Developing a hazardous waste management system with consideration of health, safety, and environment

Farrokhi-Asl, Hamed; Makui, Ahmad; Ghousi, Rouzbeh; Rabbani, Masoud

doi:10.1016/j.compeleceng.2020.106553

Cited by 14 publications

(7 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nowadays awareness of the safety of employees working in the production and manufacturing operations in different industrial environments that involve hazardous substances and materials has become of utmost interest (Farrokhi-Asl et al, 2020). Thus, monitoring the concentration and leakage of pollutants (Fung et al, 2019) and development of safe and efficient methods (Fung et al, 2019) to reduce the exposure of humans and the detrimental risks for the environment, with fewer accidents provoked by hazardous substances (Wong et al, 2018;Wang et al, 2020), are becoming a priority in the implementation of safety plan management in many sectors of the economy (Binajjaj et al, 2018).…”

Section: Maximizing Safety In Hazardous Environments With Ai-driven M...mentioning

confidence: 99%

Artificial intelligence and IoT driven technologies for environmental pollution monitoring and management

Popescu,

Mansoor,

Wani

et al. 2024

Front. Environ. Sci.

View full text Add to dashboard Cite

Detecting hazardous substances in the environment is crucial for protecting human wellbeing and ecosystems. As technology continues to advance, artificial intelligence (AI) has emerged as a promising tool for creating sensors that can effectively detect and analyze these hazardous substances. The increasing advancements in information technology have led to a growing interest in utilizing this technology for environmental pollution detection. AI-driven sensor systems, AI and Internet of Things (IoT) can be efficiently used for environmental monitoring, such as those for detecting air pollutants, water contaminants, and soil toxins. With the increasing concerns about the detrimental impact of legacy and emerging hazardous substances on ecosystems and human health, it is necessary to develop advanced monitoring systems that can efficiently detect, analyze, and respond to potential risks. Therefore, this review aims to explore recent advancements in using AI, sensors and IOTs for environmental pollution monitoring, taking into account the complexities of predicting and tracking pollution changes due to the dynamic nature of the environment. Integrating machine learning (ML) methods has the potential to revolutionize environmental science, but it also poses challenges. Important considerations include balancing model performance and interpretability, understanding ML model requirements, selecting appropriate models, and addressing concerns related to data sharing. Through examining these issues, this study seeks to highlight the latest trends in leveraging AI and IOT for environmental pollution monitoring.

show abstract

Section: Maximizing Safety In Hazardous Environments With Ai-driven M...mentioning

confidence: 99%

Artificial intelligence and IoT driven technologies for environmental pollution monitoring and management

Popescu,

Mansoor,

Wani

et al. 2024

Front. Environ. Sci.

View full text Add to dashboard Cite

show abstract

“…Crowding distance is a factor for better selection of solutions in terms of dispersion. More information about this method can be in some other studies ( Taleizadeh et al, 2008 , Taleizadeh et al, 2011 , Farrokhi-Asl et al, 2020 , Ghasemi et al, 2019 , Ghasemi et al, 2020 , Zhang et al, 2017 ). The main steps and the pseudo code of the NSGA-II are presented in Fig.…”

Section: Solution Approachmentioning

confidence: 99%

“… MID ( Goodarzian et al, 2021b ) MS ( Farrokhi-Asl et al, 2020 ) IGD ( Zhang et al, 2009 ) SNS ( Goodarzian et al, 2020 ) NPS ( Zahiri et al, 2018 , Govindan et al, 2015 ) …”

Section: Computational Experimentsmentioning

confidence: 99%

Designing a new sustainable Test Kit supply chain network utilizing Internet of Things

Navaei

Taleizadeh

Goodarzian

2023

Engineering Applications of Artificial Intelligence

View full text Add to dashboard Cite

“…The authors analyzed the distribution of reference papers among the sustainability dimensions and found that more than half (55%) of the reference papers (52 papers) focused on economic and environmental combinations and 42% of the reference papers (40 papers) focused on all three dimensions of sustainability. Two papers focused on the economic and social combination [39,40], and only one paper focused on environmental and social combinations [41]. Furthermore, most of the papers (99%) considered economic and environmental pillars as one of the objectives in SSCLM.…”

Section: Analysis Of Articles By Sustainability Dimensions and Indica...mentioning

confidence: 99%

“…Overall, the highest number of papers ( 52) have considered economic and environmental aspects of sustainability, whereas 40 papers have incorporated all three aspects of sustainability. Two papers have focused on economic and social aspects of the overall supply chain [40] and reverse logistics [39], only one paper looked at the environmental and social aspects of sustainability, which is also focused on the overall supply chain [72]. In terms of sustainability aspects of decision levels (Table 5), 58% of the SSCLM was used to make strategic decisions and 40% of which have focused on all three dimensions of sustainability, and 56% of which focused on economic and environmental aspects; 21% of the SSCLM were used for tactical decisions, 30% of which focused on all three dimensions, and 65% focused on economic and environmental aspects.…”

Section: Supply Chain Phases and Decision Levels From A Sustainable P...mentioning

confidence: 99%

Multi-Objective Optimization for Sustainable Supply Chain and Logistics: A Review

et al. 2021

View full text Add to dashboard Cite

There are several methods available for modeling sustainable supply chain and logistics (SSCL) issues. Multi-objective optimization (MOO) has been a widely used method in SSCL modeling (SSCLM), nonetheless selecting a suitable optimization technique and solution method is still of interest as model performance is highly dependent on decision-making variables of the model development process. This study provides insights from the analysis of 95 scholarly articles to identify research gaps in the MOO for SSCLM and to assist decision-makers in selecting suitable MOO techniques and solution methods. The results of the analysis indicate that economic and environmental aspects of sustainability are the main context of SSCLM, where the social aspect is still limited. More SSCLMs for sourcing, distribution, and transportation phases of the supply chain are required. Additionally, more sophisticated techniques and solution methods, including hybrid metaheuristics approaches, are needed in SSCLM.

show abstract

Developing a hazardous waste management system with consideration of health, safety, and environment

Cited by 14 publications

References 15 publications

Artificial intelligence and IoT driven technologies for environmental pollution monitoring and management

Artificial intelligence and IoT driven technologies for environmental pollution monitoring and management

Designing a new sustainable Test Kit supply chain network utilizing Internet of Things

Multi-Objective Optimization for Sustainable Supply Chain and Logistics: A Review

Contact Info

Product

Resources

About