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Urban Bio-waste Treatment System Synergy Mechanism And Technology Selection Simulation

Posted on:2020-06-09Degree:DoctorType:Dissertation
Country:ChinaCandidate:F FeiFull Text:PDF
GTID:1361330626464400Subject:Environmental Science and Engineering
Abstract/Summary:PDF Full Text Request
Urban bio-waste is the key component of municipal solid waste with a production amount of over 120 million tons per year in China.As urban bio-waste was mixed in the treatment system of municipal solid waste,with the high moisture content and easily degradable portion,it was the main cause of odor and leachate pollution.Besides,urban bio-waste contains nutrient elements and organic material,and makes it have the potential for recycling.With the improvement of urban waste sustainable management standard,the aim of urban bio-waste include amount reduce,pollution reduction and resources recovery.However,as there are many kinds of urban bio-waste,and synergy mechanism exists between wastes,links and technoloties,the system was unable to meet the needs of collaborative promotion of multiple management objectives through the improvement of a single technology or link.This paper conducted the simulation of the synergy mechanism of urban bio-waste management system,and realized the dynamic and quantitative simulation of the complex numerical matching and influence conduction inside the system.Using material flow analysis,life cycle assessment and project net present value assessment,this paper set uo the many objectives evaluation system for the urban bio-waste management system.Based on that,the study constructed urban bio-waste treatment and disposal system technology selection model,the influence mechanism simulation of urban characteristics was included,many objective optimization algorithm NSGA-? was used to achieve technology route selection,while uncertainty analysis mathods using Regional Sensitivity Analysis were used to distinguish the system risks and give policy suggestions.The research results showed that:(1)System synergy mechanism significantly influence technology chain: co-treatment of waste and waste source classification have the potential to reduce the life cycle environmental impact of 24-60% and 10-109%,respectively,and the treatment of waste water and other residues treatment contributed for 10-90% of the life cycle environmental impact,which was far in excess than that of direct emissions.(2)the technology selection integrated model provides a strong support for the city to realize the system optimization based on local conditions: based on the optimization simulation of the urban bio-waste treatment and disposal system of Suzhou in 2020,the energy saving and emission reduction level of 94-401% can be improved by improving the two key schemes originally planned,and the economic cost can be reduced by 15-29%;in 2025,under the optimization scheme of waste co-treatment mode,the energy saving and emission reduction can be increased by 51-335% when the total amount of waste increases by 23.7%.Through the identification and analysis of the system's long-term uncertainties,the treatment amounts distribution and profit probability of treatment facilities can be quantitatively evaluated,sensitive parameters can be identified,and effective early warning and avoidance measures can be provided for the overload operation of treatment facilities and the risk of project loss.(3)the system management objectives are diverse and non-linear: under the separate waste treatment mode,economic costs are positively correlated with energy and environmental benefits,and the energy saving and emission reduction level of the system can be continuously improved by increasing investment;Under the waste co-treatment mode,the marginal cost of energy saving and emission reduction per unit keeps increasing,and the greenhouse effect and the emission reduction of particulate matter have obvious inflection points,indicating that they are close to the potential boundary of existing technologies.
Keywords/Search Tags:urban bio-waste, synergy mechanism, technology selection, many-objective optimization
PDF Full Text Request
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