垃圾焚烧发电厂脱硝技术及应用

介绍了垃圾焚烧烟气烧脱硝控制的主要技术及应用情况,以及非催化还原技术的应用实例,采用该技术,氮氧化物排放值可满足现阶段排放要求。     

电子废弃物聚丙烯制备木塑复合材的研究

探讨了电子废弃物聚丙烯(PP)制备木塑复合材料(WPC)的可行性及其主要影响因子。研究结果表明:电子废弃物PP具备制备木塑复合材料的可行性,PP和木纤维混合比例是影响WPC性能的关键因子之一,加入适量的PPg-MAH有利于提高WPC的力学性能,并大幅度降低其吸水性,当PP与木纤维比例为60∶40,PP-g-MAH使用量为10%时,WPC可获得较好的综合性能指标。     

完善废旧资源进口和加工贸易环境的思考

进口利用废旧资源在解决我国原生资源不足、节约能源、降低环境负荷、满足日益增长的物资需要具有重要的意义,但进口废旧资源会因夹带、处置加工等环节带来环境污染.文章梳理了我国允许废旧资源进口利用的背景需求,探讨了科学识别进口废旧资源、加工利用相关行业产生的环境问题,提出了进一步加强环境监管的对策方案,以推进废旧资源进口和加工贸易的健康发展.     

PW—W膜分离式活性污泥法处理中小规模高浓度有机废水

阐述ＰＷ－Ｗ膜分离式活性污泥法处理高浓度有机废水的原理、工艺流程及特点。处理系统集生化处理功能和固液分离于一体，对高浓度有机废水的处理取得了良好的效果，且剩余污泥量少，处理水质稳定，维护管理方便，运行费用低。     

Environmental input-output model and its analysis with a focus on the solid waste management sectors

ＩｎｔｒｏｄｕｃｔｉｏｎＳｉｎｃｅｔｈｅｆｉｒｓｔｅｎｖｉｒｏｎｍｅｎｔａｌｉｎｐｕｔｏｕｔｐｕｔｍｏｄｅｌ(ＥＩＯＭ)(Ｃｕｍｂｅｒｌａｎｄ,1966)ｗａｓｅｓｔａｂｌｉｓｈｅｄｂｙｉｎｃｏｒｐｏｒａｔｉｎｇｅｎｖｉｒｏｎｍｅｎｔａｌｆａｃｔｏｒｓｉｎｔ?..     

Assessing of Environmental Quality in Six Areas in India,Sri Lanka and Bangladesh

Environmental quality for environmental health has been examined visually by describing general conditions and drinking water supply, sanitation and solid waste treatment conditions and practices in six South-East Asian villages with a dense population. The environmental review was supplemented by discussions with local people and political and administrative decision makers. Some drinking water analyses were done in order to show the water quality to the local people. The quality of the general conditions and the conditions and practices of drinking water supply, excreta disposal and solid waste treatment practices have been graded with the grading system presented below. The grading could be done in 1 or 2 days in each area by two persons, of which one was an environmental scientist and the other a social scientist, who also knew the local culture and worked as a translator.     

Is Municipal Solid Waste Recycling Economically Efficient?

It has traditionally been argued that recycling municipal solid waste (MSW) is usually not economically viable and that only when externalities, long-term dynamic considerations, and/or the entire product life cycle are taken into account, recycling becomes worthwhile from a social point of view. This article explores the results of a wide study conducted in Israel in the years 2000–2004. Our results reveal that recycling is optimal more often than usually claimed, even when externality considerations are ignored. The study is unique in the tools it uses to explore the efficiency of recycling: a computer-based simulation applied to an extensive database. We developed a simulation for assessing the costs of handling and treating MSW under different waste-management systems and used this simulation to explore possible cost reductions obtained by designating some of the waste (otherwise sent to landfill) to recycling. We ran the simulation on data from 79 municipalities in Israel that produce over 60% of MSW in Israel. For each municipality, we were able to arrive at an optimal method of waste management and compare the costs associated with 100% landfilling to the costs born by the municipality when some of the waste is recycled. Our results indicate that for 51% of the municipalities, it would be efficient to adopt recycling, even without accounting for externality costs. We found that by adopting recycling, municipalities would be able to reduce direct costs by an average of 11%. Through interviews conducted with representatives of municipalities, we were also able to identify obstacles to the utilization of recycling, answering in part the question of why actual recycling levels in Israel are lower than our model predicts they should be.     

Effects of planned expansion of waste incineration in the Swedish district heating systems

Recent targets for reduced amounts of waste to landfills in Sweden will result in a large increase in waste incineration with recovery of energy, used primarily for district heating. The aim of this study is to investigate what changes in the usage of other fuels and technologies for district heat production would be caused by this increase. A questionnaire was sent out to the largest district heating companies, and simulations in an energy systems model were carried out. The analysis shows that increased waste incineration reduces the demand for other fuels, especially biomass, for district heat production. The effects include reductions in operating hours as well as the avoidance or postponement of investments in new plants for district heat production. Increased waste incineration will also lead to a greater use of district heating in Sweden.     

MSWNet: A visual deep machine learning method adopting transfer learning based upon ResNet 50 for municipal solid waste sorting

● MSWNet was proposed to classify municipal solid waste. ● Transfer learning could promote the performance of MSWNet. ● Cyclical learning rate was adopted to quickly tune hyperparameters. An intelligent and efficient methodology is needed owning to the continuous increase of global municipal solid waste (MSW). This is because the common methods of manual and semi-mechanical screenings not only consume large amount of manpower and material resources but also accelerate virus community transmission. As the categories of MSW are diverse considering their compositions, chemical reactions, and processing procedures, etc., resulting in low efficiencies in MSW sorting using the traditional methods. Deep machine learning can help MSW sorting becoming into a smarter and more efficient mode. This study for the first time applied MSWNet in MSW sorting, a ResNet-50 with transfer learning. The method of cyclical learning rate was taken to avoid blind finding, and tests were repeated until accidentally encountering a good value. Measures of visualization were also considered to make the MSWNet model more transparent and accountable. Results showed transfer learning enhanced the efficiency of training time (from 741 s to 598.5 s), and improved the accuracy of recognition performance (from 88.50% to 93.50%); MSWNet showed a better performance in MSW classsification in terms of sensitivity (93.50%), precision (93.40%), F1-score (93.40%), accuracy (93.50%) and AUC (92.00%). The findings of this study can be taken as a reference for building the model MSW classification by deep learning, quantifying a suitable learning rate, and changing the data from high dimensions to two dimensions.