城市固体废物优化管理模型及管理成本影响因素研究

采用不确定性多目标动态优化模型,以优化环境和经济为目标,对中国佛山市固体废物管理进行规划.结果表明,该模型能大幅度降低固体废物管理与处理成本,节省财政支出.研究得出影响固体废物总处理费用的3个重要影响因素为产生量、回收量、处理容量.针对以上3个因素深入分析比较,提出了广义和狭义的综合处理技术,这是经济、环保、可行的技术策略:首先,将广义综合处理应用到实际中,采用分类回收、压缩收集、优化模型对废物进行合理配置,尽量降低经济成本和环境影响;其次,从狭义角度出发,采用多种技术组合,达到处理率高、资源化程度高、环境影响小的目的.     

A multi-objective optimization framework for surfactant-enhanced remediation of DNAPL contaminations

The occurrence of Dense Non-Aqueous Phase Liquid (DNAPL) contaminations in the subsurface is a threat for drinkwater resources in the western world. Surfactant-Enhanced Aquifer Remediation (SEAR) is widely considered as one of the most promising techniques to remediate DNAPL contaminations in-situ, be it with considerable additional costs compared to classical pump-and-treat remediations. A cost-effective design of the remediation set-up is therefore essential. In this work, a pilot SEAR test is executed at a DNAPL contaminated site in Belgium in order to collect data for the calibration of a multi-phase multi-component model. The calibrated model is used to assess a series of scenario-analyses for the full-scale remediation of the site. The remediation variables that were varied were the injection and extraction rate, the injection and extraction duration, and the surfactant injection concentrations. A constrained multi-objective optimization of the model was applied to obtain a Pareto set of optimal remediation strategies with different weights for the two objectives of the remediation: (i) the maximal removal of DNAPL and (ii) a total minimal cost. These Pareto curves can help decision makers to select an optimal remediation strategy in terms of cost and remediation efficiency. The Pareto front shows a considerable trade-off between the total remediation cost and the removed DNAPL mass.     

Modeling Pareto efficient PM10 control policies in Northern Italy to reduce health effects

High PM10 concentrations can cause human health problems, both related to short-term and long-term exposure to particles. In this work the impact of efficient PM10 control problems in Northern Italy is assessed by means of a two-stage methodology. In the first stage a multi-objective optimization approach is applied. The multi-objective problem defines two control objectives (the emission reduction costs and the air quality index) to be minimized varying the decision variables (precursor emission reductions). The solution of the multi-objective problem is the Pareto efficient PM10 control policies. In the second stage, the ExternE methodology is applied to estimate health impacts and external costs for the efficient emission reduction scenarios computed in the first stage. The methodology has been applied over Lombardia region, one of the most polluted areas in Europe.     

A multiobjective optimization model and an orthogonal design–based hybrid heuristic algorithm for regional urban mining management problems

In this paper, a multiobjective mixed-integer piecewise nonlinear programming model (MOMIPNLP) is built to formulate the management problem of urban mining system, where the decision variables are associated with buy-back pricing, choices of sites, transportation planning, and adjustment of production capacity. Different from the existing approaches, the social negative effect, generated from structural optimization of the recycling system, is minimized in our model, as well as the total recycling profit and utility from environmental improvement are jointly maximized. For solving the problem, the MOMIPNLP model is first transformed into an ordinary mixed-integer nonlinear programming model by variable substitution such that the piecewise feature of the model is removed. Then, based on technique of orthogonal design, a hybrid heuristic algorithm is developed to find an approximate Pareto-optimal solution, where genetic algorithm is used to optimize the structure of search neighborhood, and both local branching algorithm and relaxation-induced neighborhood search algorithm are employed to cut the searching branches and reduce the number of variables in each branch. Numerical experiments indicate that this algorithm spends less CPU (central processing unit) time in solving large-scale regional urban mining management problems, especially in comparison with the similar ones available in literature. By case study and sensitivity analysis, a number of practical managerial implications are revealed from the model.

Implications: Since the metal stocks in society are reliable overground mineral sources, urban mining has been paid great attention as emerging strategic resources in an era of resource shortage. By mathematical modeling and development of efficient algorithms, this paper provides decision makers with useful suggestions on the optimal design of recycling system in urban mining. For example, this paper can answer how to encourage enterprises to join the recycling activities by government’s support and subsidies, whether the existing recycling system can meet the developmental requirements or not, and what is a reasonable adjustment of production capacity.     

Compromising economic cost and air pollutant emissions of municipal solid waste management strategies by fuzzy multiobjective optimization model

Municipal solid waste management (MSWM) is an important environmental challenge and subject in urban planning. A sustainable MSWM strategy should consider not only economic efficiency but also life-cycle assessment of environmental impact. This study employs the fuzzy multiobjective linear programming (FMOLP) technique to find the optimal compromise between economic optimization and pollutant emission reduction for the MSWM strategy. Taichung City in Taiwan is evaluated as a case study. The results indicate that the optimal compromise MSWM strategy can reduce significant amounts of pollutant emissions and still achieve positive net profits. Minimization of the sulfur oxide (SO_x) and nitrogen oxide (NO_x) emissions are the two major priorities in achieving this optimal compromise strategy when recyclables recovery rate is lower; however, minimization of the carbon monoxide (CO) and particulate matter (PM) emissions become priority factors when recovery rate is higher.

Implications: This paper applied the multiobjective optimization model to find the optimal compromise municipal solid waste management (MSWM) strategy, which minimizes both life-cycle operating cost and air pollutant emissions, and also to analyze the correlation between recyclables recovery rates and optimal compromise strategies. It is different from past studies, which only consider economic optimization or environmental impacts of the MSWM system. The result shows that optimal compromise MSWM strategy can achieve a net profit and reduce air pollution emission. In addition, scenario investigation of recyclables recovery rates indicates that resource recycling is beneficial for both economic optimization and air pollutant emission minimization.     

Game theory approach for the design of an optimal air pollution control system for thermal power plants

A procedure, based on the concept of game theory, for the optimum design of an air pollution control system in thermal power plants is described. The problem is formulated as a four-criteria optimisation problem, with the cost of the electrostatic precipitator, the cost of the stack, the maximum ground-level concentration of particulate matter and the maximum ground-level concentration of sulfur dioxide as the objectives. The efficiency of the precipitator and the height of the stack are treated as the design variables. Geometric constraints in the form of lower and upper bounds on the design variables are imposed on the problem. The design problem is formulated as a four-person game, and the Nash non-cooperative solution is evaluated for irrational play to determine the starting point of the game. For the cooperative game, a supercriterion is formulated for the overall benefit of the players. The game is terminated when the optimal trade-off between the objectives is reached with the maximisation of the supercriterion. The methodology is demonstrated by solving a practical problem related to the design of an air pollution control system for a 210 MW thermal power plant.     

Application of Genetic Algorithms for the Design of Ozone Control Strategies

ABSTRACT

Designing air quality management strategies is complicated by the difficulty in simultaneously considering large amounts of relevant data, sophisticated air quality models, competing design objectives, and unquantifiable issues. For many problems, mathematical optimization can be used to simplify the design process by identifying cost-effective solutions. Optimization applications for controlling nonlinearly reactive pollutants such as tropospheric ozone, however, have been lacking because of the difficulty in representing nonlinear chemistry in mathematical programming models.

We discuss the use of genetic algorithms (GAs) as an alternative optimization approach for developing ozone control strategies. A GA formulation is described and demonstrated for an urban-scale ozone control problem in which controls are considered for thousands of pollutant sources simultaneously. A simple air quality model is integrated into the GA to represent ozone transport and chemistry. Variations of the GA formulation for multiobjective and chance-constrained optimization are also presented. The paper concludes with a discussion of the practicality of using more sophisticated, regulatory-scale air quality models with the GA. We anticipate that such an approach will be practical in the near term for supporting regulatory decision-making.     

Greenhouse Gas Mitigation-Induced Rough-Interval Programming for Municipal Solid Waste Management

Abstract

A greenhouse gas (GHG) mitigation-induced rough-interval programming model is proposed in this study. Components of GHG emission and environmental pollution control are incorporated into the objective function and a series of relevant constraints. To explicitly examine more complexities existing in many parameters, rough intervals are also communicated into the modeling framework. The proposed model presents satisfactory capabilities in analyzing complicated interrelationships among municipal solid waste (MSW) management, climate-change impact, and environmental pollution control. It can also provide optimal allocation schemes and facilitate decision-makers regulating environmentally sustainable strategies. The developed model is then applied to a case study for demonstrating its applicability. Two representative scenarios (relatively representing two potential management policies that may be implemented in the future years) are considered. The results indicate that the developed model presents advantages in mitigating GHG emissions and the associated climate-change impact. The comparison between the GHG mitigation-induced model with and without rough-interval parameters is also investigated. Completely different solutions of the two models imply the significant impact of dual-uncertain information on the system, which can hardly be addressed through the existing optimization approaches.     

Two-Stage Fuzzy-Stochastic Robust Programming: A Hybrid Model for Regional Air Quality Management

Abstract

In this study, a hybrid two-stage fuzzy-stochastic robust programming (TFSRP) model is developed and applied to the planning of an air-quality management system. As an extension of existing fuzzy-robust programming and two-stage stochastic programming methods, the TFSRP can explicitly address complexities and uncertainties of the study system without unrealistic simplifications. Uncertain parameters can be expressed as probability density and/or fuzzy membership functions, such that robustness of the optimization efforts can be enhanced. Moreover, economic penalties as corrective measures against any infeasibilities arising from the uncertainties are taken into account. This method can, thus, provide a linkage to predefined policies determined by authorities that have to be respected when a modeling effort is undertaken. In its solution algorithm, the fuzzy decision space can be delimited through specification of the uncertainties using dimensional enlargement of the original fuzzy constraints. The developed model is applied to a case study of regional air quality management. The results indicate that reasonable solutions have been obtained. The solutions can be used for further generating pollution-mitigation alternatives with minimized system costs and for providing a more solid support for sound environmental decisions.     

Application of Unknown Groundwater Pollution Source Release History Estimation Methodology to Distributed Sources Incorporating Surface-Groundwater Interactions

Sources of contamination of groundwater are often difficult to characterize. However, it is essential for effective remediation of polluted groundwater resources. This study demonstrates an application of the linked simulation-optimization based methodology to estimate the release history from spatially distributed sources of pollution at an illustrative abandoned mine-site. In linked simulation-optimization approaches a numerical groundwater flow and transport simulation model is linked to the optimization model. In this study, topographic and geologic characteristics of the abandoned mine-site were simulated using a three-dimensional (3D) numerical groundwater flow model. Transport of contaminant in the groundwater was simulated using a 3D transient advective-dispersive contaminant transport model. Adsorption or chemical reaction of the contaminant was not considered in the contaminant transport model. Adaptive simulated annealing (ASA) was employed for solving the optimization problem. An optimization algorithm generates the candidate solutions corresponding to various unknown groundwater source characteristics. The candidate solutions are used as input in the numerical groundwater transport simulation model to generate the concentration of pollutant in the study area. This information is used to calculate the objective function value, which is utilized by the optimization algorithm to improve the candidate solution. This process continues until an optimal solution is obtained. Optimal solutions obtained in this study show that the linked simulation-optimization based methodology is potentially applicable for the characterization of spatially distributed pollutant sources, typically present at abandoned mine-sites.     

M-AHP-熵权组合赋权法在垃圾渗滤液处理技术评价中的应用

通过分析目前综合评价赋权方法存在的弊端,提出了主客观相结合对指标进行赋权的方法,即采用基于MATLAB优化工具箱的层次分析法(M-AHP)建立主观权重,采用熵权法建立客观权重,将主客观权重加以综合来确定评价指标的权重。最后将该方法应用于重庆市卫生填埋场的处理技术的评价指标,然后按照模糊积分评价模型得出综合评价值,从3种备选方案中得出了MBR(膜生物反应器)+NF(纳滤)工艺为最佳方案。     

An Intelligent Emissions Controller for Fuel Lean Gas Reburn in Coal-Fired Power Plants

ABSTRACT

The application of artificial intelligence techniques for performance optimization of the fuel lean gas reburn (FLGR) system is investigated. A multilayer, feedforward artificial neural network is applied to model static nonlinear relationships between the distribution of injected natural gas into the upper region of the furnace of a coal-fired boiler and the corresponding oxides of nitrogen (NO_x) emissions exiting the furnace. Based on this model, optimal distributions of injected gas are determined such that the largest NO_x reduction is achieved for each value of total injected gas. This optimization is accomplished through the development of a new optimization method based on neural networks. This new optimal control algorithm, which can be used as an alternative generic tool for solving multidimensional nonlinear constrained optimization problems, is described and its results are successfully validated against an off-the-shelf tool for solving mathematical programming problems. Encouraging results obtained using plant data from one of Commonwealth Edison's coal-fired electric power plants demonstrate the feasibility of the overall approach.

Preliminary results show that the use of this intelligent controller will also enable the determination of the most cost-effective operating conditions of the FLGR system by considering, along with the optimal distribution of the injected gas, the cost differential between natural gas and coal and the open-market price of NO_x emission credits. Further study, however, is necessary, including the construction of a more comprehensive database, needed to develop high-fidelity process models and to add carbon monoxide (CO) emissions to the model of the gas reburn system.     

Multi-criteria analysis for PM10 planning

To implement sound air quality policies, Regulatory Agencies require tools to evaluate outcomes and costs associated to different emission reduction strategies. These tools are even more useful when considering atmospheric PM10 concentrations due to the complex nonlinear processes that affect production and accumulation of the secondary fraction of this pollutant. The approaches presented in the literature (Integrated Assessment Modeling) are mainly cost-benefit and cost-effective analysis. In this work, the formulation of a multi-objective problem to control particulate matter is proposed. The methodology defines: (a) the control objectives (the air quality indicator and the emission reduction cost functions); (b) the decision variables (precursor emission reductions); (c) the problem constraints (maximum feasible technology reductions). The cause-effect relations between air quality indicators and decision variables are identified tuning nonlinear source–receptor models. The multi-objective problem solution provides to the decision maker a set of not-dominated scenarios representing the efficient trade-off between the air quality benefit and the internal costs (emission reduction technology costs). The methodology has been implemented for Northern Italy, often affected by high long-term exposure to PM10. The source–receptor models used in the multi-objective analysis are identified processing long-term simulations of GAMES multiphase modeling system, performed in the framework of CAFE-Citydelta project.     

春季典型天气太原市大气环境监测时段性研究

为了从一些具有代表性的监测时段数据中快速而直观地反映太原市大气环境状况,运用特征分析法对太原市中心城区7个监测点的16个时间点位的样本数据联系性进行了分析。借助MATLAB软件对其进行可视化,得到各监测点的时间点优化分组。通过对7个优化分组综合比选,提出了能整体反映春季典型天气条件下太原市大气环境状况的优化时段点位,分别为7:00、12:00、14:00、15:00、18:00、19:00和22:00。将5 d相同或相似天气状况的优化时段点位进行对比,得到的结果一致,体现了代表性。太原市总体优化时段点位数据由反映各区域状况的各监测点优化时段点数据平均值或乘以相应的权重得出。     

Optimization of low-temperature drying of laying-hen manure using response surface methodology

ABSTRACT

Manure-drying system using exhausted air from laying hen houses or ambient air has been extensively used in China to dewater the manure for easy transportation and to reduce pathogen levels prior to land application. Due to the climate influence or inappropriate setting of technological parameters, there are some issues in this manure-drying system, such as low dehydration rate, high energy consumption, and high ammonia emission. A purpose-designed experimental drying apparatus was set up to simulate the commercial manure drying system. Drying experiments were carried out to assess the impacts of hot air temperature (15–35°C), air velocity (0.6–1.8 m/s) and manure layer thickness (60–140 mm) on fan’s energy consumption, dehydration rate, and nitrogen loss rate. The response surface analysis method and sub-stepping method was used to analyze the relationships between the response variables and the influence factors. The drying curves were drawn, and the quadratic regression mathematical models that described the relations between the experimental indices and the influence factors were established. The optimal combination of technological parameters for drying laying-hen manure was obtained through conducting a multi-objective function optimization by function-expected optimization. The optimal parameters are as follows: hot air temperature of 35°C, air velocity of 1.60 m/s, and manure layer thickness of 85 mm. The results also indicate that raising the hot air temperature increased the value of synthesis objective function when the hot air temperature was in 26–35°C. The results can provide a theoretical basis for low-temperature drying of laying-hen manure in actual production.

Implications: A large amount of poultry manure is produced yearly in China, which has become a tremendous pressure on the environment when it cannot be utilized as resources. A more sustainable solution using the residual heat from the poultry house ventilation or ambient hot air has been widely used in China. This drying method can significantly reduce energy consumption compared to the traditional way. However, due to the influence of climate or inappropriate setting of technological parameters, issues such as high energy consumption and high ammonia emission still exist in this method. It is necessary to optimize the low-temperature drying process of laying-hen manure, to reduce energy consumption and nitrogen loss rate.     

Application of genetic algorithms for the design of ozone control strategies

Designing air quality management strategies is complicated by the difficulty in simultaneously considering large amounts of relevant data, sophisticated air quality models, competing design objectives, and unquantifiable issues. For many problems, mathematical optimization can be used to simplify the design process by identifying cost-effective solutions. Optimization applications for controlling nonlinearly reactive pollutants such as tropospheric ozone, however, have been lacking because of the difficulty in representing nonlinear chemistry in mathematical programming models. We discuss the use of genetic algorithms (GAs) as an alternative optimization approach for developing ozone control strategies. A GA formulation is described and demonstrated for an urban-scale ozone control problem in which controls are considered for thousands of pollutant sources simultaneously. A simple air quality model is integrated into the GA to represent ozone transport and chemistry. Variations of the GA formulation for multiobjective and chance-constrained optimization are also presented. The paper concludes with a discussion of the practically of using more sophisticated, regulatory-scale air quality models with the GA. We anticipate that such an approach will be practical in the near term for supporting regulatory decision-making.     

Modeling Municipal Solid Waste Management System under Uncertainty

Abstract

In this study, a dynamic inexact waste management (DIWM) model is developed for identifying optimal waste-flow-allocation and facility-capacity-expansion strategies under uncertainty and is based on an inexact scenario-based probabilistic programming (ISPP) approach. The DIWM model can handle uncertainties presented as interval values and probability distributions, and it can support assessing the risk of violating system constraints. Several violation levels for facility-capacity and waste-diversion constraints are examined. Solutions associated with different risks of constraint violation were generated. The modeling results are valuable for supporting the planning of the study city’s municipal solid waste (MSW) management practices, the long-term capacity expansion for waste management system, and the identification of desired policies regarding waste diversion. Sensitivity analyses are also undertaken to demonstrate that the violations of different constraints have varied effects on the planning of waste-flow allocation, facility expansion, and waste management cost.     

Two-stage fuzzy-stochastic robust programming: a hybrid model for regional air quality management

In this study, a hybrid two-stage fuzzy-stochastic robust programming (TFSRP) model is developed and applied to the planning of an air-quality management system. As an extension of existing fuzzy-robust programming and two-stage stochastic programming methods, the TFSRP can explicitly address complexities and uncertainties of the study system without unrealistic simplifications. Uncertain parameters can be expressed as probability density and/or fuzzy membership functions, such that robustness of the optimization efforts can be enhanced. Moreover, economic penalties as corrective measures against any infeasibilities arising from the uncertainties are taken into account. This method can, thus, provide a linkage to predefined policies determined by authorities that have to be respected when a modeling effort is undertaken. In its solution algorithm, the fuzzy decision space can be delimited through specification of the uncertainties using dimensional enlargement of the original fuzzy constraints. The developed model is applied to a case study of regional air quality management. The results indicate that reasonable solutions have been obtained. The solutions can be used for further generating pollution-mitigation alternatives with minimized system costs and for providing a more solid support for sound environmental decisions.     

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.     

81st APCA Annual Meeting & Exhibition

Abstract

This study introduces a two-stage interval-stochastic programming (TISP) model for the planning of solid-waste management systems under uncertainty. The model is derived by incorporating the concept of two-stage stochastic programming within an interval-parameter optimization framework. The approach has the advantage that policy determined by the authorities, and uncertain information expressed as intervals and probability distributions, can be effectively communicated into the optimization processes and resulting solutions. In the modeling formulation, penalties are imposed when policies expressed as allowable waste-loading levels are violated. In its solution algorithm, the TISP model is converted into two deterministic submodels, which correspond to the lower and upper bounds for the desired objective-function value. Interval solutions, which are stable in the given decision space with associated levels of system-failure risk, can then be obtained by solving the two submodels sequentially. Two special characteristics of the proposed approach make it unique compared with other optimization techniques that deal with uncertainties. First, the TISP model provides a linkage to prede?ned policies determined by authorities that have to be respected when a modeling effort is undertaken; second, it furnishes the reflection of uncertainties presented as both probabilities and intervals. The developed model is applied to a hypothetical case study of regional solid-waste management. The results indicate that reasonable solutions have been generated. They provide desired waste-flow patterns with minimized system costs and maximized system feasibility. The solutions present as stable interval solutions with different risk levels in violating the waste-loading criterion and can be used for generating decision alternatives.