scholarly journals Health-Care Waste Treatment Technology Selection Using the Interval 2-Tuple Induced TOPSIS Method

2016 ◽  
Vol 13 (6) ◽  
pp. 562 ◽  
Author(s):  
Chao Lu ◽  
Jian-Xin You ◽  
Hu-Chen Liu ◽  
Ping Li
Keyword(s):  
Health Care ◽  
Waste Treatment ◽  
Technology Selection ◽  
Topsis Method ◽  
Treatment Technology ◽  
Health Care Waste

Cloud decision support framework for treatment technology selection of health-care waste

2021 ◽  
pp. 1-26
Author(s):  
Rui-Lu Huang ◽  
Min-hui Deng ◽  
Yong-yi Li ◽  
Jian-qiang Wang ◽  
Jun-Bo Li
Keyword(s):  
Health Care ◽  
Decision Support ◽  
Assessment Process ◽  
Technology Selection ◽  
Human Beings ◽  
Treatment Technology ◽  
Decision Support Framework ◽  
Health Care Waste ◽  
Evaluation Information ◽  
Selection Of

With the attention of people to environmental and health issues, health-care waste (HCW) management has become one of the focus of researchers. The selection of appropriate HCW treatment technology is vital to the survival and development of human beings. In the assessment process of HCW disposal alternative, the evaluation information given by decision makers (DMs) often has uncertainty and ambiguity. The expression, transformation and integration of this information need to be further studied. We develop an applicable decision support framework of HCW treatment technology to provide reference for relevant staff. Firstly, the evaluation information of DMs is represented by interval 2-tuple linguistic term sets (ITLTs). To effectively express qualitative information, the cloud model theory is used to process the linguistic information, a novel concept of interval 2-tuple linguistic integrated cloud (ITLIC) is proposed, and the relevant operations, distance measure and possibility degree of ITLICs are defined. Moreover, a weighted Heronian mean (HM) operator based ITLIC is presented to fuse cloud information. Secondly, the HCW treatment technology decision support model based on the BWM and PROMETHEE is established. Finally, the proposed model is demonstrated through an empirical example, and the effectiveness and feasibility of the model is verified by comparison with extant methods.

scholarly journals Indicators of Sustainable Development for Health Care Waste Treatment Industry

2017 ◽  
Vol 73 (2) ◽  
Author(s):  
Olivia Maamari ◽  
Jounaid Maurice ◽  
Cedric Brandam ◽  
Roger Lteif ◽  
Dominique Salameh
Keyword(s):  
Health Care ◽  
Sustainable Development ◽  
Waste Treatment ◽  
Health Care Waste

Management practices and modeling the seasonal variation in health care waste

2017 ◽  
Vol 12 (1) ◽  
pp. 162-174 ◽  
Author(s):  
Vikas Thakur ◽  
Ramesh Anbanandam
Keyword(s):  
Health Care ◽  
Seasonal Variation ◽  
Coming Out ◽  
Management Practices ◽  
Waste Treatment ◽  
Polynomial Regression ◽  
Health Care Facilities ◽  
Content Type ◽  
Biomedical Waste ◽  
Health Care Waste

Purpose Management of hazardous waste is a big challenge to a common biomedical waste treatment facility (CBWTF) because of variations in the amount of different kinds of waste collected and treated from various health-care facilities (HCFs). Hence, prediction of health-care waste (HCW) will be very helpful for the CBWTF in allocation of resources, transportation, storage and disposal of medical waste (MW). This study aims to focus on the current MW handling and disposal practices at CBWTF in Uttarakhand, India. The study also models the seasonal variation in the HCW quantities collected and treated in CBWTF at Uttarakhand (India). Design/methodology/approach Data were collected for two years (2013 and 2014) from CBWTF, and polynomial regression models were used to represent the complex nonlinear relationship among the variables. Findings The fixed trends in the waste generated in two years represent the seasonal variations and illness patterns. The load of approximately 527 kg/day biomedical waste, including all the three categories (red, yellow and blue), was estimated at CBWTF at Uttarakhand, India. The composition of the total waste was calculated as: yellow category (327 kg/day, 62.23 per cent), red category (190 kg/day, 36.66 per cent) and blue category (10 kg/day, 1.44 per cent). CBWTF needs to run an incinerator for 3.30 h, autoclaving machine for 4 h and shredder for 20 min daily as per the calculated load. Research limitations/implications This study is focused on only one CBWTF in Uttarakhand, so the model needs to be validated considering other facilities. Practical implications The model will help the CBWTF to plan its capacity and allocate resources. Social implications Infectious waste coming out from HCFs can be managed in a proper way. Originality/value This study is the first of its kind conducted for CBWTF, Uttarakhand, India.

Technology Selection for Emergency Radioactive Wastewater Treatment

2013 ◽  
Vol 724-725 ◽  
pp. 708-712
Author(s):  
Jun Feng Li ◽  
Jian Long Wang
Keyword(s):  
Wastewater Treatment ◽  
Radioactive Waste ◽  
Emergency Treatment ◽  
Waste Treatment ◽  
Technology Selection ◽  
Treatment Technology ◽  
Radioactive Wastewater ◽  
Different Characteristics ◽  
Accident Case

Radioactive wastewater on accident cases is characterized by large quantity and complex quality, so the treatment process must be able to meet the request of large quantity, low energy consumption and mobile processing. The quantity and quality of radioactive wastewater under core melt accident case was estimated. The different characteristics of the radioactive waste treatment technology were compared. The practicable technologies and processes for emergency treatment of radioactive waste was analyzed. The recommended flow chart for emergency radioactive wastewater treatment was given out. Some suggestions were given for the research on the emergency treatment of radioactive wastewater technology.

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