烟塔合一技术特点和工程数据

剖析了德国烟塔合一技术特点和工程数据.烟塔合一技术可以提高能源效率,简化烟气系统设计,减少烟囱和GGH换热器,可以合并锅炉引风机和脱硫增压风机,降低电厂建设费用,有利于降低发电成本.更为重要的是,烟塔合一技术可提高脱硫后净烟气的抬升高度,有利于降低污染.      

高流量负荷下低浓度VOCs废气的生物法处理

高流量负荷下生物膜填料塔净化低浓度甲苯废气的实验结果表明,当气体流量在0.8m/h,入口气体甲苯浓度为105mg/m,停留时间18.3s时,甲苯的净化效率可达到61.9%,出口气体甲苯浓度低于国家对现有企业的排放标准(≤60mg/m³).适宜的操作温度应控制在20～25℃之间,氮磷营养添加量的配比应控制为C:N:P=200:5:1.依据实验结果数据,对相关的机理问题进行了分析探讨.     

热能喷雾处理反渗透浓盐水的实验研究

目前,反渗透废水中,高浓度的含盐量会对环境带来严重危害。本研究运用热能喷雾处理反渗透浓盐水技术,对反渗透（Reverse Osmosis,RO）出水进行处理。结果表明,该技术在热量值为4.8×106 kcal、空气流量为60 m3／h的条件下,处理效率达到了90％以上。产出的冷凝水的水质符合饮用水的相关标准;在处理量为10 L／h～12 L／h时,可达到废水零排放的效果;在处理量大于12 L／h时,排出的废水总溶解物（ Total dissolved sol-ids,TDS）很高。     

生物过滤法处理污泥干化尾气

城市污水处理过程中产生大量污泥,其热干化所产生的尾气主要存在以下特点：气味属刺激性臭气,主体为各种无机、有机化合性气体;热干化过程产生的尾气含有大量的热值,容易对环境造成二次污染。关于污泥干化尾气的处理,目前国内采用的工艺并不很成熟。为了避免污泥干化尾气的二次污染,本着经济适用的原则,本文根据尾气的性质,对生物过滤法处理污泥尾气进行了研究,具体分析了原结构的不合理之处,提出了增湿塔与生物滤塔的改进性建议。     

无色硫细菌处理含硫废水的试验研究

在升流式填料塔中,通过污泥训化产生大量的硫杆菌,从而形成稳定生物膜.采用循环单因素实验研究和分析硫化物容积负荷与溶解氧(DO)共同作用下硫化物(S2-)的去除规律、硫酸盐的生成规律以及单质硫的生成率,并对建立的模拟方程进行验证.结果表明,DO是影响反应器顺利启动和稳定运行的主要因素;同时pH值对处理效果的影响也比较大;高浓度S2-有利于其本身的去除;有机物负荷对生物除硫效果影响不大;在水力停留时间(HRT)大于30min时,除硫效果基本没有很大变化.     

两种不锈钢在冷却塔冷凝酸液中的耐蚀性能

目的分析304不锈钢和316L不锈钢在电厂冷却塔内海水及烟气形成的模拟冷凝酸液环境中的耐蚀性能。方法利用浸泡试验和电化学试验方法测试两种不锈钢在模拟冷凝酸液中的腐蚀形貌、腐蚀率和极化曲线。结果 304不锈钢在模拟冷凝酸液中的耐蚀性能较差,其腐蚀率及钝态稳定性受冷凝酸液pH值的影响较大;316L不锈钢在模拟冷凝酸液中的耐蚀性能较好,其腐蚀率及钝态稳定性受冷凝酸液pH值的影响较小。结论 316L不锈钢在冷却塔冷凝酸液中的耐蚀性能明显优于304不锈钢。     

双碱法脱硫工艺改进及脱硫塔设计分析

改进优化传统双碱法烟气脱硫工艺，对脱硫塔及附属设施设计要点进行分析，在山西某化工厂2×55t/h三废炉烟气脱硫工程中得到较好运用，为同类烟气脱硫工程提供参考。     

冷却塔的噪声特性与控制

某住宅小区建有大型冷却塔,运行时产生的噪声严重干扰了居民的生活。在现场进行噪声测量的基础上,着重分析了冷却塔的噪声特性以及噪声源,进而设计并详细分析了针对不同噪声源的噪声治理方案。实施结果证明,采取的控制措施有效,降噪效果明显,能完全满足国家技术标准的要求。     

冷却塔的噪声特性与控制

以某小区冷却塔的噪声治理为例,通过对其噪声数据的测量,详细分析了冷却塔噪声的主要来源和特性,并从消声、吸声、隔声等方面阐述了一种实用的控制措施。结果表明,此方案不仅降噪效果明显,而且能够完全满足设备所需通风量和温度的要求,在工程实际过程中取得了良好的降噪效果。     

Evaluating weather effects on interannual variation in net ecosystem productivity of a coastal temperate forest landscape: A model intercomparison

Forest productivity is strongly affected by seasonal weather patterns and by natural or anthropogenic disturbances. However weather effects on forest productivity are not currently represented in inventory-based models such as CBM-CFS3 used in national forest C accounting programs. To evaluate different approaches to modelling these effects, a model intercomparison was conducted among CBM-CFS3 and four process models (ecosys, CN-CLASS, Can-IBIS and 3PG) over a 2500 ha landscape in the Oyster River (OR) area of British Columbia, Canada. The process models used local weather data to simulate net primary productivity (NPP), net ecosystem productivity (NEP) and net biome productivity (NBP) from 1920 to 2005. Other inputs used by the process and inventory models were generated from soil, land cover and disturbance records. During a period of intense disturbance from 1928 to 1943, simulated NBP diverged considerably among the models. This divergence was attributed to differences among models in the sizes of detrital and humus C stocks in different soil layers to which a uniform set of soil C transformation coefficients was applied during disturbances. After the disturbance period, divergence in modelled NBP among models was much smaller, and attributed mainly to differences in simulated NPP caused by different approaches to modelling weather effects on productivity. In spite of these differences, age-detrended variation in annual NPP and NEP of closed canopy forest stands was negatively correlated with mean daily maximum air temperature during July-September (T_amax) in all process models (R² = 0.4-0.6), indicating that these correlations were robust. The negative correlation between T_amax and NEP was attributed to different processes in different models, which were tested by comparing CO₂ fluxes from these models with those measured by eddy covariance (EC) under contrasting air temperatures (T_a). The general agreement in sensitivity of annual NPP to T_amax among the process models led to the development of a generalized algorithm for weather effects on NPP of coastal temperate coniferous forests for use in inventory-based models such as CBM-CFS3: NPP′ = NPP − 57.1 (T_amax − 18.6), where NPP and NPP′ are the current and temperature-adjusted annual NPP estimates from the inventory-based model, 18.6 is the long-term mean daily maximum air temperature during July-September, and T_amax is the mean value for the current year. Our analysis indicated that the sensitivity of NPP to T_amax was nonlinear, so that this algorithm should not be extrapolated beyond the conditions of this study. However the process-based methodology to estimate weather effects on NPP and NEP developed in this study is widely applicable to other forest types and may be adopted for other inventory based forest carbon cycle models.