A Scenario-Based Optimization Model for Planning Sustainable Water-Resources Process Management under Uncertainty

Miao, Hongchang; Li, Donglin; Zuo, Qiting; Yu, Li; Fei, Xiaoxia; Hao, Lingang

doi:10.3390/pr7050312

Cited by 10 publications

(5 citation statements)

References 45 publications

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“…Their main findings are discussed and presented by emphasizing the effects of several parameters on the system's performance. Miao et al [28] present a scenario-based optimization model for planning sustainable water-resource process management under uncertainty. Their results reveal that different water-distribution proportion scenarios and uncertainties can lead to changed water allocations, sewage discharges, chemical oxygen demand (COD) emissions and system benefits, and the variation of scenarios (i.e., from S2 to S3) can result in a change of 9% over the planning horizon for water allocation in the industrial sector.…”

Section: Other Issuesmentioning

confidence: 99%

Special Issue on “Energy, Economy and Environment for Industrial Production Processes”

et al. 2020

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Section: Other Issuesmentioning

confidence: 99%

Special Issue on “Energy, Economy and Environment for Industrial Production Processes”

et al. 2020

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“…Epigraph re f ormulation o f ob jective f unction (13) Mass balance and energy balance constraints (1)-(3) Sha f t power demand constraint (6) Process demand constraints (7), (14) Variables range constraints (8), (15), (16) (DDROSS)…”

Section: Robust Cqmip Model Of the Steam Systemmentioning

confidence: 99%

“…Research [13] proposed a worst-case optimization approach for real-time optimization of a gas lifted well system. Research [14] presented a fuzzy-credibility constrained programming approach for water resource planning under uncertainty. Research [15] utilized stochastic programming to handle uncertainties in the site utility system.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Robust Optimization for Steam Systems in Ethylene Plants under Uncertainty

Zhao

Zhong

2019

Processes

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In an ethylene plant, steam system provides shaft power to compressors and pumps and heats the process streams. Modeling and optimization of a steam system is a powerful tool to bring benefits and save energy for ethylene plants. However, the uncertainty of device efficiencies and the fluctuation of the process demands cause great difficulties to traditional mathematical programming methods, which could result in suboptimal or infeasible solution. The growing data-driven optimization approaches offer new techniques to eliminate uncertainty in the process system engineering community. A data-driven robust optimization (DDRO) methodology is proposed to deal with uncertainty in the optimization of steam system in an ethylene plant. A hybrid model of extraction–exhausting steam turbine is developed, and its coefficients are considered as uncertain parameters. A deterministic mixed integer linear programming model of the steam system is formulated based on the model of the components to minimize the operating cost of the ethylene plant. The uncertain parameter set of the proposed model is derived from the historical data, and the Dirichlet process mixture model is employed to capture the features for the construction of the uncertainty set. In combination with the derived uncertainty set, a data-driven conic quadratic mixed-integer programming model is reformulated for the optimization of the steam system under uncertainty. An actual case study is utilized to validate the performance of the proposed DDRO method.

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“…Combined with the weights determined by the AHP method, the PP method is used to further explore the characteristics of the data itself and optimize the AHP weights. The PP-AHP method is combined with the set-to-potential theory to construct the water environment carrying capacity evaluation model based on the PP-AHP method to determine the weights [27][28][29][30]. The validity of this model was verified by taking Harbin City water environment carrying capacity evaluation as an example.…”

Section: Introductionmentioning

confidence: 99%

Sustainability Analysis of the Water Environment Carrying Capacity of Harbin City Based on an Optimized Set Pair Analysis Posture-Deviation Coefficient Method Evaluation Model

Sun

Yao

Xie

et al. 2023

Water

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To scientifically measure the water environment carrying capacity of Harbin City and its change trend, based on analysis of the implications of the sustainability of the urban water environment’s carrying capacity, an evaluation index system for the sustainability of the water environment carrying capacity of Harbin City was constructed. Most existing evaluation methods rely on static data to construct correlation functions between research objects and rank criteria, while the dynamic nature of the information is not considered enough. In this paper, we use hierarchical analysis (analytic hierarchy process, AHP) to determine the weights of each index of the system and then apply the projection tracing method (projection pursuit, PP) to optimize the determined weights. Combining the set pair analysis posture evaluation method and bias coefficients method to explore the dynamic balance mechanism between different index factor levels, a sustainability evaluation model for water environment carrying capacity integrating informational evolution is constructed. Finally, the applicability of the optimization model is tested by comparing the confidence criterion judging method. The model realizes quantitative evaluation of the carrying capacity of the urban water environment. It provides a new and effective means for accurate and reasonable determination of the coefficient of variance and the number of links and dynamic analysis of the water environment carrying capacity system and judgment of its sustainable development trend. The results show that the weight for water resource quality is 0.55, which is the subsystem with the greatest overall impact on the carrying capacity of the water environment in Harbin. The evaluation level of the set-to-potential eigenvalue for 2010–2017 is biased positive 2, and the rest of the years are quasi 2. The reduced value of the coefficient of oppositeness corresponding to the years 2010–2017 is more significant. The maximum value of the dynamic evolution of the load-carrying capacity level is nearly −0.35. From the vertical comparison of different levels, it is found that the water environment carrying capacity of Harbin City gradually recovered to the normal loadable level over time. This overall shows an improving trend.

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A Scenario-Based Optimization Model for Planning Sustainable Water-Resources Process Management under Uncertainty

Cited by 10 publications

References 45 publications

Special Issue on “Energy, Economy and Environment for Industrial Production Processes”

Special Issue on “Energy, Economy and Environment for Industrial Production Processes”

Data-Driven Robust Optimization for Steam Systems in Ethylene Plants under Uncertainty

Sustainability Analysis of the Water Environment Carrying Capacity of Harbin City Based on an Optimized Set Pair Analysis Posture-Deviation Coefficient Method Evaluation Model

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