固体废弃物资源化利用评价体系及研究方法

从经济、社会政策和环境影响三个角度,运用层次分析法、模糊决策理论和多目标分析3种评价方法以及矩阵、德尔斐法和混合优序图等数学工具,分别对上海市固体废弃物资源化利用所采用的不同方案进行全面综合评价,不同评价方法的研究结果具有一致性,均表明环境影响是相对最重要的影响因素,固体废弃物双向分离系统的建立是最优化的固体废弃物资源化利用方案,也是今后上海固体废弃物资源化利用研究的重点以及努力发展的方向.     

膜生物反应器的环境经济影响动态模拟研究

膜生物反应器(MBR)污水处理技术比传统活性污泥法具有更高的水污染物去除效率,然而较高的投资和运行成本制约了该项技术的推广。决策者需要综合分析环境经济影响,才能决定是否使用该项技术。基于物质平衡、价值平衡和能源平衡,建立环境经济综合评价模型,利用多目标最优化方法,通过计算机模拟实验,综合分析和预测采用MBR污水处理技术产生的环境经济影响。模拟实验结果表明,从社会经济和环境可持续发展的角度来看,采用该技术处理污水是可行的。     

基于Agent的城市污水处理可持续发展工艺选型定量建模研究

从可持续发展的角度分析和研究中国目前的城市污水处理工艺,选出符合中国国情的工艺流程日益成为迫切需要解决的问题.在广泛调研和资料分析的基础上,基于可持续发展的原则,通过对中国城市污水处理的现状进行分析,建立了可供筛选的处理技术数据库,并通过基于生命周期的Agent模型,对每项组合工艺流程进行功能模拟和性能评价,按照所建立的评价模型和指标体系,综合考察各项处理工艺的污染物处理水平、投资成本和附属产物的环境影响等因素.按照可持续发展的技术准则进行判定,从而选择满足可持续发展要求的城市污水处理组合工艺.     

废弃电器再制造/再利用逆向物流库存模型

构建废弃电器再制造/再利用库存模型对优化回收网络、控制回收成本、提升绿色化水平具有重要意义。通过分析二手市场需求量与再制造/再利用处理后获得的成品速率的关系,综合考虑回收品与成品的库存成本、再利用和再制造处理费用以及缺货成本等,以平均总成本最小为控制目标,建立了基于PULL(拉式)策略的逆向物流库存模型。通过算例,获得最优处理周期与最低费用,探讨模型中的不同参数对平均总成本的影响,同时进行显著性分析,找出了关键参数。     

应用生命周期模型评价餐厨垃圾处理技术

运用生命周期模型对餐厨垃圾常用处理技术——厌氧发酵、好氧堆肥、饲料化技术、生化处理机进行评价和筛选。结果表明,厌氧发酵、好氧堆肥、饲料化技术、小型生化处理机的环境影响潜力分别为1.35×10-3、4.42×10-3、3.78×10-2和1.18×10-2,单位处理成本为2.1、5.65、0.37和1.4元/（人.a）,综合评估得出厌氧发酵和饲料化是当前具有优势的餐厨垃圾处理技术。     

基于DEA的中国工业污染治理效率

以用于工业污染治理的资金和设备为投入指标,以工业废水达标排放率和废水重复利用率、工业废气排放削减率、工业固体废物综合利用率和处置率为产出指标,利用DEA模型将污染治理的综合技术效率分解为纯技术效率和规模效率,分析影响污染治理效果的制约因素,为改善治理效果提供依据.结果表明,以1999年为转折点,工业废水治理的制约因素由纯技术效率转为规模效率.1997-2002年,工业废气的种类和性质变化以及废气的复合型污染提升了对废气处理技术的要求,纯技术效率偏低;2002年以后,纯技术效率和规模效率发展不相协调,废气治理投入的资源利用率偏低.1995-2005年,固体废物污染治理的规模效率较高,纯技术效率为污染治理的制约因素;2005年以后,纯技术效率有所提升,但波动较大.整体而言,工业废水治理的多年平均综合技术效率、纯技术效率和规模效率均最高,废气次之,固体废物最低,表现出明显的不均衡性.     

危险固体废物焚烧残渣的稳定化处理研究

采用常规危险固体废物稳定剂Na3 PO4、Na2S、腐殖酸以及工业固体废物电石渣对台州典型危险固体废物焚烧残渣进行稳定化处理.以残渣中污染最严重的重金属Pb、Ni、Cu、Zn的去除率为衡量指标,进行稳定化时间优化试验.结果表明,Na3 PO4、电石渣可在7d后达到较好的稳定化效果,且随着时间的延长效果稳定;而Na2S和...     

基于机械物理法的废旧电路板回收经济性分析

废旧电路板的有效回收对保护环境、实现资源的优化配置和社会可持续发展具有十分重要的意义。通过分析废旧电路板的回收方法,给出了机械物理法中3种典型回收方案的工艺流程,建立了其回收经济性评价模型,量化评估回收成本与收益。结合一个具体的回收实例,运用经济性评价模型对其回收成本与收益进行具体分析,验证了评价模型的可靠性,实现了回收方案的优选。最后,对提高废旧电路板回收经济性提出一些建议。     

抗生素制药菌渣处理处置技术评价与分析

通过选取涵盖技术性能、经济效益、环境影响和社会影响4个方面的评估指标体系,建立了关于抗生素制药菌渣的处理处置技术评估层次分析-模糊数学综合筛选模型,其中,层次分析法用于确定一级指标、二级指标的权重,专家打分法与定量法相结合构成了模糊数学综合评估模型。采用评估模型对堆肥、厌氧消化、水煤浆、制备酵母膏/粉代用品、吸附材料、焚烧及填埋7种技术进行了综合评估。结果显示,社会影响和环境影响两项指标权重较大,均为0.338 9,经济效益权重为0.153 5,技术性能权重为0.068 7。堆肥技术为Ⅳ级,厌氧消化、制备酵母膏/粉代用品、水煤浆、吸附材料等技术为Ⅲ级,焚烧技术为Ⅱ级,填埋技术为Ⅰ级。最后,结合各种技术的一级指标和二级指标的得分结果,对各种技术存在的具体问题进行了分析。     

对城市不同燃料供热系统方案的分析评价方法研究

针对土地资源的商品化和传统燃煤供热方面的价值观及其对大气环境的影响,综合环境影响分析、建筑方案、动态投资费用和动态成本等四方面因素,提出了对城市不同燃料供热方案的分析评价方法,为推广使用清洁燃料,改善城市大气环境质量,实现环境、经济、社会三个效益的统一提供了依据.     

An economic evaluation and assessment of environmental impact of the municipal solid waste management system for Taichung City in Taiwan

Municipal solid waste management (MSWM) is an important environmental challenge and subject in urban planning. For sustainable MSWM strategies, the critical management factors to be considered include not only economic efficiency of MSW treatment but also life-cycle assessment of the environmental impact. This paper employed linear programming technique to establish optimal MSWM strategies considering economic efficiency and the air pollutant emissions during the life cycle of a MSWM system, and investigated the correlations between the economical optimization and pollutant emissions. A case study based on real-world MSW operating parameters in Taichung City is also presented. The results showed that the costs, benefits, streams of MSW, and throughputs of incinerators and landfills will be affected if pollution emission reductions are implemented in the MSWM strategies. In addition, the quantity of particulate matter is the best pollutant indicator for the MSWM system performance of emission reduction. In particular this model will assist the decision maker in drawing up a friendly MSWM strategy for Taichung City in Taiwan. Implications: Recently, life-cycle assessments of municipal solid waste management (MSWM) strategies have been given more considerations. However, what seems to be lacking is the consideration of economic factors and environmental impacts simultaneously. This work analyzed real-world data to establish optimal MSWM strategies considering economic efficiency and the air pollutant emissions during the life cycle of the MSWM system. The results indicated that the consideration of environmental impacts will affect the costs, benefits, streams of MSW, and throughputs of incinerators and landfills. This work is relevant to public discussion and may establish useful guidelines for the MSWM policies.     

An optimal model and its application for the management of municipal solid waste from regional small cities in China

Based on the basic characteristics of municipal solid waste (MSW) from regional small cities in China, some optimal management principles have been put forward: regional optimization, long-term optimization, and integrated treatment/disposal optimization. According to these principles, an optimal MSW management model for regional small cities is developed and provides a useful method to manage MSW from regional small cities. A case study application of the optimal model is described and shows that the optimal management scenarios in the controlling region can be gained, adequately validating and accounting for the advantages of the optimal model.     

An economic evaluation and assessment of environmental impact of the municipal solid waste management system for Taichung City in Taiwan

Municipal solid waste management (MSWM) is an important environmental challenge and subject in urban planning. For sustainable MSWM strategies, the critical management factors to be considered include not only economic efficiency of MSW treatment but also life-cycle assessment of the environmental impact. This paper employed linear programming technique to establish optimal MSWM strategies considering economic efficiency and the air pollutant emissions during the life cycle of a MSWM system, and investigated the correlations between the economical optimization and pollutant emissions. A case study based on real-world MSW operating parameters in Taichung City is also presented. The results showed that the costs, benefits, streams of MSW, and throughputs of incinerators and landfills will be affected if pollution emission reductions are implemented in the MSWM strategies. In addition, the quantity of particulate matter is the best pollutant indicator for the MSWM system performance of emission reduction. In particular, this model will assist the decision maker in drawing up a friendly MSWM strategy for Taichung City in Taiwan.

Implications: Recently, life-cycle assessments of municipal solid waste management (MSWM) strategies have been given more considerations. However, what seems to be lacking is the consideration of economic factors and environmental impacts simultaneously. This work analyzed real-world data to establish optimal MSWM strategies considering economic efficiency and the air pollutant emissions during the life cycle of the MSWM system. The results indicated that the consideration of environmental impacts will affect the costs, benefits, streams of MSW, and throughputs of incinerators and landfills. This work is relevant to public discussion and may establish useful guidelines for the MSWM policies.     

An Optimal Model and Its Application for the Management of Municipal Solid Waste from Regional Small Cities in China

Abstract

Based on the basic characteristics of municipal solid waste (MSW) from regional small cities in China, some optimal management principles have been put forward: regional optimization, long-term optimization, and integrated treatment/disposal optimization. According to these principles, an optimal MSW management model for regional small cities is developed and provides a useful method to manage MSW from regional small cities. A case study application of the optimal model is described and shows that the optimal management scenarios in the controlling region can be gained, adequately validating and accounting for the advantages of the optimal model.     

Evaluation of solid waste management strategies in the Taipei metropolitan area of Taiwan

Municipal solid waste (MSW) management is a major concern for highly urbanized societies. Among proposed MSW management systems, regionalization programs generally have received considerable attention. This study analyzes real-world operational data to assess different MSW management policies, especially regionalization strategies, and their impact on MSW management systems in the Taipei metropolitan area. Linear programming is also used to identify the minimum costs sustained by each policy. The linear programming results show that regionalization programs are more economical and also improve incinerator operation efficiency. Sensitivity analysis indicates that the minimum treatment requirement of incinerators is a very sensitive influence on the MSW flows distributed through the entire region. The MSW of several "sensitive" administrative districts will be allocated to different treatment facilities according to different management strategies. A list of preferential sequences of MSW treatment and disposal facilities can also be identified by the model presented in this study. The results of this study may provide a useful tool for aiding decision-making related to real-world MSW management problems.     

Evaluation of Solid Waste Management Strategies in the Taipei Metropolitan Area of Taiwan

Abstract

Chenfang Lin is with the Department of Soil and Environmental Science at National Chung Hsing University.Municipal solid waste (MSW) management is a major concern for highly urbanized societies. Among proposed MSW management systems, regionalization programs generally have received considerable attention. This study analyzes real-world operational data to assess different MSW management policies, especially regionalization strategies, and their impact on MSW management systems in the Taipei metropolitan area. Linear programming is also used to identify the minimum costs sustained by each policy. The linear programming results show that regionalization programs are more economical and also improve incinerator operation efficiency. Sensitivity analysis indicates that the minimum treatment requirement of incinerators is a very sensitive influence on the MSW flows distributed through the entire region. The MSW of several “sensitive” administrative districts will be allocated to different treatment facilities according to different management strategies. A list of preferential sequences of MSW treatment and disposal facilities can also be identified by the model presented in this study. The results of this study may provide a useful tool for aiding decision-making related to real-world MSW management problems.     

Subregion districting analysis for municipal solid waste collection privatization

Privatization of municipal solid waste (MSW) collection can improve service quality and reduce cost. To reduce the risk of an incapable company serving an entire collection area and to establish a competitive market, a large collection area should be divided into two or more subregions, with each subregion served by a different company. The MSW subregion districting is generally done manually, based on the planner's intuition. Major drawbacks of a manual approach include the creation of a districting plan with poor road network integrity for which it is difficult to design an efficient collection route. The other drawbacks are difficulty in finding the optimal districting plan and the lack of a way to consistently measure the differences among subregions to avoid unfair competition. To determine an MSW collection subregion districting plan, this study presents a mixed-integer optimization model that incorporates factors such as compactness, road network integrity, collection cost, and regional proximity. Two cases are presented to demonstrate the applicability of the proposed model. In both cases, districting plans with good road network integrity and regional proximity have been generated successfully.     

Long short-term memory neural network and improved particle swarm optimization–based modeling and scenario analysis for municipal solid waste generation in Shanghai,China

Accurate estimations of municipal solid waste (MSW) generation are vital to effective MSW management systems. While various single-point estimation approaches have been developed, the non-linearity and multiple site-specific influencing factors associated with MSW management systems make it challenging to forecast MSW generation quantities precisely. To address these concerns, this study developed a two-stage modeling and scenario analysis procedure for MSW generation and taking Shanghai as a test case demonstrated its viability. In the first stage, nine influencing factors were selected, and a hybrid novel forecasting model based on a long short-term memory neural network and an improved particle swarm optimization (IPSO-LSTM) was proposed for the forecasting of the MSW generation quantities, after which actual Shanghai data from 1980 to 2019 were used to test the performance. In the second stage, the future influencing variable values in different scenarios were predicted using an improved grey model, after which the predicted Shanghai MSW generation quantities from 2025 to 2035 were evaluated under various scenarios. It was found that (1) the proposed IPSO-LSTM had higher accuracy than the benchmark models; (2) the MSW generation quantities are expected to respectively increase to 9.971, 9.684, and 9.090 million tons by 2025 and 11.402, 11.285, and 10.240 by 2035 under the low, benchmark, and high scenarios; and (3) the MSW generation differences between the high and medium scenarios were decreasing.

     

Factors influencing regional municipal solid waste management strategies

Although regionalization policies have been proven as good strategies for municipal solid waste (MSW) management in previous studies, the optimal allocation of the waste stream is significantly affected by several influential factors, thus further investigation of the impacts of these factors on regional MSW management strategies is necessary. This study demonstrated the impacts of waste-to-electricity transformation coefficient (WETC) of incinerators and the fluctuation of unit tipping fees on the regional MSW flow/allocation of the Taipei metropolitan area from practical and economic perspectives. Real-world data and linear programming were used to obtain the least-cost alternatives under different scenarios. Analytical results indicated some treatment facilities had geographic superiority and their priorities changed when actual WETCs of incinerators were considered. Treatment facilities located at weighted centers were identified. The allocation of MSW among incinerators and landfills is affected by fluctuation of unit tipping fees within a certain range. Treatment facilities sensitive to the changes in unit tipping fees were also identified. Regression equations were also established that can estimate the cost items of MSW management scenarios with different unit tipping fees. The results of this study are very useful for daily basis regulation of MSW administration.     

Minimax regret analysis for municipal solid waste management: an interval-stochastic programming approach

In this study, an interval minimax regret programming (IMMRP) method is developed for the planning of municipal solid waste (MSW) management under uncertainty. It improves on the existing interval programming and minimax regret analysis methods by allowing uncertainties presented as both intervals and random variables to be effectively communicated into the optimization process. The IMMRP can account for economic consequences under all possible scenarios without any assumption on their probabilities. The developed method is applied to a case study of long-term MSW management planning under uncertainty. Multiple scenarios associated with different cost and risk levels are analyzed. Reasonable solutions are generated, demonstrating complex tradeoffs among system cost, regret level, and system-failure risk. The method can also facilitate examination of the difference between the cost incurred with identified strategy and the least cost under an ideal condition. The results can help determine desired plans and policies for waste management under a variety of uncertainties.