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Flood, as a serious worldwide environment problem, can lead to detrimental economic losses and fatalities. Effective flood control is desired to mitigate the adverse impacts of flooding and the associated flood risk through development of cost-effective and efficient flood management decisions and policies. A bi-level fuzzy two-stage stochastic programming model, named BIFS model is developed in this study to provide decision support for economic analysis of flood management. The BIFS model is capable of not only addressing the sequential decision making issue involving the two-level decision makers, but also correcting the pre-regulated flood management decisions before the occurrence of a flood event in the two-stage environment. The probabilistic and non-probabilistic uncertainties expressed as probability density functions and fuzzy sets are quantitatively analyzed. The overall satisfaction solution is obtained for meeting the goals of the two-level decision makers by compromising, reflecting the tradeoffs among various decision makers in the two decision-making levels. The results of application of the BIFS model to a representative case study indicate informed decision strategies for flood management. Tradeoffs between economic objectives and uncertainty-averse attitudes of decision makers are quantified.
Hong Wang; Xiaodong Zhang. A Decentralized Bi-Level Fuzzy Two-Stage Decision Model for Flood Management. Water Resources Management 2018, 32, 1615 -1629.
AMA StyleHong Wang, Xiaodong Zhang. A Decentralized Bi-Level Fuzzy Two-Stage Decision Model for Flood Management. Water Resources Management. 2018; 32 (5):1615-1629.
Chicago/Turabian StyleHong Wang; Xiaodong Zhang. 2018. "A Decentralized Bi-Level Fuzzy Two-Stage Decision Model for Flood Management." Water Resources Management 32, no. 5: 1615-1629.
Xiaodong Zhang; Alexander Y. Sun; Ian J. Duncan; Velimir V. Vesselinov. Two-Stage Fracturing Wastewater Management in Shale Gas Development. Industrial & Engineering Chemistry Research 2017, 56, 1570 -1579.
AMA StyleXiaodong Zhang, Alexander Y. Sun, Ian J. Duncan, Velimir V. Vesselinov. Two-Stage Fracturing Wastewater Management in Shale Gas Development. Industrial & Engineering Chemistry Research. 2017; 56 (6):1570-1579.
Chicago/Turabian StyleXiaodong Zhang; Alexander Y. Sun; Ian J. Duncan; Velimir V. Vesselinov. 2017. "Two-Stage Fracturing Wastewater Management in Shale Gas Development." Industrial & Engineering Chemistry Research 56, no. 6: 1570-1579.
Xiaodong Zhang; Velimir Vesselinov. Energy-water nexus: Balancing the tradeoffs between two-level decision makers. Applied Energy 2016, 183, 77 -87.
AMA StyleXiaodong Zhang, Velimir Vesselinov. Energy-water nexus: Balancing the tradeoffs between two-level decision makers. Applied Energy. 2016; 183 ():77-87.
Chicago/Turabian StyleXiaodong Zhang; Velimir Vesselinov. 2016. "Energy-water nexus: Balancing the tradeoffs between two-level decision makers." Applied Energy 183, no. : 77-87.
This work presents an optimization framework for evaluating different wastewater treatment/disposal options for water management during hydraulic fracturing (HF) operations. This framework takes into account both cost-effectiveness and system uncertainty. HF has enabled rapid development of shale gas resources. However, wastewater management has been one of the most contentious and widely publicized issues in shale gas production. The flowback and produced water (known as FP water) generated by HF may pose a serious risk to the surrounding environment and public health because this wastewater usually contains many toxic chemicals and high levels of total dissolved solids (TDS). Various treatment/disposal options are available for FP water management, such as underground injection, hazardous wastewater treatment plants, and/or reuse. In order to cost-effectively plan FP water management practices, including allocating FP water to different options and planning treatment facility capacity expansion, an optimization model named UO-FPW is developed in this study. The UO-FPW model can handle the uncertain information expressed in the form of fuzzy membership functions and probability density functions in the modeling parameters. The UO-FPW model is applied to a representative hypothetical case study to demonstrate its applicability in practice. The modeling results reflect the tradeoffs between economic objective (i.e., minimizing total-system cost) and system reliability (i.e., risk of violating fuzzy and/or random constraints, and meeting FP water treatment/disposal requirements). Using the developed optimization model, decision makers can make and adjust appropriate FP water management strategies through refining the values of feasibility degrees for fuzzy constraints and the probability levels for random constraints if the solutions are not satisfactory. The optimization model can be easily integrated into decision support systems for shale oil/gas lifecycle management.
Xiaodong Zhang; Alexander Y. Sun; Ian J. Duncan. Shale gas wastewater management under uncertainty. Journal of Environmental Management 2016, 165, 188 -198.
AMA StyleXiaodong Zhang, Alexander Y. Sun, Ian J. Duncan. Shale gas wastewater management under uncertainty. Journal of Environmental Management. 2016; 165 ():188-198.
Chicago/Turabian StyleXiaodong Zhang; Alexander Y. Sun; Ian J. Duncan. 2016. "Shale gas wastewater management under uncertainty." Journal of Environmental Management 165, no. : 188-198.
Climate change can result in significant impacts on regional and global surface water and groundwater resources. Using groundwater as a complimentary source of water has provided an effective means to satisfy the ever-increasing water demands and deal with surface water shortages problems due to robust capability of groundwater in responding to climate change. Conjunctive use of surface water and groundwater is crucial for integrated water resources management. It is helpful to reduce vulnerabilities of water supply systems and mitigate the water supply stress in responding to climate change. Some critical challenges and perspectives are discussed to help decision/policy makers develop more effective management and adaptation strategies for conjunctive water resources use in facing climate change under complex uncertainties.
Xiaodong Zhang. Conjunctive surface water and groundwater management under climate change. Frontiers in Environmental Science 2015, 3, 1 .
AMA StyleXiaodong Zhang. Conjunctive surface water and groundwater management under climate change. Frontiers in Environmental Science. 2015; 3 ():1.
Chicago/Turabian StyleXiaodong Zhang. 2015. "Conjunctive surface water and groundwater management under climate change." Frontiers in Environmental Science 3, no. : 1.
Waste management activities can release greenhouse gases (GHGs) to the atmosphere, intensifying global climate change. Mitigation of the associated GHG emissions is vital and should be considered within integrated municipal solid waste (MSW) management planning. In this study, a fuzzy possibilistic integer programming (FPIM) model has been developed for waste management facility expansion and waste flow allocation planning with consideration of GHG emission trading in an MSW management system. It can address the interrelationships between MSW management planning and GHG emission control. The scenario of total system GHG emission control is analyzed for reflecting the feature that GHG emission credits may be tradable. An interactive solution algorithm is used to solve the FPIM model based on the uncertainty-averse preferences of decision makers in terms of p-necessity level, which represents the certainty degree of the imprecise objective. The FPIM model has been applied to a hypothetical MSW planning problem, where optimal decision schemes for facility expansion and waste flow allocation have been achieved with consideration of GHG emission control. The results indicate that GHG emission credit trading can decrease total system cost through re-allocation of GHG emission credits within the entire MSW management system. This will be helpful for decision makers to effectively determine the allowable GHG emission permits in practices.
Xiaodong Zhang; Guohe (Gordon) Huang. Municipal solid waste management planning considering greenhouse gas emission trading under fuzzy environment. Journal of Environmental Management 2014, 135, 11 -18.
AMA StyleXiaodong Zhang, Guohe (Gordon) Huang. Municipal solid waste management planning considering greenhouse gas emission trading under fuzzy environment. Journal of Environmental Management. 2014; 135 ():11-18.
Chicago/Turabian StyleXiaodong Zhang; Guohe (Gordon) Huang. 2014. "Municipal solid waste management planning considering greenhouse gas emission trading under fuzzy environment." Journal of Environmental Management 135, no. : 11-18.
Xiaodong Zhang; Ian J. Duncan; Gordon Huang; Gongchen Li. Identification of management strategies for CO2 capture and sequestration under uncertainty through inexact modeling. Applied Energy 2014, 113, 310 -317.
AMA StyleXiaodong Zhang, Ian J. Duncan, Gordon Huang, Gongchen Li. Identification of management strategies for CO2 capture and sequestration under uncertainty through inexact modeling. Applied Energy. 2014; 113 ():310-317.
Chicago/Turabian StyleXiaodong Zhang; Ian J. Duncan; Gordon Huang; Gongchen Li. 2014. "Identification of management strategies for CO2 capture and sequestration under uncertainty through inexact modeling." Applied Energy 113, no. : 310-317.
Alexander Y. Sun; Jean-Philippe Nicot; Xiaodong Zhang. Optimal design of pressure-based, leakage detection monitoring networks for geologic carbon sequestration repositories. International Journal of Greenhouse Gas Control 2013, 19, 251 -261.
AMA StyleAlexander Y. Sun, Jean-Philippe Nicot, Xiaodong Zhang. Optimal design of pressure-based, leakage detection monitoring networks for geologic carbon sequestration repositories. International Journal of Greenhouse Gas Control. 2013; 19 ():251-261.
Chicago/Turabian StyleAlexander Y. Sun; Jean-Philippe Nicot; Xiaodong Zhang. 2013. "Optimal design of pressure-based, leakage detection monitoring networks for geologic carbon sequestration repositories." International Journal of Greenhouse Gas Control 19, no. : 251-261.
Greenhouse gas (GHG) emissions from municipal solid waste (MSW) management facilities have become a serious environmental issue. In MSW management, not only economic objectives but also environmental objectives should be considered simultaneously. In this study, a dynamic stochastic possibilistic multiobjective programming (DSPMP) model is developed for supporting MSW management and associated GHG emission control. The DSPMP model improves upon the existing waste management optimization methods through incorporation of fuzzy possibilistic programming and chance-constrained programming into a general mixed-integer multiobjective linear programming (MOP) framework where various uncertainties expressed as fuzzy possibility distributions and probability distributions can be effectively reflected. Two conflicting objectives are integrally considered, including minimization of total system cost and minimization of total GHG emissions from waste management facilities. Three planning scenarios are analyzed and compared, representing different preferences of the decision makers for economic development and environmental-impact (i.e. GHG-emission) issues in integrated MSW management. Optimal decision schemes under three scenarios and different pi levels (representing the probability that the constraints would be violated) are generated for planning waste flow allocation and facility capacity expansions as well as GHG emission control. The results indicate that economic and environmental tradeoffs can be effectively reflected through the proposed DSPMP model. The generated decision variables can help the decision makers justify and/or adjust their waste management strategies based on their implicit knowledge and preferences.
Xiaodong Zhang; Guohe (Gordon) Huang. Optimization of environmental management strategies through a dynamic stochastic possibilistic multiobjective program. Journal of Hazardous Materials 2013, 246-247, 257 -266.
AMA StyleXiaodong Zhang, Guohe (Gordon) Huang. Optimization of environmental management strategies through a dynamic stochastic possibilistic multiobjective program. Journal of Hazardous Materials. 2013; 246-247 ():257-266.
Chicago/Turabian StyleXiaodong Zhang; Guohe (Gordon) Huang. 2013. "Optimization of environmental management strategies through a dynamic stochastic possibilistic multiobjective program." Journal of Hazardous Materials 246-247, no. : 257-266.
A key impediment to carbon capture and storage is the cost of CO2 capture, particularly for conventional power plants whose flue gas is dominated by gases other than CO2. Waste-gas streams from power plants that use novel technologies (such as oxyfuel, the focus of this paper) can circumvent the capture step thanks to their CO2-rich composition (CO2>90%), but at the expense of stream CO2 purity (N2, O2, Ar, and other minor species may be present). Relatively high purity levels must be achieved to avoid compression and complications in pipeline transportation (two-phase flow) and, potentially, subsurface impacts. The CO2 Capture Project Phase 3 (CCP3) has started investigating the latter, which, in turn, inform techno-economic assessments of capture and transportation economics. Subsurface impacts of an impure CO2 stream could be twofold: (1) complicate flow behavior and reduce static capacity because of density and viscosity differences and (2) undermine reservoir and top seal integrity due to reaction with reactive species (O2, CO, SOx). Using a range of potential oxyfuel waste-gas compositions, we approached the first issue through a desktop study using the numerical modeling tool. So that we could work with accurate flow parameters, we performed laboratory experiments in order to determine the actual viscosity and density of the mixtures. Information on solubility of these various mixture components in the aqueous phase under various pressure, temperature, and salinity conditions was also collected. An important observation controlling all results of the study was that viscosity and density of mixtures are lower than those of pure CO2 at the same temperatures and pressures. It follows that a plume of CO2 with impurities, moving updip with no barrier, will migrate farther from the point of injection but will be trapped through residual saturation sooner than will a plume of pure CO2. A larger plume means that a larger area must be inspected for leakage pathways, such as faults and wells, but faster trapping means a shorter monitoring period. Equally important is that contrasts of viscosity and density between pure CO2 and a CO2 mixture decrease with depth, suggesting that differences in flow behavior and storage capacity are similarly reduced with depth. Whereas flow behavior may impact the whole field, geochemical impacts are more likely to be restricted to the well-bore environment and the near field. Batch experiments conducted in high-pressure, high- temperature autoclaves with rocks immersed in synthetic brine and exposed to supercritical CO2 with and without admixed O2 suggest that O2 may change the geochemistry of subsurface systems in ways that the pure CO2 case does not. Results of the study, therefore, present the CO2 project developer with tradeoffs in capacity, pressure evolution, and monitoring scenarios, with additional costs likely more than offset by reduced capture costs
Jean-Philippe Nicot; Silvia Solano; Jiemin Lu; Patrick Mickler; Katherine Romanak; Changbing Yang; Xiaodong Zhang. Potential Subsurface Impacts of CO2 Stream Impurities on Geologic Carbon Storage. Energy Procedia 2013, 37, 4552 -4559.
AMA StyleJean-Philippe Nicot, Silvia Solano, Jiemin Lu, Patrick Mickler, Katherine Romanak, Changbing Yang, Xiaodong Zhang. Potential Subsurface Impacts of CO2 Stream Impurities on Geologic Carbon Storage. Energy Procedia. 2013; 37 ():4552-4559.
Chicago/Turabian StyleJean-Philippe Nicot; Silvia Solano; Jiemin Lu; Patrick Mickler; Katherine Romanak; Changbing Yang; Xiaodong Zhang. 2013. "Potential Subsurface Impacts of CO2 Stream Impurities on Geologic Carbon Storage." Energy Procedia 37, no. : 4552-4559.
A fuzzy simulation-based optimization approach (FSOA) is developed for identifying optimal design of a benzene-contaminated groundwater remediation system under uncertainty. FSOA integrates remediation processes (i.e., biodegradation and pump-and-treat), fuzzy simulation, and fuzzy-mean-value-based optimization technique into a general management framework. This approach offers the advantages of (1) considering an integrated remediation alternative, (2) handling simulation and optimization problems under uncertainty, and (3) providing a direct linkage between remediation strategies and remediation performance through proxy models. The results demonstrate that optimal remediation alternatives can be obtained to mitigate benzene concentration to satisfy environmental standards with a minimum system cost.
A. L. Yang; G. H. Huang; Y. R. Fan; Xiaodong Zhang. A Fuzzy Simulation-Based Optimization Approach for Groundwater Remediation Design at Contaminated Aquifers. Mathematical Problems in Engineering 2012, 2012, 1 -13.
AMA StyleA. L. Yang, G. H. Huang, Y. R. Fan, Xiaodong Zhang. A Fuzzy Simulation-Based Optimization Approach for Groundwater Remediation Design at Contaminated Aquifers. Mathematical Problems in Engineering. 2012; 2012 (1):1-13.
Chicago/Turabian StyleA. L. Yang; G. H. Huang; Y. R. Fan; Xiaodong Zhang. 2012. "A Fuzzy Simulation-Based Optimization Approach for Groundwater Remediation Design at Contaminated Aquifers." Mathematical Problems in Engineering 2012, no. 1: 1-13.
[1] Groundwater pollution has gathered more and more attention in the past decades. Conducting an assessment of groundwater contamination risk is desired to provide sound bases for supporting risk‐based management decisions. Therefore, the objective of this study is to develop an integrated fuzzy stochastic approach to evaluate risks of BTEX‐contaminated groundwater under multiple uncertainties. It consists of an integrated interval fuzzy subsurface modeling system (IIFMS) and an integrated fuzzy second‐order stochastic risk assessment (IFSOSRA) model. The IIFMS is developed based on factorial design, interval analysis, and fuzzy sets approach to predict contaminant concentrations under hybrid uncertainties. Two input parameters (longitudinal dispersivity and porosity) are considered to be uncertain with known fuzzy membership functions, and intrinsic permeability is considered to be an interval number with unknown distribution information. A factorial design is conducted to evaluate interactive effects of the three uncertain factors on the modeling outputs through the developed IIFMS. The IFSOSRA model can systematically quantify variability and uncertainty, as well as their hybrids, presented as fuzzy, stochastic and second‐order stochastic parameters in health risk assessment. The developed approach haw been applied to the management of a real‐world petroleum‐contaminated site within a western Canada context. The results indicate that multiple uncertainties, under a combination of information with various data‐quality levels, can be effectively addressed to provide supports in identifying proper remedial efforts. A unique contribution of this research is the development of an integrated fuzzy stochastic approach for handling various forms of uncertainties associated with simulation and risk assessment efforts.
Xiaodong Zhang; Guo H. Huang. Assessment of BTEX-induced health risk under multiple uncertainties at a petroleum-contaminated site: An integrated fuzzy stochastic approach. Water Resources Research 2011, 47, 1 .
AMA StyleXiaodong Zhang, Guo H. Huang. Assessment of BTEX-induced health risk under multiple uncertainties at a petroleum-contaminated site: An integrated fuzzy stochastic approach. Water Resources Research. 2011; 47 (12):1.
Chicago/Turabian StyleXiaodong Zhang; Guo H. Huang. 2011. "Assessment of BTEX-induced health risk under multiple uncertainties at a petroleum-contaminated site: An integrated fuzzy stochastic approach." Water Resources Research 47, no. 12: 1.
Eutrophication of small prairie reservoirs presents a major challenge in water quality management and has led to a need for predictive water quality modeling. Studies are lacking in effectively integrating watershed models and reservoir models to explore nutrient dynamics and eutrophication pattern. A water quality model specific to small prairie water bodies is also desired in order to highlight key biogeochemical processes with an acceptable degree of parameterization. This study presents a Multi-level Watershed-Reservoir Modeling System (MWRMS) to simulate hydrological and biogeochemical processes in small prairie watersheds. It integrated a watershed model, a hydrodynamic model and an eutrophication model into a flexible modeling framework. It can comprehensively describe hydrological and biogeochemical processes across different spatial scales and effectively deal with the special drainage structure of small prairie watersheds. As a key component of MWRMS, a three-dimensional Willows Reservoir Eutrophication Model (WREM) is developed to addresses essential biogeochemical processes in prairie reservoirs and to generate 3D distributions of various water quality constituents; with a modest degree of parameterization, WREM is able to meet the limit of data availability that often confronts the modeling practices in small watersheds. MWRMS was applied to the Assiniboia Watershed in southern Saskatchewan, Canada. Extensive efforts of field work and lab analysis were undertaken to support model calibration and validation. MWRMS demonstrated its ability to reproduce the observed watershed water yield, reservoir water levels and temperatures, and concentrations of several water constituents. Results showed that the aquatic systems in the Assiniboia Watershed were nitrogen-limited and sediment flux played a crucial role in reservoir nutrient budget and dynamics. MWRMS can provide a broad context of decision support for water resources management and water quality protection in the prairie region.
Hua Zhang; Guo H. Huang; Dunling Wang; Xiaodong Zhang; Gongchen Li; ChunJiang An; Zheng Cui; Renfei Liao; Xianghui Nie. An integrated multi-level watershed-reservoir modeling system for examining hydrological and biogeochemical processes in small prairie watersheds. Water Research 2011, 46, 1207 -1224.
AMA StyleHua Zhang, Guo H. Huang, Dunling Wang, Xiaodong Zhang, Gongchen Li, ChunJiang An, Zheng Cui, Renfei Liao, Xianghui Nie. An integrated multi-level watershed-reservoir modeling system for examining hydrological and biogeochemical processes in small prairie watersheds. Water Research. 2011; 46 (4):1207-1224.
Chicago/Turabian StyleHua Zhang; Guo H. Huang; Dunling Wang; Xiaodong Zhang; Gongchen Li; ChunJiang An; Zheng Cui; Renfei Liao; Xianghui Nie. 2011. "An integrated multi-level watershed-reservoir modeling system for examining hydrological and biogeochemical processes in small prairie watersheds." Water Research 46, no. 4: 1207-1224.
Changing climatic conditions contribute to a time varying nature of hydrological responses over different temporal scales. The temporal dynamics of hydrological systems bring uncertainties into hydrological simulation which are different to uncertainties from spatial heterogeneity of soil and land use. This study develops a new approach to improve the calibration of hydrological based on hydroclimatic similarities. Six climatic indexes are integrated using Principal Component Analysis and Fuzzy C-mean Clustering methods to transform hydrological years into hydroclimatic periods. Parameter sets of SWAT model are calibrated independently for each period and used together to generate continuous simulation for a prairie watershed in southern Canada. Results indicate that the multi-period model exhibits comprehensive advantages over the traditional single-period model under various flow conditions. The simulation ability of the model is improved through using period-specific parameter sets in fitting the observations to compensate for deficiencies in the model structure or input data.
Hua Zhang; Guo H. Huang; Dunling Wang; Xiaodong Zhang. Multi-period calibration of a semi-distributed hydrological model based on hydroclimatic clustering. Advances in Water Resources 2011, 34, 1292 -1303.
AMA StyleHua Zhang, Guo H. Huang, Dunling Wang, Xiaodong Zhang. Multi-period calibration of a semi-distributed hydrological model based on hydroclimatic clustering. Advances in Water Resources. 2011; 34 (10):1292-1303.
Chicago/Turabian StyleHua Zhang; Guo H. Huang; Dunling Wang; Xiaodong Zhang. 2011. "Multi-period calibration of a semi-distributed hydrological model based on hydroclimatic clustering." Advances in Water Resources 34, no. 10: 1292-1303.
The planning of energy systems is associated with various uncertainties. Such uncertainties may only be expressed by interval numbers or fuzzy sets rather than probability distributions. In addition, issues of capacity expansion related to timing, sizing and siting under such uncertainties needs to be addressed. Therefore, the objective of this research is to develop a dynamic optimization model for energy systems planning under uncertainty (DESPU) through integrating interval-parameter, fuzzy and mixed integer programming techniques within an energy systems management framework. The developed methodology is then applied to a hypothetical regional energy system. The results indicate that DESPU has advantages in reflecting complexities of various uncertainties as well as dealing with problems of capacity expansion within energy systems.
Q. G. Lin; Guohe (Gordon) Huang; B. Bass; Y. F. Huang; Xiaodong Zhang. DESPU: Dynamic Optimization for Energy Systems Planning Under Uncertainty. Energy Sources, Part B: Economics, Planning, and Policy 2011, 6, 321 -338.
AMA StyleQ. G. Lin, Guohe (Gordon) Huang, B. Bass, Y. F. Huang, Xiaodong Zhang. DESPU: Dynamic Optimization for Energy Systems Planning Under Uncertainty. Energy Sources, Part B: Economics, Planning, and Policy. 2011; 6 (4):321-338.
Chicago/Turabian StyleQ. G. Lin; Guohe (Gordon) Huang; B. Bass; Y. F. Huang; Xiaodong Zhang. 2011. "DESPU: Dynamic Optimization for Energy Systems Planning Under Uncertainty." Energy Sources, Part B: Economics, Planning, and Policy 6, no. 4: 321-338.
A regional energy system consists of diverse forms of energy. Energy‐related issues such as utilization of renewable energy and reduction of greenhouse gas (GHG) emission are confronting decision makers. Meanwhile, various uncertainties and dynamics of the energy system are posing difficulties for the energy system planning, especially for those under multiple stages. In this study, an interval multi‐stage stochastic programming regional energy systems planning model (IMSP‐REM) was developed to support regional energy systems management and GHG control under uncertainty. The IMSP‐REM is a hybrid methodology of inexact optimization and multi‐stage stochastic programming. Not only can it handle uncertainties presented as intervals and probability density functions but also reflect dynamics of system conditions over multiple planning stages. The developed IMSP‐REM was applied to a hypothetical regional energy system. The results indicate that the IMSP‐REM can effectively reflect issues of GHG reduction and renewable energy utilization within an energy system planning framework. In addition, the model has advantages in incorporating multiple uncertainties and dynamics within energy management systems. Copyright © 2011 John Wiley & Sons, Ltd.
Gongchen Li; Guohe Huang; Qianguo Lin; Yanpeng Cai; Yumin Chen; Xiaodong Zhang. Development of an interval multi-stage stochastic programming model for regional energy systems planning and GHG emission control under uncertainty. International Journal of Energy Research 2011, 36, 1161 -1174.
AMA StyleGongchen Li, Guohe Huang, Qianguo Lin, Yanpeng Cai, Yumin Chen, Xiaodong Zhang. Development of an interval multi-stage stochastic programming model for regional energy systems planning and GHG emission control under uncertainty. International Journal of Energy Research. 2011; 36 (12):1161-1174.
Chicago/Turabian StyleGongchen Li; Guohe Huang; Qianguo Lin; Yanpeng Cai; Yumin Chen; Xiaodong Zhang. 2011. "Development of an interval multi-stage stochastic programming model for regional energy systems planning and GHG emission control under uncertainty." International Journal of Energy Research 36, no. 12: 1161-1174.
Multiple dynamics and uncertainties are involved in regional energy and greenhouse gas management (REGM) system, confronting decision makers during plan/policy makings. In this study, a greenhouse gas (GHG)-mitigation oriented inexact dynamic energy system management model (IFMP-REGM) is developed for a REGM system. The IFMP-REGM is a hybrid methodology of interval mathematical programming, fuzzy linear programming and multi-stage stochastic programming. It can not only handle uncertainties presented as discrete intervals, fuzzy sets and probability distributions, but also reflect dynamic variations of system conditions, particularly for large-scale multistage problems with sequential structures. The uncertain information can be incorporated within a multi-layer scenario tree; revised decisions are permitted in each time period based on the realized values of the uncertain events. The developed IFMP-REGM model was then applied to a hypothetical regional energy and GHG management system. The results indicate that the IFMP-REGM can effectively address complexities of various system uncertainties as well as dealing with multi-stage stochastic decision problems within energy systems.
G.C. Li; G.H. Huang; Q.G. Lin; Xiaodong Zhang; Q. Tan; Y.M. Chen. Development of a GHG-mitigation oriented inexact dynamic model for regional energy system management. Energy 2011, 36, 3388 -3398.
AMA StyleG.C. Li, G.H. Huang, Q.G. Lin, Xiaodong Zhang, Q. Tan, Y.M. Chen. Development of a GHG-mitigation oriented inexact dynamic model for regional energy system management. Energy. 2011; 36 (5):3388-3398.
Chicago/Turabian StyleG.C. Li; G.H. Huang; Q.G. Lin; Xiaodong Zhang; Q. Tan; Y.M. Chen. 2011. "Development of a GHG-mitigation oriented inexact dynamic model for regional energy system management." Energy 36, no. 5: 3388-3398.
Water quality management is inevitably complicated since it involves a number of environmental, socio-economic, technical, and political factors with dynamic and interactive features. In planning water quality management systems, uncertainties exist in many system components and may affect the system behaviours. It is thus desired that such complexities and uncertainties be effectively addressed for providing decision support for practical water quality management. The objective of this study is to develop a model-based decision support system for supporting water quality management under hybrid uncertainties, named FICMDSS, which is based on a hybrid uncertain programming (HFICP) model with fuzzy and interval coefficients. The system provides an effective tool for the decision makers in dealing with water quality management problems and formulating desired policies and strategies. The user can easily operate the system and obtain the decision support through user-friendly graphical interfaces. The HFICP model improves upon the existing inexact programming methods through incorporation of hybrid fuzzy and interval uncertainties into the optimization management processes and resulting solutions. Results of a water quality management case study indicated that the developed FICMDSS can facilitate the decision making in planning agricultural activities for water quality management in agricultural systems. Feasible decision alternatives for cropping area, amounts of manure and fertilizer application, and sizes of livestock husbandry can be generated for achieving the maximum agricultural system benefit subject to the given water-related constraints. The user can better make the decisions for water quality management under hybrid uncertainties with the help of FICMDSS.
Xiaodong Zhang; Guo H. Huang; Xianghui Nie; Qianguo Lin. Model-based decision support system for water quality management under hybrid uncertainty. Expert Systems with Applications 2011, 38, 2809 -2816.
AMA StyleXiaodong Zhang, Guo H. Huang, Xianghui Nie, Qianguo Lin. Model-based decision support system for water quality management under hybrid uncertainty. Expert Systems with Applications. 2011; 38 (3):2809-2816.
Chicago/Turabian StyleXiaodong Zhang; Guo H. Huang; Xianghui Nie; Qianguo Lin. 2011. "Model-based decision support system for water quality management under hybrid uncertainty." Expert Systems with Applications 38, no. 3: 2809-2816.
With increasing evidences of climate change in the prairie region, there is an urgent need to understand the future climate and the responses of small prairie wetlands. This study integrated two regional climate models (RCMs), two weather generators and a distributed hydrological model to examine uncertainties in hydrological responses to climate change in the Assiniboia watershed, Canada. Comparing to baseline conditions (1971–2000), annual water yield and evapotranspiration in the period of 2041–2070 were generally unchanged, while annual reservoir storage was generally reduced. However, projected hydrological regimes were less consistent at monthly level, particularly for March and July. Such uncertainties in simulated hydrological responses were derived from the implementations of different integrated downscaling methods, reflecting our imperfect knowledge of the future climate. We identified a warming temperature trend from climatic projections, but had less confidence in the future pattern of precipitation. Uncertainties in integrated downscaling were primarily derived from the choice of RCM, and were amplified through the incorporation of different weather generators. Results of any climate change study based on only one RCM and/or one weather generator should be interpreted with caution, and the ensemble framework should be advised to generate a comprehensive vision of the future climate. This study demonstrated that the incorporation of precipitation occurrence change contributed to a full translation of RCM outputs, but also introduced additional uncertainty. A balance is thus desired between the information loss and the additional uncertainty in order to effectively utilize RCM outputs.
Hua Zhang; Guo H. Huang; Dunling Wang; Xiaodong Zhang. Uncertainty assessment of climate change impacts on the hydrology of small prairie wetlands. Journal of Hydrology 2011, 396, 94 -103.
AMA StyleHua Zhang, Guo H. Huang, Dunling Wang, Xiaodong Zhang. Uncertainty assessment of climate change impacts on the hydrology of small prairie wetlands. Journal of Hydrology. 2011; 396 (1-2):94-103.
Chicago/Turabian StyleHua Zhang; Guo H. Huang; Dunling Wang; Xiaodong Zhang. 2011. "Uncertainty assessment of climate change impacts on the hydrology of small prairie wetlands." Journal of Hydrology 396, no. 1-2: 94-103.
Agricultural activities are the main contributors of nonpoint source water pollution within agricultural systems. In this study, a possibilistic stochastic water management (PSWM) model is developed and applied to a case study of water quality management within an agricultural system in China. This study is a first application of hybrid possibilistic chance-constrained programming approach to nonpoint source water quality management problems within an agricultural system. Hybrid uncertainties with the synergy of fuzzy and stochastic implications are effectively characterized by the PSWM model with the following advantages: (1) it improves upon the existing possibilistic and chance-constrained programming methods through direct incorporation of fuzziness and randomness within a general optimization framework; (2) it will not lead to more complicated intermediate models and thus have lower computational requirements; (3) its solutions offer flexibility in interpreting the results and reflect the interactional effects of uncertain parameters on system conditions variations; and (4) it can help examine the risk of violating system constraints and the associated consequences. The results of the case study show useful information for feasible decision schemes of agricultural activities, including the trade-offs between economic and environmental considerations. Moreover, a strong desire to acquire high agricultural income will run into the risk of potentially violating the related water quality standards, while willingness to accept low agricultural income will increase the risk of potential system failure (violating system constraints). The results suggest that the developed approach is also applicable to many practical problems where hybrid uncertainties exist.
Xiaodong Zhang; Guo H. Huang; Xianghui Nie. Possibilistic Stochastic Water Management Model for Agricultural Nonpoint Source Pollution. Journal of Water Resources Planning and Management 2011, 137, 101 -112.
AMA StyleXiaodong Zhang, Guo H. Huang, Xianghui Nie. Possibilistic Stochastic Water Management Model for Agricultural Nonpoint Source Pollution. Journal of Water Resources Planning and Management. 2011; 137 (1):101-112.
Chicago/Turabian StyleXiaodong Zhang; Guo H. Huang; Xianghui Nie. 2011. "Possibilistic Stochastic Water Management Model for Agricultural Nonpoint Source Pollution." Journal of Water Resources Planning and Management 137, no. 1: 101-112.