An optimization model for air pollution control decision making

Shaban, Habib I.; Elkamel, Ali; Gharbi, Ridha

doi:10.1016/s1364-8152(96)00008-4

Cited by 26 publications

(10 citation statements)

References 6 publications

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“…Because of the environmental and health impacts of air pollutants and added expenses of air pollution control technology, chemical engineers have explored and implemented many solutions for improving air pollution conditions through analysis, design, and application 11–14 . Shaban et al developed a mixed integer program which solved for the cheapest air pollution control strategy to meet emissions regulations, selecting from a number of different control technologies 15 . Shih et al added atmospheric chemistry and reaction kinetics between photochemical pollutants and their precursors into a pollution control optimization formulation, again minimizing cost 16 .…”

Section: Introductionmentioning

confidence: 99%

Designing industrial landscapes for mitigating air pollution with spatially‐explicit techno‐ecological synergy

Charles

Bakshi

2021

AIChE Journal

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Air pollution has posed health and environmental threats since the Industrial Revolution. Technological solutions present major expenses for industry, yet nature's ecosystems also provide pollution uptake. In the pursuit of techno‐ecological sustainable design, this work presents a framework for spatially‐explicit industrial site design that determines where and when ecological restoration should be considered. The framework considers land use changes and identifies the cheapest balance between technological and ecological uptake for industrial landscapes, including the impacts of long term ecological growth dynamics. This work presents the framework's construction along with a case study conducted for a coal‐fired power station in Ohio. The results provide spatial maps of proposed restoration areas, projected savings values, and spatial‐temporal maps that consider annual budget constraints. The results demonstrate a significant sensitivity to land use restoration costs and highlights ecological advantages, like simultaneous uptake of different chemical species.

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Section: Introductionmentioning

confidence: 99%

Designing industrial landscapes for mitigating air pollution with spatially‐explicit techno‐ecological synergy

Charles

Bakshi

2021

AIChE Journal

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“…The recent studies take into account economic as well as environmental impact into consideration Clift 1995, 1999). An optimization model for selection of suitable control option to minimize pollution load and maximize the profits has been described by Shaban et al (1997). The mixed integer linear programming (MILP) model combines the minimization of costs with minimization of environmental impact that is assessed in terms of life cycle (Papandreou and Shang 2008).…”

Section: Introductionmentioning

confidence: 99%

Optimal Air Pollution Control Strategy Based on Particle Size Distribution

Deshmukh¹,

Srinivasarao²

2015

IJETT

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Air pollution prevention and control is one of the key factors for sustainable Environmental Management System. Among all air pollutants, particulate matter attracted considerable attention of researchers. Particles smaller than 10 μm (PM10) & 2.5 μm (PM2.5) are associated with a range of respiratory and cardiovascular diseases. Overall collection efficiency of each one of the air pollution control devices depends on particle size distribution (PSD) of inlet stream. Existing air pollution control strategies does not consider PSD of particulate matter emitted into atmosphere and its impact on the environment. In the present work a Mixed Integer Non Linear model has been presented for selection of optimal air pollution control strategy. Collection efficiency of control equipments based on PSD has been considered. The Optimization model presented minimizes total cost of air pollution control strategy including economical and environmental cost meeting prescribed emission norms. The efficacy of the model has been successfully evaluated using a case study of typical process plant. The model presented can be used for selection of optimal emission norms with minimum social cost

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“…This approach provides a potentially powerful decision making tool which may help process industries identify sustainable options for the future Clift 1995, 1999). An optimization model for selection of suitable control option to minimize pollution load and maximize the profits has been described by Shaban et al (1997).The existing optimization models consider economic cost and various constraints like _______________________________________________________________________________________ Volume: 04 Issue: 02 | Feb-2015, Available @ http://www.ijret.org 108 pollutant emission norms. A multi-objective optimization strategy to identify the sustainable design of utility systems that satisfy both economic and environmental goals is developed.…”

Section: Introductionmentioning

confidence: 99%

Selection of Optimal Air Pollution Control Strategies

.¹

2015

IJRET

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Sustainable development needs social, economic, energy and environmental sustainability. We propose a model to minimize the total cost consisting of economic cost of pollution control equipment, health cost because of pollutants emissions. We developed a Mixed Integer Non Linear Programming (MINLP) based model for selection of optimal air pollution control strategy. The model considers multiple pollutants, multiple emission sources and multiple control equipments. Constraints like budget and efficiency of control equipment are included in the model. Affect of emission norms on total cost is discussed. The efficacy of the proposed model is explained by considering simulation case studies of a typical cement plant and a thermal power plant.

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An optimization model for air pollution control decision making

Cited by 26 publications

References 6 publications

Designing industrial landscapes for mitigating air pollution with spatially‐explicit techno‐ecological synergy

Designing industrial landscapes for mitigating air pollution with spatially‐explicit techno‐ecological synergy

Optimal Air Pollution Control Strategy Based on Particle Size Distribution

Selection of Optimal Air Pollution Control Strategies

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