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DALIAN UNIVERSITY OF TECHNOLOGY Login 中文
张树深

Researcher
Supervisor of Doctorate Candidates
Supervisor of Master's Candidates


Gender:Male
Alma Mater:北京师范大学
Degree:Master's Degree
School/Department:环境学院
E-Mail:zhangss@dlut.edu.cn
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Current position: Home >> Scientific Research >> Paper Publications

Management of Occupational Exposure to Engineered Nanoparticles Through a Chance-Constrained Nonlinear Programming Approach

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Indexed by:期刊论文

Date of Publication:2013-04-01

Journal:INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH

Included Journals:SCIE、Scopus

Volume:10

Issue:4

Page Number:1231-1249

ISSN No.:1660-4601

Key Words:manufactured nanomaterials (MNMs); occupational exposure; environmental health and safety standards (EHS); emerging risk management; nonlinear programming; chance-constrained programming; uncertainty analysis

Abstract:Critical environmental and human health concerns are associated with the rapidly growing fields of nanotechnology and manufactured nanomaterials (MNMs). The main risk arises from occupational exposure via chronic inhalation of nanoparticles. This research presents a chance-constrained nonlinear programming (CCNLP) optimization approach, which is developed to maximize the nanaomaterial production and minimize the risks of workplace exposure to MNMs. The CCNLP method integrates nonlinear programming (NLP) and chance-constrained programming (CCP), and handles uncertainties associated with both the nanomaterial production and workplace exposure control. The CCNLP method was examined through a single-walled carbon nanotube (SWNT) manufacturing process. The study results provide optimal production strategies and alternatives. It reveal that a high control measure guarantees that environmental health and safety (EHS) standards regulations are met, while a lower control level leads to increased risk of violating EHS regulations. The CCNLP optimization approach is a decision support tool for the optimization of the increasing MNMS manufacturing with workplace safety constraints under uncertainties.