Abstract

Critical environmental and human health concerns are associated with the rapidly growing fields of nanotechnology and Engineered nanomaterials (ENMs). The main risk arises from occupational exposure via chronic inhalation of nanoparticles. This research presents a fuzzy chance-constrained nonlinear programming (FCCNLP) optimization approach, which is developed to maximize the nanomaterial production and minimize the risks of workplace exposure to ENMs. The FCCNLP method integrates fuzzy mathematical programming (FMP) and chance-constrained programming (CCP) into nonlinear programming (NLP) optimization framework, and could be used to deal with uncertainties expressed as not only probability distributions and fuzzy values associated with components of constraints but ambiguity of the objective function as well.
The FCCNLP method was examined through a single-walled carbon nanotube (SWNT) manufacturing process. Solutions of the compromise decision alternatives associated with different risk levels of relaxed constraint violations were obtained. This study confirmed that a high level control strategy through strict occupational exposure limits (OELs) combined with a high enforcement of OELs would lower the nanomaterial exposure risks to workers. The related cost and nanomaterial production have also been optimized for different operational scenarios under multi-layer system uncertainties. The results were helpful for decision makers to identify desirable schemes under uncertainties to maximize the economic benefits and ensure workplace safety through minimizing the nanomaterial-related health risks. The developed technology has technical novelty to help finding cost-effective measures for the sustainable development of nanotechnology.