Collaborative profitable pollution prevention: an approach for the sustainable development of complex industrial zones under uncertain information

Piluso, Cristina; Huang, Yinlun

doi:10.1007/s10098-008-0187-y

Cited by 17 publications

(6 citation statements)

References 4 publications

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“…And making an adjustment of the pre-allocation schemes is also necessary after the variables of total water resources are ascertained. The inexact two-stage stochastic programming that incorporated with intervalparameter programming (Huang 1996;Xia et al 1997;Piluso & Huang 2009) and two-stage stochastic programming (Dai et al 2000;Darby-Dowman et al 2000;Maqsood et al 2005) could successfully reflect the decision process of industrial water resources management and effectively handle the interval and random information as the representation of system uncertainties. (2) For making reasonable and balanced water allocation strategies among industrial sectors, a comprehensive risk-aversion approach is proposed through integrated with the traditional risk measurement and the risk-mitigation coefficient that are related with the characteristics of water resources utilization of each sector into a general framework.…”

Section: Model Frameworkmentioning

confidence: 99%

Water management for industrial development, energy conservation, and subjective attitudes: a comprehensive risk-oriented model to explore the tolerance of unbalanced allocation problem

Xie

et al. 2021

Journal of Water and Climate Change

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In this study, a new concept concerning comprehensive characteristics of water resources utilization as an index for risk modeling within the water allocation management model is proposed to explore the tolerance of unbalanced allocation problem under water–energy nexus. The model is integrated with interval two-stage stochastic programming for reflecting system uncertainties. These uncertainties are associated with the industrial production feature and the decision-making process. With respect to water–energy nexus, energy proposed is mainly focused on the consumption intensity of water purification and transportation from different water sources. The developed model is applied for industrial water resources allocation management in Henan province, China. Multiple scenarios related to disparate energy consumption control and the comprehensive risk levels are simulated to obtain a reasonable trade-off among system profit, comprehensive risk, and energy consumption. The results indicated that the strict comprehensive risk management or energy consumption control measures could cause damage to system benefit owing to decreasing the flexibility of industrial water resources distributions, and the preliminary energy consumption or the comprehensive risk control would be beneficial to moderate the conflict between industrial sectors and water resources, and accelerate industrial structure transformation in the future.

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Section: Model Frameworkmentioning

confidence: 99%

Water management for industrial development, energy conservation, and subjective attitudes: a comprehensive risk-oriented model to explore the tolerance of unbalanced allocation problem

Xie

et al. 2021

Journal of Water and Climate Change

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“…In most decision-making processes, information and data uncertainty is not thoroughly considered, which could make the derived decisions not sufficiently effective in the implementation phase. Note that the uncertainty associated with sustainability problems is frequently pervasive, which appears due to incomplete or imprecise information and lack of deep knowledge about the interactions of participating industrial units within the system or surrounding environment (Piluso and Huang 2009).…”

Section: Introductionmentioning

confidence: 99%

Sustainability enhancement under uncertainty: a Monte Carlo-based simulation and system optimization method

Liu

Huang

2015

Clean Techn Environ Policy

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Known methods for sustainability enhancement are typically scenario based, and the uncertainty of surrounding available data and information is usually not addressed holistically, due to inherent problem complexity. Thus the solutions identified by those methods could be not sufficiently effective in many industrial applications. In this paper, we introduce a Monte Carlo-based simulation and system optimization method for deriving sustainability enhancement strategies, where uncertainties are systematically taken into account. The methodological efficacy is illustrated through the study of an industrial sustainability enhancement problem involving a number of sectors.

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“…In the work by Guilleń-Gosaĺbez et al, 9 an LCA framework for optimal design of chemical processes shows an approach for studying multiobjective optimization problems. The Collaborative Profitable Pollution Prevention (CP3) idea was introduced by Piluso and Huang, 10 which aids in decision making for the study of sustainable development of industrial zones. Liu and Huang 11 presented a simple, yet systematic interval-parameter-based methodology for identifying quickly superior solutions under uncertainty for sustainability performance improvement.…”

Section: ■ Introductionmentioning

confidence: 99%

Decision Support for Enhancement of Manufacturing Sustainability: A Hierarchical Control Approach

Moradi-Aliabadi

Huang

2018

ACS Sustainable Chem. Eng.

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From the sustainability science point of view, improvement of manufacturing sustainability involves a series of system (performance) state transitions in a sustainable development (SD) space. The transitions occur based on the system information obtained through stage-wised sustainability assessment and the actions on the system taken based on the derived stagewised decisions. From the standpoint of control engineering, this is a multiobjective system control problem. In this paper, we introduce a general decision-support framework for deriving strategies to achieve short-to-longterm sustainability goals. In this framework, a two-layered hierarchical control scheme is introduced to implement strategic and tactical control for dynamic sustainability control. A comprehensive case study on biodiesel manufacturing is illustrated to demonstrate methodological efficacy.

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Collaborative profitable pollution prevention: an approach for the sustainable development of complex industrial zones under uncertain information

Cited by 17 publications

References 4 publications

Water management for industrial development, energy conservation, and subjective attitudes: a comprehensive risk-oriented model to explore the tolerance of unbalanced allocation problem

Water management for industrial development, energy conservation, and subjective attitudes: a comprehensive risk-oriented model to explore the tolerance of unbalanced allocation problem

Sustainability enhancement under uncertainty: a Monte Carlo-based simulation and system optimization method

Decision Support for Enhancement of Manufacturing Sustainability: A Hierarchical Control Approach

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