of the sugar-beet slices and the microbial biomass, while the limited degree of vertical mixing helps to avoid the elutriation of the useful microbiome. The suggested mixing model will be combined with the formerly elaborated model involving 9 bacterial groups.
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