Sulfate removal effect of molasses alcohol wastewater pretreated by physical and chemical methods and its biogas production performance

Molasses alcohol wastewater normarlly contains high quantity of SO4 which can restrain the production of the biogas. In this paper, we used the activated carbon, Ba Cl2·2H2O, Ca（OH）2 and iron carbon micro cell to pretreat the molasses alcohol wastew

物化法去除糖蜜酒精废水硫酸盐效果及其产气性能的比较

糖蜜酒精废水中高浓度的硫酸盐会抑制生化处理产甲烷性能。该文采用活性炭、Ba Cl2·2H2O、Ca（OH）2及铁碳微电池法对糖蜜酒精废水中的硫酸盐进行预处理,并分析了预处理前后硫酸根及化学需氧量COD的变化,同时,也分析评价了几种方法对产气性能的影响。结果表明：在500.0 g糖蜜酒精废水中加入8.0 g活性炭,硫酸根去除率达80.0%以上,且产气性能相对最好,累计产气率可达437.3 m L/g。在500.0 g糖蜜酒精废水中,根据S元素含量和Ba SO4的分子组成比例,添加70%的化学试剂Ba Cl2·2H2O,能全部去除硫酸根,累计产气率可达483.2 m L/g,产气效果相对最好,同等条件下,添加70%的Ca（OH）2,硫酸根也能全部去除,累计产气率可达436.6 m L/g。铁碳微电池处理的糖蜜酒精废水,能有效抑制硫酸根的影响,并且可以提高废水的可生化性,累计产气率为451.0 m L/g,是对照处理293.1 m L/g的1.5倍。各处理效果的产气性能排序为70%Ba Cl2·2H2O〉铁碳微电池〉8.0 g活性炭吸附〉70%Ca（OH）2〉对照组

Research Progress on Mechanism and Treatment of Sulfate on the Anaerobic Degradation of Molasses Alcohol Wastewater

High concentration of sulfate is an important factor affecting the degradation of molasses alcohol wastewater. This paper described the sulfate-reducing bacteria effect of anaerobic fermentation process and sulfate reduction products of the anaerobic met

二价铁离子对糖蜜酒精废水厌氧发酵的影响

为提高糖蜜酒精废水的厌氧发酵效率，实现其资源化再利用，本文研究了添加一定量Fe2+对糖蜜酒精废水厌氧发酵处理过程的影响。结果表明：添加Fe2+后发酵液中SO42.的去除率由77.12%提高至90.79%；COD去除率由57.34%提高至81.14%；累积沼气产量由386mL·g.1VS提高至475mL·g.1VS；产气周期由29天缩短为26天；其氧化还原电位（ORP）升高，pH值降低

Effect of Fe2+ on Anaerobic Digestion of Molasses Alcohol Wastewater

In order to improve the anaerobic digestion efficiency and realize the recycle of molasses alcohol wastewater,the effects of Fe2+ on the digestion process were investigated. The results showed that with the addition of Fe2+, the SO42?removal rate increas

Characteristics of Anaerobic Digestion of Marine Macro-algae

The effects of salinity on anaerobic digestion performance of Laminaria Japonica were investigated under the different inoculation rate adopting batch mesophilic experiments. The results indicated that the biogas production performance of the non-washed

JUNO Sensitivity on Proton Decay p→νˉK+p\to \bar\nu K^+ Searches

The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator detector designed to explore many topics in fundamental physics. In this paper, the potential on searching for proton decay in $p\to \bar\nu K^+$ mode with JUNO is investigated.The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification. Moreover, the excellent energy resolution of JUNO permits to suppress the sizable background caused by other delayed signals. Based on these advantages, the detection efficiency for the proton decay via $p\to \bar\nu K^+$ is 36.9% with a background level of 0.2 events after 10 years of data taking. The estimated sensitivity based on 200 kton-years exposure is $9.6 \times 10^{33}$ years, competitive with the current best limits on the proton lifetime in this channel

JUNO sensitivity on proton decay p → ν K + searches*

The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator detector designed to explore many topics in fundamental physics. In this study, the potential of searching for proton decay in the $p\to \bar{\nu} K^+$ mode with JUNO is investigated. The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification. Moreover, the excellent energy resolution of JUNO permits suppression of the sizable background caused by other delayed signals. Based on these advantages, the detection efficiency for the proton decay via $p\to \bar{\nu} K^+$ is 36.9% ± 4.9% with a background level of $0.2\pm 0.05({\rm syst})\pm$$0.2({\rm stat})$ events after 10 years of data collection. The estimated sensitivity based on 200 kton-years of exposure is $9.6 \times 10^{33}$ years, which is competitive with the current best limits on the proton lifetime in this channel and complements the use of different detection technologies