Search

Inter-regional electricity transmission inevitably brings about unbalanced carbon emission and air pollution among different regions. It is vital to compensate for the economic and environmental loss of power output region by taking the carbon subsidy strategy into account. In this study, a risk explicit interval multistage stochastic programming model considering inter-regional carbon subsidy is developed to support an optimal energy system planning in Inner Mongolia, China. This model can not only deal with uncertainties embedded in the complex energy system described as interval values and random variables but also reflect dynamic linkage between multiple stages over a time series. It advanced the existing optimization methods by introducing the cost-risk tradeoff information based on the risk preferences of decision-makers. The obtained crisp solutions are more feasible, effective and optimum for the practical policymaking process. Different scenarios associated with carbon subsidy policy implementation, renewable energy share adjustment and carbon emission reduction strategy are employed to project the optimal power generation scheme, net system cost, installed electricity capacity, carbon intensity and pollution emission in the future. The results indicate that the amount of exported electricity will keep increasing, and the carbon subsidy strategy will prominently promote the economic growth of the electric power sector by 3.05%. The further power supply pattern is going to transform from fossil fuel to renewable energy, by which wind and solar power will occupy around 44% of total power generation capacity in West Inner Mongolia. Raising the share of renewable energy will be the most effective approach to adjust the power generation scheme; meanwhile, reducing carbon intensity will significantly contribute to enlarge green energy capacity, enhance low-carbon emission and mitigate air pollution. These findings will help decision-makers gain insights into a more scientific energy system planning under various uncertainties.

Integrated water resources management from a water-energy nexus perspective is necessary to realize sustainable development. However, the inherent uncertainties and complexities in the nexus system have posed great challenges for decision makers. In this study, a novel two-stage fuzzy stochastic programming approach capable of addressing uncertainties with both possibility and probability distribution is developed for water resources management problems under water-energy nexus. Under this framework, the future uncertain events can be effectively defined as a scenario tree with fuzzy random information and fuzzy probability distribution. Then, the proposed method is examined through a real case of Tianjin, a coastal water-stressed city in northeast China. A series of decision alternatives are obtained under various water resource availability scenarios with the consideration of decision makers’ risk preferences. Results showed that the surface water and the imported water accounting for 70% of the total water supply would still be the main contributors to support local socioeconomic development and that the estimated electricity consumption embodied in per-unit water supply ranges from 0.56 kWh/m3 to 0.61 kWh/m3 through the system nexus relationship modeling. The results also disclosed that due to climate change, water scarcity would require more exploitation of groundwater and utilization of desalination water and recycle water, which would exacerbate environmental pressure and increase energy demand for water supply. These findings can provide managerial insights and suggestions for decision makers to achieve flexible water management with the consideration of system uncertainties, water-energy nexus, water shortage, and decision maker’s risk attitude.

The carbon-price mechanism has been proved to be an effective measure for promoting energy revolution and mitigating climate change. It is of vital importance to develop optimal energy development strategy for electric power-dependent regions by considering the complex interaction among carbon price, carbon emission control, and carbon-responsibility transfer. In order to fill the research gap on the optimal choice of carbon-price mechanism at the urban level, this study is the first attempt to express uncertainties embodied in the carbon price mechanism as interval values, probability distribution and downside risks. The developed risk-aversion-based interval two-stage stochastic programming (RITSP) model is effective in analyzing the effect of internal and electric-transmission related carbon-tax on power system structure. It is discovered that carbon compensation policy for imported electricity is more suitable for Tianjin's power system development. Tianjin would primarily purchase electricity from Inner-Mongolia. With the increase of carbon emission tax, Tianjin would import increasing proportion of electricity from Gansu. Due to the limited endowment of renewable energy in Tianjin, the impact of carbon emission limitations on the renewable energy power generation structure of is trivial, and it has a greater impact on stimulating the development of CCS technology. What's more, Tianjin's future power system planning is more inclined to develop CCS rather than renewable energy.

Synergic management of the local crop-biomass coproduction system is the potential to increase resource efficiency and promote sustainable development. In this paper, an interval fuzzy linear fractional programming (IFLFP) model is developed for planning regional food production with the consideration of ecological protection, water resource conservation, biomass energy supply, and food-energy-water (FEW) nexus. The main advantages of the proposed IFLFP model are the abilities to reflect uncertainties with different characters as interval values and fuzzy sets and to provide system efficiency measurement by the ratio form of conflict objective functions. The IFLFP model is then tailored for the application of the crop-biomass coproduction management with FEW nexus in Jiangsu Province, China. The influences of different optimization goals (i.e. economic benefits and environmental benefits per unit irrigation water consumption) and multiple water resource scenarios under climate change are examined and discussed to provide more managerial insights. The results reveal that wheat, barley, and tuber crops would be more easily affected by water resource availability and optimization goals. Besides, recycle water would be considered as an important source for irrigation under scare water scenario, and gain the priority for the economic purpose. The results also imply that it is impossible to realize “double-win” of environmental and economic goals simultaneously, and decision makers should make compromises among different strategies.

This study attempts to characterize the literature related to input&ndash, output analysis between 1990&ndash, 2017 through bibliometric analysis technology based on the Science Citation Index and Social Sciences Citation Index databases. By means of bibliometric tools, this paper provides deep insights on the patterns of these articles, the most influential works and authors, and the emerging research topics. The results imply that China and the United States (USA) are the leading countries in terms of publication output. The Chinese Academy of Sciences is the most productive research institution, followed by Beijing Normal University and the University of Sydney. The Journal of Cleaner Production, Ecological Economics, and Energy Policy are the top mainstream journals in the input&ndash, output analysis-related field. Based on network analysis, this paper also discovers the hidden collaboration patterns and interrelations of countries, institutions, and authors. The bibliographic coupling and keywords concurrence networks are adopted to illustrate the input&ndash, output analysis evolution over time, and identify the current key research hotspots. The obtained results will help scientific researchers better understand the research status and frontier trends in this field, permit researchers to know the current research interests in the input&ndash, output analysis field, and provide useful information for further investigation and publication strategies.

Aerosol particulate matter with dynamic diameter smaller than 2.5 μm (PM

In this study, in order to improve regional energy system adjustment, a multistage stochastic inexact robust programming (MSIRP) is proposed for electric-power generation planning and structure adjustment management under uncertainty. Scenario-based inexact multistage stochastic programming and stochastic robust optimization were integrated into general programming to reflect uncertainties that were expressed as interval values and probability distributions in the objective function and constraints. An MSIRP-based energy system optimization model is proposed for electric-power structure management of Zibo City in Shandong Province, China. Three power demand scenarios associated with electric-power structure adjustment, imported electricity, and emission reduction were designed to obtain multiple decision schemes for supporting regional sustainable energy system development. The power generation schemes, imported electricity, and emissions of CO2 and air pollutants were analyzed. The results indicated that the model can effectively not only provide a more stable energy supply strategies and electric-power structure adjustment schemes, but also improve the balanced development between conventional and new clear power generation technologies under uncertainty.

In this study, a fuzzy probability distribution- based multi-stage stochastic robust programming method has been developed for supporting regional water supply management. In the proposed model, methods of interval-parameter programming and robust stochastic optimization, and fuzzy probability distribution are introduced into a multi-stage stochastic programming framework, and the developed model can tackle uncertainties described in terms of interval values and fuzzy probability distributions. The developed model was applied to a water resources management system with three water users. A number of scenarios corresponding to different river inflow and α-cut levels are examined; the results suggest that reasonable solutions have been generated for regional water resources management. The results indicated that the optimization model’s outputs were highly dependent on the complex uncertain features of the study system, and the α-cut level of fuzzy probability had few significant impacts on the system objective. The results also implied that the developed method can be used for analyzing a variety of policy scenarios that are associated with different levels of economic consequences when the promised water-allocation targets are violated. Dynamics and uncertainties of water availability (and thus water allocation and shortage) could be taken into account through generation of a set of representative scenarios within a multistage context. The proposed method could be used by environmental managers to evaluate trade-offs of system benefits and risk involving fuzzy probability condition, as well as identify management solutions that sufficiently hedge against dual uncertainties.

In recent years, Binhai New Area of Tianjin has been suffering severe water shortage due to climate change and industrial activities. Integrated and effective water resources management approaches are urgent for the sustainable development of industrial parks in Binhai New Area. However, uncertainties exist in many aspects of the water resources system and are inevitably problematic for water resources planning and policy-making. To address these uncertainties, an interval multiple-objective programming model was developed here to support the long-term planning of industrial water resources management in Binhai New Area, Tianjin, China. The model incorporated both multiple-objective programming and interval linear programming into a general programming framework. The developed model could handle the uncertainties and complexities of the water management system, and also allowed decision makers to adjust fuzzy objective control decision variables to satisfy multiple holistic and interactive objectives. The solutions are useful for planning adjustments of the existing water allocation patterns in Binhai New Area.

In this study, a fuzzy credibility constrained programming (FCCP) model is developed for water quantity and quality management with uncertainties in water quality parameters. The proposed method could reflect not only inexact uncertainties in the objective function, variables and parameters, but also fuzzy uncertainties in the right-hand side. Credibility levels which represent satisfaction degrees of the constraints can be analysed. The developed model is applied to a case study of water resources management within one river basin, three subareas and three water users regarding water environment security. According to the different confidence levels and sewage recovery, scenario analysis is conducted to analyse possible events in water allocation and water quality control. The resulting solutions obtained show that the proposed method can help decision-makers to provide scientific bases for water quantity and quality management and energy system planning, solid waste management and other environmental system problems.

In this study, a multistage stochastic inexact chance-constraint programming (MSICCP) model is developed for power supply management under uncertainties. In the MSICCP model, methods of multistage stochastic programming (MSP), interval-parameter programming (IPP), and chance-constraint programming (CCP) are introduced into a general optimization framework, such that the developed model can tackle uncertainties described in terms of interval values and probability distribution s over a multistage context. Moreover, it can reflect dynamic and randomness of energy resources during the planning horizon. The developed method has been applied to a case of managing the process of power supply in an integrated biomass-municipal solid waste power plant. Useful solutions for the power supply management have been generated. Interval solutions associated with different risk levels of constraint violation have been obtained. The generated solutions can provide desired energy resource allocation with a minimized system cost, maximized system reliability and a maximized energy security. Tradeoffs between system costs and constraint-violation risks can also be tackled. Higher costs will increase system stability, while a desire for lower system costs will run into a risk of potential instability of the management system. They are helpful for supporting (a) adjustment or justification of allocation patterns of energy resources, and (b) analysis of interactions among economic cost, environmental requirement, and power supply security.

In this paper, an inexact two-stage stochastic programming model was developed for supporting regional water resource allocation management under uncertainties. The proposed model is an integrated framework of interval parameter programming and two-stage stochastic programming, which can tackle uncertain parameters expressed as interval values with probability distribution information. The proposed model was successfully applied to a typical heavy industrial city suffering water shortage in the east of China, Tianjin. The uncertainties of future water demand were taken into account through generating a set of representative scenarios. The results indicated that different scenarios would affect the water distribution patterns, water shortages, total benefits and system cost. The results revealed that the transferred water from Luan River and Changjiang River would still be the main water resource for each water user. Besides, nearly all water demand in planning horizon would be guaranteed through the reasonable dispatch except under high demand level scenario, in which a small proportion of water requirement in agricultural, municipal and environmental sectors would not be satisfied. The developed method could be used by environmental managers to identify the optimal water supply plan from multiple sources to different end-user sectors under system uncertainties.

In this study, a multistage inexact stochastic robust model was developed for regional energy system management in Jining City, China. Three scenarios about the electric power structure adjustment, clean power generation, and the emission reduction target are designed. Methods of interval parameter programming (IPP), stochastic robust optimization (SRO), and multistage stochastic programming (MSP) were incorporated into the developed model to tackle uncertainties described by interval values and probability distributions. The results indicated that the model can provide an effective linkage between conflicting economic cost and the system stability, and different power demand levels correspond to different electricity generation schemes with varied system cost and system-failure risk. The net system cost, power generation schemes, exported electricity, and captured CO2 amount were analyzed. The results indicated that the power generation by traditional technology would be decreased with the improvement of regional energy structure adjustment and environmental protection request. The modeling results are valuable for supporting the adjustment or justification of the existing power generation schemes within a complicated energy system under uncertainty.

CdS nanoparticles (NPs) on the edges of reduced graphene oxide (RGO) sheets are prepared. Carboxyl groups at the periphery of RGO sheets are converted to amine groups and a free-radical polymerization initiator is anchored to the RGO sheets. Poly(2-vinyl pyridine) (P2VP) brushes on RGO sheets are synthesized by in situ free-radical polymerization. Upon the addition of cadmium ions into the dispersion of RGO–P2VP nanocomposites in methanol, P2VP brushes form complexes with Cd 2+ , and CdS NPs protected by P2VP can be prepared upon the addition of sodium sulfide. A transmission electron microscope (TEM), Fourier Transform Infrared absorption spectra (FT-IR) and thermogravimetric analysis (TGA) are employed to characterize the composites. TEM results provide direct evidence for the location of CdS NPs at the periphery of RGO sheets. Fluorescence results indicate that there is a strong interaction between CdS NPs and RGO sheets. The synthesized composites show strong optical limiting ability.

In this study, an inexact two-stage water resources management model was developed for multi-regional water resources planning in the Nansihu lake Basin, China. Four planning districts, four water users, and five water sources were considered in the optimization model, with net system benefit, recourse cost, water supply cost, and wastewater treatment cost being analyzed. Methods of interval-parameter programming (IPP) and two-stage stochastic programming (TSP) were incorporated into the model to tackle uncertainties described by both interval values and probability distributions. A number of scenarios corresponding to different river inflow levels were examined, and the results indicated that different inflow levels could lead to different water allocation schemes with varied system benefit and system-failure risk. In general, the developed model can provide an effective linkage between economic benefits and the associated penalties attributed to the violation of predefined policies. The modeling results were valuable for supporting the adjustment or justification of the existing water allocation schemes within a complicated water resources system under uncertainty.

Non-point source (NPS) pollution from agricultural lands has aroused widespread concerns throughout the world. In this study, an interval fuzzy two-stage stochastic non-point source pollution mitigation (IFTNS) model was developed for agricultural systems management under uncertainty through land retirement and nutrients discharge trading programs. The developed IFTNS model incorporated fuzzy possibilistic programming (FPP) and interval linear programming (ILP) within a general two-stage stochastic programming (TSP) framework. The IFTNS model could not only deal with uncertainties presented as probability density functions, fuzzy membership functions, and discrete interval numbers, but also provide an effective linkage between the pre-regulated agricultural systems management policies and the associated economic implications. Moreover, the IFTNS model could help to analyze various trading scenarios associated with different plausibility degree levels. The IFTNS model was applied to a case study with a nutrients discharge trading program within a subcatchment of the Lake Tai Basin in China. The results indicated that reasonable solutions under different plausibility degree levels were generated, which could be used for generating decision alternatives and thus help decision makers to identify desired agricultural NPS pollution mitigation policies. The results also indicated that land retirement and nutrients discharge trading programs could effectively achieve agricultural NPS pollution mitigation and maximize net system benefits.

In this study, an interval probability-based inexact two-stage stochastic (IP-ITSP) model is developed for environmental pollutants control and greenhouse gas (GHG) emissions reduction management in regional energy system under uncertainties. In the IP-ITSP model, methods of interval probability, interval-parameter programming (IPP) and two-stage stochastic programming (TSP) are introduced into an integer programming framework; the developed model can tackle uncertainties described in terms of interval values and interval probability distributions. The developed model is applied to a case of planning GHG -emission mitigation in a regional electricity system, demonstrating that IP-ITSP is applicable to reflecting complexities of multi-uncertainty, and capable of addressing the problem of GHG-emission reduction. 4 scenarios corresponding to different GHG -emission mitigation levels are examined; the results indicates that the model could help decision makers identify desired GHG mitigation policies under various economic costs and environmental requirements.

An interval linear traffic planning model is developed for supporting vehicle emissions limited under uncertainty. The interval linear traffic planning model can address uncertainties of traffic system and vehicle emissions related to system costs and limitation of emission. The interval linear traffic planning model is applicable to complex traffic system. One virtual city as our study object was taken by using the interval linear traffic planning model. In this study, one virtual case and a scenario are provided for three planning periods. The results indicate that the interval linear traffic planning model can effectively reduce the vehicles emission and provide strategies for authorities to deal with problems of transportation system.

In this study, a traffic energy system model is developed to optimize the traffic system cost of Urumqi, considering energy consumption, pollution emission and travel time. Meanwhile, scenario analysis method is proposed to solve the problem of the extreme weather of traffic, and three scenarios (i.e. 10%, 20% and 30%) of reductions of traffic flow quantity and pollutant emission are examined. The results demonstrate that the medium-type coach will be the promising selection under different scenarios especially in the extreme conditions and the traffic flow reduction scenarios are not the better option for the decision owing to the same cost under the different reduction levels. Moreover, encouraging the medium-type coach traveling and restricting the small vehicle driving would be attractive alternatives for the extreme situation. The proposed model would provide reasonable references for decision makers.

In this study, a fuzzy probability-based Markov chain model is developed for forecasting regional long-term electric power demand. The model can deal with the uncertainties in electric power system and reflect the vague and ambiguous during the process of power load forecasting through allowing uncertainties expressed as fuzzy parameters and discrete intervals. The developed model is applied to predict the electric power demand of Beijing from 2011 to 2019. Different satisfaction degrees of fuzzy parameters are considered as different levels of detail of the statistic data. The results indicate that the model can reflect the high uncertainty of long term power demand, which could support the programming and management of power system. The fuzzy probability Markov chain model is helpful for regional electricity power system managers in not only predicting a long term power load under uncertainty but also providing a basis for making multi-scenarios power generation/development plans.

Volatile organic compounds (VOCs) decomposition by non-thermal plasma (NTP) has been receiving increasing attention from the scientific communities due to its advantages of easy operation, high efficiency, energy saving, and non-secondary pollution. But most of the researches are doing experiments and existing experiment methods cannot observe the micro physical and chemical processes. In order to make up for the deficiency of the experiment, herein, a numerical method was developed to analyze the decomposition behavior of HCN, C3H3N, C3H8, C3H6, CO, and NO in the VOCs treatment by NTP. Results indicated that increasing electron density or energy was beneficial to VOCs decomposition, but when the electron density and energy was too high, the promotion would be weakened. The influences of initial concentration of O2 and H2O on different VOCs decomposition were totally different. The increase of initial concentration of oxygen was beneficial to the decomposition of HCN, C3H8, CO, and NO, but the high concentration of oxygen could promote to generate C3H6 at the initial reaction stage. The decomposition of HCN and C3H3N are not restricted by dry or wet conditions, but the increase of the concentration of water vapor is advantageous to the decomposition of C3H8, CO, and NO.

Water scarcity and aquatic environment deterioration have become increasingly serious problems in Urumqi, China. To alleviate the conflicts arising from competitive water users and retard the environment degradation, an optimization model was developed for regional water resources management under considering the dual total amount of water quantity and pollutant emissions control. Water allocation schemes and guarantee rates that combine diverse water sources and economic activities were obtained through the solutions of the proposed model. The interactions among water allocation schemes, various activities benefits, and pollutant emissions were analyzed synchronously. Policies for industrial structure adjustment and pollutants emission reduction were acquired through the analysis of the results. The modeling results can be valuable for water resources planning and justification of the existing industrial structure.

A simulation-based interval quadratic programming (IQP) method was developed for river water quality management, where uncertainties associated with water quality parameters, cost functions, and environmental requirements were described as interval values. IQP was applied to a real-case study of the Xiangxihe River, China. A multisegment simulation model of one river with one tributary was used to generate water-quality transformation matrices and vectors, which were used to establish the river water quality constraints; in addition, the cost function of wastewater treatment were expressed as quadratic form. System cost, wastewater treatment efficiencies, and river pollution situations were analyzed under two scenarios and three criteria. A number of cost-effective schemes were generated by adjusting different combinations of decision variables within their solution intervals. Results can help decision makers generate alternatives between wastewater treatment cost and stream water quality require...

In this study, an interval-based regret-analysis (IBRA) model is developed for supporting long-term planning of municipal solid waste (MSW) management activities in the City of Changchun, the capital of Jilin Province, China. The developed IBRA model incorporates approaches of interval-parameter programming (IPP) and minimax-regret (MMR) analysis within an integer programming framework, such that uncertainties expressed as both interval values and random variables can be reflected. The IBRA can account for economic consequences under all possible scenarios associated with different system costs and risk levels without making assumptions on probabilistic distributions for random variables. A regret matrix with interval elements is generated based on a matrix of interval system costs, such that desired decision alternatives can be identified according to the interval minimax regret (IMMR) criterion. The results indicate that reasonable solutions have been generated. They can help decision makers identify the desired alternatives regarding long-term MSW management with a compromise between minimized system cost and minimized system-failure risk.

In this study, an interval fixed-mix stochastic programming (IFSP) model is developed for greenhouse gas (GHG) emissions reduction management under uncertainties. In the IFSP model, methods of interval-parameter programming (IPP) and fixed-mix stochastic programming (FSP) are introduced into an integer programming framework, such that the developed model can tackle uncertainties described in terms of interval values and probability distributions over a multi-stage context. Moreover, it can reflect dynamic decisions for facility-capacity expansion during the planning horizon. The developed model is applied to a case of planning GHG-emission mitigation, demonstrating that IFSP is applicable to reflecting complexities of multi-uncertainty, dynamic and interactive energy management systems, and capable of addressing the problem of GHG-emission reduction. A number of scenarios corresponding to different GHG-emission mitigation levels are examined; the results suggest that reasonable solutions have been generated. They can be used for generating plans for energy resource/electricity allocation and capacity expansion and help decision makers identify desired GHG mitigation policies under various economic costs and environmental requirements.

In this study, an interval-fuzzy multiobjective programming (IFMOP) model is developed for supporting planning of environmental and economic management activities of chemical industry district in Tianjin, China. In the IFMOP model, both interval-parameter programming (IPP) and fuzzy programming (FP) methods are introduced into a multiobjective programming framework. The modeling approach inherits advantages of IPP and FP, and allows uncertainties and decision-makers’ aspirations to be directly communicated into the optimization process and resulting solutions. The developed model can help tackle dynamic, uncertain and multiobjective characteristics of the water environmental and economic management system in the chemical industry district, and can address issues concerning plans for cost-effective industrial production.

A model based on economic structure adjustment and pollutants mitigation was proposed and applied in Urumqi. Best-worst case analysis and scenarios analysis were performed in the model to guarantee the parameters accuracy, and to analyze the effect of changes of emission reduction styles. Results indicated that pollutant-mitigations of electric power industry, iron and steel industry, and traffic relied mainly on technological transformation measures, engineering transformation measures and structure emission reduction measures, respectively; Pollutant-mitigations of cement industry relied mainly on structure emission reduction measures and technological transformation measures; Pollutant-mitigations of thermal industry relied mainly on the four mitigation measures. They also indicated that structure emission reduction was a better measure for pollutants mitigation of Urumqi. Iron and steel industry contributed greatly in SO2, NOx and PM (particulate matters) emission reduction and should be given special attention in pollutants emission reduction. In addition, the scales of iron and steel industry should be reduced with the decrease of SO2 mitigation amounts. The scales of traffic and electric power industry should be reduced with the decrease of NOx mitigation amounts, and the scales of cement industry and iron and steel industry should be reduced with the decrease of PM mitigation amounts. The study can provide references of pollutants mitigation schemes to decision-makers for regional economic and environmental development in the 12th Five-Year Plan on National Economic and Social Development of Urumqi.

In this study, a novel inexact two-stage stochastic robust-compensation programming (ITSP-RC) model is developed for CO2 emission reduction management under uncertainties. This model is attempted to integrate ITSP and stochastic RC programming into a general framework and apply the ITSP-RC for power management and CO2 emission reduction management, such that the developed model can tackle uncertainties described in terms of interval values and probability distributions over a two-stage context. Moreover, it can reflect dynamic and randomness of the energy systems during the planning horizon. The developed method has been applied to a case to solve CO2 emission management problem in electric supply environmental management. A number of scenarios corresponding to different adoption rate levels of carbon capture, utilization, and storage technology are examined. With the RC programming, regional energy systems would have a stable financial budget. The result suggests that the methodology is applicable for reflecting complexities of large-scale energy management systems and addressing CO2 emissions reduction issue with the planning period.

Water shortage and water pollution have become major problems hindering socio-economic development. Due to the scarcity of water resources, the conflict between water supply and demand is becoming more and more prominent, especially in urban areas. In order to ensure the safety of urban water supply, many cities have begun to build reservoirs. However, few previous studies have focused on the optimal allocation of water resources considering storage reservoirs. In this study, a multi-water resources and multiple users chance-constrained dynamic programming (MMCDP) model has been developed for water resources allocation in Beijing, China, which introduces reservoir and chance-constrained programming into the dynamic programming decision-making framework. The proposed model can distribute water to different departments according to their respective demands in different periods. Specifically, under the objective of maximal benefits, the water allocation planning and the amount of water stored in a reservoir for each season under different feasibility degrees (violating constraints or available water resources situations) can be obtained. At the same time, the model can be helpful for decision-makers to identify the uncertainty of water-allocation schemes and make a desired compromise between the satisfaction degree of the economic benefits and the feasibility degree of constraints.

An inexact two-stage stochastic risk-aversion model was developed in this study for supporting regional energy system planning and management. It can tackle uncertainties described in terms of probability distributions and permits the in-depth analysis of various policy scenarios when the promised policy targets are violated. Moreover, it can help local decision-makers evaluate trade-offs between energy system economy and stability associated with different robust criteria (risk-aversion levels). An actual case study in the Beijing-Tianjin-Hebei (BTH) region, China, was provided for demonstrating the applicability of the developed model. The results indicated that electricity generated by coal-burning (coal-fired power and coal-fired cogeneration) and gas-fired heating would be the main power forms for power and heat supply to get the maximum security. Furthermore, some policy implications can be concluded as follows: (a) more attention should be focused on increasing the local power generation capacity i...

Uncertainties exist in the water resources system, while traditional two-stage stochastic programming is risk-neutral and compares the random variables (e.g., total benefit) to identify the best decisions. To deal with the risk issues, a risk-aversion inexact two-stage stochastic programming model is developed for water resources management under uncertainty. The model was a hybrid methodology of interval-parameter programming, conditional value-at-risk measure, and a general two-stage stochastic programming framework. The method extends on the traditional two-stage stochastic programming method by enabling uncertainties presented as probability density functions and discrete intervals to be effectively incorporated within the optimization framework. It could not only provide information on the benefits of the allocation plan to the decision makers but also measure the extreme expected loss on the second-stage penalty cost. The developed model was applied to a hypothetical case of water resources management. Results showed that that could help managers generate feasible and balanced risk-aversion allocation plans, and analyze the trade-offs between system stability and economy.

Carboxyl groups at the periphery of reduced graphene oxide (RGO) sheets are converted to amine groups by reaction with N-hydroxysuccinimide and 1,3-diaminopropane, and a free-radical polymerization initiator is anchored to the RGO sheets. Poly(acrylamide) (PAM) polymer brushes on RGO sheets (RGO/PAM) are synthesized by in situ free-radical polymerization. The heavy metals, Pb(II), and the benzenoid compounds, methylene blue, (MB) were selected and adsorbed by RGO/PAM composites, and the adsorption capacity of RGO/PAM for Pb(II) and MB was measured. The experimental data of RGO/PAM isotherms for Pb(II) and MB followed the Langmuir isotherm model. The RGO/PAM displays adsorption capacities as high as 1000 and 1530 mg/g for Pb(II) and MB, respectively, indicating RGO/PAM is a good adsorbent for the adsorption of Pb(II) and MB. The adsorption kinetics of Pb(II) and MB onto RGO/PAM can be well fitted to the pseudo-second-order model. The adsorption processes of Pb(II) and MB onto RGO/PAM are spontaneous at 298, 308, and 318 K.

In this study, an inexact-chance-constrained water quality management (ICC-WQM) model is developed for planning regional environmental management under uncertainty. This method is based on an integration of interval linear programming (ILP) and chance-constrained programming (CCP) techniques. ICC-WQM allows uncertainties presented as both probability distributions and interval values to be incorporated within a general optimization framework. Complexities in environmental management systems can be systematically reflected, thus applicability of the modeling process can be highly enhanced. The developed method is applied to planning chemical-industry development in Binhai New Area of Tianjin, China. Interval solutions associated with different risk levels of constraint violation have been obtained. They can be used for generating decision alternatives and thus help decision makers identify desired policies under various system-reliability constraints of water environmental capacity of pollutant. Tradeoffs between system benefits and constraint-violation risks can also be tackled. They are helpful for supporting (a) decision of wastewater discharge and government investment, (b) formulation of local policies regarding water consumption, economic development and industry structure, and (c) analysis of interactions among economic benefits, system reliability and pollutant discharges.

In this study, an inexact optimisation model is developed for regional power system management under uncertainty, where interval-parameter programming and two stage-stochastic programming were integrated in a framework for dealing with the long-term energy systems planning. The developed method can deal with multiple forms of uncertainties and dynamics related to capacity expansions within energy management systems. The proposed model was applied to a case study of power system management and air pollutants mitigation in Heilongjiang Province, China. Six scenarios are considered based on different emissions reduction and mode of power structure adjustments. The results show that in order to keep a sustainable development and safe power/energy supply, a diversified power structure (primarily on coal power generation, supplemented by renewable energy generation) would be established in the region. In addition, the pollutants-emission control is more effective than power structure adjustment for improving rationality of the electricity economic budget in Heilongjiang Province. The model is helpful for supporting adjustment of allocation patterns of regional energy resources, formulating energy structure and energy consumption of local policies.